# Receptiviti Documentation > Our custom-built models are powered by proprietary language psychology science, helping you uncover insights about people to enhance predictive models, guide decisions, and improve interpersonal interactions. ## Overview - [Overview](https://docs.receptiviti.com/overview.md): What is Receptiviti? - [Receptiviti API Dashboard](https://docs.receptiviti.com/overview/dashboard.md): The dashboard is the main user interface to find information about your account. - [CSV Upload Tool](https://docs.receptiviti.com/overview/dashboard/csv-upload-tool.md): This is a basic user interface we provide where you can upload a CSV file of text to have it scored and returned as a separate CSV. The CSV upload tool is found in your account dashboard. You can initiate an account by going here. - [Custom Norming Contexts from Dashboard](https://docs.receptiviti.com/overview/dashboard/custom-norming-from-dashboard.md): This guide walks you through creating and managing custom norming contexts from your user Dashboard. - [Getting Started](https://docs.receptiviti.com/overview/getting-started.md): Calling the API - [Word Count Guidelines](https://docs.receptiviti.com/overview/word-count-guidelines.md): This table outlines the minimum word count requirements for generating reliable psycholinguistic insights across Receptiviti and LIWC measures. These word count thresholds reflect the amount of text needed to produce statistically valid and interpretable results. Shorter texts may yield noisy or incomplete outputs, while meeting or exceeding ideal counts enhances reliability, particularly for nuanced psychological interpretation. In general, more language yields more robust the analysis. ## Frameworks - [Frameworks](https://docs.receptiviti.com/frameworks.md): Receptiviti’s measures can be separated into two categories: proportional measures and normed measures. This is an important distinction for those who are looking to combine and compare multiple measures for the purpose of extracting insights. - [Cognition](https://docs.receptiviti.com/frameworks/normed-frameworks/cognition.md): Receptiviti’s Cognition framework provides access to nine measures that quantify levels of multiple aspects of cognitive processing and analytical thinking. - [Drives](https://docs.receptiviti.com/frameworks/normed-frameworks/drives.md): Receptiviti’s Drives framework contains six measures that provide insight into what motivates people. Drives can be strong predictors of individual or group behaviour, offering insight into whether a person is driven by a need for achievement and self actualization, a need for domination, a need for reward, or a focus on risk. - [Interpersonal Circumplex](https://docs.receptiviti.com/frameworks/normed-frameworks/interpersonal-circumplex.md): The Interpersonal Circumplex displays users' language on the axes based on scores from our agentic and communal measures. - [Needs and Values](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md): Receptiviti’s Needs and Values framework comprises 17 measures that evaluate aspects of what motivates a person’s preferences, habits, and decision-making. - [Personality - Big 5](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md): The Big Five, also known as the Five Factor Model, is one of the dominant factor models of personality in psychology today. It proposes that every aspect of how we see each other and ourselves can be organized into five theoretically independent clusters of characteristics, called traits. These traits are as follows (see Schmidt et al., 2007): - [Personality - DISC](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-disc.md): DISC is a versatile psychological framework designed to help leaders understand how people in groups relate to their peers and collaborate with each other. Receptiviti’s DISC measures require that text samples contain at least 350 words to generate results. - [Social Dynamics](https://docs.receptiviti.com/frameworks/normed-frameworks/social-dynamics.md): Receptiviti’s Social Dynamics framework provides access to seven measures that evaluate a number of important aspects of how people are focused on themselves, focused on other people, whether they communicate with authenticity, clout, hesitation, the degree to which they communicate formally or informally, and more. - [Thinking Fast and Slow](https://docs.receptiviti.com/frameworks/normed-frameworks/thinking-fast-and-slow.md): Receptiviti’s Thinking Fast and Slow framework is adapted from the dual systems model of cognition to measure two fundamental thinking modes: Slow thinking (effortful, careful, incremental) and Fast thinking (intuitive, reflexive, holistic). Thinking Fast and Slow is also known as System One and System Two thinking. Both modes of thinking have implications for the nature, quality, and speed of decision-making and reasoning. These measures are crucial for understanding how people think in various scenarios, including market research, audience segmentation, personnel selection, and leadership assessment. Neither way of thinking is inherently superior or inferior. Without flexible use of both modes of thought, it would be nearly impossible to effectively process our environments or make decisions. - [Toxicity](https://docs.receptiviti.com/frameworks/other-frameworks/toxicity.md): Hate speech is a serious and growing problem for online publishers, e-gaming companies, comment moderation platforms, and social media sites. In addition to the ethical reasons for combating online hate speech, governments across the globe are beginning to implement new legislation that requires platforms to remove hateful content within hours or face significant financial penalties. - [Emotions](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md): Receptiviti’s Emotions engine, called SALLEE (Syntax-Aware LexicaL Emotion Engine; pronounced Sally), detects emotions and sentiment expressed in text. It is designed to score the emotions a person is expressing, which can include emotions they’re feeling in the present, emotions they've felt in the past or expect to feel in the future, or emotions they see or assume others are feeling. Each emotion can be seen as negative, neutral, or positive. - [LIWC](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md): Linguistic Inquiry and Word Count (LIWC) is the gold standard for research in the field of Language Psychology. Created by Dr. James W. Pennebaker at the University of Texas, the software was originally used to examine the therapeutic value of writing by analyzing the frequency of psychologically-relevant linguistic features of text. Since its inception, the various LIWC dimensions have been validated and addressed in published research, and LIWC has been the basis for over 25,000 academic publications in a variety of fields covering topics such as power dynamics, thinking styles, motivations, communication dynamics,personality, consumer behavior, group dynamics, culture, and interpersonal relationships, among others. - [LIWC Extension](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md): Receptiviti’s LIWC Extension framework provides measures focused on understanding communication dynamics and determinants of interpersonal support. These measures are paired to review several opposing forces in communication style. For example, the demonstration of a low or high amount of empathy, or the use of agentic (ambitious) versus communal (caring) language. - [Temporal and Orientation](https://docs.receptiviti.com/frameworks/proportional-frameworks/temporal-and-orientation.md): Receptiviti’s Temporal and Orientation measures provide access to three measures of Temporal Orientation and two measures of Attentional Focus. Temporal and Orientation measures provide insight into whether a person’s language and thoughts are rooted in the past, present, or future, while the Attentional Focus measures provide insight into whether a person is focused on themselves or on external entities. ## Developer Resources - [Developer Resources](https://docs.receptiviti.com/developer-resources.md): Explore our developer packages designed to streamline your workflow: ## Visualization UI - [Getting Started](https://docs.receptiviti.com/visualization-ui.md): Learn how to log in and get started with a Receptiviti UI project. - [Creating a Project](https://docs.receptiviti.com/visualization-ui/creating-a-project.md): Learn how to create a Receptiviti UI project. - [Export to Highlights](https://docs.receptiviti.com/visualization-ui/export-to-highlights.md): Learn how to export highlights of a text sample in the Receptiviti UI. - [Menu](https://docs.receptiviti.com/visualization-ui/menu.md): Learn about the menu items in the Receptiviti UI. - [Saving Worksheets as Local Files](https://docs.receptiviti.com/visualization-ui/saving-worksheets-as-local-files.md): Learn how to save worksheets as local files in the Receptiviti UI. - [Template Gallery](https://docs.receptiviti.com/visualization-ui/template-gallery.md): Using templates in the Receptiviti UI. - [Visualizing with Charts and Graphs](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs.md): Learn how to use the Receptiviti UI to analyze your language. - [Bar Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/bar-charts.md): Learn how to use the Receptiviti UI's bar chart to analyze your language. - [Circumplex Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/circumplex-chart.md): Learn how to use the Receptiviti UI's Circumplex chart to analyze your language. - [DISC Quadrant Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/disc-chart.md): Learn how to use the Receptiviti UI DISC Quadrant chart to analyze your language. - [Emotions by Topic](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/emotions-by-topic.md): Learn how to use the Receptiviti UI's Emotions by Topic graph to analyze your language. - [Heatmap](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/heatmap.md): Learn how to use the Receptiviti UI's Heatmap to analyze your language. - [Highlights](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/highlights.md): Learn how to use the Receptiviti UI's Highlights chart to analyze your language. - [Language Style Matching Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/language-style-matching-chart.md): Learn how to use the Receptiviti UI Language Style Matching chart to analyze your language. - [Line Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/line-charts.md): Learn how to use the Receptiviti UI's line charts to analyze your language. - [Lollipop Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/lollipop-chart.md): Learn how to use the Receptiviti UI's Lollipop chart to analyze your language. - [Radar Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/radar-chart.md): Learn how to use the Receptiviti UI's Score Radar chart to analyze your language. - [Scatterplot](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/scatterplot.md): Learn how to use the Receptiviti UI's scatterplot chart to analyze your language. - [Score Proportion](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/score-proportion.md): Learn how to use the Receptiviti UI's Score Proportion chart to analyze your language. - [Table](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/table.md): Learn how to use the Receptiviti UI's table to analyze your language. - [Topics by Emotion](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/topics-by-emotions.md): Learn how to use the Receptiviti UI's topics by emotion graph to analyze your language. - [Worksheets and Dashboards](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/worksheets-and-dashboards.md): Learn how to use the Receptiviti UI's worksheets and dashhboards to analyze your language. ## api-ref - [API Reference](https://docs.receptiviti.com/api-ref.md) ## Data Preparation - [Preparing Your Data](https://docs.receptiviti.com/data-preparation.md): This page will help you determine what text is valid and what could potentially skew your results. Our technology performs best when samples come from written or spoken language, including conversational language, formal or informal language from a variety of sources including blog posts, survey responses, social media posts, transcribed calls, short text samples, or text messages. - [Optimizing Outcomes by Manipulating Text](https://docs.receptiviti.com/data-preparation/optimizing-outcomes-by-manipulating-text.md): Depending on the level of insight you aim to produce, it can be necessary to take different approaches to structuring your text samples for analysis. For example, sentence-level analysis works best when you want to identify emotions related to key topics of interest. Or, you can split conversational language into chronological segments to evaluate linguistic progression over time. Below are several examples of different useful text concatenation methods: - [Remove Unwanted Mentions Programmatically](https://docs.receptiviti.com/data-preparation/remove-unwanted-mentions-programmatically.md): @Mentions ## FAQ - [FAQ](https://docs.receptiviti.com/faq.md): Frequently Asked Questions ## From Scores to Insights - [From Scores to Insights](https://docs.receptiviti.com/from-scores-to-insights.md): Once you have obtained language analysis scores, the next step is understanding how to use them effectively. This involves applying statistical methods and interpretation techniques to extract meaningful insights. Below are key approaches to consider: - [I Scored My Dataset - Now What?](https://docs.receptiviti.com/from-scores-to-insights/i-scored-my-data-now-what.md): Once you have used the Receptiviti API to analyze your language data, you can use a variety of statistical methods and tools to further explore and understand the nuances of your dataset. By applying techniques like z-scoring, rank norming, and statistical tests such as t-tests and ANOVAs, you can identify patterns, differences, and relationships within the data. - [Score Interpretation Guide](https://docs.receptiviti.com/from-scores-to-insights/score-interpretation-guide.md): Methods for Normed Measures ## Knowledge Base - [Knowledge Base](https://docs.receptiviti.com/knowledge-base.md): Advanced explanations and interpretive guidance on psycholinguistic and language-based insights. ## Norming and Base Rates - [Norming and Base Rates](https://docs.receptiviti.com/norming-and-base-rates.md): This section provides guidance on understanding and applying norming and base rates effectively. Establishing your own norms allows for tailored comparisons that reflect the specific populations or contexts relevant to your analysis. Using different norming contexts—such as industry-specific language, cultural variations, or genre-based benchmarks—enhances the precision and relevance of your insights. - [Custom Norming](https://docs.receptiviti.com/norming-and-base-rates/custom-norming.md): To gain meaningful insights from language analysis, it’s essential to interpret scores within the context that makes sense for the language source. While Receptiviti provides norming through the use of our extensive proprietary datasets, custom norming allows you to tailor the norms to your specific dataset or context. For instance, you can create norms based on a specific dataset to generate scores that are directly relevant to that particular data. Alternatively, you can establish norms that apply more broadly to a context, enabling analysis of new datasets within the same contextual framework even if they were not part of the original norming data. This flexibility ensures that normed measures better reflect either localized conditions or broader contextual trends, enhancing the relevance and accuracy of your insights. - [Streamlined Custom Norming](https://docs.receptiviti.com/norming-and-base-rates/custom-norming/streamlined-custom-norming.md): This guide provides a streamlined approach designed for users who want effective results without diving deep into code. While the Developer Resources offer greater flexibility, this method balances ease of use with reliable outputs, making it an option for those looking to implement custom norming. - [Norming](https://docs.receptiviti.com/norming-and-base-rates/norming.md): Norming Options - [Receptiviti Base Rates](https://docs.receptiviti.com/norming-and-base-rates/receptiviti-base-rates.md): The table below outlines base rates for Receptiviti and LIWC API measures. Base rates are the mean scores of a measure in a specific context. Because language is context dependent, base rates are not universally applicable (e.g., base rates calculated using spoken language should not be used as reference points when interpreting analysis of written language). ## Selecting Measures for Analysis - [Selecting Frameworks and Measures](https://docs.receptiviti.com/selecting-measures-for-analysis.md): This section provides tailored framework recommendations based on specific use cases to help you select the most appropriate tools for your project needs. - [Measure Bundles](https://docs.receptiviti.com/selecting-measures-for-analysis/measure-bundles.md): Measure Bundles are recommended groups of measures that each reflect different aspects of the same underlying psychological concept. The bundle approach is different from our frameworks, which usually involve algorithmic measures based on psychological models such as the Big Five or DISC. Bundles instead facilitate exploratory analyses by providing users with a menu of relevant measures that will help make sense of patterns of results. - [What's Your Receptiviti Use Case?](https://docs.receptiviti.com/selecting-measures-for-analysis/whats-your-receptiviti-use-case.md): Explore framework suggestions categorized by their strengths and intended use cases, making it easier to align your goals with the right technologies. ## Optional - [API Reference](https://docs.receptiviti.com/api-reference): Interactive reference for the Receptiviti API (v2). - [OpenAPI Specification (v2)](https://docs.receptiviti.com/api/v2.openapi.yaml): Machine-readable OpenAPI spec for the Receptiviti API (v2). --- # Full Documentation Content # API Reference --- # Preparing Your Data This page will help you determine what text is valid and what could potentially skew your results. Our technology performs best when samples come from written or spoken language, including conversational language, formal or informal language from a variety of sources including blog posts, survey responses, social media posts, transcribed calls, short text samples, or text messages. Raw text works best, meaning that it's unnecessary to tokenize, lemmatize, stem, remove stop words, or remove punctuation. During data preparation, raw text may need to be aggregated or parsed depending on the goal of your analysis. Raw text should be prepared in a way that corresponds with the level of insight you aim to produce. For example, parse raw text into discrete sentences before calling the API if you aim to produce sentence-level insights; aggregate raw text into paragraphs before calling the API if you aim to produce paragraph-level insights. The API will analyze input text submissions as a whole unit. Refer to the table below for details surrounding what to include and what to exclude from your text before using the Receptiviti API. | Element | Example(s) | Include? | Action | Comment | | --------------------------------------------- | -------------------------------------------------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Text Encoding | | utf-8 | Encode your text strings in utf-8 | The API currently accepts only JSON as input. JSONs are encoded in unicode with a default encoding of utf-8. More details [here](https://www.ietf.org/rfc/rfc4627.txt) and [here](https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/). | | @Mentions | `@bigScaryPup` | Yes | Leave in your text if this is relevant to your use case. Exclude, if not. | For most use cases, @Mentions are data noise and not natural language and do not indicate underlying psychology or emotion. Currently, an @Mention adds 1 to the word count (wc). | | Hashtags | `#lolnotlol` | Yes | Retain hashtags: we score them. | Hashtags are separated and parsed by the API. The individual components of the hashtags count towards word count. **#thiswillbescored** will be split up into `this will be scored` and count as 4 words. Currently, hashtags adds the number of tokens in the hashtag to wc and 1 to hashtags. | | Emojis | `\xf0\x9f\x8c\xbb` 😂😡 | Yes | Retain emojis in your text. | Emojis are visual representations of emotions, common objects and situations. They are powerful tools to uncover psychological and emotional meaning in language. | | URLs | `http://receptiviti.com` | Yes | Leave in your text if this is relevant to your use case. Exclude, if not. | For most use cases, URLs are data noise and not natural language and do not indicate underlying psychology or emotion. However, if they are relevant to your use case, feel free to leave them in your text. Currently, a URL adds 1 to the wc and to the urls category. | | Email headers | `From: no-reply@receptiviti.com` | No | Remove all email headers, and only use email body as text. Remember, if your email body is in html, follow the instructions below to strip html tags from your text. | Email headers are data noise and not natural language for Receptiviti’s metrics. They do not indicate underlying psychology or emotion. They will count towards the total number of words and thereby skew scores. | | Email metadata | `Mon, 24 Aug 2024 10:16:07 -0700 (PDT)` | No | Remove all email metadata, and only use email body as text. Remember, if your email body is in html, follow the instructions below to strip html tags from your text. | Email metadata are data noise and not natural language for Receptiviti’s metrics. They do not indicate underlying psychology or emotion. They will count towards the total number of words and thereby skew scores. | | Email footers and confidentiality disclaimers | `Head office: 150 Bloor St. West, Suite 310, Toronto, Ontario` | No | Remove all email footers and legal disclaimers from your email. Remember to use only email body as text. | Email footers and confidentiality disclaimers are data noise and not natural language for Receptiviti's metrics. They do not indicate underlying psychology or emotion. They will count towards the total number of words and thereby skew scores. | | HTML | `` | No | Strip all HTML tags and only retain relevant content within the tags e.g., text within the `

` tags could be natural language and therefore valid for analysis. | HTML tags specify formatting, not naturally spoken or written language. The text within some HTML tags may be useful (depending on your application). Tools like [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) can help you do this. | | Code | `Print("Hello World")` | No | Remove all code snippets from your text. | Code snippets are not natural language and do not indicate underlying psychology or emotion. They will count towards the total number of words and thereby skew scores. | ## Language Support in the Receptiviti Platform[​](#language-support-in-the-receptiviti-platform "Direct link to Language Support in the Receptiviti Platform") Our platform only supports English in the API. However, it is highly compatible with machine-translated text derived from languages that are sufficiently similar to English (Spanish, French, German, Italian, Dutch, Portuguese, etc.). Independent research confirms the validity of leveraging machine translation prior to analyzing text using Receptiviti measures (See citation below). This process can be applied across our frameworks, and many of our clients successfully use machine translation in their workflows. For users looking to integrate machine translation, numerous advanced machine translation services are readily available online. 👉 [Download the research paper here](https://39959461.fs1.hubspotusercontent-na1.net/hubfs/39959461/Machine-translated%20texts%20as%20an%20alternative%20to%20translated%20dictionaries%20for%20LIWC.pdf) Citation * Boot, P. (2021, January 12). Machine-translated texts as an alternative to translated dictionaries for LIWC. ## Understanding Word Count Discrepancies[​](#understanding-word-count-discrepancies "Direct link to Understanding Word Count Discrepancies") You might notice that the API response item under `summary`/`word_count` displays a word count that differs from the count you see in the text editor document that contains your samples. This is because the Receptiviti API separates words on hyphens and apostrophes, so for example, `he's our go-to guy` is six 'words' counted as `he` `s` `our` `go` `to` `guy`. From a counting perspective, the Receptiviti API regards words primarily as tokens, and in some cases emphasizes their individual units rather than recognizing them as complete, contextual words. This means that it dissects the text into discrete components, such as `it's` becoming `it` and `s` and subsequently assesses and processes these fragments separately. --- # Optimizing Outcomes by Manipulating Text Depending on the level of insight you aim to produce, it can be necessary to take different approaches to structuring your text samples for analysis. For example, sentence-level analysis works best when you want to identify emotions related to key topics of interest. Or, you can split conversational language into chronological segments to evaluate linguistic progression over time. Below are several examples of different useful text concatenation methods: ### Analysis of the Individual[​](#analysis-of-the-individual "Direct link to Analysis of the Individual") * **Use case:** Understanding the character traits of a single person (i.e., speaker, author, leader, etc.). * **Method:** Concatenate all language data from a single speaker. * **Example Objective:** Assess the communication style of an executive leader to align and boost employee engagement and effectiveness. Collect the leader’s written or spoken language from company emails, instant messages, speeches, etc., and compile it into one cell of your CSV file. ### Analysis of the Group[​](#analysis-of-the-group "Direct link to Analysis of the Group") * **Use case:** Understanding the character traits of multiple individuals as a collective (i.e., speaker, author, leader, etc.). * **Method:** Concatenate all language data for each individual in the group separately. After scoring, calculate the average score for all individuals to ensure each person is weighted equally in the group average. Alternatively, if you prefer the group average to be weighted by word count—allowing more verbose individuals to contribute more to the final score—concatenate all language data for the group into a single sample before scoring. * **Example Objective:** Assess the personality and motivations of an audience segment to improve marketing and ad effectiveness. Collect audience-generated content (i.e., social media posts, focus group transcripts) and compile the language data based on author or segment depending on your preferred method. ### Analysis of Answers to Key Questions[​](#analysis-of-answers-to-key-questions "Direct link to Analysis of Answers to Key Questions") * **Use case:** Exploring and comparing trends in responses across individual questions. * **Method:** Aggregate language data based on individual questions. * **Example Objective:** Analyze and improve the communication style of customer support representatives to enhance customer satisfaction and resolution efficiency. Collect all written communications from customer support reps over a specified period, including emails and chat transcripts and compile the responses that were given in answer to each question. ### Analysis of Answers by Question Theme[​](#analysis-of-answers-by-question-theme "Direct link to Analysis of Answers by Question Theme") * **Use case:** Exploring and comparing trends in responses across multiple related questions. * **Method:** Aggregate language data based on question theme. * **Example Objective:** Analyze and categorize customer feedback and product reviews by thematic elements to better address common concerns, enhance product and service offerings, and improve marketing strategies. Collect reviews and combine the responses that were given in answer to each set of related questions. ### Analysis of Conversation Over Time (Longitudinal)[​](#analysis-of-conversation-over-time-longitudinal "Direct link to Analysis of Conversation Over Time (Longitudinal)") * **Use case:** Evaluating trends in conversation progression or long-term progression. * **Method:** Split the language data into chronological segments; segments can be split based on word count, time, a conversation outline, days, weeks, etc. * **Example Objective:** Evaluate the changes in employee sentiment and communication trends across different fiscal quarters to better understand workforce morale and engagement. Compile transcripts of quarterly all-hands meetings into a single document, allowing for a comprehensive analysis of overarching themes and shifts. ### Analysis of Intra-Person Variance (Non-Temporal)[​](#analysis-of-intra-person-variance-non-temporal "Direct link to Analysis of Intra-Person Variance (Non-Temporal)") * **Use case:** Understanding internal variability within an individual's communication style, such as thinking agility and tonal shifts in messaging—even when time is not the primary factor. * **Method:** Split a single individual's language into multiple equal-sized or contextually meaningful segments (e.g., paragraphs, questions, or topic transitions), regardless of timeline. * **Example Objective:** Evaluate the thinking agility of a speaker by dividing their full transcript into segments based on thematic shifts or rhetorical structure. This allows insight into how their tone, emotional stance, or thinking agility (as measured by fast and slow thinking) may vary across different parts of the same transcript—even without a temporal progression. ### Analysis of Topics by Emotion[​](#analysis-of-topics-by-emotion "Direct link to Analysis of Topics by Emotion") * **Use case:** Identifying emotions related to key topics of interest. * **Method:** Parse language data into sentences and score using pre-built or custom taxonomies and our emotions framework. * **Example Objective:** Evaluate the emotional responses elicited by different elements of marketing campaigns to optimize messaging and content for targeted audience engagement. Compile all customer interactions and feedback related to specific marketing campaigns, such as social media comments, survey responses, and customer service transcripts. ### Analysis of Rapport (Language Style Matching)[​](#analysis-of-rapport-language-style-matching "Direct link to Analysis of Rapport (Language Style Matching)") * **Use case:** Understanding the extent to which two or more speakers are attentively engaged. * **Method:** Analyze and compare each speaker pair’s language. * **Example Objective:** Evaluate the linguistic alignment between job candidates and interviewers during interviews to identify strong rapport, which can indicate better team fit and communication compatibility. Use turn-by-turn text in VTT files or row-by-row in CVS files. note For best results, text should be split at sentence boundaries if possible — not in the middle of a sentence. This helps preserve linguistic coherence and helps ensure more accurate scoring. Each of the created sample segments should still meet word count minimum thresholds to ensure valid output. --- # Remove Unwanted Mentions Programmatically ### @Mentions[​](#mentions "Direct link to @Mentions") ``` # python import re def remove_at_mentions(line): return re.sub("@\w+", "", line) def scrub_text(text): scrubbed_text = remove_at_mentions(text).strip() return scrubbed_text scrubbed_text = scrub_text("@dogwalker I hope you had a nice long walk!") ``` ### URLs[​](#urls "Direct link to URLs") ``` # python # Regex from https://gist.github.com/dperini/729294 import re def remove_urls(line): URL_REGEX = "(?:(?:(?:https?|ftp):)?\/\/)(?:\S+(?::\S*)?@)?(?:(?!(?:10|127)(?:\.\d{1,3}){3})(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))|(?:(?:[a-z0-9\u00a1-\uffff][a-z0-9\u00a1-\uffff_-]{0,62})?[a-z0-9\u00a1-\uffff]\.)+(?:[a-z\u00a1-\uffff]{2,}\.?))(?::\d{2,5})?(?:[/?#]\S*)?" return re.sub(URL_REGEX, "", line) remove_urls("have you visited https://www.receptiviti.com/company to find out more") ``` ### Email headers and Email Metadata[​](#email-headers-and-email-metadata "Direct link to Email headers and Email Metadata") ``` # python from email import message_from_string def extract_body_from_email(text): msg = message_from_string(text) if msg.is_multipart(): for part in msg.walk(): content_type = part.get_content_type() content_disposition = str(part.get('Content-Disposition')) found_body = False if content_type == 'text/plain' and 'attachment' not in content_disposition and not found_body: body = part.get_payload(decode=True) else: body = msg.get_payload(decode=True) return body text = """Delivered-To: jdoe@receptiviti.com Received: by 2002:a05:6000:1188:0:0:0:0 with SMTP id g8csp2511771wrx; Mon, 14 Feb 2024 05:11:19 -0700 (PDT) MIME-Version: 1.0 In-Reply-To: From: Test User Date: on, 14 Feb 2024 08:11:06 -0400 Subject: Fwd: Spam email from you To: All full-time employees Content-Type: multipart/related; boundary="000000000000754a2e05af44eeac" --000000000000754a2e05af44eeac Content-Type: multipart/alternative; boundary="000000000000754a2c05af44eeab" --000000000000754a2c05af44eeab Content-Type: text/plain; charset="UTF-8" As part of our ongoing security awareness, if you see emails like this, please mark them as phishing. Note the warning signs - the actual email address doesn't match sender, the signature isn't right, and there's a giant red warning banner :) [image: Screen Shot 2020-09-14 at 9.24.41 am.png] Marking something as phishing is slightly different than using the spam button in gmail. The email goes through different processes / to a different team and helps Google prevent these from landing in our inboxes. Thanks all! *Test User* Desig, Nation | Head, Software @ Receptiviti | testuser@receptiviti.com This message is intended only for the use of the intended recipients, and it may be privileged and confidential. If you are not the intended recipient, you are hereby notified that any review, re-transmission, conversion to hard copy, copying, circulation or other use of this message is strictly prohibited and may be illegal. If you are not the intended recipient, please notify me immediately by return email and delete this message from your system. Thank you. --000000000000754a2c05af44eeab Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable

As part of our ongoing security awareness, if you see emails like this, please mark them as phishing.
Note the wa rning signs - the actual email address doesn't match sender, the signat ure isn't right, and there's a giant red warning banner :)

3D"Screen
This message is intended only for the use of the intended recipients, and it may be privileged and confidential. If you are not the intended recipient, you are hereby notified that any review, re-transmission, conversion to hard copy, copying, circulation or other use of this message is strictly prohibited and may be illegal. If you are not the intended recipient, please notify me immediately by return email and delete this message from your system. Thank you.
--000000000000754a2c05af44eeab-- --000000000000754a2e05af44eeac Content-Type: image/png; name="Screen Shot 2020-09-14 at 9.24.41 am.png" Content-Disposition: inline; filename="Screen Shot 2020-09-14 at 9.24.41 am.png" Content-Transfer-Encoding: base64 Content-ID: <1748c828c6b7ef93e481> X-Attachment-Id: 1748c828c6b7ef93e481 --000000000000754a2e05af44eeac--""" extract_body_from_email(text) ``` ### HTML[​](#html "Direct link to HTML") ``` # python # pip3 install bs4 from bs4 import BeautifulSoup def strip_html_basic(message_string, parser="lxml-xml"): soup = BeautifulSoup(message_string, parser) for tag in soup("style"): tag.decompose() plain = soup.get_text("\n", strip=True) return plain html_doc = """ The Receptiviti Story

Every word counts... I, Fountain and Puppy; When language first emerged, they were not made equally.

...

""" strip_html_basic(html_doc) ``` --- # Developer Resources Explore our developer packages designed to streamline your workflow: * [**R Package**](https://receptiviti.github.io/receptiviti-r/) * [**Python Package**](https://receptiviti.github.io/receptiviti-python/) These packages offer integration with external software libraries and tools, enhancing your Receptiviti API development experience. Also, you can access our **API Reference documentation** by going [here](https://docs.receptiviti.com/api-reference). --- # FAQ ## Frequently Asked Questions[​](#frequently-asked-questions "Direct link to Frequently Asked Questions") This section includes answers to questions that users frequently encounter. If you don't find an answer to your question, please [contact us](https://dashboard.receptiviti.com/contact) and let us know how we can help. ## Trials[​](#trials "Direct link to Trials") **Q: What is the free trial?** **A:** We provide a free 10-day trial of the Receptiviti API so that you can try it before committing to a paid plan. During the trial period you can select among a limited group of frameworks you'd like to try . Go [here](https://www.receptiviti.com/request-trial-account) to apply for a trial account. **Q: What happens at the end of my free trial?** **A:** Your trial will expire 10 days after you sign up, at which point you will automatically convert to the fee-based plan that you signed up for at the beginning of the trial. You can see the details of your account on the [dashboard](http://dashboard.receptiviti.com/). **Q: How do I cancel my trial?** **A:** If you wish to cancel your account before the end of the trial period, simply [contact us](https://dashboard.receptiviti.com/contact) at least three days before your trial expiry date and include the word "cancel" in the subject line. Please note that cancellations made with less than three days prior to trial expiry will be subject to an administrative fee that will be deducted from any available refund. ## Plans & Pricing[​](#plans--pricing "Direct link to Plans & Pricing") **Q: How does your pricing work?** **A:** Our pricing is based on the number of words analyzed each month. We offer a range of plans that include a fixed monthly word allowance, with overage fees applied if that limit is exceeded. For added flexibility, annual plans are also available. All paid plans require a three-month commitment. **Q: What are your accepted Payment Methods?** **A:** We accept VISA, Mastercard, American Express, and JCB Payments. Wire transfer payments are available upon request. **Q: When do you process payments?** **A:** You will pre-pay in advance at the beginning of your month for access to the API and the plan you select. If you exceed the number of words included in your plan you will be charged a per-word overage fee at the end of the month. Immediately upon completion of your 10 day free-trial you will be charged for the first fee-based month of your plan. **Q: Do you offer any discounts?** **A:** If you're working on a project that involves social impact or philanthropy, we'd love to learn more about you and your initiative. We often offer discounted pricing for projects that make a significant social impact. Please [contact us](https://dashboard.receptiviti.com/contact) for more information. **Q: Do you offer customized plans?** **A:** Generally no. If you have a specific need, please [contact us](https://dashboard.receptiviti.com/contact) and we would be happy to work with you to make sure you get what you need. **Q: Can I make changes to my plan?** **A:** Absolutely, we want you to be happy and have a plan that fits your needs. If you have started using our platform and need to analyze more volume than our advertised plans include, please [contact us](https://dashboard.receptiviti.com/contact) to find the right pricing tier and package for your needs. Any upgrades and downgrades to your plan that are processed mid-month (at least 5 business days before month-end) will be retroactive to the beginning of the month. Mid-month downgrades can be accommodated if your usage is below the upper limit of the plan that you wish to downgrade to. In all other circumstances, you will be downgraded in the next billing cycle. Cancellations subject to our cancellation policy will take effect at the end of the month. **Q: How do I cancel my plan?** **A:** To cancel your plan, please [contact us](https://dashboard.receptiviti.com/contact) at least 10-days before your monthly renewal date and include the word "cancel" in the subject line. Please note that paid plans require a minimum three-month commitment, and fees for the first three months are not refundable. We do not offer prorated refunds for partial month use. All overage fees incurred each month will be charged at the end of the month. **Q: What happens if I miss a payment?** **A:** If you miss a payment, we will attempt to contact you using the email address you provided upon registration. Unpaid accounts may be deactivated upon non-payment. ## The API[​](#the-api "Direct link to The API") **Q: How much text can I include in one API call?** **A:** Each API request must not exceed 10MB in total payload size, regardless of the number of text inputs. When sending multiple texts in a single call, you may include up to 1,000 individual texts within that 10MB limit. More information about API calls can be found [here](https://docs.receptiviti.com/api-reference). ## Data & Privacy[​](#data--privacy "Direct link to Data & Privacy") **Q: Where are the Terms of Use and Privacy Policy?** **A:** You can find the Terms of Use [here](https://www.receptiviti.com/terms-of-use) and Privacy Policy [here](https://www.receptiviti.com/terms-of-use). **Q: Does Receptiviti store my data?** **A:** No. And no human can or will ever read the text that you send to the API. **Q: My data cannot be transmitted to a web-based API. Do you have other options for me?** **A:** Yes, in addition to the web-based API, we also offer a containerized, Dockerized version of the API that can be installed within your technology environment. Contact for containerized API pricing information. ## LIWC[​](#liwc "Direct link to LIWC") **Q: How do the LIWC measures differ from the academic version of LIWC?** **A:** The academic version of LIWC and the LIWC API are built on the same specs, but do have some minor differences in the way they count words. For example, "10,000" is counted as one word in the LIWC API, but as two words (ten thousand) in the academic version. If you need more information, please [contact us](https://dashboard.receptiviti.com/contact). **Q: How do I sign up for LIWC?** **A:** LIWC is not available via self-checkout, so you need to contact our sales team to add the package to your plan. You can do so [here](https://dashboard.receptiviti.com/contact). --- # Frameworks Receptiviti’s measures can be separated into two categories: proportional measures and normed measures. This is an important distinction for those who are looking to combine and compare multiple measures for the purpose of extracting insights. ## Proportional Measures[​](#proportional-measures "Direct link to Proportional Measures") These measures produce proportion-based scores, meaning the score output represents the portion of the analyzed text that consists of words related to the psychological construct being measured. The [LIWC](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md), [LIWC Extension](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md), [Emotions](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md) (SALLEE), [Temporal and Orientation](https://docs.receptiviti.com/frameworks/proportional-frameworks/temporal-and-orientation.md), and [Toxicity](https://docs.receptiviti.com/frameworks/other-frameworks/toxicity.md) frameworks all contain proportional measures: for each submitted text sample, Receptiviti analyzes one word at a time. As each word is processed, the dictionary file is searched by category, looking for a category match with the current word. If the target word is matched with a category word, the appropriate word category scale (or scales) for that word is incremented. While SALLEE operates slightly differently than LIWC, LIWC Extension, Temporal and Orientation, and Cognition, these six frameworks count words in a similar fashion. Proportional measures will always provide scores in a range of `0` to `1`, except for SALLEE’s sentiment measures, which will always fall between `-1.0` to `+1.0`. Proportional Measures are not normalized or baselined against a dataset or population. Rather, they produce raw scores. note Toxicity's `toxicity_measures` are proportional but `toxicity_likelihood` are likelihoods in the `0` to `1` range. ## Normed Measures[​](#normed-measures "Direct link to Normed Measures") The [Big 5 Personality](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md), [Social Dynamics](https://docs.receptiviti.com/frameworks/normed-frameworks/social-dynamics.md), [Drives](https://docs.receptiviti.com/frameworks/normed-frameworks/drives.md), [Cognition](https://docs.receptiviti.com/frameworks/normed-frameworks/cognition.md), [Needs and Values](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md), [Interpersonal Circumplex](https://docs.receptiviti.com/frameworks/normed-frameworks/interpersonal-circumplex.md), [Fast and Slow Thinking Index](https://docs.receptiviti.com/frameworks/normed-frameworks/thinking-fast-and-slow.md), and [DISC](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-disc.md) frameworks all contain normed measures. Normed measures are algorithms that have building block components (i.e., ingredients) that contribute to the psychological phenomenon being measured. Our proportional measure frameworks (i.e., LIWC, SALLEE, etc.) are primary sources of ingredients for the algorithms. Some measures are comprised of one component, while other measures are comprised of multiple components. important A minimum of 350 words per analyzed text sample is required to produce valid scores when analyzing text using normed measures. By default, scores for these measures are normed using Z-scoring. Z-scoring transforms raw scores into standardized scores that show how far a value is from the mean, measured in standard deviations. For example, if someone scores a Z-score of `2` on a psychometric test, it means their score is two standard deviations above the average for that test. This results in a normal distribution. Scores are then projected onto a range from `0` to `100`. A normed score of `80` indicates that the sample is `2.4` standard deviations away from the mean of the norming dataset. Users can choose from Receptiviti's Spoken or Written norming datasets, or create a custom one. info For those who would prefer percentiles, normed measures can alternatively be normed using Rank Norming. Rank Norming adjusts scores by ranking them within a group and then converting those ranks into a new scale, allowing for comparisons across different groups. A percentile score of `80` indicates that 80% of the samples in the norming dataset — Receptiviti curated or custom — have scores lower than the analyzed language sample. Please reach out to if you would like to adjust your output from default Z-scoring to rank normed percentiles. --- # Cognition Receptiviti’s Cognition framework provides access to nine measures that quantify levels of multiple aspects of cognitive processing and analytical thinking. This framework makes it possible to analyze how people think, digest information, problem-solve, and make decisions. We recommend using text samples containing at least 350 words to generate the most accurate Cognition results. Larger text samples will better reflect a person’s typical way of talking and thinking (in the same way that larger samples of research participants tend to better represent behavior in the human population). ``` { "plan_usage": { "word_limit": 250000, "words_used": 1438, "words_remaining": 248562, "percent_used": 0.58, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "ff75ed78-7373-45c8-8bc9-fe67a5980fac", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": {...}, "social_dynamics": {...}, "drives": {...}, "cognition": { "analytical_thinking": 33.856809727230385, "cognitive_processes": 41.161966073028886, "causation": 41.816615890086716, "certainty": 46.67367268871232, "comparisons": 32.258545640565885, "differentiation": 48.857016607516705, "discrepancies": 24.6284597745971, "insight": 60.38766301589613, "tentative": 28.076814219888607 }, "cognition_proportional": {...}, } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Measure | Summary | High Score Definition | Low Score Definition | | --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | | `analytical_thinking` | Reflects structured, hierarchical thinking, complex problem solving, and higher order executive functioning. | Reflects formal, logical, hierarchical and strategic thinking. | Reflects more informal, here-and-now, and narrative thinking. | | `cognitive_processes` | A measure of the automatic cognitive processes involved with paying attention, or processing environmental inputs and the world around us. | Suggests that unnecessary or attentional demands are being imposed on a person, making the task of processing information more difficult. | Suggests attentional demands are less burdensome or more manageable. | | `causation` | The degree to which a person is engaged in causal thinking, or understanding the relationship between a cause and its effect. | Suggests significant causal thinking or focus on understanding the relationship between a cause and its effect. | Suggests little-to-no causal thinking or focus on understanding the relationship between a cause and its effect. | | `certainty` | The degree to which a person is using language that reflects concepts such as certainty, specificity, and completeness, with the intention of persuading themselves or someone else that something is true. | Suggests a significant focus on persuading themselves or someone else that something is true. | Suggests little-to-no intention of persuading themselves or someone else that something is true. | | `comparisons` | The degree to which language is being used to compare one entity with another. | Suggests a significant amount of language being used to compare one entity to another. | Suggests attentional demands are less burdensome or more manageable. | | `differentiation` | The degree to which language is being used to distinguish between entities, people, or ideas. | Suggests a significant amount of language being used to distinguish between entities, people, or ideas. | Suggests little-to-no language being used to distinguish between entities, people, or ideas. | | `discrepancies` | The degree to which a person is comparing or articulating the difference between a current state with an alternative state, as often seen in expressions of inferiority, desires, or expectations. | Suggests significant language being used to articulate the difference between a current state with an alternative state. | Suggests little-to-no language being used to articulate the difference between a current state with an alternative state. | | `insight` | The degree to which a person is focused on understanding, insight or gaining clarity into themselves, someone else or an entity. | Suggests a significant focus on understanding, insight, or gaining clarity. | Suggests little-to-no focus on understanding, insight, or gaining clarity. | | `tentative` | The degree to which a person is signalling uncertainty or the using non-definitive or hedging language. | Suggests significant signalling of uncertainty or significant use of non-definitive or hedging language. | Suggests little-to-no signalling of uncertainty and little-to-no use of non-definitive or hedging language. | ## Additional Information on the Cognition Framework[​](#additional-information-on-the-cognition-framework "Direct link to Additional Information on the Cognition Framework") ### Analytical Thinking[​](#analytical-thinking "Direct link to Analytical Thinking") The Analytical Thinking measure indicates the degree to which language shows markers of deliberate, structured, and complex thinking. Lower levels of Analytical Thinking is indicative of less productive, less structured and less hierarchical thinking. For example, highly analytical language is typical of scientific writing and intellectual speech. Language with lower scores on this measure is typically seen in highly social environments, such as casual gatherings among friends. A drop from baseline in the Analytical Thinking style of an individual is highly correlated with significant events in an individual’s personal life, especially in the case of negative events. A significant event will disrupt pre-existing cognitive patterns, and lead to a temporarily less structured way of thinking and communicating. With finite mental resources available, when increased mental energy is dedicated to coping, less mental energy is available for higher-level thinking. There is a vast body of research examining Analytical Thinking and behaviour. For example, researchers have used this measure to investigate [the relationship between higher grades and graduation rates in university settings](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115844), the [speeches of political leaders](https://psycnet.apa.org/record/2017-41186-008), long-term [language trends in our politics and culture](https://www.pnas.org/content/pnas/116/9/3476.full.pdf), and the [helpfulness of online customer reviews](https://www.mdpi.com/2071-1050/10/6/1735/htm), and much more. ### Cognitive Processing[​](#cognitive-processing "Direct link to Cognitive Processing") The Cognitive Processing measure - also referred to as Cognitive Load - looks at the markers present in language that indicate someone is using increased mental energy to process environmental or situational stimuli. Words found here are broad, and include certain adjectives (i.e., *obvious, essential, specific*), verbs (i.e., *distinguish, suppose, consider*), nouns (i.e., *secret, question, findings*), that reflect increased levels of cognitive processing. When individuals are trying to understand the world around them, they often use words that demonstrate this behaviour. If this mental processing is continuous or rigorous, it can increase an individual’s Cognitive Load. This increase can occur due to the [complexity or format of a task, time pressure, a significant event or change that impacts them, and other factors](https://www.researchgate.net/publication/252083119_Cognitive_Load_Measurement_as_a_Means_to_Advance_Cognitive_Load_Theory). Elevated attentional demands can have a significant and negative impact on analytical thinking, decision-making, and one’s ability to carry out complex mental tasks. In combination with other frameworks such as Social Dynamics, this framework can be immensely helpful in understanding how individuals process the world around them. There is extensive research examining Cognitive Load, behaviour, and language. For example, researchers investigating Cognitive Load have shown that it can play a role in [physicians’ decision-making](https://pubmed.ncbi.nlm.nih.gov/32304298/), lead to [more risk-averse behaviour](https://www.researchgate.net/publication/278036112_The_Effect_Of_Cognitive_Load_On_Economic_Decision_Making_A_Survey_And_New_Experiments), cause [more impatience with money](https://www.researchgate.net/publication/278036112_The_Effect_Of_Cognitive_Load_On_Economic_Decision_Making_A_Survey_And_New_Experiments), and has a [relationship with lying and deception](https://link.springer.com/article/10.1023/B:GRUP.0000011944.62889.6f). ### Causation[​](#causation "Direct link to Causation") The Causation measure includes language associated with the cause and effect of an action (i.e., *change, create, initiate, solve*). Individuals may also use more Causation words when dealing with an unexpected or surprising situation. For example, some research has shown that the presence of Causation and Insight words when describing a past event could suggest that an [individual is actively reappraising the event, and possibly shifting their feelings or thoughts towards it](https://www.cs.cmu.edu/~ylataus/files/TausczikPennebaker2010.pdf). ### Certainty[​](#certainty "Direct link to Certainty") The Certainty measure evaluates a range of certain adjectives (i.e., complete, apparent, undeniable) and adverbs (i.e., confidently, absolutely, definitely) relating to Certainty and specificity. Researchers have used the Certainty measure to examine many important behaviours, such as [risk propensity](http://www.integraorg.com/wp-content/Lenguaje%20verbal%20y%20no%20verbal%20para%20la%20toma%20de%20decisiones.pdf), [dogmatism](https://arxiv.org/abs/1609.00425), [extremism](https://www.tandfonline.com/doi/abs/10.1080/19434472.2019.1651751), and more. ### Comparison[​](#comparison "Direct link to Comparison") The Comparison measure evaluates certain adjectives (i.e., *cleanest, wittiest, newest*) and prepositions (i.e., *before, after,*) that are used to compare one or more entities to each other. ### Differentiation[​](#differentiation "Direct link to Differentiation") The Differentiation measure includes certain verbs (i.e., differ, hasn’t, can’t), adverbs (i.e., *actually, differently, exclusively*), conjunctions (i.e., *unless, although, whereas*), and other language related to difference and contrast. While the Differentiation and Discrepancy groups are similar, Discrepancy is typically used to point out inconsistencies, while Differentiation is typically used to discern among the qualities of two or more items. ### Discrepancy[​](#discrepancy "Direct link to Discrepancy") The Discrepancy measure includes certain adjectives (i.e., *abnormal, lacking, unnecessary*), adverbs (i.e., *normally, hopefully*), and verbs (i.e., *mustn’t, shouldn’t, ought*) related to concepts of inconsistency and deviation. While the Differentiation and Discrepancy groups are similar, Discrepancy is used to point out inconsistencies, while Differentiation is used to discern among the qualities of two or more objects or concepts. ### Insight[​](#insight "Direct link to Insight") Words in the Insight measure are broad, and include certain verbs (i.e., *accepted, comprehend, define*), nouns (i.e., *solution, reflection, complexity*), and adjectives (i.e., *perspective, question*) related to understanding. Some examples of research have shown that the presence of Insight and Causation words when describing a past event could suggest that an [individual is actively reappraising the event, and possibly shifting their feelings or thoughts towards it](https://www.cs.cmu.edu/~ylataus/files/TausczikPennebaker2010.pdf). ### Tentative[​](#tentative "Direct link to Tentative") The Tentative measure includes certain adverbs (i.e., *approximately, hopefully*), verbs (i.e., *guess, depending*), and adjectives (i.e., *indefinite, vague*) related to non-definitive or hedging behaviour. For example, [women](https://www.researchgate.net/publication/258181378_Women_Are_More_Likely_Than_Men_to_Use_Tentative_Language_Aren't_They_A_Meta-Analysis_Testing_for_Gender_Differences_and_Moderators) and individuals who are [lower in status](https://www.cs.cmu.edu/~ylataus/files/TausczikPennebaker2010.pdf) can sometimes use more hedging language than men or those in positions of power. Some researchers have used this measure in examining the [relationship between language markers and grandiose narcissism](https://journals.sagepub.com/doi/abs/10.1177/0261927X19871084). ## Specifications[​](#specifications "Direct link to Specifications") The Cognition framework consists of measures that are indicative of cognitive processing and analytical thinking. A score of `0` implies that there was no detectable cognitive focus, while anything greater than `0` implies that there was some kind of cognitive focus for that measure. --- # Drives Receptiviti’s Drives framework contains six measures that provide insight into what motivates people. Drives can be strong predictors of individual or group behaviour, offering insight into whether a person is driven by a need for achievement and self actualization, a need for domination, a need for reward, or a focus on risk. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1594, "words_remaining": 248406, "percent_used": 0.64, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "576920d5-ac3b-4fac-b7f5-a00189a6f194", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": {...}, "social_dynamics": {...}, "drives": { "affiliation": 45.80194813696906, "achievement": 57.02246226126686, "risk_seeking": 61.54968706606685, "risk_aversion": 34.820768247909285, "risk_focus": 46.19190784979832, "power": 39.347034412685794, "reward": 69.14634980415481 }, }, "cognition": {...}, "additional_indicators": {...}, "sallee": {...}, "liwc": {...} } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Measure | Summary | High Score Definition | | --------------- | ------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------- | | `affiliation` | Reflects a person's drive for connection with individuals or groups. | Indicates a strong need for affiliation with others. | | `achievement` | Reflects a person's drive for success and accomplishment. | Indicates a strong internal drive for achievement and self-actualization. | | `power` | Reflects a person's drive for power or influence. | Indicates a strong need for domination, control, or status. | | `risk_focus` | Reflects a person’s focus on risk. | Indicates a strong psychological focus on downsides, negative outcomes, avoidant behaviors, etc. | | `reward` | Reflects a person's drive for gaining rewards. | Indicates a strong internal drive for reward. | | `risk_seeking` | Reflects the degree of focus on both risk and reward | Indicates a strong focus on seeking or engaging in informed risky (risk and reward-aware) behaviors. | | `risk_aversion` | Reflects a focus on risk without a substantially complementary focus on reward | Indicates a strong focus on avoiding risk. | ## Additional Information on the Drives Framework[​](#additional-information-on-the-drives-framework "Direct link to Additional Information on the Drives Framework") ### Affiliation[​](#affiliation "Direct link to Affiliation") Affiliation drive involves the desire for social bonds. The Affiliation measure evaluates language that relates to connecting and being in the presence of other people. Words in this category are related to the Social measure, but measure different phenomena. The Social measure is a marker of social engagement and is associated with awareness of other people, while the affiliation measure captures the need for interpersonal closeness The Affiliation indicator has been used extensively in research. For example, it has been used to examine [gender differences in evaluations of emergency medicine residents and their approach to patient care](https://onlinelibrary.wiley.com/doi/full/10.1002/aet2.10057). Research has also shown that the [feeling of affiliation, or the need to affiliate with others, can also play a role in promoting positive or negative health behaviours](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3225964/). ### Achievement[​](#achievement "Direct link to Achievement") Achievement drive involves the desire for self-mastery or success and the need to to outperform or accomplish goals. Note: Achievement drive is distinct from reward drive, as those high in achievement are not necessarily motivated by external rewards such as praise or money (those motivations are more comprehensively captured by Reward). The Achievement measure evaluates language related to actualization and fulfillment. Words in this category include certain achievement-related verbs (e.g., *advance, obtain*), and nouns (e.g., *plan, award, prize*). While research on Achievement and motivational drives is extensive, studies rooted in implicit motive theory—such as work by Schultheiss (2013)—have shown that language patterns, particularly the use of achievement-related words, can reliably indicate underlying motivational traits like the need for Achievement and Power, even when those motives are not explicitly stated. ### Power[​](#power "Direct link to Power") Power drive involves a desire for control, authority, or influence. The Power measure is broad, and evaluates language related to status (e.g., beginner, president, authority), dominance (e.g., *conquest, destroy*), wealth (e.g., *rich, poor*), and fame (e.g., *famous, royal*). ​Power motives can be an important driver of cognition and behaviour, directing attention and influencing goal-directed actions. Power language should be viewed as indicative of motivation (i.e., how much a person cares about and is influenced by power) rather than actual dominance or social influence. That is, rather than directly reflecting control of a situation or group, power language indicates the degree to which a person views the world through the lens of dominance and hierarchies, which sometimes contrasts with a more affiliative worldview (Fetterman et al., 2015) or, in organizations, more lateral power structures. Though power words don't directly map onto a person's actual status or place in a hierarchy, they do influence perceptions of power, with people who talk about power dynamics coming across as more powerful in some settings. ​Power can be an important driver of behaviour. For example, some [research](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797396/) has shown that [marker words can be important in detecting implicit motives, such as Achievement and Power](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797396/). ### Risk Focus & Reward[​](#risk-focus--reward "Direct link to Risk Focus & Reward") The Risk focus measure evaluates language related to caution (e.g., *avoid, danger*), failure (e.g., *lose, disaster*), and behavior (e.g., *apprehensive, reluctant, tentative*). This measure has been used in research to evaluate many aspects of personality and behaviour. For example, researchers have used it to investigate [how risk-taking evolves with age](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3565580/), [how risk interacts with certainty](http://www.integraorg.com/wp-content/Lenguaje%20verbal%20y%20no%20verbal%20para%20la%20toma%20de%20decisiones.pdf), and more. The Reward measure is narrower in scope as compared to the Power and Achievement categories. The measure evaluates language related to benefits (e.g., award, goal), opportunity (e.g., bet, wager, score), and feelings (e.g., eager, fearless, excited). Studies investigating the relationship between reward and behaviour are vast. For example, some researchers have used the Reward measure to investigate the [relationship between goal-setting, hopes, duties, and rewards](https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00757/full). In summary, Risk focus and Reward are both measurements of the amount that a person is thinking about or focusing on risks and rewards, respectively. These measures are independent of one another, meaning people can be high in only one or the other, or high in both or neither. For this reason, we like to plot them on a grid with four quadrants to better understand a person’s “risk profile” or risk-reward balance. Be sure to consider the context that language is taken from – a person’s risk profile is often different in their career vs. in their personal life, and can change from topic to topic or decision to decision based on how important they consider the relevant risks and rewards to be. In order to understand a person’s risk-profile in general (as a trait as opposed to a state), it is important to take samples of that person’s language from a range of different contexts. | | Low Risk-focus | High Risk-focus | | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Low Reward** | **Not Focused on Risks or Rewards.** This person is relatively indifferent to risk and rewards in the context their language was sampled from. This person may be laid-back or unambitious, or they may simply be comfortable and content in the context their language has been sampled from – for example, risks and rewards may not be relevant in a good-natured discussion about what to have for dinner. | **Risk-averse.** This person is highly focused on risks, without much focus on rewards that might be gained by taking those risks. In some cases, the rewards are not present or not relevant – people are often appropriately risk-averse in no-win situations, where risk-seeking would be pointless. In other cases, where real rewards are present, risk-aversion can hold someone back from advancing their career, their business, or their personal life. | | **High Reward** | **Reward-seeking.** This person is highly aware of rewards, without much awareness of risks. This person may be naive to legitimate risks, or there may actually be little risk present in the topic of decision at hand. Whether this person believes risks are not present or not significant, it is worth it is worth stopping to consider this assumption and its implications on decision-making. | **Focused on both Risks and Rewards.** This person is highly aware of both risks and rewards. They may be seeking to understand all possible risks before taking advantage of a great opportunity for rewards, wanting to avoid leaping without looking. In a group setting, they may be the voice of balance in a group with both reward-seekers and risk-avoiders. In general, because taking risks (even measured ones) often involves some degree of acceptance of or comfort with uncertainty, those who focus on both risks and rewards are considered risk seeking (willing to take risks). | ### Risk Seeking & Risk Aversion[​](#risk-seeking--risk-aversion "Direct link to Risk Seeking & Risk Aversion") **Risk-seeking** and **Risk-aversion** measures are derived from Risk focus and Reward. **Risk-seeking** measures the degree to which one’s language aligns with the lower right quadrant of the chart above, in which people are highly focused on both risks and rewards. These people are likely to take calculated, strategic risks with full awareness of the potential drawbacks – the type of risk-seeking behavior that can be beneficial and prevent stagnation. **Risk-aversion** measures the degree to which one’s language aligns with the upper right quadrant of the chart above, in which people are concerned about risks without considering the possible rewards to be gained. This mindset can be associated with a more cautious approach. In other words, risk-seeking and risk aversion do not measure perfectly negatively correlated constructs. Instead they capture the degree to which one shows higher risk focus combined with either low or high reward focus. ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") Scores in the Drives framework are always in the range of `0` to `100`. note Normed measures require a text sample of at least 350-500 words per person. Let's look at a couple of examples: High-scoring example Our local community has always been close to my heart. Recently, we decided to donate a small box to the local food bank. It wasn’t much, but we hoped it would make a difference for our neighbors. Seeing people come together in times of need is truly inspiring. It reminds me of how important it is to support one another, no matter how big or small the gesture. This isn’t the first time we’ve contributed, and it certainly won’t be the last. Whenever there is a drive or a community initiative, we try our best to be involved, whether through donations, volunteering, or simply spreading the word. Building relationships with those around us creates a sense of belonging that goes beyond individual achievements. It’s about knowing that you can rely on your neighbors and that they can count on you. Last year, during a particularly harsh winter, a group of us banded together to shovel driveways for elderly residents and check in on families who might need assistance. The warmth and gratitude we received in return was priceless and only reinforced how interconnected we all are. Participating in community events like park clean-ups, neighborhood watch meetings, and local charity drives brings a sense of fulfillment and joy. It’s not about recognition; it’s about creating a stronger, more caring community. I believe we all have a role to play in building a better environment for everyone around us. My family and I often reflect on how our small efforts contribute to the bigger picture, and we take pride in being part of something that transcends ourselves. Connecting with others in meaningful ways brings us all closer together, and that’s why we continue to engage in acts of kindness whenever possible. Even something as simple as checking in on a neighbor, offering a helping hand, or sharing a warm meal can brighten someone’s day. These experiences make life richer and create lasting bonds that weave the fabric of our community. We’ll continue to give what we can and encourage others to do the same because when we lift each other up, everyone benefits. ``` // partial response { "drives": { "affiliation": 100 } } ``` The paragraph above describes the value and impact of fostering community connection through acts of kindness, collaboration, and support. This is reflected in a very high `affiliation` score of 100. This means that 100% of all samples in our curated baseline dataset scores less than this sample paragraph. Lower-scoring example A few weeks ago, I dropped off a box of supplies at the local food bank. It was a straightforward decision—something to do with excess items I wasn’t going to use. The food bank serves an important function for people who need short-term assistance, so I figured it was better to pass the items along than let them go to waste. Small contributions like this seem practical for addressing immediate needs. Last winter, I shoveled some driveways after a heavy snowstorm. It made sense to help neighbors who couldn’t do it themselves. The task didn’t take much time, and it was a way to deal with the immediate challenges posed by the weather. Similarly, I’ve joined park clean-ups a couple of times, mainly because they seemed like efficient ways to deal with public maintenance issues. It’s good when straightforward solutions can be implemented without unnecessary complications. Donation drives or neighborhood initiatives are something I participate in occasionally, depending on what’s needed. It’s less about ongoing involvement and more about handling specific tasks that come up. For example, I’ve volunteered during events that required extra hands, but only when it fit into my schedule. These kinds of contributions are manageable, as they don’t demand a long-term commitment or significant effort. What I’ve found is that smaller, focused actions tend to yield results that are easier to measure. While not every problem has an immediate solution, addressing what’s within reach often works better than trying to take on broader, abstract goals. The more manageable the effort, the more likely it is to be repeated. Overall, these actions are about responding to clear and immediate needs. It’s not about building connections or strengthening community bonds but about practical, results-oriented contributions. This kind of approach feels sustainable and avoids unnecessary complexity. ``` // partial response { "drives": { "affiliation": 47.321319688241495 } } ``` In this paragraph, we describe a different scenario, which emphasizes practical, results-driven contributions to address immediate needs without focusing on community connection or emotional fulfillment. The approach prioritizes manageable, independent actions over broader or ongoing involvement. Accordingly, the `affiliation` measure returns a high score of `47.3`. This means that 47.3% of all samples in our curated baseline dataset scored below this paragraph on `affiliation`. We could surmise that the individual in Example 2 is less focused on community affiliation than the first. ## References[​](#references "Direct link to References") References * Körner, R., Overbeck, J. R., Körner, E., & Schütz, A. (2024). The language of power: Interpersonal perceptions of sense of power, dominance, and prestige based on word usage. European Journal of Personality, 38(5), 812-838. 3:31 * Fetterman, A. K., Boyd, R. L., & Robinson, M. D. (2015). Power versus affiliation in political ideology: Robust linguistic evidence for distinct motivation-related signatures. Personality and Social Psychology Bulletin, 41(9), 1195-1206. * Schultheiss O. C. (2013). Are implicit motives revealed in mere words? Testing the marker-word hypothesis with computer-based text analysis. Frontiers in psychology, 4, 748. --- # Interpersonal Circumplex The Interpersonal Circumplex displays users' language on the axes based on scores from our `agentic` and `communal` measures. The `agentic` and `communal` measures capture two primary ways of navigating social environments: pursuing personal goals (agency) and building and maintaining relationships with other people (communality). The two tendencies can operate together or alone, with all possible combinations together forming the Interpersonal Circumplex. Speakers who use highly `agentic` language are likely exerting willpower to pursue personal goals, indicating that they prioritize things individually and for personal motivations or desires. Using `communal` language suggests that a person is cooperating and connecting with others to improve social relationships, indicating that they are likely doing things with other people to help meet the group's goals. These two form the “Big Two” dimensions of social cognition. The successful balance of `agentic` and `communal` leadership behaviors involves the ability to exhibit both nurturing and supportive qualities, as well as assertiveness and drive towards achieving goals. The effective integration of Communal-Agentic leadership styles is associated with more effective leadership, as it allows leaders to build positive relationships, foster collaboration, and drive results simultaneously. Our research has shown that companies run by CEOs who effectively balance high scores on these two dimensions (the upper right quadrant of the Interpersonal Circumplex) generate higher returns than those who appear elsewhere in the circumplex. ![CALI circumplex](/assets/images/circumplex-04c0e081084b69a69ee966a3d6887a89.jpg) ## Subfacets of the Interpersonal Circumplex[​](#subfacets-of-the-interpersonal-circumplex "Direct link to Subfacets of the Interpersonal Circumplex") The radial axes contain sub-facets of `agentic` and `communal`, describing communication dynamics that combine the two main measures. These areas can represent combinations of `agentic` and `communal` and are defined as follows: `Authoritative`: Combines moderate-to-high `agentic` and low `communal`. Represents individuals who are assertive and confident in their decision-making, and may come across as more aloof or less approachable than leaders with a more `communal` focus. Some Authoritative leaders are seen as commanding. They may work to establish and maintain workplace hierarchies and can be perceived as cold. `Directive`: Combines high `agentic` and moderate-to-low `communal`. Represents individuals who are decisive and take charge, but also, at times, take into account the perspectives and input of others. Capable of balancing the needs of others while driving business results, they also have a bias toward action and can risk coming across as ruthless. These may be naturally highly `agentic` individuals who push themselves to be warmer and more relatable. `Inspirational`: Combines high `agentic` and moderate-to-high `communal`. Represents individuals who reflect a combination or balance of the two traits and can inspire and motivate others to achieve `agentic` goals while also showing empathy and concern for their well-being. This leader knows that strong relationships are required for a group to take decisive action together but also recognizes that achievements are necessary for the group’s relationships and well-being. `Coaching`: Combines moderate-to-high `agentic` and high `communal`. Represents individuals who balance the two traits while emphasizing getting along and understanding each other’s perspectives. These leaders may excel in guiding and supporting others in achieving their goals. Coaching-style leaders strike a balance between focusing on results and people, prioritizing relationships even in cases where it may be tempting to focus solely on getting ahead. `Methodical`: Combines low `communal` and moderate-to-low `agentic`. Represents individuals who are more focused on actions and tasks than they are on people and relationships. Such leaders may care about the work itself more than worldly accomplishments and tend to find it satisfying to check items off the to-do list. By leveraging their preference for tasks, methodical leaders can be effective at driving results but could benefit from remembering that they may accomplish more with the buy-in from their team. `Laissez-faire`: Combines moderate-to-low `communal` and low `agentic`. Represents individuals who are unobtrusive, laid-back leaders who take a hands-off approach to strengthening social relationships and avoid directly making bold changes. They are more likely to be happy with the status quo, maintaining their group's existing strengths — in terms of both relationships and work projects — and keeping their group on a steady, reliable course. They can be effective, low-stress leaders, especially when in charge of expert team members who need little direct oversight. However, they may risk coming across as distant or indecisive. `Democratic`: Combines moderate-to-high `communal` and low `agentic`. Represents individuals who are highly-people focused leaders and who do well encouraging collaboration and building strong teams. This leader is focused on relationships and may compromise assertiveness in order to get along with team members. At times, their focus on collaboration over competition may make it difficult for them to correct problematic team members or more ruthless competitors. `Participative`: Combines high `communal` and moderate-to-low `agentic`. Represents individuals who are people-focused and guide teams to results in a participatory, collaborative way. These leaders may shy away from the spotlight and may be seen as more supporting contributors than dominant leaders. Although this style will thrive in flat organizational structures, they may risk coming across as too indecisive or easygoing in situations where action or ruthlessness are expected. note The image of the circumplex above is generated through the Interpersonal Circumplex chart type in our [Visualization UI](https://ui.receptiviti.com/) tool. ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") Scores in the Interpersonal Circumplex framework are always in the range of `0` to `100`. note Normed measures require a text sample of at least 350-500 words per person. > **Example 1:** Let me share a remarkable experience we had with our sales teams. I kid you not, we had a whopping 15 different teams all reaching out to the same clients, making it incredibly challenging for our customers to do business with us. That's when we realized the need for a change. We decided to bring all our teams together, creating a unified point of entry to improve scalability and drive bottom-line revenue. As I approach my 10th anniversary with the company at the end of this year, I can't help but reflect on our incredible journey. I vividly remember celebrating my first anniversary with the company over dinner with my husband, and in that moment, tears welled up in my eyes. He asked what was wrong, and I confessed that this job felt incredibly lonely. However, that realization became a turning point for us. We started having more good days than bad days, witnessing progress and the rallying of our team alongside us. Gradually, our culture improved, and now we have a cohesive and caring team of people who share our vision. It's an amazing feeling to have such a strong sense of unity and agreement within the team. ``` "interpersonal_circumplex": { "agentic": 59.409121684003196, "communal": 85.2677468519442, "category": "coaching", "level": "high" ``` This sample depicts a company culture shift as told by a person in a leadership position, indicating a move toward heightened collaboration, teamwork, and a shared approach to enhance scalability and drive revenue. Additionally, the sample portrays the personal journey and emotional experience of the leader, touching upon feelings of loneliness initially but then describing a transformation toward a more positive and supportive team environment. The mention of progress, unity, and shared vision highlights the communal nature of the speaker and the sense of togetherness they have achieved, as seen by the `communal` score of `85`, and the level being `high` within the `coaching` category of `communal` vs. `agentic` leadership styles High-scoring example What has very much surprised me is the way in which American institutions have metabolized the lessons of the last software wave. So they have a much deeper understanding of what the problem would be that you would solve and the operators. So the people actually running the business in America realized the determinant variable for changing their margins, changing their profit and outmaneuvering their competition is AI. And so then you have the dynamic with the Board. The Board is insisting on governance. The operators are insisting on adoption. And this is very, very different than anything I’ve ever seen. Typically, with software intervention from the outside, you’ve got to convince the CIO, the Board doesn’t care and the operator is focused on business metrics. Here, the operators are saying, I know, for example, I was dealing with a large -- I was talking to one of the largest transporters of people in the world, and the operator is saying, okay, I know we have a problem with churn. I know we have a problem with logistics. These problems can only be solved algorithmatically, but I can’t solve them unless I have a governance structure that allows me to solve them, because my Board will go nuts. That is basically a shape of a problem only we can solve and this is happening all over America, various problems like this. I’ll give you another example. One of the advantages of AI is that, if you want to do manufacturing, so you want to do Japanese manufacturing in America, you’re actually having to use Japanese methodologies with American workers. Share the force \[ph] is not impossible. AI can actually allow you to manage the internal dynamics of your workforce so that you get the Japanese culture with American workers in the U.S. And this is just absolutely game-changing for our country. And why is it that -- now there are other countries, but Europe here is really going to struggle, but other countries, why is this game shaming particularly for America? Because it allows us to do manufacturing at a level we typically could not do in use cases that we were not good at. It allows us to change the margins. It allows us to do it safely. It realigns the institution. So now the IT person, the operator and the CEO are very focused on a use case that will change the share price. It’s very unusual for institutions to be fully aligned unless someone’s shooting at you. So the only institutions I’ve ever seen that are fully aligned, literally someone is shooting at you. And even then, the IT person is often like, well, why can’t I buy this from my cousins. It’s -- and literally, the special operator kicks the IT. So this is a completely different moment. And then I’ll say something else, typically, this moment would be captured by non-incumbents, people early-stage -- early-stage companies. But the market is moving too quickly. The barriers to get inside these companies are still there. And then the other incumbents, some of them are very interesting, but most of them just don’t build products. They do not -- they are companies with thin -- beautiful companies, they do things we can’t do, but they don’t really have many engineers. The engineers have been there for 50 years. They have huge sales forces. They cannot build something that’s relevant and if they could, it’s going to take years and there’s nothing to acquire. We’re not for sale. ``` "interpersonal_circumplex": { "agentic": 84.85258817012371, "communal": 46.59799243574429, "category": "directive", "level": "high" ``` The speaker of this sample demonstrates an agentic nature through their confident and proactive approach in their language. They assertively state that their company has gained a significant advantage in recruiting, highlighting their success in attracting top talent and positioning themselves ahead of larger companies. The speaker's emphasis on recognizing potential inefficiencies and addressing them reflects their task-oriented mindset and keen awareness of challenges. Their mention of the company's expansion and embrace of division and specialization of labor shows a deliberate and strategic decision-making process. Overall, the speaker's tone and language convey a sense of control, initiative, and determination, which characterize their agentic nature, as reflected in their score of `84` for `agentic` and the level being `high` within the `directive` category of `communal` vs. `agentic` leadership styles. ## Correlation between `communion_language` and `agency_language` in LIWC Extension In addition to the `communal` and `agentic` measures that are part of the Interpersonal Circumplex framework, the [LIWC Extension](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md) framework also features two dimensions that have similar labels, namely `communion_language` and `agency_language`. While similar in name, these measures are calculated differently and will generate different scores when used on the same text sample. `Communion_language` and `agency_language` are categories in [LIWC Extension](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md) that measure the frequency of utterances that are communion- or agency-focused, whereas `communal` and `agentic` in the Interpersonal Circumplex are measures that identify the degree to which people's language reflect agentic and communal psychological traits. The `communal` and `agentic` algorithms consist of multiple input categories with varying weights, that take into account the specific psycholinguistic parameters that comprise the measures. These measures are also normed against our extensive datasets to allow us to express the scores as percentiles that can be compared against the general population. It is worth noting that someone with a high score in `communion_language` is likely to be a very communal person, whereas the inverse isn’t necessarily true - someone with a low score in `communion_language` might still be a very communal person but they express it in a different way. Similarly, high levels of \`\`agency\_language`is not the only linguistic marker that correlates with a strong sense of agency. Because the`communal`and`agentic\` measures are based on formulas, we are able to get a more nuanced view of the psychological contexts and are able to capture people who may be, for example, very communal and express it through positivity and community focus, or are highly agentic but demonstrate it through collaborative decision-making and cooperative problem-solving methods rather than assertive language. --- # Needs and Values Receptiviti’s Needs and Values framework comprises 17 measures that evaluate aspects of what motivates a person’s preferences, habits, and decision-making. **Needs:** Each of the 12 Needs measures should be interpreted as traits that can impact decisions and can be predictive of their consumption preferences and habits. **Values:** Each of the 5 Values measures should be interpreted as factors that motivate and influence a person’s decision-making. Receptiviti customers often use Needs and Values to understand their audience, employees, and people who are important to their businesses, and create messaging that aligns with their unique needs and values. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1594, "words_remaining": 248406, "percent_used": 0.64, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "576920d5-ac3b-4fac-b7f5-a00189a6f194", "language": "en", "version": "v1.0.0", "summary": {...} “liwc”: {...} “needs”: { “challenge”: 65.27562618024193, “closeness”: 71.48750044929895, “curiosity”: 48.9476513488303, “excitement”: 43.18118628827615, “harmony”: 46.11177008197975, “ideal”: 44.66317116045183, “liberty”: 38.072214302224985, “love”: 46.992509890505325, “practicality”: 69.49822653570534, “self_expression”: 30.9596279050637, “stability”: 52.900419551226044, “structure”: 56.45318425385919 }, “values”: { “conservative”: 67.71114620465193, “hedonism”: 44.98279785270812, “open_to_change”: 36.462844784708935, “self_enhancement”: 49.60027230025912, “self_transcendence”: 22.849709647897217 }, “custom”: {...}, ``` ## Measures[​](#measures "Direct link to Measures") **Needs** | Score | Summary | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `challenge` | The tendency towards overcoming obstacles and achieving difficult things. | | `closeness` | The need or prioritization of intimate and affectionate bonds with loved ones. | | `curiosity` | The need or prioritization of exploring concepts that are either new to oneself or new to one’s society. | | `excitement` | The need for energetic activity in life, whether it be physical, social, or mental. In extreme cases, these people may have trouble with down-time or quiet time. | | `harmony` | The need for peace and agreement among those around a person. This may appear as an aversion to conflict or as strong conflict-management skills. | | `ideal` | To strive for a utopian or idealistic reality – one may become disillusioned when this is out of reach, or may be resilient in persisting toward the vision of a better world. | | `liberty` | To value freedom above all things, often at the expense of safety or equality. | | `love` | To very highly value affection, typically romantic affection. | | `practicality` | The main driver of decisions for someone who scores highly in practicality is what is the most logical or pragmatic choice. | | `self_expression` | The enjoyment of discovering and asserting one’s own identity, sexuality, artistry, and creativity. | | `stability` | The prioritization of comfort from things one relies on, and possible reticence of destabilizing changes such as leaving one's job or selling one's home. | | `structure` | The appreciation of a methodical, careful, and logical approach to accomplishing tasks. Not likely to be spontaneous and not error-prone in one's daily work. | **Values** | Score | Summary | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `self_transcendence` | Associated with the expansion of one’s personal boundaries, greater awareness of oneself and others, increased calmness and spirituality, and the experience of seeking coherence, integration, and meaning across all dimensions of life. | | `conservative` | To hold conservative values and place significant weight on the ways of life that one is used to, while tending to believe that change should happen slowly. | | `self_enhancement` | The need for inflating one’s own accomplishments or skills, often with the goal of elevating oneself above others. | | `hedonism` | To prioritize physical pleasures, including (but not limited to) food, alcohol, drugs, and sex. | | `open_to_change` | To value open-mindedness and to listen to input from a variety of sources. | ## Science Behind the Framework[​](#science-behind-the-framework "Direct link to Science Behind the Framework") ### Needs Identifying and understanding customer needs is incredibly useful in determining how they will respond to brands, product offerings, marketing campaigns, incentive programs, etc. ([Armstrong, Kotler 2013](https://www.amazon.ca/Principles-Marketing-17th-Philip-Kotler/dp/013449251X)). The Receptiviti **Needs** framework is rooted in the Universal Needs Map ([Ford 2005](https://www.amazon.ca/Brands-Laid-Bare-Evidence-Based-Management/dp/0470012838)). Ford’s work describes each need, as well as highlights how they complement each other, and how successful brands can appeal to strategic combinations of needs. The Receptiviti **Needs** measures unlock immense potential for marketers, employers, and any organization seeking to understand their customers. We provide data on all 12 of Ford’s needs, namely: * `Challenge` * `Closeness` * `Curiosity` * `Excitement` * `Harmony` * `Ideals` * `Liberty` * `Love` * `Practicality` * `Self-Expression` * `Stability` * `Structure` See the [Measures](#measures) section for definitions. ### Values Fundamental human values have been studied extensively throughout the development of social sciences. These values help define how people organize themselves individually and into groups, as well as how they react to change ([Schwartz 2006](https://www.cairn-int.info/journal-revue-francaise-de-sociologie-1-2006-4-page-929.htm)). Over the years, the labeling of these human values has varied. Schwartz’s work aimed at solidifying a core set of universal human values derived through empirical study in many countries across the world. His first iteration analyzed 20 countries to construct his initial framework of values ([Schwartz 1992](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.220.3674\&rep=rep1\&type=pdf)). He has since applied this framework to 68 countries to demonstrate its universality across humankind ([Schwartz 2006](https://www.cairn-int.info/journal-revue-francaise-de-sociologie-1-2006-4-page-929.htm)). Schwartz’s work boils down the core values into five unique dimensions, which are calculated by the Receptiviti **Values** framework. These dimensions are: * `Conservatism` * `Hedonism` * `Openness to Change` * `Self-Enhancement` * `Self-Transcendence` See the [Measures](#measures) section for definitions. ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") Needs and Values scores are between `0` to `100`. note Normed measures require a text sample of at least 350-500 words per person. > **Example 1:** I do transcendental meditation. It's—it's great. And so I do that. And when I slip up on it, you know it's not the best because it's like—it's better when I do. And sometimes I can be in a ton of pain and meditate and it goes away. It's amazing. I also work out every day. But I also listen to my body. So if I'm in a lot of pain and deep stress, I might not do either as hard of a workout or I might, you know, not work out at all. I do listen to my body and I listen to what it's telling me. I do talk therapy. Dialectical behavioral therapy. And I also do lots of other things like opposite action, for example. So let's say you're feeling really depressed and you're at home and you've been at home for seven days straight and you just can't leave the house and you just—you practice opposite action. Someone invites you to go somewhere, or you reach out to a friend and you say, hey, you want to play a game of poker? Get up, get in your car, and go. Opposite action. That's something that I do all the time. All the time I actively work on myself. I have to. If I don't, I will sit and I will be in pain all day. ``` // partial response { "needs": { "challenge": 79.52765735952477, "structure": 30.38962307483074 } } ``` This sample scores quite high in `challenge` at `79.5`. This means that almost 80% of text samples in our baseline dataset score less than this text sample for `challenge`, which evaluates a person's tendency towards overcoming obstacles and achieving difficult things. On the other hand, this sample scores low in `structure`, at `30.3`, indicating that the speaker does not have a particularly methodical or logical approach to things. > **Example 2:** I’m proudly the first person to graduate college in my family, making my parents very happy and proud. I’m also a very successful business owner. We’ve grown our company from one State to 11 States. I’m a very hard worker. I’ve always paid my taxes. I’ve never been arrested. I’ve never done drugs, but I’ve gotten a few speeding tickets in my day. What you need to know about me is I’m a very regular person. ``` // partial response { "needs": { "conservative": 74.26351907779349, "curiosity": 17.42638362795728, "excitement": 9.530917749551895 } } ``` This sample scores quite high in `conservative`, at `74.2`, indicating that the speaker is likely to hold conservative values and might tend to be resistant to change, preferring to live within the boundaries what they are used to. Meanwhile, `curiosity` scores quite low at `17.4`, indicating that the speaker is not someone who enjoys exploring concepts that are unfamiliar to them or that might be out of their comfort zone. Similarly, the speaker scores very low in `excitement`, at just `9.5`, which indicates that they may not have the need for much energetic activity, whether it be physical, social, or mental. --- # Personality - Big 5 The Big Five, also known as the Five Factor Model, is one of the dominant factor models of personality in psychology today. It proposes that every aspect of how we see each other and ourselves can be organized into five theoretically independent clusters of characteristics, called traits. These traits are as follows (see Schmidt et al., 2007): * **Openness to experience** - Openness to new ideas and feelings; interest in art, complex thoughts, emotions, and progressive politics. * **Conscientiousness** - Adherence to order, rules, and duty; involves self-control, a strong work ethic, and a desire for tidiness or organization. * **Extraversion** - Sociability and social dominance; a tendency to be positive, friendly, and active, seeking out others’ attention and respect. * **Agreeableness** - Easygoingness and prosociality; desire to make others happy, help people, fit in, and be a good or moral person. * **Neuroticism** - Vulnerability to stress; tendency to experience negative emotions such as sadness, anxiety, and self-consciousness or embarrassment. Each trait is made up of a number of narrower characteristics, called facets, that all correlate with each other more strongly than they correlate with other facets and traits. For example, people who enjoy large parties also tend to like to stay busy; thus, sociability and activity are facets of the overarching trait Extraversion. The Big Five can be divided into either three or six facets per trait. Both approaches have been similarly statistically validated; the 30-facet model provides greater granularity and differentiation among the separate aspects of each trait (see the table below for definitions and interpretations of each trait and facet score). Traits are relatively stable over time and across situations. Even in situations that powerfully constrain or influence a person’s behavior, a person’s rank order relative to other people on a given trait tends to remain similar. For example, a highly emotionally stable person (i.e., low in Neuroticism) will be more tense in a high-stakes negotiation than they would be chatting with an old friend – but they will still be more laid-back than most people would be in the same stressful situation. The Big Five is not domain specific: It describes universal aspects of personality that are not limited to specific contexts or use cases. Big Five measures have been used for a myriad of purposes, ranging from predicting depression vulnerability and relationship quality to placing personnel in roles where they will perform best and predicting how different people will respond to crises. The language-based personality scores generated by the API are normed against a large, diverse corpus of baseline language that are representative of how people naturally write and talk in everyday life. A normed personality score of 90, for example, indicates that 90 percent of people in Receptiviti’s baseline corpus had lower scores on that trait or facet. Receptiviti’s Big Five measures are based on verbal behavior (patterns of natural language use) rather than self-reports. Some gaps between behavioral (linguistic) and survey-based (self-report) measures are to be expected – partly because there are always going to be some traits that are more obvious to other people than to ourselves. For example, even self-aware people may be unaware of how (dis)agreeable or (dis)organized they seem to others. We recommend using text samples containing at least 350 words to generate the most accurate Big Five results. Larger text samples will better reflect a person’s typical way of talking and thinking (in the same way that larger samples of research participants tend to better represent behavior in the human population). info Our measures are baselined against our proprietary personality datasets, which are comprised of hundreds of thousands of personality-labelled language samples that exceed 350 words. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1594, "words_remaining": 248406, "percent_used": 0.64, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "576920d5-ac3b-4fac-b7f5-a00189a6f194", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": { "extraversion": 38.376797968177726, "active": 43.54976845660127, "assertive": 23.413367474901293, "cheerful": 41.683272292714385, "energetic": 49.66502347590169, "friendly": 46.019838073790424, "sociable": 31.71679439852739, "openness": 48.510129205463244, "adventurous": 59.52121777987507, "artistic": 56.07331452847811, "emotionally_aware": 22.422942272399894, "imaginative": 40.36145843687435, "intellectual": 50.480342122839446, "liberal": 55.33647325525235, "conscientiousness": 29.271006599653877, "ambitious": 24.60101067813019, "cautious": 48.755724740818984, "disciplined": 21.756998699364903, "dutiful": 15.564867422030998, "organized": 1.0959348508415885, "self_assured": 63.49038255719855, "neuroticism": 27.567116936762485, "aggressive": 16.557837752477873, "anxiety_prone": 40.588156125165526, "impulsive": 55.30945336924723, "melancholy": 19.17393584015485, "self_conscious": 33.99859821354556, "stress_prone": 30.60302323914423, "agreeableness": 45.08628021530888, "cooperative": 49.0963123235805, "empathetic": 54.03126544927001, "genuine": 20.232439533115723, "generous": 39.565857799875396, "humble": 62.604514523910886, "trusting": 53.10217715031157 }, "social_dynamics": {...}, "drives": {...}, "cognition": {...}, "additional_indicators": {...}, "sallee": {...}, "liwc": {...} } ] } ``` ## Categories and Facets[​](#categories-and-facets "Direct link to Categories and Facets") | Category | Summary | High Score | Low Score | | ------------------- | --------------------------------------------------------------- | --------------------------------------------------------------- | ------------------------------------------------------------------- | | `openness` | Examines how open a person is to new ideas and experiences. | Suggests creativity, emotional expressiveness, and imagination. | Suggests a more conventional, predictable, and practical approach. | | **Facets** | | | | | `artistic` | Reflects appreciation and enjoyment of the arts. | Indicates a strong appreciation and enjoyment of the arts. | Indicates a lower likelihood of appreciating and enjoying the arts. | | `adventurous` | Reflects interest in and seeking out of adventure. | Indicates a preference for seeking out adventure. | Indicates less likelihood of seeking out adventure. | | `intellectual` | Reflects inclination towards intellectual or academic pursuits. | Indicates an intellectual or academically inclined mindset. | Indicates less inclination towards intellectual pursuits. | | `liberal` | Reflects social and ideological liberalism. | Indicates socially and ideologically liberal views. | Indicates less socially and ideologically liberal views. | | `imaginative` | Reflects a vivid imagination. | Indicates a strong imaginative capacity. | Indicates a lower imaginative capacity. | | `emotionally_aware` | Reflects awareness and connection with one's emotions. | Indicates strong emotional awareness and connection. | Indicates lower emotional awareness and connection. |
| Category | Summary | High Score | Low Score | | ------------------- | ----------------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------- | | `conscientiousness` | Assesses reliability, organization, discipline, and deliberation. | Indicates strong organization, discipline, and deliberation. | Indicates impulsivity, carelessness, or disorganization. | | **Facets** | | | | | `self_assured` | Reflects self-confidence. | Indicates a high level of self-confidence. | Indicates lower self-confidence. | | `disciplined` | Reflects adherence to routines and rules. | Indicates a strong likelihood to follow routines and rules. | Indicates a lower likelihood of following routines and rules. | | `ambitious` | Reflects drive and aspiration for achievement. | Indicates ambition and drive for success. | Indicates lower ambition or drive. | | `dutiful` | Reflects respect for expectations and authority. | Indicates respect for expectations or authority. | Indicates less respect for expectations or authority. | | `cautious` | Reflects carefulness and prudence. | Indicates a cautious approach. | Indicates a less cautious approach. | | `organized` | Reflects orderliness and organization. | Indicates a high level of organization. | Indicates lower organization. |
| Measure | Summary | High Score | Low Score | | -------------- | ------------------------------------------------------------------- | ---------------------------------------------------------- | ---------------------------------------------------------------------- | | `extraversion` | Assesses how energized a person feels when interacting with others. | Indicates sociability, assertiveness, and outgoing nature. | Indicates reserved, reflective, and introverted tendencies. | | **Facets** | | | | | `sociable` | Reflects enjoyment of social situations. | Indicates seeking out and enjoying social settings. | Indicates less likelihood of seeking out and enjoying social settings. | | `friendly` | Reflects warmth and positivity in social interactions. | Indicates friendliness and positivity. | Indicates less friendliness and positivity. | | `assertive` | Reflects confidence in expressing ideas and needs. | Indicates assertiveness and comfort in self-expression. | Indicates less assertiveness and comfort in self-expression. | | `active` | Reflects a need for activity and engagement. | Indicates a strong drive for activity and engagement. | Indicates a lower need for activity and engagement. | | `energetic` | Reflects energy and enthusiasm. | Indicates a high level of energy and enthusiasm. | Indicates lower energy and enthusiasm. | | `cheerful` | Reflects happiness and cheerfulness. | Indicates strong cheerfulness and positivity. | Indicates lower levels of cheerfulness and positivity. |
| Measure | Summary | High Score | Low Score | | --------------- | -------------------------------------------------------- | --------------------------------------------------- | ----------------------------------------------------------- | | `agreeableness` | Assesses inclination to please others. | Indicates cooperation, trust, and likability. | Indicates critical, demanding, or unsympathetic tendencies. | | **Facets** | | | | | `generous` | Reflects enjoyment of helping others with time or money. | Indicates strong generosity. | Indicates less likelihood of generosity. | | `trusting` | Reflects ease of trusting others. | Indicates high trust in others. | Indicates lower trust in others. | | `cooperative` | Reflects consideration for others' needs. | Indicates consideration for others' needs. | Indicates less frequent consideration for others' needs. | | `empathetic` | Reflects internalizing the feelings of others. | Indicates strong empathy. | Indicates lower empathy. | | `genuine` | Reflects authenticity and honesty. | Indicates a high level of authenticity and honesty. | Indicates lower authenticity and honesty. | | `humble` | Reflects humility and modesty. | Indicates strong humility and modesty. | Indicates lower humility and modesty. |
| Measure | Summary | High Score | Low Score | | ---------------- | --------------------------------------------------------------------------- | ----------------------------------------------- | ----------------------------------------------- | | `neuroticism` | Assesses tendencies towards anxiety, unhappiness, pessimism, or depression. | Indicates anxiety, unhappiness, or pessimism. | Indicates calmness, resilience, and confidence. | | **Facets** | | | | | `impulsive` | Reflects impulsive behavior. | Indicates a tendency towards impulsive actions. | Indicates less impulsivity. | | `stress_prone` | Reflects susceptibility to stress. | Indicates strong reactions to stress. | Indicates lower susceptibility to stress. | | `anxiety_prone` | Reflects tendency towards anxiety. | Indicates a strong tendency towards anxiety. | Indicates lower tendency towards anxiety. | | `aggressive` | Reflects aggressive behavior. | Indicates a tendency towards aggression. | Indicates less aggression. | | `melancholy` | Reflects a tendency towards melancholy. | Indicates a melancholic disposition. | Indicates less melancholy. | | `self_conscious` | Reflects feelings of embarrassment or anxiety about oneself. | Indicates strong self-consciousness or anxiety. | Indicates lower self-consciousness or anxiety. | ## Big Five Research Background[​](#big-five-research-background "Direct link to Big Five Research Background") ### History Trying to simplify our worlds and finding patterns in seemingly random information are both built-in tendencies of the human brain that help us deal with life more efficiently (Gigerenzer & Gaissmaier, 2011). At least since Ancient Greece (and no doubt before), philosophers, psychologists, and others have attempted to identify the several mostly independent trait dimensions along which all people vary (McAdams, 1997). The dominant view today is that rather than each person falling into one of a few broad categories (e.g., phlegmatic, choleric, sanguine, or melancholic), people can vary along several semi-independent dimensions, resulting in a unique personality constellation or profile that remains relatively stable across the lifespan. In the first half of the 20th century, Gordon Allport (1937) and colleagues approached the task of enumerating and defining the basic dimensions of personality by taking the thousands of person descriptors in the English language and manually grouping them into categories. Later, Cattell (1945) condensed these categories down to a smaller group of adjectives and asked people to rate themselves and others on them on a quantitative scale in order to study how the words statistically cluster when put into practice. His methodological approach finally later became the basis of the Big Five, also known as the Five Factor Model, devised by McCrae and Costa (1987). The Big Five has since become the consensus model in personality psychology, although elaborations on the basic five factors (such as the more recent six-factor HEXACO model, which separates honesty-humility from the other facets of agreeableness; Lee & Ashton, 2004) have gained some traction as well (Ashton et al., 2014; Pletzer et al., 2020). ## Mental and Physical Health[​](#mental-and-physical-health "Direct link to Mental and Physical Health") Understanding a person’s personality helps predict what mental and physical health conditions they are most vulnerable to and can help calibrate treatment. For example, most mental health conditions are correlated with neuroticism. People who are neurotic are not necessarily chronically distressed, but they do tend to be more negative (including interpreting ambiguous information, like a friend not responding to a text or an oddly-shaped mole, negatively) and experience more negative emotions in response to life stressors. Extraverts tend to be more positive, active, and socially connected – characteristics associated with well-being. Extraverts therefore tend to be physically and mentally happier than people at the opposite end of the spectrum (i.e., withdrawn, low-energy, not positive). Eminent personality researchers, such as Jack Block, have argued that people at either extreme end of any trait are at increased risk of mental disorders or psychological distress. For example, conscientiousness, despite being socially valued, is associated with overcontrolled disorders like obsessive-compulsive disorder. Extreme emotional stability can manifest as flat affect, socially inappropriate affect, or insensibility to stressors (“fiddling while Rome burns”; see Harenski et al., 2019). ## Leadership and the Workplace[​](#leadership-and-the-workplace "Direct link to Leadership and the Workplace") In the workplace, the Big Five can help human resources and people analytics experts in placing employees in positions that are the best fit for their strengths and vulnerabilities. Along the same lines, the Big Five has relevance for assembling teams that have a balance of different styles of thinking and diverse approaches to problem solving. Research shows that teams with diverse ways of looking at a task sometimes need to work harder to understand each other but ultimately tend to come up with better solutions. A common misconception of the Big Five is that some traits are universally good (extraversion, agreeableness, conscientiousness, and openness) and others are bad (neuroticism and its facets, e.g., depression, anxiety). Although there are some broad correlations between personality (primarily conscientiousness and emotional stability) and overall job satisfaction and success (Sutin et al., 2009), research on personality in the workplace confirms that people along each trait dimension spectrum have ways they can contribute to the group. For example, people who are higher in neuroticism (more negative, more sensitive to threats) may excel in jobs where it is necessary to be aware of and plan ahead for worst case scenarios; people low in agreeableness may do much better than easygoing, compliant people in positions that regularly require conflict and thick skin, such as many legal and academic professions. The degree to which personality determines performance in specific roles is also quite variable, depending on the nature of the role. When roles are highly structured and employees don’t have much leeway in how they work or make decisions, personality tends to be strongly predictive of performance (i.e., you need to have the right person for the right job) – however, personality matters less in positions with more flexibility that allow people to have some control over how they structure their work (Judge & Zapata, 2015). The same research has found that some positions, regardless of how structured they are, also tend to activate specific personality traits, meaning that people without that trait are not likely to be as successful as those that do (e.g., openness may be necessary – not just desirable – in roles that demand innovation and creativity). ## Measure Development[​](#measure-development "Direct link to Measure Development") As with any framework that Receptiviti develops, we created the Big Five measures using a version of a multi-trait multi-method approach (see Campbell et al., 1959). We studied the Big Five traits in self-reports, observer reports, and behavior in several waves of testing, including replicating effects across text samples representing a range of social contexts to ensure that the effects replicate (i.e., work equally well) across different samples. Triangulating personality using these methods is the gold standard for research in personality psychology. In all cases, Receptiviti follows a similar methodology: First, we collect ground truth data and analyze it for patterns in language use. Next, a panel of experts works to build algorithms reflecting that ground truth while remaining consistent with prior research. Finally, we test algorithms across different observations, different contexts, and different groups of people to understand the algorithms' consistency, reliability, expected performance, and best use cases. ## Text Samples[​](#text-samples "Direct link to Text Samples") We conduct validation with three main datasets: (1) a large online writing sample (including stream-of-consciousness writing and writings based on the Picture Story Exercise) with self-reported personality, (2) longitudinal naturalistic samples of workplace interactions (from video transcripts and chat messages) including self and peer ratings, and (3) another naturalistic sample of business leaders’ quarterly earning calls. ## Panel of Experts[​](#panel-of-experts "Direct link to Panel of Experts") Our frameworks are built and refined by a panel of experts. Each member of the panel brings years of experience working with LIWC and language psychology, as well as a unique perspective from their background in academia and industry. The core team has over 50 years of combined academic and applied domain expertise, and regularly consults a broader network of domain experts for specialized tasks. In evaluating a new algorithm, the panel members conduct independent tests and then resolve discrepancies or disagreements regarding algorithm design through discussion. Every algorithm Receptiviti uses is a team product that the panel of experts has reached a consensus on through intensive testing. Part of Receptiviti’s Expert Panel’s role is to be familiar with empirical research on personality and keep pace with the state of the art in language-based models of personality and individual differences. Receptiviti's panel’s collective expertise in social-personality psychology, linguistics, computational linguistics, economics, and mathematics gives us a big-picture perspective on academic and applied advances in personality science. ## Results[​](#results "Direct link to Results") ### Internal Consistency The Big Five metrics have been evaluated for internal consistency using standard statistical measures of intercorrelations among their component parts (Cronbach’s α). Internal consistency is desirable for algorithms that are meant to be internally coherent. The logic is that if different linguistic cues correlate with each other, they are more likely to all reflect a common underlying trait or characteristic. Note that there are some cases where the goal of a measure is conjunctive – to measure multiple mostly independent variables rather than one single variable – and internal consistency is not expected or required. For facets below with low reliability (α < .30), that simply means that they are measuring a combination of behavioral cues that do not correlate with each other in all contexts or groups of people (e.g., self-focus correlates with negativity for depression-prone people but not others). Along the same lines, some of the lower-reliability traits, like openness to experience, are conceptually less internally coherent than other traits. Whereas most agreeable people more or less resemble each other and the facets positively correlate with each other (e.g., if someone is cooperative, they are probably also empathetic), there are different types of people who are open to new experiences. For example, artists, political liberals, and intellectuals are all examples of people who are high in openness to new experiences, yet those groups only partly overlap: many intellectuals are politically conservative, and many people who love art wouldn’t consider themselves to be intellectual or philosophical. | Trait | Raw α | Standardized α | | ----------------- | ----- | -------------- | | Extraversion | .62 | .64 | | Agreeableness | .64 | .66 | | Openness | .37 | .36 | | Conscientiousness | .40 | .43 | | Neuroticism | .82 | .80 | :::noteFor all tables, α = Cronbach’s alpha, which is based on an average of every pairwise correlation within the component ingredients of a measure. Standardized alpha is more appropriate than raw alpha for measures made up of ingredients that are on different scales (e.g., low and high frequency categories, or raw and normed scores). For a thorough discussion of all currently available reliability metrics’ strengths and weaknesses for different personality applications, see Revelle and Condon (2018). ## Test-Retest Reliability[​](#test-retest-reliability "Direct link to Test-Retest Reliability") The temporal consistency of measures is a critical component of reliability. If a trait is expected to remain stable over time, measures of that trait should likewise show a high degree of test-retest stability – scores at one point in time should strongly positively correlate with scores at a later time (McCrae et al., 2011). If a person takes a personality test repeatedly and it sometimes or often gives them different results, that suggests that the ingredients of the measure are unstable (i.e., it measures behaviors that vary randomly and are not tied to a stable internal trait), the measurement method itself is flawed (e.g., measuring continuous traits using binary forced-choice questions on surveys, or using invalid dictionaries in language research), or both. In a longitudinal validation study that took place over approximately two years, Receptiviti assessed stability between earlier and later language-based measures of personality profiles in work meeting transcripts totaling over 1.1 million words. Specifically, we converted Pearson’s *r* from within-person profile correlations of Receptiviti’s personality measures to Fisher’s *z*, calculated averages, and then converted mean *z*'s back to *r*. Personality profiles from the two time periods were highly correlated over the 30 facets, *r* = .96 (min *r* = .78, max *r* = .99). Looking at each trait individually, every trait was similarly highly consistent (mean *r* = .98), with extraversion showing the least (*r* = .96) and neuroticism showing the most test-retest reliability over time (*r* = .995). There was some individual variation among participants in the study, but even the lowest within-person test-retest reliability was acceptably high (*r* = .70 for extraversion facets); most individual correlations were in the *r* = .90-.98 range. ## Robustness Across Samples[​](#robustness-across-samples "Direct link to Robustness Across Samples") Robust algorithms should generalize across groups that differ from those they are initially tested on, with little to no difference in error rates. We tested our personality algorithms across a variety of relevant populations (e.g., college students, CEOs, startup employees) and contexts (e.g., creative writing, self-descriptions, earnings calls) to ensure that the traits and facet measures are similarly accurate and reliable across contexts. In Receptiviti’s internal testing, typical correlations across contexts (e.g., video calls and online chats) for the same person average around *r* = .60 across all traits. Not surprisingly, we see stronger correlations for traits that tend to be more external and readily observable, such as extraversion (*r* = .88). Traits that are less outwardly apparent, such as neuroticism and openness to experience, tend to have lower correlations across contexts. You can expect more consistency across situations and language samples for people who are equally comfortable sharing those more internal aspects of their personalities across social contexts. For example, some people openly discuss negative emotions in public or at work, but it is more common for people to suppress or mask those feelings in certain settings, such as work meetings. ## Correlations with Self-Reports[​](#correlations-with-self-reports "Direct link to Correlations with Self-Reports") In one validation study of over 1.1 million words spoken in video meetings, our language measures were shown to correlate with self-reports and peer reports. Correlations between self-reports and language measures of personality tend to be higher with higher word counts. We recommend at least 350 words per sample; we suggest using a 500-word minimum if you have larger samples available, such as longitudinal samples of individuals’ language use over time. In interpreting correlations with any survey data, it is important to remember a few psychometric issues: **common method variance** and **self-other knowledge asymmetry**. **Common method variance** refers to the fact that some of the variance in any measure is due to the methods used to collect the data. One behavioral measure will usually correlate more strongly with another behavioral measure than with survey measures, and vice versa (Eastwick et al., 2011). Thus, Receptiviti's language measures will correlate more strongly with objective behavioral measures (e.g., job performance) than they do with self-report measures. **Self-other knowledge asymmetry** refers to the observation that some traits, such as neuroticism in particular, will always be more easily assessed through introspection, whereas others, like agreeableness and conscientiousness, will typically be easier for others to judge accurately (Carlson et al., 2013). Not being able to see our facial expressions or hear our words from a more objective outside perspective, we will always be somewhat blind to some of our personality characteristics. Any time a close friend has commented on some aspect of your behavior that you weren’t previously aware of (e.g., “your eyes really light up whenever you talk about psychology” or “did you know you wring your hands when your mom visits?”), that’s evidence of self-other knowledge asymmetries in real life. ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") Scores in the Big 5 Personality framework are always in the range of `0` to `100`. note Normed measures require a text sample of at least 350-500 words per person. Following are examples of how to interpret the Personality scores derived from sample language: High-scoring example I started my career as a developer 15 years ago. Java used to be all the rage back then. I loved the intricacies of working with code; each day felt like a challenge to overcome, a mountain to climb - it was exhilarating! Life as a developer means needing to continuously upskill oneself or risk becoming irrelevant. As a lifelong learner, I embraced this with joy and enthusiasm. I worked in the same company for 12 years and found the team and environment to be excellent! When the company had to shut down due to financial woes, I found myself in another dev shop - this one focused on building next-gen AI solutions for interplanetary robots. Over time, I realized that adapting to new technologies was not just a necessity but a passion. Working on AI solutions meant that I had to stay ahead of the curve, constantly learning and experimenting with new methods and tools. This kept me energized and motivated, even during challenging times. I took pride in mentoring junior developers and watching them grow into skilled engineers, knowing that I had a hand in their success. Collaboration became an integral part of my daily routine, and I thrived in an environment where everyone pushed each other to excel. I wouldn't call myself overly ambitious - not like my friends in investment banking. However, I have always been determined to do my best, even if my ambitions don’t align with those in high-pressure careers. With the strength of 15 years of experience behind me, I moved up the ranks fairly quickly. At the moment, I am CTO for the company that builds those AI solutions I just mentioned. I still get to code a lot - which is the whole point I even got into this career in the first place - so that's excellent! Coding brings joy and fulfillment to my work life; it is what gives me the spark to wake up each day ready to create and innovate. Despite the added managerial responsibilities of being a CTO, I have stayed hands-on with technical challenges and coding projects. This keeps me connected to the core of what drew me to this field initially. Mentoring team members and engaging in problem-solving sessions has allowed me to grow further as both a leader and a developer, contributing positively to my own self-assurance. `self_assured` evaluates the degree to which a person uses language that suggests they are confident in themselves. A high score suggests an individual is using language that makes them appear highly confident in themselves, whereas a low score indicates that they are using language that makes them appear less confident in themselves. The paragraph above returns a score of `79.7` for `self_assured`. This means that 79.7% of text samples in our baseline dataset score less than the current text sample for `self_assured`. This is a good indication that the paragraph above indicates moderately high self-assuredness. ``` // partial response { "personality": { "self_assured": 79.70697852638573 } } ``` Details Low-scoring example I started my career as a developer 15 years ago. Java used to be all the rage back then. I despised the complexities of working with code; each day felt monotonous and draining - I was and still am tired. Life as a developer requires continuously upskilling oneself to stay relevant, and that constant need felt more burdensome than exciting. As someone who has always leaned toward a laid-back, less demanding life, this aspect of the job was nothing but a source of frustration for me. I remained in the same company for 12 years, not because of loyalty or satisfaction, but because I simply didn't know what else to do with my life. When the company eventually shut down due to financial problems, I found myself completely lost. I wasn't devastated because this work had been my calling—I knew it never really was—but because change terrifies me. Having to start over felt like an insurmountable challenge. I eventually managed to land another job, but it was just more of the same—a development shop with similar work. I can’t say I felt any ambition or spark driving me forward. My career has never felt like a meaningful pursuit; instead, it has been a series of necessary steps to keep life moving forward without any real passion or joy. In truth, I have always struggled with identifying what I truly want out of my career or my life in general. Moving from one job to the next has been more about survival than growth or self-fulfillment. I see people around me thriving, pursuing goals with enthusiasm, but I have never felt that drive myself. Most days, I find it hard to muster any motivation or excitement about my work, and I often feel like I’m simply drifting through. Meetings and daily tasks feel like obstacles to endure rather than opportunities to learn or grow. There is a persistent sense of disconnect and aimlessness, and I often find myself wondering if there is something more, but I have no idea what that might be. ``` // partial response { "personality": { "self_assured": 19.726702063408283 } } ``` This second example is a modified version of the first example, changed to include language that is decidedly less self-assured. As a result, we see that the `self_assured` score falls to `19.7`. This means that only 19.7% of text samples in our baseline dataset score less than what this sample returned for `self-assured`. ## Personality FAQ[​](#personality-faq "Direct link to Personality FAQ") **Personality scores for the same person differ across samples. Is that a problem?** Language, like any other social behavior, shows both stability and change across contexts (Damian et al., 2019). For example, no matter how emotional you typically are, you’ll naturally talk more about your feelings in a therapy session than you will on an average work call. However, your rank will remain similar across situations, something called rank order stability. Let’s say you submit two samples from your team: 1) responses to open-ended survey questions about people’s feelings regarding a recent company reorganization and 2) earnings call transcripts. Scores on the responses to survey questions about feelings will likely show high normed emotionality across the board (above the 60th percentile), while the earnings call language data will likely have low emotionality scores (below the 40th percentile) – yet the most and least emotional people in the company will probably be the same in each context, if you rank team members relative to others in the same sample. **My scores don’t match how I see myself or how my friends see me. What might be causing that?** These scores are a simple, theory-consistent reflection of personality traits and facets. They describe how a person comes across to other people in a specific context based on their verbal behavior. If a CEO scores high on openness to experience based on the language they use in a quarterly earnings call, for example, it means that in that earnings call they are behaving like a textbook example of a person high in openness to experience (using a rich and varied vocabulary, talking about abstract ideas, etc.). If you disagree with the results, it may be due to two different aspects of the social context: individuals’ social groups in everyday life, and the situations in which the language samples were captured. The first context effect is the reference group effect (Heine et al., 2002). Simply put, it means that we see ourselves in the context of our friend or peer group. If you are an open person surrounded by other people who are highly open to new experiences, you may think you’re not particularly intellectual, artistic, or creative when in fact you are extremely high on that trait relative to the rest of the human population – most of whom you don’t regularly encounter in everyday life. The second set of context effects concern the situation in which the text samples were produced. Certain situations are strong or highly constrained, compelling everyone to behave in a certain way. For example, nearly everyone tries to be polite in a job interview, and most people will be anxious in a life-or-death crisis. If your language-based Big Five scores don’t match how you see yourself, consider whether the context you were speaking or writing in was a *strong situation* that may have constrained your behavior or compelled you to speak or write out of character (Cooper & Withey, 2009). **Language-based models of personality work best when people have a fair amount of freedom to “be themselves” (e.g., anonymous open-ended survey questions, stream-of-consciousness writing tasks).** If you believe that a language sample doesn’t represent a person accurately, that fact alone can be useful. If, for example, a manager sees that a friendly, assertive sales team member is scored as very reserved in transcripts of sales calls, they can pass that information onto the sales team member as constructive feedback and encourage them to open up more when speaking with customers. **I’ve seen other Big Five models with different trait labels or different numbers of facets. Which is correct?** Trait and facet labels vary between theoretical models and research groups. For example, in order for all traits to be in the same socially-valued direction (where higher is presumed to be better), neuroticism is sometimes reversed and called “emotional stability.” The five traits remain conceptually the same across all Big Five models, however, regardless of labeling differences. Likewise, there is some disagreement – partly statistical, partly practical – about how many distinct facets each trait should have, with different principled approaches finding two, three, or six facets per trait. Although most language research on personality is at the trait level, some research on the Big Five facets has found distinct language patterns associated with up to six facets per trait. That model, which provides the most granularity of the available approaches, is what Receptiviti’s Big Five framework offers. In places where Receptiviti uses different facet labels than others, that is simply a pragmatic labeling choice and does not reflect any differences in the underlying construct. For example, we use the term melancholy rather than depression for the facet of neuroticism that indicates a tendency to feel sad; this was a labeling choice made because prior research has demonstrated that people can be sad frequently without meeting clinical criteria for a depression diagnosis, and we have chosen to avoid conflating personality with psychopathology. **What are the best personality traits? Are some good and others bad?** Which traits are socially valued and the degree to which they are encouraged or stigmatized tends to vary across cultures (Hofstede & McCrae, 2004). Broadly speaking though, agreeableness, conscientiousness, and emotional stability (low neuroticism) tend to be socially valued, with extraversion and openness being similarly (although less pervasively) desired across different social groups and regions. Neuroticism and negative affectivity in particular tend to be less stigmatized and more accepted in some East Asian and Latina/o cultures (Bastian et al., 2012; Campos et al., 2014). There is evidence to suggest that being somewhat but not extremely high (\~60-70th percentile) on all socially valued traits is the optimal spot for mental and physical health as well as job success and social integration (Block, 2010). Very high scores, even on the most universally appealing traits, have some costs. For example, extremely agreeable people may be too compliant and thus easily manipulated; very conscientious people are more vulnerable to mental health issues having to do with excessive self-control, like obsessive compulsive disorder, specific hygiene phobias, or perfectionism. It is important to remember that every personality trait can have advantages and disadvantages. Even neuroticism, which reflects vulnerability to stress and mental health conditions, can be beneficial in situations where a person needs to be attuned to threats and risks. **I want to change my results. Unfortunately, I read that personality can’t be changed. If that’s true, what can I do with my language-based Big Five scores?** Personality is relatively stable over time, although there are some reliable, slow changes over the lifespan that occur naturally. For example, most people become calmer, tidier, and more confident as they age from adolescents to older adults, consistent with typical increases in maturity across adulthood (Damian et al., 2019). People can also (moderately and slowly) shift their personality traits intentionally over time, something that tends to improve well-being when successful (Hudson et al., 2016). Knowing that personality is stable and difficult to change may make people feel helpless if they don’t like what they see on a personality test. However, especially for language-based personality evaluation, it is important to recognize that your personality scores reflect your observable verbal behavior in a specific context. Although it is challenging to change your core personality and temperament, you can more easily change how you come across to others in certain important situations, like job interviews or first dates. Changing how people see you is especially straightforward in cases where your behavior is masking your true personality – for example, if a friendly person comes across as cold due to shyness or lack of confidence, they simply need to learn to relax and be themselves in order for other people (and language-based personality assessment) to see them as more friendly. Alternatively, for someone who may not realize how disagreeable they seem in the workplace, seeing low scores for agreeableness may motivate them to resolve their workplace conflicts or try to be more easygoing in meetings. As the examples above illustrate, Receptiviti’s Big Five measures can provide an impetus for personal growth, giving people objective, behavioral information on how they are presenting themselves in specific situations, like in workplace meetings. Gaining better self-awareness is the foundation of self-change. Once a person realizes how they may be coming across to others, they will better understand their social interactions and be better equipped to overcome social barriers. **I’m a team manager, and I’m hiring. Should I look for people whose personalities are similar to mine so that we will get along better?** Research suggests that people get along better initially with people whose personalities or other attributes match their own, but diverse groups perform better in the long run (see Roberge et al., 2010). It makes sense that diverse viewpoints all working together on the same problem will be more creative and versatile than groups with highly overlapping skills and interests. In fact, Receptiviti’s personality scores can be a useful way of ensuring diversity in groups or teams – people tend to like similarity, so without consciously intending to, a manager may assemble a team of people whose skills and personalities are redundant with their own. Using Receptiviti's language-based personality scores can help managers create teams of people with complementary traits, diverse abilities, and versatile ways of approaching problems. ## References[​](#references "Direct link to References") References * Allport, G. W. (1937). *Personality: a psychological interpretation*. Holt. * Ashton, M. C., Lee, K., & De Vries, R. E. (2014). The HEXACO Honesty-Humility, Agreeableness, and Emotionality factors: A review of research and theory. *Personality and Social Psychology Review, 18*, 139-152. * Bastian, B., Kuppens, P., Hornsey, M. J., Park, J., Koval, P., & Uchida, Y. (2012). Feeling bad about being sad: the role of social expectancies in amplifying negative mood. *Emotion, 12*, 69. * Block, J. (2010). The five-factor framing of personality and beyond: Some ruminations. *Psychological Inquiry, 21*, 2-25. * Campos, B., Busse, D., Yim, I. S., Dayan, A., Chevez, L., & Schoebi, D. (2014). Are the costs of neuroticism inevitable? Evidence of attenuated effects in US Latinas. *Cultural Diversity and Ethnic Minority Psychology, 20*, 430. * Carlson, E. N., Vazire, S., & Oltmanns, T. F. (2013). Self‐other knowledge asymmetries in personality pathology. *Journal of Personality, 81*, 155-170. * Cattell, R. B. (1945). The principal trait clusters for describing personality. *Psychological Bulletin, 42*, 129. * Cooper, W. H., & Withey, M. J. (2009). The strong situation hypothesis. *Personality and Social Psychology Review, 13*, 62-72. * Eastwick, P. W., Eagly, A. H., Finkel, E. J., & Johnson, S. E. (2011). Implicit and explicit preferences for physical attractiveness in a romantic partner: a double dissociation in predictive validity. *Journal of Personality and Social Psychology, 101*, 993. * Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. *Annual Review of Psychology, 62*, 451-482. * Heine, S. J., Lehman, D. R., Peng, K., & Greenholtz, J. (2002). What's wrong with cross-cultural comparisons of subjective Likert scales?: The reference-group effect. *Journal of Personality and Social Psychology, 82*, 903. * Hofstede, G., & McCrae, R. R. (2004). Personality and culture revisited: Linking traits and dimensions of culture. *Cross-Cultural Research, 38*, 52-88. * Hudson, N. W., & Fraley, R. C. (2016). Changing for the better? Longitudinal associations between volitional personality change and psychological well-being. *Personality and Social Psychology Bulletin, 42*, 603-615. * Judge, T. A., & Zapata, C. P. (2015). The person–situation debate revisited: Effect of situation strength and trait activation on the validity of the Big Five personality traits in predicting job performance. *Academy of Management Journal, 58*, 1149-1179. * Lahey, B. B. (2009). Public health significance of neuroticism. *American Psychologist, 64*, 241. * Lee, K., & Ashton, M. C. (2004). Psychometric properties of the HEXACO personality inventory. *Multivariate Behavioral Research, 39*, 329-358. * Lönnqvist, J. E., Verkasalo, M., Haukka, J., Nyman, K., Tiihonen, J., Laaksonen, I., Leskinen, J., Lönnqvist, J., & Henriksson, M. (2009). Premorbid personality factors in schizophrenia and bipolar disorder: results from a large cohort study of male conscripts. *Journal of Abnormal Psychology, 118*, 418. * McAdams, D. P. (1997). A conceptual history of personality psychology. In *Handbook of personality psychology* (pp. 3-39). Academic Press. * McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. *Journal of Personality and Social Psychology, 52*, 81. * Pletzer, J. L., Oostrom, J. K., Bentvelzen, M., & de Vries, R. E. (2020). Comparing domain-and facet-level relations of the HEXACO personality model with workplace deviance: A meta-analysis. *Personality and Individual Differences, 152*, 109539. * Revelle, W., & Condon, D. M. (2019). Reliability from α to ω: A tutorial. *Psychological assessment, 31*, 1395. * Roberge, M. É., & Van Dick, R. (2010). Recognizing the benefits of diversity: When and how does diversity increase group performance? *Human Resource Management Review, 20*, 295-308. * Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martínez, V. (2007). The geographic distribution of Big Five personality traits: Patterns and profiles of human self-description across 56 nations. *Journal of Cross-Cultural Psychology, 38*, 173-212. * Sutin, A. R., Costa, P. T., Miech, R., & Eaton, W. W. (2009). Personality and career success: Concurrent and longitudinal relations. *European Journal of Personality, 23*, 71-84. * Van Os, J., Park, S. B. G., & Jones, P. B. (2001). Neuroticism, life events and mental health: evidence for person-environment correlation. *The British Journal of Psychiatry, 178*, s72-s77. * Zhang, F., Baranova, A., Zhou, C., Cao, H., Chen, J., Zhang, X., & Xu, M. (2024). Causal influences of neuroticism on mental health and cardiovascular disease. *Human Genetics, 140*, 1267-1281. --- # Personality - DISC DISC is a versatile psychological framework designed to help leaders understand how people in groups relate to their peers and collaborate with each other. Receptiviti’s DISC measures require that text samples contain at least 350 words to generate results. DISC is typically defined by four DISC styles, each representing a style of interacting with one’s environment: * **D** is normally referred to as dominant; D-type people tend to be ambitious, active, bold leaders; * **I** is alternately referred to as influence or inducement; I-type people lead through connections, creativity, and collaboration; * **S** is called stable, submissive, steady, or supportive; S-type people tend to be faithful, modest, methodical people who value relationships; and * **C** is labeled compliant, conscientious, or cautious; C-type people prefer to do their jobs accurately, unobtrusively, and impersonally. A person’s score on the four DISC styles is derived from their scores on two dimensions of the DISC axis: * **Bold** vs. **Reserved**; and * **People-focused** vs. **Task-focused** The Receptiviti API measures these two axes and produces a score for each of the four component measures, Bold, Reserved, People, and Task. By studying language across these four measures, we are able to produce a score for each of the D, I, S, and C styles (more on this below). Some users find it useful to compute a full axis continuum, so we have outlined an approach to do so and describe this under [User Computed Definitions](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-disc.md#user-computed-measures) below. People focus and task focus are more modestly negatively correlated (one can score high or low in people focus and task focus at the same time, and they are only negatively correlated in some contexts). Therefore, to capture those who have similar scores on both ends of that axis, the Receptiviti API measures people focus and task focus using two separate scores, labeled `people_relationship_emotion_focus` and `task_system_object_focus`. Bold and reserved traits are strongly negatively correlated (one cannot be both bold and reserved at the same time) and thus are each measured on a single dimension. To parallel the output for people focus and task focus, the Receptiviti API output for this dimension is divided into two measures: `bold_assertive_outgoing` and `calm_methodical_reserved`, with the latter score being the inverse of the first (i.e., the sum of the normed bold and reserved scores will add to roughly 100). The D, I, S, and C measures produce four proportional DISC types, which add up to 100%. A person's dominant style is the highest score produced from the D, I, S, or C-type output. A person may have similarly high scores for two DISC types. When that occurs, they are near the midpoint on one of the dimensions. A person’s DISC type is generated as a lowercase letter if they are greater than or equal to 25% and a capital letter if they are greater than or equal to 35%. As an example, imagine that a person, K, is reserved, unambitious, and tends to ruminate about past mistakes. K is interested in both technical tasks and people: they are very single-minded and meticulous most of the time, but they are also very empathic and warm in interactions with peers. Based on those traits, K might score 10% D, 10% I, 50% C, and 30% S, making them a C/s type overall. Scores from the following measures are normed: * `bold_assertive_outgoing` * `calm_methodical_reserved` * `people_relationship_emotion_focus` * `task_system_object_focus` For example, someone whose score is `70` for `bold_assertive_outgoing` ranks higher than 70% of people in the norming dataset on that measure. Scores from the following measures are comprised of normed components: * `d_axis` * `i_axis` * `s_axis` * `c_axis` Scores from the following measures are proportional, and together will add up to 1: * `d_axis_proportional` * `i_axis_proportional` * `s_axis_proportional` * `c_axis_proportional` ![disc axis](../../static/img/disc-axis.gif) ``` "disc_dimensions": { "bold_assertive_outgoing": 40.78590202076781, "calm_methodical_reserved": 59.2244722102542, "people_relationship_emotion_focus": 39.98276604911676, "task_system_object_focus": 36.130978026712626, "d_axis": 38.387947714250316, "i_axis": 40.38233745833143, "s_axis": 48.66167092039699, "c_axis": 46.25838414895561, "d_axis_proportional": 0.2210137170597945, "i_axis_proportional": 0.23249616185956354, "s_axis_proportional": 0.2801633692041587, "c_axis_proportional": 0.2663267518764833 }, ``` ## API Response Measures[​](#api-response-measures "Direct link to API Response Measures") | Measure | Summary | High Score | Low Score | | ----------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | `bold_assertive_outgoing` | Fast-paced, decisive, assertive, sociable, natural leader, approach-oriented, focused on the future. | Dominant and charismatic leaders; like to move forward quickly, stay busy, and be looked up to. | Unassertive, methodical, focused on fewer tasks at a time, prefer following to leading. | | `calm_methodical_reserved` | Reserved, calm, methodical, careful, avoidant, unemotional, happy to follow, focused on the past. | Slower-paced, impersonal, verbally inhibited, unemotional; prefer unobtrusive positions. | Impulsive, verbally fluent, emotional, not concerned with the past, not afraid to make mistakes. | | `people_relationship_emotion_focus` | Interested in others’ thoughts and feelings, good at understanding other people, warm, agreeable. | Friendly, interested in others’ lives, supportive, social, easygoing, joking, polite. | Uninterested in small talk, serious, not confident in social skills, can seem disagreeable or cold. | | `task_system_object_focus` | Interested in well-defined problem spaces and structured tasks, focused on work tasks and accomplishments. | Detail-oriented, interested in numbers, objects, and abstract systems; impersonal, likes completing to-do lists. | Unambitious, works in a more intuitive manner, less of a strategic worker, can be less work-focused and more distractible. | | `d_axis` | Bold and task-focused. Dominant; interested in accomplishing a lot quickly, leading, and planning aggressively for the future. | Dominant, ambitious, forward-thinking, aggressive, controlling, task-focused leaders. | Unassertive, unambitious, focused on the past; similar to `s_axis`. | | `i_axis` | Bold and people-focused. Influential; interested in being admired, creativity, and accomplishing things through relationships. | Social, creative, charismatic, well-connected leader who cares about peers’ thoughts and feelings. | Uninterested in relationships or leadership, careful, not focused on the future; similar to `c_axis`. | | `s_axis` | Calm and people-focused. Guided more by relationships and loyalty than big picture goals. Reserved and not assertive. | Loyal, modest, kind, agreeable, supportive; driven by relationships and a desire to help others. | Active, impulsive, ambitious, disagreeable, more interested in accomplishing tasks than relationships; similar to `d_axis`. | | `c_axis` | Calm and task-focused. Interested in avoiding mistakes, and getting things done meticulously and impersonally. Inhibited, not interested in standing out. | Methodical, perfectionist, task and goal-oriented; socially distant or finds social interactions draining; needs time to react. | Not interested in structured work or completing to-do lists; disinhibited, emotional, creative; similar to `i_axis`. | | `d_axis_proportional` | Same as `d_axis` but sums to 1. | | | | `i_axis_proportional` | Same as `i_axis` but sums to 1. | | | | `s_axis_proportional` | Same as `s_axis` but sums to 1. | | | | `c_axis_proportional` | Same as `c_axis` but sums to 1. | | | ## User Computed Measures[​](#user-computed-measures "Direct link to User Computed Measures") In some contexts it will be useful to simplify the four DISC measures by combining them into two axes. The formulas for these two axes are as follows: `bold_vs_reserved_energy_axis` = (((`bold_assertive_outgoing` - `calm_methodical_reserved`) + 100) / 200) \* 100 `people_vs_task_focus_axis` = (((`people_relationship_emotion_focus` - `task_system_object_focus`) + 100) / 200) \* 100 For the vertical axis, higher scores indicate that a person is bolder and less reserved. For the horizontal axis, higher scores indicate that a person is focused more on people and less on tasks. As mentioned above, a person can be both people focused and task focused but not both bold and reserved. To scale the scores from 0 to 100, we use min-max normalization. We prefer min-max over other standardization methods (e.g., z-scoring) because it is not influenced by the distribution of a particular sample. That is, given a pair of scores, the above formulas will produce the same scaled axis scores every time. | Measure\* | Summary | High Score | Low Score | | ------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | | `bold_vs_reserved_energy_axis` | Vertical DISC axis. Assertive, fast-paced or active, sociable; natural leadership ability and charisma. | Bold, fast-acting, decisive, fearless, ambitious, future-thinking, and interested in and/or naturally good at leading. | Cautious, slower and more methodical, reserved, verbally inhibited, happier in a follower or mid-level position. | | `people_vs_task_focus_axis` | Horizontal DISC axis. Warm, supportive, inquisitive about others’ lives, agreeable, joking, polite, interested in conversations. | Empathic, emotional, social, socially adaptable, more interested in one-on-one interactions than abstract concepts. | Unemotional, impersonal, more interested in tasks, achievement, and rule-based systems than other people. | \*Note that this is not in the API output but is user computed. ## Further Details about DISC[​](#further-details-about-disc "Direct link to Further Details about DISC") The following documentation first summarizes classic and recent research on how DISC can be applied in specific use cases, such as assembling work teams or collaborative groups outside of a work setting. Next, we describe the creation of the Receptiviti language-based DISC measures through analysis of the relationships between naturalistic conversational language use and DISC dimensions. Specifically, in addition to the four main styles (D, I, S, and C), four additional dimensions were developed to measure each end of the two main DISC axes, allowing for greater measurement flexibility than a two-dimensional measure alone (particularly useful for people who are balanced, combining characteristics from both ends of an axis). Finally, in an FAQ section, we answer questions you may have about the science behind these measures and their applications in specific scenarios. ### DISC Background The DISC model was originally developed in the 1920s by William Moulton Marston, an American psychologist who was educated at Harvard and held faculty positions at Tufts and American University (in addition to his work in the entertainment industry as the creator of Wonder Woman). He was interested in emotions, physiology, and practical applications of psychology. His best known research, beyond the DISC model, was on polygraph testing. Through the DISC model, Marston aimed to provide a holistic account of how individual differences in emotional and communication styles affect behavior in everyday social interactions and relationships. DISC has been most commonly applied to workplace interactions: how people prefer to go about their work, relate to coworkers, and seek out an appropriate place in their workplace hierarchies. The DISC framework applies equally well to other collaborative groups, however – especially those with hierarchical structures (i.e., leaders and followers). Two dimensions define the four DISC styles: 1. **Boldness** vs. **Reserved**; and 2. **People-focused** vs. **Task-focused**. Scores on each of the two dimensions are mapped to the four DISC quadrants, which are labeled D, I, S, and C, each representing a style of interacting with one’s environment. The labels for each of the resulting four DISC categories sometimes vary depending on the researcher and business application. D is normally referred to as dominant; I is alternately referred to as influence or inducement; S is called stable, submissive, steady, or supportive; and C is labeled compliant, conscientious, or cautious. Because of the variation among labels, it tends to be more straightforward to simply refer to each type by its letter. * **D-type people** tend to be ambitious, active, bold leaders; * **I-type people** lead through connections, creativity, and collaboration; * **S-type people** tend to be faithful, modest, methodical group members who value relationships; and * **C-type people** prefer to do their jobs accurately, unobtrusively, and impersonally. ### Bold vs. Reserved Energy & Extraversion The vertical DISC dimension of bold vs. reserved energy captures some aspects of extraversion, including social boldness, friendliness, activity or assertiveness, and positive emotion (Goldberg, 1999). We use the labels “bold” vs. “reserved” energy rather than extraversion (or related terms like outgoing vs. introverted) because this dimension is not identical to extraversion as measured in academic personality science. This dimension has more to do with being bold, dominant, and fast-paced than being friendly or talkative. People who have more bold energy, as defined by DISC, are mentally and physically active and decisive, and are either interested in or naturally good at leadership in a work group or other collaborative context. ### People-Task Focus & Empathizing-Systemizing The horizontal DISC dimension of people focus vs. task focus involves prosociality, agreeableness, and empathy on the people-focused end of the spectrum. On the task-focused end of the spectrum, there is a preference for well-structured tasks and non-human aspects of one’s surroundings (e.g., numbers, rule-based systems) over human connections. This dimension resembles the empathizing and systemizing dimensions conceptualized by Simon Baron-Cohen and colleagues (2003). ### Recent DISC Research Peer-reviewed academic work on DISC is relatively sparse. However, there are a few empirical studies that support the idea that DISC measures can predict performance and assist in personnel selection. For example, in a crowdsourcing experiment that asked groups of strangers to collaborate online in an advertising design task, Lykourentzou et al. (2016) found that balanced teams (at least one of each DISC type, and no more than one D or I-type) outperformed imbalanced teams (3 or more D-types) in terms of objective performance (advertisement quality, novelty, and attention to detail rated by observers). This group arrangement also outperformed sentiment in transcripts of their group discussions (linguistic positivity-to-negativity ratio), and subjective team member experiences (better communication, more acceptance, greater satisfaction with the group’s ads). Another study that tracked students over their final two years of college found that DISC scores from junior and senior year predicted GPA upon graduating from or leaving college (Deviney et al., 2010). In terms of basic personality science, a few studies have compared DISC measures with other established measures of personality or creativity. For example, a recent study found that DISC is not redundant with the Big Five model of personality, although there are some significant facet-level correlations (e.g., I-type personalities tend to be less introverted, and S-types tend to be more emotionally stable; Jones & Hartley, 2013). Related personality research has found theory-consistent correlates between DISC categories and aspects of the creative process, with social, people-focused I-types preferring the big picture, brainstorming phase of creativity, while more task-focused, methodical C types prefer to take on a detail-oriented, clarifying role (Puccio & Grivas, 2009). ## Survey Measures[​](#survey-measures "Direct link to Survey Measures") The Receptiviti DISC measures were refined using surveys: (1) peer assessments with one item per dimension and a single item for each of the four DISC types and (2) self-assessments including validated personality questionnaires that are aligned with the DISC dimensions (extraversion facet measures for bold and reserved, and brief systemizing-empathizing scales for people focus and task focus). ### Single-Item Peer Rating Survey Measures Peers rated each other on the two DISC dimensions in two items: 1. “What is this person's usual working style?” `1` = *Active, fast-paced, assertive, dynamic, bold,* `5` = *Thoughtful, calm, methodical, moderate-paced, careful;* and 2. People focus vs. task focus: “What is this person’s attentional focus at work?” `1` = *People-focused, accepting, empathic, receptive, agreeable,* `5` = *Task-focused, logical, objective, skeptical, challenging*. The two dimensions are intended to be independent of each other. Partly consistent with that aim, ratings on the two scales were modestly but not significantly negatively correlated with each other, r = -.30. Notably, there are theoretical reasons to believe that there should be a modest negative correlation between the vertical and horizontal DISC dimensions. The bold-reserved dimension involves assertiveness (desire for dominance) and leadership (being someone people naturally follow), and therefore will likely be correlated with leadership positions in many organizations. People with more power or status tend to be less empathic or less likely to take others’ perspectives (Galinsky et al, 2006), which may be reflected in lower scores on the horizontal people focus vs. task focus dimension. Participants additionally rated both themselves and peers on the four DISC types, with one item per type on a five-point scale (*very inaccurate, moderately inaccurate, neither accurate nor inaccurate, moderately accurate,* and *very accurate*) with the following instruction: Indicate the degree to which you feel each description fits this person's behavior in the workplace. * **D**: Ambitious, aggressive, decisive, controlling, dominant, quick reactions, goal oriented. * **I**: Optimistic, social, enthusiastic, spontaneous, charming, open, creative. * **S**: Patient, modest, loyal, discreet, kind, supportive, reliable. * **C**: Conventional, objective, structured, analytical, perfectionist, socially distant, needs time to react. According to the models’ design, DISC types should be moderately negatively correlated or uncorrelated (independent of) each other. Consistent with that prediction, types within the same column were generally uncorrelated. As we would predict, the diagonals (types with opposite categorization on both the bold-reserved and people-task dimensions) were negatively correlated with each other: D-type and S-type (*r* = -.587) and I-type and C-type (*r* = -.287). ### Four-Facet Extraversion (12 Items) The vertical dimension of DISC (higher for D and I, lower for S and C) is alternately labeled as activity (active vs. inactive), pace (fast-paced vs. slow-paced), and extraversion (outgoing vs. reserved). To measure extraversion comprehensively, capturing all possible facets, we administered Goldberg’s (1999) full AB5C 9-facet scale including 3 questions for each facet. We initially viewed five extraversion facets as theoretically relevant to both D and I styles: assertiveness (i.e., dominance, activity), poise (i.e., social skills, confidence), leadership (i.e., magnetism, communication skill), sociability (i.e., preferring to be around people), and provocativeness (i.e., liking attention and strong reactions from others). Reliability analysis showed that provocativeness (items had to do with making noise and being loud) was negatively correlated with the other facets, so that facet was removed. The four remaining facets had good internal reliability (Cronbach’s standardized α = .74, raw α = .70). Demonstrating convergent validity, the resulting four-facet measure of extraversion (assertiveness, poise, leadership, and sociability) was positively correlated with a composite measure of the vertical DISC dimension based on the four DISC self-ratings (dominance + influencer – submissive – compliant), *r* = .60, p < .01. The items and facets that were retained for this measure are as follows (*r* = reverse-scored): **Assertiveness:** 1. Automatically take charge. 2. Come up with a solution right away. 3. Try to lead others. **Poise**: 1. Feel comfortable around people. 2. Am comfortable in unfamiliar situations. 3. Find it difficult to approach others. (R) **Leadership:** 1. Know how to captivate people. 2. Express myself easily. 3. Have little to say. (R) **Sociability**: 1. Can't do without the company of others. 2. Like to be alone. (R) 3. Seek quiet. (R) ### SQ-EQ (13 Items) DISC’s horizontal dimension (sometimes referred to as people vs. task oriented, agreeable vs. challenging, or empathic vs. logical) is essentially the same as the empathizing-systemizing spectrum conceptualized by Simon Baron-Cohen and colleagues, who started studying these traits in the context of autism spectrum disorder. The theory was that people who have more extreme autism symptoms tend to be more interested in rule-based systems than other people's thoughts and feelings, finding objects and their mathematical and physical properties more interesting than humans and their mental states. In the academic literature, the empathizing quotient (EQ) and systemizing quotient (SQ) are reported to be moderately negatively correlated, and the creators of the scale suggest that it be treated as a continuous spectrum with interest in people or empathy at one end and interest in rule-based systems or the physical world at the other. To measure systemizing and empathizing, we used abbreviated versions of the short SQ and EQ scales (Wakabayashi et al., 2006) developed by Micah Iserman based on his research with Molly Ireland at Texas Tech University. The very brief SQ was supplemented by five items from the full-length scale that had to do with interest in natural systems (plants, animals, humans’ physical appearance). This was in order to ensure that the scale would have a better chance of performing similarly across people who are and are not from engineering backgrounds, resulting in 10 total items for the SQ and five items for the EQ. After removing two SQ items with poor reliability, the eight-item version of the brief SQ had acceptable reliability, standardized α = .66. The brief five-item EQ was highly internally consistent, standardized α = .91. Demonstrating convergent validity, the single-item people focus vs. task-focus measure correlated strongly with the EQ-SQ difference score (higher numbers indicating more interest in people and empathy), *r* = -.54. It was also negatively correlated with the composite measure of self-rated task focus (dominant + compliant – influencer – submissive), *r* = -.28. The final items for the SQ and EQ were as follows: **Empathizing:** 1. I am quick to spot when someone in a group is feeling awkward or uncomfortable. 2. I can tell if someone is masking their true emotion. 3. I can tune into how someone else feels rapidly and intuitively. 4. Other people tell me I am good at understanding how they are feeling and what they are thinking. 5. I am good at predicting how someone will feel. **Systemizing:** 1. I am interested in knowing the path a river takes from its source to the sea. 2. I am curious about life on other planets. 3. When I look at a building, I am curious about the precise way it was constructed. 4. When I am walking outdoors or hiking, I am curious about how the various kinds of trees differ. 5. When traveling by train, I often wonder exactly how the rail networks are coordinated. 6. When I travel, I like to learn specific details about the culture of the place I am visiting. 7. I can easily visualize how the motorways in my region link up. 8. I am fascinated by how machines work. ## DISC Language Measures[​](#disc-language-measures "Direct link to DISC Language Measures") The composite language measures are based on a combination of correlates with the two DISC dimensions based on self-assessments (the four-facet extraversion measure and short EQ-SQ score described above) and the two-dimensional (bold vs. reserved and people focus vs. task focus) items on the coworker rating survey. ### Formula Design Strategy Linguistic correlates of self-report measures were not included in the language metrics even if they were (a) very low base rate (< 1%) and therefore less likely to be reliable across samples or (b) intuitively unrelated to the underlying DISC constructs and therefore likely to be unique to our sample. We used both self-assessed and peer-assessed DISC dimensions as different components of “ground truth” because DISC is inherently a social, group processes model that encompasses not only how people view themselves but also how they relate to and are viewed by others. Such an approach was important in this case because DISC captures a range of characteristics that are more internal or difficult to read in casual workplace interactions (e.g., preference for order, negativity) and more external, social characteristics that others will be better judges of than oneself (e.g., assertiveness, sociability; see Vazire, 2010). Measures were constructed using text samples of at least 350 words, all taken from transcripts of spoken conversations. The average conversation we analyzed had about 1,700 words. ### Formulas to Infer DISC Dimensions The resulting language dimensions capture multiple aspects of the vertical and horizontal DISC dimensions: The vertical bold vs. reserved energy dimension is measured by words relating to activity, sociability, ambition, dominance, emotional intensity, fearlessness, and future orientation on the bold end of the spectrum. Words relating to compliance, lower activity levels, fearfulness, tentativeness, and cognitive load indicate a more reserved, cautious style. The horizontal people focus vs. task focus dimension is measured by socially engaged, warm language on the people-focused end of the spectrum. More impersonal, evaluative, complex, formal, and goal-oriented language indicates a more task-focused style. The four DISC styles and two dimensions can be calculated from the three formulas for `bold_reserved_energy`, `people_focus`, and `task_focus`. From those three base scores, the remaining DISC framework scores are calculated as follows: * `d_axis` = `bold_reserved_energy` + `task_focus` * `i_axis` = `bold_reserved_energy` + `people_focus` * `s_axis` = (- `bold_reserved_energy`) + `people_focus` * `c_axis` = (- `bold_reserved_energy`) + `task_focus` In this formulation, each DISC type is unique and largely independent of the others: none can be perfectly predicted from the other scores. ### Linguistic DISC Measure Correlates In the DISC validation study, plotting individuals’ places on the two dimensions according to each measure shows that the behavioral (language-based) measures often represent an average of peer and self reports. The language measures correlated in the expected direction with both peer and self-assessments. The language-based bold-reserved measure was strongly associated with peer (*r* = .53) and self-assessments (four-facet extraversion *r* = .70; DISC measures *r* = .73). The language-based people-task focus measure was moderately correlated with self-assessments (EQ-SQ *r* = .29, DISC measures *r* = .30) but only modestly positively correlated with peer ratings (*r* = .10). Given that the bold-reserved axis is strongly correlated with extraversion and includes easy-to-judge characteristics like dominance and sociability, it makes sense that that measure would be more strongly correlated with survey-based assessments. People focus and task focus are defined by more internal characteristics (i.e., abstract interests and empathy) that may be difficult to judge accurately from an outside perspective (see Vazire, 2010, for research on self-other knowledge asymmetries for various traits). ## DISC FAQs[​](#disc-faqs "Direct link to DISC FAQs") **Do DISC scores or types change over time or across different social contexts, like work and home?** DISC is a personality framework, meaning that it describes characteristics of people that are relatively stable across situations and over time. There are some caveats to that general rule, though. First, although personality has rank-order stability, personality traits and their behavioral indicators do change across contexts. For example, someone with a positive personality may not appear to be joyful in serious or stressful scenarios, such as when dealing with a crisis, but they’ll still probably be more optimistic or lighthearted than the average person in the same scenario (that is, their rank-order positivity will remain high, despite not being very positive in the moment). This also means that people can manifest different personality states or traits depending on the people they are talking with (i.e., the other person’s personality and/or goals) and the nature of the conversation (especially how volatile or high-pressure the conversation is). Second, personality slowly changes over time. Research tracking the same people over several decades has found that people gradually become more emotionally stable, self-confident, warm, and dominant over the lifetime as a result of normal developmental processes; people also gradually gain better self-control, becoming less impulsive as a result (Roberts & Mroczek, 2008; Roberts et al., 2006). Because the vertical bold-reserved DISC dimension partly reflects dominance, we would expect people to slowly move up on that dimension as they gain expertise and confidence and have more leadership opportunities. However, it’s important to note that, because of the rank-order stability of personality, a person who becomes bolder over time will still probably be less bold than someone with her same level of experience who started at a higher point on the bold-reserved spectrum. That is, they will both become bolder as they gain seniority in an organization, thus preserving their rank relative to each other as they slowly become more confident and assertive. People will be more likely to change DISC types over time, or consciously adjust their type through their own efforts, if they are near a borderline to start with (i.e., scoring close to the midpoint of either the bold-reserved or people-task dimensions). Having shorter text samples and fewer text samples in a given category will increase the volatility of a person’s DISC scores. When assessed using language, estimates of a person’s DISC style (or any personality measure) will be more stable when you’re analyzing substantial text samples (each >350 words) across a range of representative contexts (for example, conversations with multiple people, including both written and spoken communication). **Some of the DISC types sound less appealing than others. Are any DISC types bad, or should any be viewed as a red flag?** Like research in other areas of diversity and inclusivity, DISC research suggests that balance should be the aim in collaborative groups rather than selecting only people with a certain orientation or type. Especially with traits like dominance or extraversion, research shows that people are better off with partners or team members at both ends of the personality spectrum (e.g., Kristoff-Brown et al., 2005). For example, leaders like to lead and do better when they’re leading people who are happy to follow them. More broadly, teams with diverse perspectives (based on gender, ethnicity, expertise, or personality) tend to outperform more homogeneous teams. They don’t always communicate with each other as easily at first, because they have less common ground, but in the end, research finds that diverse organizations tend to be more successful in terms of earnings and customer satisfaction (Herring, 2009). **How can DISC insights be applied at work? Should DISC feedback be viewed as something that offers insights, motivates change, or both?** Like other personality measures, DISC can be used to gain insight or to make important changes in how people work together. For example, it may be useful in many cases to simply understand an organization’s leaders or teams better. Knowing how much a person cares about task-focus, empathy, or dominance can help make sense of their past behavior. DISC feedback can also provide information that can help leaders motivate people to do better work and feel more at home in an organization in the future. DISC information can help with personnel selection, team building, and choosing effective incentives. For example, recent research has found that teams produce better work and get along better when they have a balance of DISC types as opposed to a majority of D-types (bold, task-focused people who prefer to lead; Lykourentzou et al., 2016). **Does DISC reflect how people see themselves or how others perceive them?** The linguistic DISC measure was based on a combination of psychological theory, peers’ perceptions, and self-views. There are a few reasons for this. DISC is meant to predict and explain social behavior in groups, so it’s important to capture the reputational, social component of DISC in the language measure. Different personality traits are also differently visible to the self and others. There are aspects of one's personality that other people (including coworkers, friends, and group members) will always know better than a person does themselves – especially aspects of social behaviour, like how dominant a person acts or how they behave in large groups (Vazire, 2010). DISC involves both kinds of traits: characteristics that are most easily perceived by the self, and behavior patterns that are easiest to see from an outside perspective. **How does DISC relate to other popular personality frameworks, like the Big Five?** Both DISC dimensions have some similarities with other personality models. The bold vs. reserved energy dimension involves a few extraversion facets (i.e., fine-grained characteristics that make up larger traits): assertiveness, sociability, leadership, and poise. The people focus vs. task focus dimension has some parallels with agreeableness, or the desire to get along with and support other people, on the people-focused end of the spectrum. As described above, our validation studies suggest that the language-based DISC measures have good convergent and divergent validity -- that is, they correlate with other personality measures that it should and should not correlate with in the right directions -- but are not identical to or redundant with other personality measures. **My DISC type doesn’t match how I see myself or how I want to be perceived. What does that mean?** That could mean a few things. The simplest answer is that, in some cases, you will be correct: Your DISC scores could be wrong. Linguistic measures of any kind are only as good as the words that are fed into the model. If the data we analyze for you is not representative of how you usually behave in a group setting, then your DISC scores will probably not reflect your true DISC type. It is also possible that the language we analyzed does accurately reflect how you interact with people in groups but doesn’t bear much resemblance to how you see yourself – or who you would like to be. If that’s the case, it could mean that you’re not being yourself in a particular group, in a sense, or that, in the text samples we’ve analyzed, you’re dealing with stressors at work that make you behave atypically. In that case, linguistic DISC measures can provide insight into how others might be seeing you, giving you a chance to change how you interact with people in your group in order to bring your reputation in that context more in line with how you see yourself. See also the answer above to “Does DISC reflect how people see themselves or how others perceive them?” **Can a person “trick” the language-based measures into scoring them as a particular DISC style by using words associated with that style?** Each linguistic DISC measure is based on a complex combination of word categories. That alone makes it unlikely that a person could alter their DISC scores much by intentionally using certain keywords. A person would need to simultaneously modify a large number of language categories while talking or writing to change their DISC scores. The language measures are also based on several word categories that are difficult to monitor or regulate in everyday language use. For example, the formulas include a number of grammatical language categories known as “function words,” which tend to be produced and processed automatically (without much conscious thought) in conversation. Think of a recent conversation: you will probably have a good sense of the conversation’s emotional tone and topic but will struggle to remember how you or your conversation partner used words like and, the, for, I, and it. That is because these words perform mainly supporting roles in language and do not have much meaning on their own, outside of the context of a sentence. For that reason, function words are psychometrically “clean” language categories – you can be relatively sure when you’re analyzing them that you’re getting a true picture of a person’s mental state, as opposed to seeing whatever a person wants you to see. ## References[​](#references "Direct link to References") References * Baron-Cohen, S., Richler, J., Bisarya, D., Gurunathan, N., & Wheelwright, S. (2003). The systemizing quotient: an investigation of adults with Asperger syndrome or high–functioning autism, and normal sex differences. *Philosophical Transactions of the Royal Society of London. 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[DOI](https://doi.org/10.1111/j.1467-8691.2009.00535.x) * Roberts, B. W., & Mroczek, D. (2008). Personality trait change in adulthood. *Current directions in psychological science, 17*, 31-35. * Roberts, B. W., Walton, K. E., & Viechtbauer, W. (2006). Patterns of mean-level change in personality traits across the life course: A meta-analysis of longitudinal studies. *Psychological Bulletin, 132*, 1–25. [doi:10.1037/0033-2909.132.1.1](https://doi.org/10.1037/0033-2909.132.1.1) * Vazire, S. (2010). Who knows what about a person? The self–other knowledge asymmetry (SOKA) model. *Journal of Personality and Social Psychology, 98*, 281. [DOI](https://doi.org/10.1037/a0017908) [Link](https://www.simine.com/Vazire_JPSP_2010.pdf) * Wakabayashi, A., Baron-Cohen, S., Wheelwright, S., Goldenfeld, N., Delaney, J., Fine, D., Smith, R. & Weil, L. (2006). Development of short forms of the Empathy Quotient (EQ-Short) and the Systemizing Quotient (SQ-Short). *Personality and individual differences, 41*, 929-940. [DOI](https://doi.org/10.1016/j.paid.2006.03.017) --- # Social Dynamics Receptiviti’s Social Dynamics framework provides access to seven measures that evaluate a number of important aspects of how people are focused on themselves, focused on other people, whether they communicate with authenticity, clout, hesitation, the degree to which they communicate formally or informally, and more. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1594, "words_remaining": 248406, "percent_used": 0.64, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "576920d5-ac3b-4fac-b7f5-a00189a6f194", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": {...}, "social_dynamics": { "social": 57.2093235584043, "affiliation": 45.868725701575585, "inward_focus": 40.45137386358836, "outward_focus": 60.98081570000119, "authentic": 34.37457365649723, "negations": 37.327443401793744, "clout": 61.090716652475116 }, "drives": {...}, "cognition": {...}, "additional_indicators": {...}, "sallee": {...}, "liwc": {...} } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Category | Summary | High Score | Low Score | | --------------- | ----------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | | `social` | Reflects focus on social engagement and awareness of others. | Indicates a strong desire for social engagement or high awareness of others. | Indicates little interest in social engagement or minimal awareness of others. | | `affiliation` | Reflects an internal drive for forming connections with individuals or groups. | Indicates a strong need for affiliation with others. | Indicates a low need for affiliation with others. | | `inward_focus` | Reflects self-directed language and attention. | Indicates increased focus on oneself. | Indicates minimal self-focus. | | `outward_focus` | Reflects attention directed towards others or external entities. | Indicates a strong focus on people or entities other than oneself. | Indicates minimal focus on others or external entities. | | `authentic` | Reflects communication that is either open and personal or more closed and guarded. | Indicates a personal, honest, and open communication style. | Indicates a guarded and distanced communication style. | | `negations` | Reflects use of language to negate, refute, or contradict. | Indicates significant use of language to negate or contradict. | Indicates minimal use of negating or refuting language. | | `clout` | Reflects communication marked by confidence and certainty. | Indicates highly confident language. | Indicates a more tentative or humble communication style. | ## Additional Information on the Social Dynamics Measures[​](#additional-information-on-the-social-dynamics-measures "Direct link to Additional Information on the Social Dynamics Measures") ### Social[​](#social "Direct link to Social") Social words are a marker of social engagement and are associated with awareness of other people. This vast category of words makes reference to other people, and includes certain pronouns, possessives, social nouns (i.e., *brother*, *team*), social verbs (i.e., *participate*, *listen*), social adjectives (i.e., *trusting*, *secret*), and more. When individuals use Social words, they are inherently thinking about or interacting with other people. Therefore, people who communicate using a higher level of Social words are generally more socially-conscious. ### Affiliation[​](#affiliation "Direct link to Affiliation") The Affiliation measure includes language that relates to connecting and being in the presence of other people. Words in this category are related to the Social measure but measure different phenomena. The Social measure is a marker of social engagement and is associated with awareness of other people. The Affiliation indicator has been used extensively in research. For example it has been used to [examine gender differences in evaluations of emergency medicine residents and their approach to patient care](https://onlinelibrary.wiley.com/doi/full/10.1002/aet2.10057). Research has also shown that [the feeling of affiliation or the need to affiliate with others can also play a role in promoting positive or negative health behaviours](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3225964/). ### Inward Focus[​](#inward-focus "Direct link to Inward Focus") The Inward Focus measure analyzes if an individual's language is focused on themselves, or outwardly focused on other people. The more someone uses “I” and self-related words, the more focused they are on themselves. How we see ourselves in the world is vitally important to how we interact within it. While research is still being done on the implications of self-focused language, we know it holds important information on how we communicate and behave in the world around us. Self-focus has been an important factor for researchers investigating status, age, depression, and more. For example, [lower status individuals tend to use language that is more self-focused and tentative](https://journals.sagepub.com/doi/abs/10.1177/0261927X13502654), people tend to [become less self-focused with age](https://pubmed.ncbi.nlm.nih.gov/12916571/), and [depressed individuals are more self-focused than their non-depressed counterparts](https://www.researchgate.net/publication/254221761_Language_Use_of_Depressed_and_Depression-Vulnerable_College_Students). While this category is not intended to be used for deception detection purposes, [deceptive statements have been found to be more distanced from the self than truthful ones](https://journals.sagepub.com/doi/abs/10.1177/0146167203029005010). ### Outward Focus[​](#outward-focus "Direct link to Outward Focus") The Outward Focus measure determines the degree to which a person’s language is focused on themselves or on other people by evaluating their use of “I” words and other self-referencing language. A high score suggests a significant focus on people or entities other than oneself. A low score suggests minimal to no focus on other people or entities other than oneself. Self-focus has been an important factor for researchers investigating status, age, depression, and more. For example, [lower status individuals tend to use language that is more self-focused and tentative](https://journals.sagepub.com/doi/abs/10.1177/0261927X13502654), [people tend to become more Outward Focused with age](https://pubmed.ncbi.nlm.nih.gov/12916571/), and \[depressed individuals are more self-focused than their non-depressed counterparts]\( depressed individuals are more self-focused than their non-depressed counterparts). While this category is not intended to be used for detecting deception, research has shown that \[deceptive statements have been found to be more distanced from the self]\([deceptive statements have been found to be more distanced from the self](https://journals.sagepub.com/doi/abs/10.1177/0146167203029005010)) (ie. Outward Focused) than truthful ones. ### Authentic[​](#authentic "Direct link to Authentic") The Authentic measure evaluates when someone is speaking naturally and uninhibited or whether they are carefully curating their words. A person may change their language for multiple reasons, such as to be more easily understood to align with expected tone or style or to avoid mentioning specific things. When evaluating authenticity it is important to compare samples within the same context as to ensure the accuracy of the results. When someone is communicating inauthentically they tend to distance themselves from their words. Authentic communicators tend to speak their mind use their own language and care less about the specific words they choose to use. People with high authenticity scores tend to be seen as relatable down-to-earth and honest. Studies relating to language and authenticity are vast. For example, some research has shown that [low Authenticity scores are correlated with deception](https://journals.sagepub.com/doi/abs/10.1177/0146167203029005010), how c[hanges in writing style are related to fraudulent data reporting](https://journals.sagepub.com/doi/10.1177/0261927X15614605), and how it is possible to [detect deceptive discussions in quarterly earnings calls](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1572705). ### Negations[​](#negations "Direct link to Negations") Language in this category contains a range of negative language and contractions, such as *wouldn’t*, *shouldn’t*, *don’t*, *cannot*, etc. This category, combined with other measures of Social Dynamics, Personality, and Emotions can be helpful to understand more about how people feel about topics and the world around them, as well as aspects of the dynamics of their relationships with others. Negations have been used by researchers to investigate a variety of social behaviours. For example, research has shown that [emotion words are positively correlated with negation use](https://www.cs.cmu.edu/~ylataus/files/TausczikPennebaker2010.pdf). Additionally, research suggests that [people who score high on extraversion in personality tests use negations less frequently](https://psycnet.apa.org/doiLanding?doi=10.1037%2F0022-3514.77.6.1296). ### Clout​[​](#clout "Direct link to Clout​") The Clout measure evaluates whether language is influential and leadership-like, or whether it is more passive and less persuasive. Language with lower Clout scores may not be intended to draw audiences in, or to inspire action. Clout can be context- and subject-specific; an individual with a low Clout score in one context may express a higher Clout score and have the ability to be influential in a different context. Research has shown that people with [lower status levels are more focused on themselves, whereas leaders are more focused on others and the group as a whole](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.897.7482\&rep=rep1\&type=pdf). This phenomenon has been documented across a range of social and linguistic contexts, group sizes, and settings. It is important to note that this focus on others does not suggest that leaders put others before themselves, but rather that they’re particularly attentive to the behaviour and mental states of others. As such, studies have found that [people who are attentive towards others will naturally be able to lead more effectively than those with attention directed inwards](https://www.researchgate.net/publication/232469632_Who_Is_This_We_Levels_of_Collective_Identity_and_Self_Representations). ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") Scores in the Social Dynamics framework are always in the range of `0` to `100`. note Normed measures require a text sample of at least 350-500 words per person. Let's look at a couple of examples: High-scoring example There were some real injuries from the game. Huff had a knee twist, we'll find out tomorrow on that. And Gene had a shoulder strain. There's some Real concern and we don't know for sure yet what it is. Most likely it's ACL but can't confirm it so we'll find out later. It was the first few plays. I mean it's, we always do 24 openers. There were still twelve runs, twelve passes, but it went like five and five. Just trying to do that early and then get back to running the game once we settled down a little bit. I thought he played real well. There was very few that he missed. Protected the ball extremely well when there wasn't anything there. We didn't have any intentional groundings and did a good job moving the chains also, especially at the end there on that keeper. But I thought he had a hell. I think Brock's been great because he's been consistent through it all. I mean, we never felt anything different with Brock during those three losses. You know, I think he's pretty realistic so he doesn't get into stuff that's not that accurate. Yeah, I wasn't concerned with guys looking ahead. I didn't feel that at all. You know, it's especially we got that, you know, we had won one game here in the last month and that was a big one versus Jacksonville. And I knew our guys are extremely focused on this. Once we were able to finish the game, we talked a lot about it in the locker room because just how quick it comes, everything's so important as soon as this ends, what we got to do to get ready for this quick turnaround, which is always a challenge and tough, but really glad we're going to get three days off after it. For the stretch, I thought you did a hell of a job. I mean, to lose a player like huff in the game and for him to come in and step it up big, make two huge plays. I mean, the pick was huge and then the fourth down, you know where you got all out Blitz and they just throw it up and sometimes your biggest fear and when they throw it up, it's like what happened to Buffalo on that Monday night when it's under thrown, it's so hard not to get a PI and for him to not PI the guy and to get back and from what I saw, it looked like he knocked it down. That was a hell of a play. Oh, yeah, big time. I mean, I noticed him all the time because one, he goes against the offensive scout team all the time and he's one of the guys who consistently gets better each week on all the card looks. We always split the reps with the ones and the twos anyways, when the defense goes and he's really taken off here in this last month and was ready for his opportunity. You never know when it's going to come. He's been preparing the right way. Yeah, definitely still improving. He had a really good game statistically wise, his best one I think, of his career from what they told me. But yeah, I was real happy with Ba. Has to run through the spot where no one is? We've called that play a few times. We've never done it with a bump motion. That's a play that a lot of people run. I think we ran into Tevin Coleman in the Super bowl. It's just a man play that is good for man. Everyone runs it, but we never done it with that motion and it was cool, worked out. No, I mean, teams usually do what they do. I mean teams, you don't just make up schemes every week. So every team has what they run and then you try to game plan stuff by having disguises, calling things at different times, giving a look or a blitz that people aren't ready for, but teams do everything. Brock doesn't ever come in saying we're throwing deep or throwing short or any throwing to certain people. We run plays and he goes through progressions and attacks coverages and I want to know what to say on how to defend them. He just goes through the holes in the defense. Ba has been great. A lot was made out of it because everyone talked doghouse and training camp, which I thought that was a little confused with just coaching. He was never in anyone's doghouse. We were just coaching him and Ba going back to those years, he was one of our best players halfway through that year and one of the main reasons we went to the NFC championship and got better last year and he's been better this offseason and always when you're getting better doesn't always show for a receiver because stats and all those results are dependent of a lot of other people. The bas been playing some good football for a while. It's really cool when he gets rewarded with those numbers. I mean, I think we got a lot of players who really enjoy football. I think we work at it in terms of, you know, we like to coach, we like to point things out. I think our guys, you know, if you stay healthy, I think you practice more, you play more and when that happens you get better as the year goes. So I always expect us to look better as it goes than we do at the beginning. I think one of the bigger challenges for everyone in the NFL is that there are no otas, whatever they call them, and how little training camp is. So especially running the ball and tackling and everything, you kind of evolve as the year goes and our guys work really hard and try to find a way to stay healthy while you do it. No, I don't think so. I thought our defense was awesome. I mean, they kept getting us back. I wish we would have finished it. Once you get the turnover on the first one, I want to go down in there and score. I think we went three and out and the next time we should have run the clock out, had two first downs. We got out of bounds twice so wasn't happy with that but so pumped with the defense. No concern, you know that last week, if I can remember correctly, I don't think we really got inside the ten. I thought we scored from all further out until the end when we were just trying to hook Christian up. This week, when I think about it, I know we got sacked, I believe one time down there and second down. You get sacked on second and ten or whatever it was. And it leads to third along. Most likely that drives over, but hopefully it goes full circle. We haven't done it the last couple weeks. We've done it better earlier in the year, but nothing that's different just goes like that sometimes. No, yeah, I didn't see it over there. I mean, the same thing that happened to kittle. I mean, those guys are very aware that they shouldn't go out of bounds in those situations. But when you get plays on the edge, I think kid will try to stop and go back in. That was right on our sidelines. But then people hit you and you go out and so it looks similar to me with Christian, but I was on the far sideline, so I'm not sure. I mean, you never know until people go through that. But I mean, the film was so good, so, I mean, the guy was playing unbelievable in those, in those three losses, so there was nothing to really worry about. You just got to make sure he doesn't make up stuff that other people are making up. So you just try to get him to stick with practice, stick with the film and doing what you're doing. And he had a couple back picks in those games, but there was none that he felt he had to change with what he was doing because he was playing such a high level in those losses, too. No, I didn't. I didn't know the guy would fall and I had no idea where the middle third safety was, but I knew his man coverage and he was going to go to whichever one of his go routes he had. He had Debo on one side and Ba on the other. And I'm assuming the safety cheated a little bit to the right since he threw to the left and it was right there and it was great when you saw the corner on the ground and Ba finished it. It was a hell of a play by those guys. I mean, Brock's solid, really good quarterback, so he can do, I mean, I don't know if he can run a four three when doing a lot of wildcat stuff, but everything we've asked him to do, I think he's done at a real high level. Yeah, when you get three, three weeks in a row, I mean, I love his competitiveness and he does everything to try not to get it, but if you're in your positions that you're going to get it, it really doesn't matter. So we kind of challenged him on that. They turned into sacks, which that happens, but we always want to try to get rid of it. But he had some really bad looks on those. I think we had one where we were hot, and that's when he tries hard to get rid of it. And sometimes bad things can happen, and sometimes you just got to know you had a bad play. And that's where I thought he improved on it. All right, thanks, guys. ``` // partial response { "social_dynamics": { "social": 88.50465740549186 } } ``` The paragraph above features a coach evaluating a recent game, highlighting individual and team performances, injuries, and tactical decisions. It is high on the `social` at `88.5` as it frequently acknowledges team collaboration, player development, and shared experiences, emphasizing interpersonal interactions and relationships within the team. This indicates that 88.5% of all samples in our curated baseline dataset scored equal to or below this paragraph on social. Low-scoring example Let me reflect on some of the key operational and business highlights and outlook for 2023. It was a record second quarter for us with 36% growth in deliveries versus last year. Combined with the first quarter, we delivered around 28,000 vehicles in the first six months of this year with particularly strong growth in many of our established markets and solid growth in some of our newest markets. We increased revenue by 18% to $1.2 billion for the first six months of 2023 and with continued strong momentum into the second half of the good year, we expect to deliver between 60,000 and 70,000 vehicles and a gross margin of 4% for 2023. Coming to recent business developments. Last quarter, I talked about the first customer deliveries of our upgraded Edition 2, and these have now started to ramp up, taking us past another milestone having 150,000 cars manufactured in just over three years. The upgraded Edition 2 is the best version to-date. We improved software longer range of up to 650 kilometers and faster charging with an effect of up to 205 kilowatts. All while reducing cradle-to-gate carbon emissions by 3 tonnes per car and introducing the new SmartZone face identity from Edition 3 and the 2. It is a fantastic car. J.D. Power's Tech Experience Index placed Edition 3 in the top three, and there are a number of enthusiastic independent reviews across motoring magazines and on YouTube. Just a few weeks ago, we were at the annual festival in the UK, where Edition 3 and Edition 5 had their dynamic debuts, making the traditional hill climb in front of the crowd. Seeing these two cars and what they are capable of in terms of performance and driving experience is a testament to our outstanding engineering teams. I'm delighted that Edition 3 is now available for customers to experience in many of our retail spaces around the world. And Edition 5 shows what the next steps are for our brand, reflected in its design handling and top-level spot premium positioning. We have started formally taking orders for Edition 4, our SUV coupe, less than a week ago at the Chengdu Auto Show. And now three days in reception and order take have been fantastic. As a reminder, our SUV coupe combines the great space with an amazing dynamic driving attributes. It is positioned between Edition 2 and the Edition 3 in terms of size and price. Edition 4 is on track to start production in November with the first customer deliveries in China expected before year-end. ``` // partial response { "social_dynamics": { "social": 8.420132578538823 } } ``` The paragraph above outlines a company's operational highlights and product developments, focusing on metrics such as vehicle deliveries, revenue growth, product upgrades, and future plans. It is low in `social` at just `8.42` because it emphasizes data, achievements, and business strategy rather than interpersonal connections, collaboration, or team dynamics. This indicates that only 8.4% of all samples in our curated baseline dataset scored equal to or below this paragraph on `social`. --- # Thinking Fast and Slow Receptiviti’s Thinking Fast and Slow framework is adapted from the [dual systems model](https://faculty.fortlewis.edu/burke_b/Criticalthinking/Readings/Kahneman%20-%20Of%202%20Minds.pdf) of cognition to measure two fundamental thinking modes: Slow thinking (effortful, careful, incremental) and Fast thinking (intuitive, reflexive, holistic). Thinking Fast and Slow is also known as System One and System Two thinking. Both modes of thinking have implications for the nature, quality, and speed of decision-making and reasoning. These measures are crucial for understanding how people think in various scenarios, including market research, audience segmentation, personnel selection, and leadership assessment. Neither way of thinking is inherently superior or inferior. Without flexible use of both modes of thought, it would be nearly impossible to effectively process our environments or make decisions. ``` { "plan_usage": { "word_limit": 2500000, "words_used": 1347, "words_remaining": 2498653, "percent_used": 0.05, "start_date": "2024-07-05T00:00:00Z", "end_date": "2024-08-04T23:59:59Z" }, "results": [ { "response_id": "c45ba97b-741c-4812-95dd-5e2a23655827", "language": "en", "version": "v1.0.0", "summary": { "word_count": 38, "words_per_sentence": 19, "sentence_count": 2, "six_plus_words": 0.3684210526315789, "capitals": 0.04326923076923077, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "thinking_fast_slow": { "thinking_fast_slow": 44.58847767737836 }, "interpersonal_circumplex": {...} ``` ## Interpreting Scores[​](#interpreting-scores "Direct link to Interpreting Scores") **Low scores** are indicative of Fast thinking, which involves intuitive, efficient thought processes, often based on mental shortcuts or heuristics. Decisions made using Fast thinking often rely on emotions, habits, and implicit associations rather than deliberate thought. Despite the risk of bias or irrationality, these rapid judgments are crucial in the daily mental triage process. For instance, reflexive decisions ensure personal safety, such as instinctively stopping before crossing traffic or avoiding a potentially dangerous situation. Intuitive reasoning also saves time and mental effort when performing routine tasks like making coffee or riding a bike, which benefit from *Fast and frugal* thinking. In contrast, overthinking such routine tasks can actually make them awkward and less efficient. **High scores** suggest a Slow thinking style, which is more reflective and more painstaking than Fast or intuitive thinking. It requires more attention, working memory, and conscious effort. As a result, Slow thinking is harder to do when a person is more distracted or fatigued. Slow thinking tends to be more abstract, often involving reflections and deductions based on general principles rather than immediate reactions. People use this more effortful, deliberative mode of thinking when they are carefully considering alternatives, working through challenging problems using rule-based logic, or when evaluating new ideas. ## Interplay Between Fast and Slow Thinking[​](#interplay-between-fast-and-slow-thinking "Direct link to Interplay Between Fast and Slow Thinking") Fast and slow thinking complement each other, and the ability to strategically employ either mode of thought is essential for efficiently navigating most aspects of daily life. Individuals naturally use both fast and slow thinking depending on the situation, but oftentimes people spend more time in one system or the other. When analyzing someone's language over time or in different situations, if their average leans towards Fast thinking, they are more likely to make quick, emotional, and instinctive decisions. If a person’s average is further in the direction of Slow thinking, they likely spend more time making carefully-reasoned decisions. Psychology research shows it’s best to [balance both modes of thinking](https://www.sciencedirect.com/science/article/am/pii/S0010027720302006), letting the harder work of deliberation inform later cognitive shortcuts, i.e., thinking carefully now in order to facilitate [smart, fast decisions when needed](https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01672/full). ## Using Receptiviti to Measure Fast and Slow Thinking[​](#using-receptiviti-to-measure-fast-and-slow-thinking "Direct link to Using Receptiviti to Measure Fast and Slow Thinking") Receptiviti’s Thinking Fast and Slow framework measures are based on both psychological theory and data on how people use language in real life. Using data from our proprietary corpora, we analyzed how language measures theoretically related to fast and slow thinking correlate to each other across dozens of contexts. Results showed that fast and slow thinking styles form opposite ends of a spectrum that is common across all kinds of communication. Fast thinking is measured by words reflecting concrete, narrative, lower-effort thinking and basic, instinctive emotions. The Slow end of the spectrum is measured using words reflecting abstract, formal, rule-based, effortful thinking and complex emotions. ## Specifications[​](#specifications "Direct link to Specifications") Scores in the Thinking Fast and Slow framework are normed, and thus always in the range of `0` to `100`. Our measures are baselined against our proprietary datasets, which consist of language samples that exceed 350 words. A language sample that generates a score of 80 implies that 80% of all samples in our curated baseline dataset have scores that are less than the score of the language sample being analyzed. If you are analyzing multiple people in the same context, it can be useful to compare normed scores within the same sample. For example, most language in formal, carefully constructed documents like legal decisions or scientific reports will score high on this measure, indicating slower, more careful and effortful thinking than average. In that context, a text that scores at or slightly above the median of the norming data may represent thinking that is strikingly Fast for that context. ## State versus trait measures[​](#state-versus-trait-measures "Direct link to State versus trait measures") While the Thinking Fast and Slow framework was initially intended to gauge an individual's habitual inclination towards Fast thinking, Slow thinking, or a combination of both, it can also provide insights into how someone is processing information in a specific moment, such as during a discussion about a particular problem or while making a decision. The optimal mode of thinking will vary depending on the complexity of the task at hand. Deciding whether to rely on Fast thinking, Slow thinking, or a balanced approach depends on your understanding of the task's demands and potential benefits of each thinking mode. Fast thinking may be preferred for crises where quick, decisive actions are needed, whereas slower thinking may be preferred for major decisions that require careful planning and unbiased, objective judgments. --- # Toxicity Hate speech is a serious and growing problem for online publishers, e-gaming companies, comment moderation platforms, and social media sites. In addition to the ethical reasons for combating online hate speech, governments across the globe are beginning to implement new legislation that requires platforms to remove hateful content within hours or face significant financial penalties. The Toxicity framework can be used to detect the likelihood of toxic language in your data. It can detect the probability that your language sample contains threats, hate speech, offensive language, and general toxicity. In addition to these four categories, we also provide 17 Toxicity measures that can help you identify the specific source of toxicity in your data. The Toxicity framework helps to identify language that might be considered obscene, insulting, or offensive, including lewdness, descriptions of sex or desires, direct insults to the reader of the message, or excessive swear words in a context where they aren’t welcome. The toxicity framework stands out as the only Receptiviti framework that includes ML-based measures, leveraging machine learning to assess language patterns. It's important to note that this framework was trained on social media data, making it particularly useful for analyzing similar contexts, but it may not be applicable to all forms of communication. note The toxicity framework measures the **likelihood** that language is toxic, not how toxic it is. ``` { "plan_usage": { "word_limit": 100000, "words_used": 1020, "words_remaining": 98980, "percent_used": 1.02, "start_date": "2024-03-16T20:52:15.254118Z", "end_date": "2024-04-18T23:59:59Z" }, "results": [ { "response_id": "68150a92-21c8-42f6-8b6e-7a2551f31cf1", "request_id": "req-1", "language": "en", "version": "v1.0.0", "summary": { "word_count": 17, "words_per_sentence": 8.5, "sentence_count": 2, "six_plus_words": 0.4117647058823529, "capitals": 0.02127659574468085, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "toxicity_likelihood": { "hate_speech": 0.08253253779198318, "offensive": 0, "threat": 0.14128420102382466, "toxicity": 0.4139965950424534 }, "toxicity_measures": { "authority_structures": 0, "body_size_shape": 0, "crimes": 0, "death": 0, "direct_insults": 0, "disability": 0, "ethnic_origin": 0, "gender_sex": 0, "other_sociocultural": 0, "political_affiliation": 0, "political_issues": 0.058823529411764705, "race_ethnicity": 0.11764705882352941, "religion": 0, "sexual": 0, "sexual_orientation": 0, "swears": 0, "violence": 0.058823529411764705 } } ] } ``` ## Toxicity Measure Categories[​](#toxicity-measure-categories "Direct link to Toxicity Measure Categories") | Measure | Summary | High Score Example | | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | | `hate_speech` | Provides the probability that a text sample contains an instance of hatred, anger, or disgust regarding any demographic group. Demographic groups are commonly differentiated by race, ethnicity, gender, sexual orientation, or disability. Expressions of racism or sexism are considered hate speech, while insults directed at a person or people with no mention of their race, gender, or other demographic markers are not hate speech. | Can't you see why you morons get nowhere? `0.5` | | `offensive_language` | Provides the probability that a text sample contains language that might be considered obscene or offensive. Examples of this might include lewd descriptions of sex or desires, direct insults to the reader of the message, or excessive swear words in a context where they aren’t welcome. What is considered offensive in one context (i.e. particular website, culture of website users, point in time, etc.) may not be considered offensive in another, so it’s important to recognize that this measure is based on generalizations. | Wow you're an idiot `1` | | `threat` | Provides the probability that a text sample contains a direct threat of any kind. This threat may be directed to the reader or recipient, a third party who isn’t reading the message, or to a type of person in general. | If you keep that posting content like this, I will find you. And hurt you `0.67` | | `toxicity` | Provides the probability that a text sample contains any content considered undesirable in civil discourse. This may include, but is not limited to threats, hate speech, offensive language, obscenities, bullying, direct insults, insensitive content, descriptions of violence, or overt sexual content. | Your promiscuous behaviour will get you into trouble. That isn't your place. `0.94` | ### Measures per Category: Hate Speech[​](#measures-per-category-hate-speech "Direct link to Measures per Category: Hate Speech") | Score | Summary | | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `race_ethnicity` | Includes references to skin colour as well as more specific ethnicities. Includes both rude and socially-acceptable references. | | `ethnic_origin` | Includes references to citizenship status, immigration or refugee status, and specific nationalities. Includes both rude and socially-acceptable references. | | `gender_sex` | Includes terms based on biological sex as well as gender identity and gender expression. Includes both rude and socially-acceptable references. | | `sexual_orientation` | Includes terms for all common sexual orientations. Includes both rude and socially-acceptable references. | | `religion` | Includes terms for all major world religions, as well as terms that do not refer to any specific religion but refer instead to types of faith or religious practices. Includes both rude and socially-acceptable references. | | `disability` | Includes terms associated with sensory or mobility impairments, other physical disabilities, mental or psychological health concerns, and substance abuse. Includes both rude and socially-acceptable references. | | `body_size_shape` | Includes terms used for different body sizes and shapes, including both overweight and underweight. Includes both rude and socially-acceptable references. | | `sociocultural` | Includes an assortment of social identifiers that people are assigned, such as “hippie” or “redneck”. Includes both rude and socially-acceptable references. | | `political_affiliation` | Includes terms used specifically for the political left or right, as well as terms for other political ideologies or affiliations. Includes both rude and socially-acceptable references. | ### Measures per Category: Threat[​](#measures-per-category-threat "Direct link to Measures per Category: Threat") | Score | Summary | | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `crimes` | Includes a range of specific crimes. Includes both violent and non-violent crimes. Includes both misdemeanors and felonies. | | `violence` | Contains specific references to violence, including war and gang violence, methods of murder, methods of violating or injuring people, and references to weapons. | | `death` | Contains references to death or the deceased, regardless of mechanism or cause of death. | | `authority_structures` | Contains references to specific authority structures or figures, including those found within the military, police force, government, corporations, and educational institutions. | | `political_issues` | Specific political issues relevant to current political conversation. Primarily focuses on controversial issues and issues that are considered very important by many people. | ### Measures per Category: Offensive Language[​](#measures-per-category-offensive-language "Direct link to Measures per Category: Offensive Language") | Score | Summary | | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `direct_insults` | Includes terms that are predictably used to insult people, as well as terms that insult a person’s intelligence, appearance, morality, worth, and sanity. | | `sexual` | Includes terms based on biological sex, as well as commonly used words and phrases related to sexual harassment or unwanted romantic interaction. Includes both rude and socially-acceptable references. | | `swears` | Includes words that may be considered swears, ranging from very mild exclamations acceptable in any context to very obscene language considered unacceptable in most contexts. | ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") The Toxicity framework was designed to assist both human interpreters and machine learning models working on the task of identifying specific types of toxicity in online discourse. The Toxicity framework contains specific, topical word lists that are unique to the goal of detecting toxicity (such as direct insults to intelligence and racial slurs), as well as LIWC2015’s original grammatical categories (such as personal pronouns and conjunctions) which have been studied at length for their ability to model relevant underlying social dynamics. This means that the Toxicity framework provides information about both the explicit and implicit signals in toxic discourse. The four main categories - Hate Speech, Threat, Offensive Language, and Toxicity - are derived through machine learning modelling based on the measures that correspond with them. A score is a probability that a text sample contains some kind of toxic language, and not a measure of severity of the toxicity. > **Example:** *Your promiscuous behaviour will get you into trouble. That isn't your place.* ``` // partial results "results": [ { "response_id": "4ba0550e-4df7-4b13-805b-73b23f9b0e06", "language": "en", "version": "v1.0.0", "summary": { "word_count": 13, "words_per_sentence": 6.5, "sentence_count": 2, "six_plus_words": 0.23076923076923078, "capitals": 0.03076923076923077, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "toxicity_likelihood": { "hate_speech": 0.5780701545167783, "offensive": 0.6897154921639401, "threat": 0.7026310100469049, "toxicity": 0.9442709017897967 }, "toxicity_measures": { "authority_structures": 0, "body_size_shape": 0, ...... "sexual": 0.07692307692307693, ...... } ``` In the example sentence above, the word *promiscuous* counts towards the `sexual` measure. The word frequency from that measure - in this case, one instance of a word in `sexual` divided by the length of the sample (13) - goes into the machine learning model. The model uses the percentage in that category to predict whether the sample contains toxicity or not. As can be seen by the scores, the likelihood that the language in that sample is both offensive and threatening is quite high, and is especially high in `toxicity`, at `0.94`. --- # Emotions Receptiviti’s Emotions engine, called SALLEE (Syntax-Aware LexicaL Emotion Engine; pronounced *Sally*), detects emotions and sentiment expressed in text. It is designed to score the emotions a person is expressing, which can include emotions they’re feeling in the present, emotions they've felt in the past or expect to feel in the future, or emotions they see or assume others are feeling. Each emotion can be seen as negative, neutral, or positive. SALLEE analyzes the amount of emotion proportional to the size of a particular piece of text on everything from a short tweet to a long speech. This is particularly valuable when you want to understand how emotions change over time, in different contexts, and across different individuals. For longer documents, you can analyze the entire language sample at once to understand the emotions expressed in the text. Alternatively, you can break the document into smaller sections to understand how the emotions change section by section. Emotion scores (including scores Goodfeel, Badfeel, and Ambifeel) will always fall between `0.0` and `1.0`. Sentiment scores will always fall between `-1.0` and `+1.0`. A sentiment score of `0` indicates either equal amounts of positive and negative emotions or no emotions present. Goodfeel, Badfeel, and Ambifeel will allow you to differentiate between degrees of negative, neutral, or positive emotions based on the scoring. SALLEE’s impressive accuracy comes from its ability to process grammatical structure and contextual clues. For example, it accounts for intensifiers such as *very*, softeners such as *sort of*, and negations such as *never*. It can process the different ways people use the same swear words and idioms based on context. It can tell the difference between *not really happy* and *really not happy*, and can also understand the emotional relevance of emojis and hashtags. SALLEE is particularly effective at capturing emotions from social media posts, short text samples, casual language, and mediums like conversation or text messages. note There are two modes in which SALLEE can be called from the API: Sparse mode, and Default mode. Sparse Mode is designed for use cases where false positives are especially undesirable. More information can be found in the [SALLEE Sparse mode](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#sallee-sparse-mode) section of this page. Using the Receptiviti API, you can programmatically access 20 SALLEE measures. * 7 positive emotions * Admiration * Amusement * Calmness * Excitement * Gratitude * Joy * Love * 5 negative emotions * Anger * Boredom * Disgust * Fear * Sadness * 2 ambivalent emotions * Curiosity * Surprise * 6 summary metrics of emotions * Ambifeel * Badfeel * Goodfeel * Sentiment * Emotionality * Non-emotion **Ambifeel** is a summary metric for the ambivalent emotions. **Badfeel** is a summary metric for negative emotions. **Goodfeel** is a summary metric for positive emotions. **Emotionality** is the overall degree to which a text sample contains emotion. **Non-emotion** is the overall degree to which a sample lacks emotion. **Sentiment** provides a net score for the degree of good or bad emotionality contained with a text sample. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1438, "words_remaining": 248562, "percent_used": 0.58, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "ff75ed78-7373-45c8-8bc9-fe67a5980fac", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "sallee": { "admiration": 0, "ambifeel": 0, "amusement": 0, "anger": 0, "badfeel": 0.11538461538461539, "boredom": 0, "calmness": 0, "curiosity": 0, "disgust": 0, "emotionality": 0.5769230769230769, "excitement": 0.5769230769230769, "fear": 0.11538461538461539, "goodfeel": 0.5769230769230769, "gratitude": 0, "joy": 0, "love": 0, "non_emotion": 0.42307692307692313, "sadness": 0, "sentiment": 0.46153846153846156, "surprise": 0 }, "receptiviti_measures": {...} } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Measure | Summary | High Score Samples / Score | | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------- | | `admiration` | Includes aesthetic appreciation, awe, and pride. Includes the feeling of being impressed by anything or anyone. Includes both pride in yourself and admiration of others. | oh WOW! I'm so impressed! `0.85` | | `amusement` | Includes laughter and humour, the feeling of watching good comedy, and casual laughter in conversation. | Her laugh was SO infectious! `0.83` | | `calmness` | Includes relaxation and peacefulness, such as feelings achieved from meditation or other relaxation exercises. Includes the feeling of watching a quiet landscape with a cup of tea. | Take a deep relaxing breathe and stretch #yoga `0.75` | | `excitement` | Includes excitement or anticipation, waiting for something to happen. Includes the feeling of jitters before any event, such as a work presentation or a wedding. Includes the feeling of looking forward to the release of the next movie in your favourite series. | Amp up this partay! `0.74` | | `gratitude` | Includes thankfulness, satisfaction, relief. Includes the feeling of being pleasantly full (not bloated) after a good meal. Includes the sign of relief after almost breaking a dish but catching it at the last moment. | Phew! SO VERY glad that the exam is over #RELIEF `0.7` | | `joy` | Includes happiness, enjoyment, and pleasure. Includes the feelings of being at a festival or reading a good book – whichever you prefer. Includes the feelings of playing a favourite game or doing a favourite hobby. | Lets go walking - it's #sunny! 😀 `0.72` | | `love` | Includes adoration, romance, and affection. Includes the feeling of watching a cute video of an animal you’ve never met. Includes fondness or affection for friends. | I absolutely adore rum and raisin icecream `0.79` | | `anger` | Includes annoyance, rage, and frustration. Ranges from the feeling of irritation at a fly buzzing around your head to the feeling of deep fury after being betrayed by a loved one. | Stop! That's my muffin! `0.74` | | `boredom` | Includes momentary boredom and existential boredom. Ranges from the feeling of waiting around with nothing to do, to existential boredom or ennui, such as the feeling that every day in your life is the same. | What a bland lecture! `0.78` | | `disgust` | Includes disgust and disdain, such as the visceral disgust felt about bodily fluids or rotting garbage. Includes social or conceptual disdain, the way you might feel about those with different political opinions. | yuck! That's pretty gross! `0.84` | | `fear` | Includes worry, anxiety, and horror. Includes the feeling of being terrified at a scary movie. Includes vague feelings of anxiety about unknown factors such as money or health. | I am terrified of dying :scared: `0.83` | | `sadness` | Includes disappointment, grief, and sorrow. Includes intense feelings of mourning and loss. Includes mild disappointment after everyday losses, such as not finding something you want at the store. | A singularly spectacular failure 😢 `0.84` | | `curiosity` | Includes confusion, interest, intrigue, and entrancement, such as the feeling of not being able to look away from the scene of an accident. Includes fixation on a hypnotic image or obsession with finding the answer to a mystery. | I can't look away. Scooby...I wonder? `0.7` | | `surprise` | Includes any surprise or shock, whether positive, negative, or neither. Includes coming home from work on your birthday and seeing friends jump out yelling “surprise!!!”. Includes the intense shock of finding out that a loved one has been in an accident. | OMG!! Who? `0.92` | | `ambifeel` | The proportion of the text sample that expresses ambiguous or ambivalent emotions. For example, if you are craving something, you have positive feelings about it but also do not have access to it. The emotions Curiosity and Surprise contribute to this summary score. | This is a little more unloading a secret, and searching for answers. `0.12` | | `badfeel` | The proportion of the text sample that expresses negative, or typically “bad” emotions. The emotions Boredom, Sadness, Disgust, Anger, and Fear contribute to this summary score. | I had a really BAD day :( `0.76` | | `goodfeel` | The proportion of the text sample that expresses positive, or typically “good” emotions. The emotions Love, Joy, Amusement, Gratitude, Admiration, Calmness, and Excitement contribute to this summary score. | That movie was BRILLIANT and AWESOME!!! `0.83` | | `sentiment` | Total sentiment on a scale from most negative to most positive. A sentiment score of `0` indicates either equal amounts of positive and negative emotions or no emotions present. Goodfeel, Badfeel, and Ambifeel will allow you to differentiate between degrees of negative, neutral, or positive emotions based on the scoring | How upsetting :sad: `-0.94` | | `emotionality` | The proportion of the text sample that expresses any emotion, as well as the intensity of that emotion. | I'm so very sorry :'( `0.87` | | `non_emotion` | The portion of text that does not indicate any emotions. | I have completed the task within schedule. `1` | ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") SALLEE is sensitive to punctuation and context. Adding modifiers (e.g., *very, really*), amplifiers (e.g., *!*, caps lock, explicatives), and negations (e.g., *not*) will affect how a text is scored. It is also important to note that because SALLEE measures relate to the proportion of emotion in a particular text, it is crucial that SALLEE be used to compare language within similar contexts, as you'll see in the following examples: > **Example 1:** *That was the best movie* ``` // partial response { "sallee": { "admiration": 0.2, "goodfeel": 0.2, "sentiment": 0.2 } } ``` ![count formula](/assets/images/scores-formula-8c044eba237e6168ae27f1c0f238a37c.jpg) The sentence above returns a `0.20` score for `admiration`, which means that about 20% of the statement was characterized as displaying admiration. The `goodfeel` score for this sentence is `0.2`, and `sentiment` score is also `0.2` (on a scale from `-1.0` to +`1.0`). This occurs because the word *best* counts for one out of the five words and it expresses a positive emotion with no amplifiers, softeners, or negations. The score is relatively low because *best* is not a particularly strong emotion word. > **Example 2:** *That was an awesome movie* ``` // partial response { "sallee": { "admiration": 0.6, "emotionality": 0.6, "excitement": 0.4, "goodfeel": 0.6, "non_emotion": 0.4, "sentiment": 0.6 } } ``` By changing the word from *best* to *awesome*, the phrase expresses not only a stronger emotion, but also a wider range of emotions. With *awesome*, the score is `0.6` for `admiration` and `0.4` for `excitement`. The `goodfeel`, `emotionality`, and `sentiment` scores are also all `0.6` in this context. The reason `emotionality` and `sentiment` scores are the same as the `goodfeel` score is because there are no negative emotions in the sentence. Since the sentence has an `emotionality` score of `0.6`, the `non_emotion` score is `0.4` as the remainder of the text contains no discernible emotion. Although `admiration` and `excitement` account for approximately half of the text, approximately `0.4` (close to half) of the text does not convey any emotion, and therefore the score for the `non_emotion` is `0.4`. In this example, the SALLEE scores are not quite a true reflection of emotion as a proportion of total text (as in Example 1). This is because *awesome* is associated with multiple emotions (`admiration` and `excitement`), each of which have different [valences](https://dictionary.apa.org/emotional-valence). For more information about valences and which words are associated with which emotions, please [contact us](https://dashboard.receptiviti.com/contact). > **Example 3:** *That ride was terrifying* ``` // partial response { "sallee": { "badfeel": 0.7692307692307693, "fear": 0.7692307692307693, "emotionality": 0.7692307692307693, "non_emotion": 0.23076923076923073, "sentiment": -0.7692307692307693 } } ``` This sentence returns `0.77` for `fear`, a negative emotion because *terrifying* is a very strong emotional word. The score for `sentiment` is a negative value, as fear is considered a negative emotion. Identifying the range of different emotions in longer and more complicated texts can present a challenge. As our communications grow longer, we often express a wider range of emotions in our language. At times, these emotions might even be polar opposites (e.g., *I loved the meal, but the service was awful*). Longer sentences often include more filler words (e.g., *rambling*), which can make inferring emotions even more difficult for humans. However, as mentioned earlier, it is important to remember that SALLEE measures the proportion of emotion in a particular text. Therefore, it is crucial that SALLEE be used to compare language within similar contexts. To illustrate the comparison of emotion in similar contexts, we’ll compare excerpts from two speeches: > **Gandhi on the eve of the Dandi March in 1930:** *But let there be not a semblance of breach of peace even after all of us have been arrested. We have resolved to utilize all our resources in the pursuit of an exclusively nonviolent struggle. Let no one commit a wrong in anger. This is my hope and prayer. I wish these words of mine reached every nook and corner of the land.* ``` // partial response { "sallee": { "anger": 0.1553398058252427, "calmness": 0.0970873786407767, "disgust": 0.009708737864077669, "fear": 0.08737864077669903, "joy": 0.019417475728155338, "sadness": 0.02912621359223301 } } ``` > **George Brown in Favour of Confederation in 1865:** *For myself, sir, I care not who gets the credit of this scheme, I believe it contains the best features of all the suggestions that have been made in the last ten years for the settlement of our troubles; and the whole feeling in my mind now is one of joy and thankfulness that there were found men of position and influence in Canada who, at a moment of serious crisis, had nerve and patriotism enough to cast aside political partisanship, to banish personal considerations, and unite for the accomplishment of a measure so fraught with advantage to their common country.* ``` // partial response { "sallee": { "admiration": 0.040275213962074174, "anger": 0.06041282094311126, "disgust": 0.03356267830172848, "fear": 0.14633327739553617, "gratitude": 0.07501258600436315, "joy": 0.05370028528276557, "love": 0.06712535660345696, "sadness": 0.03356267830172848 } } ``` By comparing excerpts from these two speeches, we can see that George Brown’s speech elicits twice as much Fear as Gandhi’s does. ## SALLEE Sparse Mode[​](#sallee-sparse-mode "Direct link to SALLEE Sparse Mode") For shorter language samples, the Receptiviti API can be used in Sparse mode. Sparse Mode is designed for use cases where false positives are especially undesirable. Sparse mode is available to all users who have subscribed to the Emotions package, and can also be set as the default mode on any Emotions account by [request](https://dashboard.receptiviti.com/contact). Sparse mode optimizes to minimize false positives, whereas standard has been optimized to generate fewer false negatives. ## Calling the API in Sparse mode[​](#calling-the-api-in-sparse-mode "Direct link to Calling the API in Sparse mode") To use Sparse mode, you can simply include `"sallee_mode": "sparse"` in the API request, as seen below: ``` { "content": "This is my text sample", "sallee_mode": "sparse" } ``` In Postman, for example, you would add this to the payload (Body) with the **raw** button and **JSON** selected. ![payload sparse sallee](/assets/images/payload-sparse-sallee-d6621ea49304920861c3cb925e96bbbf.jpg) You'll get a response structured like this (although individual results will vary): ``` "results": [ { "response_id": "36cb614d-c72e-47a3-8fe3-309d1a7742a5", "language": "en", "version": "v1.0.0", "sallee_mode": "sparse", "summary": { "word_count": 52, "words_per_sentence": 52, "sentence_count": 1, "six_plus_words": 0.17307692307692307, "capitals": 0.009852216748768473, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, // <...> "sallee": { "sentiment": 0, "goodfeel": 0, "badfeel": 0, "emotionality": 0, "non_emotion": 1, "ambifeel": 0, "admiration": 0, "amusement": 0, "excitement": 0, "gratitude": 0, "joy": 0, "love": 0, "anger": 0, "boredom": 0, "disgust": 0, "fear": 0, "sadness": 0, "calmness": 0, "curiosity": 0, "surprise": 0 }, ``` --- # LIWC Linguistic Inquiry and Word Count (LIWC) is the gold standard for research in the field of Language Psychology. Created by [Dr. James W. Pennebaker](https://en.wikipedia.org/wiki/James_W._Pennebaker) at the University of Texas, the software was originally used to examine the therapeutic value of writing by analyzing the frequency of psychologically-relevant linguistic features of text. Since its inception, the various LIWC dimensions have been validated and addressed in published research, and LIWC has been the basis for over 25,000 academic publications in a variety of fields covering topics such as power dynamics, thinking styles, motivations, communication dynamics,personality, consumer behavior, group dynamics, culture, and interpersonal relationships, among others. LIWC 2015 classifies language into 94 psychologically-relevant categories, and LIWC22 has expanded that number to 102. These categories are defined by curated collections of words. Some categories consist primarily of function words (e.g., articles, pronouns), others focus on content words (e.g., biological processes, positive emotion), and many blend both content and function words (e.g., affiliation). info Content words capture what people are communicating about, which is the focus of traditional Natural Language Processing (NLP) methods like topic modeling and sentiment analysis. Function words capture how people communicate. Though often discarded as “stop words” in NLP, they carry rich psychological signals, reflecting states, traits, and values. Importantly, function words account for about 55% of everyday language and are processed largely subconsciously. For example, in the sentence “We should discuss the future priorities for our team to ensure success,” the words we, should, the, for, our, and to are function words, while discuss, future, priorities, team, ensure, and success are content words. LIWC analyzes both content and function words to detect psychological patterns. While early versions of LIWC were developed manually, later updates have added semantic vector networks, thematic analysis, meaning extraction, and other machine-learning techniques, all used in conjunction with human-led analysis and decision-making. Through the Receptiviti API, LIWC can be accessed programmatically, making it possible to integrate psychological language analysis into applications at scale. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1282, "words_remaining": 248718, "percent_used": 0.51, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "f2ef969d-c96b-4adc-b78b-cd3cba8111f8", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": {...}, "social_dynamics": {...}, "drives": {...}, "cognition": {...}, "additional_indicators": {...}, "sallee": {...}, "liwc": { "analytical_thinking": 0.9325858951175406, "clout": 0.5, "authentic": 0.01, "emotional_tone": 0.99, "six_plus_words": 0.6666666666666666, "dictionary_words": 0.6666666666666666, "function_words": 0, "pronouns": 0, "personal_pronouns": 0, "i": 0, "we": 0, "you": 0, "she_he": 0, "they": 0, "impersonal_pronouns": 0, "articles": 0, "prepositions": 0, "auxiliary_verbs": 0, "adverbs": 0, "conjunctions": 0, "negations": 0, "other_grammar": 0.3333333333333333, "verbs": 0, "adjectives": 0, "comparisons": 0, "interrogatives": 0, "numbers": 0, "quantifiers": 0.3333333333333333, "affective_processes": 0.3333333333333333, "positive_emotion_words": 0.3333333333333333, "negative_emotion_words": 0, "anxiety_words": 0, "anger_words": 0, "sad_words": 0, "social_processes": 0, "family": 0, "friends": 0, "female": 0, "male": 0, "cognitive_processes": 0.3333333333333333, "insight": 0, "causation": 0, "discrepancies": 0, "tentative": 0.3333333333333333, "certainty": 0, "differentiation": 0, "perceptual_processes": 0, "see": 0, "hear": 0, "feel": 0, "biological_processes": 0, "body": 0, "health": 0, "sexual": 0, "ingestion": 0, "drives": 0, "affiliation": 0, "achievement": 0, "power": 0, "reward": 0, "risk": 0, "time_orientation": 0, "focus_past": 0, "focus_present": 0, "focus_future": 0, "relativity": 0, "motion": 0, "space": 0, "time": 0, "personal_concerns": 0, "work": 0, "leisure": 0, "home": 0, "money": 0, "religion": 0, "death": 0, "informal_language": 0, "swear_words": 0, "netspeak": 0, "assent": 0, "nonfluencies": 0, "filler_words": 0, "all_punctuation": 0.3333333333333333, "periods": 0, "commas": 0, "colons": 0, "semicolons": 0, "question_marks": 0, "exclamations": 0, "dashes": 0, "quotes": 0, "apostrophes": 0, "parentheses": 0, "other_punctuation": 0.3333333333333333 } } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Category | Measure | Examples | | -------------------------- | ------- | -------- | | Summary Language Variables | | | | Linguistic Dimensions | | | | Other Grammar | | | | Psychological Processes | | | ## The Relationship between LIWC and Receptiviti[​](#the-relationship-between-liwc-and-receptiviti "Direct link to The Relationship between LIWC and Receptiviti") Receptiviti operates as the commercial arm of LIWC (the academic offering). Members of our science and sales teams have worked in Pennebaker’s lab and contributed to various iterations of LIWC. Today, our science team continues to evolve and expand the science, while the customer-facing side of our company supports its application. The science team has developed over 2,600 additional categories using the same validated methodology established in LIWC's original design. Some of these are made available to customers as proportional frameworks (like LIWC Extension), while others are used internally. note *Categories* are a common way we refer to our proportional measures. The term is derived from the dictionary approach used to design the measures, which, at a high-level, involves determining sets of words that are psychometrically related. Receptiviti’s proportional measures, together with LIWC, serve as the foundational ingredients of Receptiviti’s normed algorithmic measures. Our normed algorithmic measures are never based on black-box machine learning models. Instead, they are built from the ground up using interpretable components—formulas shaped through a combination of theoretical insight and data-driven methods. We rely on transparent techniques such as regression and decision trees, ensuring every part of a measure remains understandable and explainable. Theory-based methods draw on more than 25,000 published studies that have used LIWC to predict traits, states, behaviors, and other outcomes. These findings inform how we construct each measure. Data-driven methods include statistical approaches such as principal component analysis, regression modeling, and embeddings to test performance and inform and validate design using our internal datasets. *** ## Working with LIWC[​](#working-with-liwc "Direct link to Working with LIWC") The LIWC dictionary is composed of approximately 6,400 words, word stems, and select emoticons. LIWC measures will range between `0` and `1`. `0` implies that a word within a category was not mentioned, and anything above zero indicates that a word in that category was mentioned, and the associated score reflects the ratio of that word to the total number of words in the submitted text sample. When using LIWC, it’s important to remember that some words can fall into more than one category. For example, the word *cried* is part of five different categories: **sadness**, **negative emotion**, **overall affect**, **verbs**, and **past focus**. Hence, if the word *cried* is found in your text sample, each of these five sub-dictionary scores will be incremented. As in the example with the word *cried*, many of the LIWC categories are arranged hierarchically. All sadness words, by definition, belong to the broader **negative emotion** category, as well as the overall **affect words** category. Word stems can also be captured by LIWC. For example, the dictionary includes the stem *hungr\**, which allows for any word in your sample that matches the first five letters to be counted as an ingestion word (including *hungry, hungrier, hungriest*). The asterisk denotes the acceptance of all letters, hyphens, or numbers following its appearance. ### LIWC API vs LIWC Academic Desktop Processing Differences: What to Expect[​](#liwc-api-vs-liwc-academic-desktop-processing-differences-what-to-expect "Direct link to LIWC API vs LIWC Academic Desktop Processing Differences: What to Expect") When using the API, there are a few key differences from the desktop application. Here’s what you should know: | Feature | API Behavior | Desktop Behavior | | ---------------------------- | ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | **URLs** | Counts the URL as a unit, but does **not** analyze the words or punctuation inside it. | Counts both punctuation and words within the URL. | | **Hashtags** | Counts `#` under `OtherP`. Attempts to split the following word unless it matches a dictionary entry exactly. | Doesn’t count `#` under `OtherP`. Only scores the word if it matches the dictionary as a whole. | | **Numbers with Punctuation** | Treats punctuated numbers as a **single item**. E.g., `20,000.00` = one number. | Counts each part separately (e.g., 20,000.00 = three numbers). | | **Parentheses** | Counts full pairs as one unit, and adds 1 to `OtherP` for a pair like `(some text)`. | Also counts parentheses in pairs, but adds 2 to `OtherP` for `(some text)`. | | **Dictionary Bigrams** | Counts each word in a bigram (e.g., "each other") **separately**. | Treats dictionary bigrams as **one token**, affecting word and six-letter word counts. | | **Titles (Mr., Ms.)** | Counts both with and without periods as words and **doesn't** trigger sentence breaks. | Period titles cause a **sentence break**; both forms are skipped unless matched in the dictionary. | | **OtherP Category** | Recognizes more symbols (including mathematical ones). | Fewer symbols are assigned to this category. | | **Ellipses (...)** | Groups all ellipsis forms under `OtherP` as a single unit. | Treats `...` as separate punctuation unless encoded as a true ellipsis. | | **Hyphens & Apostrophes** | **Always splits** on hyphens and apostrophes to count words independently. | **Does not split**, potentially causing proportional measures > 1. | *** ## Further Reading[​](#further-reading "Direct link to Further Reading") * [LIWC 2015 User Manual](https://repositories.lib.utexas.edu/bitstream/handle/2152/31333/LIWC2015_LanguageManual.pdf) * [LIWC22 User Manual](https://www.liwc.app/static/documents/LIWC-22%20Manual%20-%20Development%20and%20Psychometrics.pdf) * [The psychological meaning of words: LIWC and computerized text analysis methods](https://journals.sagepub.com/doi/abs/10.1177/0261927x09351676) * [Dr. James Pennebaker’s TED Talk](https://youtu.be/PGsQwAu3PzU) * [Psychological Aspects of Natural Language Use: Our Words, Our Selves](https://www.annualreviews.org/doi/abs/10.1146/annurev.psych.54.101601.145041) * [What do we know when we LIWC a person? Text analysis as an assessment tool for traits, personal concerns and life stories.](https://psycnet.apa.org/record/2018-21508-016) [Contact us](https://www.receptiviti.com/contact) for further reading or research materials that are specific to your use case. --- # LIWC Extension Receptiviti’s LIWC Extension framework provides measures focused on understanding communication dynamics and determinants of interpersonal support. These measures are paired to review several opposing forces in communication style. For example, the demonstration of a low or high amount of empathy, or the use of agentic (ambitious) versus communal (caring) language. These measures are helpful in the analysis of conversational language in a therapeutic scenario, in interviews, as well as in one-way communication such as a job postings or Corporate Social Responsibility reports. note The LIWC Extension framework is made up of [proportional](https://docs.receptiviti.com/frameworks/.md#proportional-measures) measures. For normed versions of `agentic` and `communal`, see our [Interpersonal Circumplex](https://docs.receptiviti.com/frameworks/normed-frameworks/interpersonal-circumplex.md) framework. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1282, "words_remaining": 248718, "percent_used": 0.51, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "f2ef969d-c96b-4adc-b78b-cd3cba8111f8", "language": "en", "version": "v1.0.0", "summary": { "word_count": 395, "words_per_sentence": 9, "sentence_count": 120, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": {...}, "social_dynamics": {...}, "drives": {...}, "cognition": {...}, "additional_indicators": {...}, "sallee": {...}, "liwc": {...}, "liwc_extension": { "low_empathy": 0.23124569855471439, "high_empathy": 0.10766995488261834, "allure": 0.09191710636996253, "absolutist": 0.01735872141928577, "action": 0.10178175422497515, "inaction": 0.012846983253039687, "abstract": 0.346180316586373, "concrete": 0.28102775866024315, "agency_language": 0.038617419897530016, "communion_language": 0.013841095052382044, "approach": 0.1571433574307583, "avoidance": 0.0572574006377751 }, } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Score | Summary | Examples | High Score Samples w/ Scores | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | ----------------------------------------------------------------------------------------------- | | `low_empathy` | Language that reflects social detachment or callousness towards others. Can indicate either impersonal, technical language or rude, insensitive words. | time, thing, IDGAF, loser | I don’t care and don’t have time for other people’s problems. `0.28` | | `high_empathy` | Language that indicates concern for others and sympathy. Reflects shared distress and interest in others’ thoughts and feelings. | love, think, help, hospital | I love to help out by volunteering at the children’s hospital. `0.42` | | `allure` | A measure derived from advertising that reflects language that is intended to persuade or attract. Includes words that are attention-grabbing and stimulate people's needs and desires. | fast, life, need, perfect, today | 40% of business owners feel that the holiday season is make or break for their business. `0.12` | | `absolutist` | Language that reflects black-and-white, all-or-nothing thinking. Indicates cognitive rigidity and dislike of ambiguity. Considered a cognitive distortion, or irrational thought process associated with mental health challenges such as anxiety and depression. | always, none, never, everyone | I am always right and never wrong, and no one else’s opinion matters. `0.28` | | `action` | Language related to increasing physical or mental activity; aiming to do something or do more. Associated with faster pace of life, keeping busy, and expending energy. | do, make, go, run | On weekday mornings I do push-ups, then go for a run, then make celery juice. `0.31` | | `inaction` | Language related to decreasing physical or mental activity; aiming to do nothing or do less. Associated with slower pace of life, restorative goals, and conserving energy. | rest, stop, wait, relax | On weekend mornings, I prefer to rest and relax with my family. `0.16` | | `abstract` | Language that reflects nonspecific, amorphous, or big-picture ideas. Indicates a higher level of construal (seeing the forest, not the trees). | spirituality, concept, risky, luck | His calling to study philosophy was a deeply meaningful experience. `0.6` | | `concrete` | Language that reflects specific or tangible actions, objects, or traits. Indicates a lower level of construal (seeing the trees, not the forest). | salty, item, person, wooden | There was an antique oak table, and a set of carved chairs with it. `0.28` | | `agency_language` | Language that suggests a person is exerting willpower to pursue personal goals. Indicates doing things as an individual for personal motivations or desires. | acquire, choose, goal, need | You need to stay late tonight if you want to earn this promotion. `0.15` | | `communion_language` | Language that suggests a person is cooperating and connecting with others to improve social relationships. Indicates they are likely doing things with other people to help meet the group's goals. | family, share, help, talking | We can only help you if you’re willing to share what’s going on. `0.13` | | `approach` | Language related to emotions that motivate people to move towards an emotional trigger. For example, love can inspire people to get closer to the object of their affection, and anger can drive people to attack whatever is frustrating them. The opposite of `avoidance` emotions. | furious, funny, awesome, beauty | I was so angry that I stormed to his house and smashed his window with a brick. `0.51` | | `avoidance` | Language related to emotions that indicate retreat or avoidance. For example, fear can make people flee or hide, and sadness can lead people to withdraw from the world. The opposite of `approach` emotions. | afraid, sad, boring, ugh, hurt | The movie was so scary that I was hiding from it behind my hands. `0.62` | ## Specifications[​](#specifications "Direct link to Specifications") All LIWC Extension measures will range between `0` and `1`. `0` implies that a word within a category was not mentioned, and anything above zero indicates that a word in that category was mentioned. The associated score reflects the ratio of that word to the total number of words in the submitted text sample. --- # Temporal and Orientation Receptiviti’s Temporal and Orientation measures provide access to three measures of Temporal Orientation and two measures of Attentional Focus. Temporal and Orientation measures provide insight into whether a person’s language and thoughts are rooted in the past, present, or future, while the Attentional Focus measures provide insight into whether a person is focused on themselves or on external entities. By combining Temporal and Orientation and/or Attentional Focus measures with other measures like those generated from SALLEE, our emotions engine, you can gain a detailed understanding of whether fear (one of SALLEE's 16 emotions) is associated with an event in the past, a current situation, or if it is associated with something in the future that has yet to occur. Temporal and Orientation measures can also serve as inputs to predictive or informative models, with applications for use cases as diverse as call centres, mental health, and product reviews. note Temporal and Orientation measures are labelled as `additional_indicators` in the JSON of the API response. ``` { "plan_usage": { "word_limit": 250000, "words_used": 1438, "words_remaining": 248562, "percent_used": 0.58, "start_date": "2024-01-01T00:00:00Z", "end_date": "2024-01-31T23:59:59Z" }, "results": [ { "response_id": "ff75ed78-7373-45c8-8bc9-fe67a5980fac", "language": "en", "version": "v1.0.0", "summary": { "word_count": 3, "words_per_sentence": 3, "sentence_count": 1, "six_plus_words": 0.6666666666666666, "capitals": 0.043478260869565216, "emojis": 0, "emoticons": 0, "hashtags": 0, "urls": 0 }, "personality": {...}, "social_dynamics": {...}, "drives": {...}, "cognition": {...}, "additional_indicators": { "focus_past": 0.5, "focus_present": 0, "focus_future": 0.66, "self_focus": 0, "external_focus": 0 }, "sallee": {...}, "liwc": {...} } ] } ``` ## Measures[​](#measures "Direct link to Measures") | Score | Summary | High Score Sample | | ---------------- | ------------------------------------------------------------------------ | ------------------------------------------ | | `focus_future` | The degree to which a person is focused on the future. | "Tomorrow will be awesome." `0.5` | | `focus_past` | The degree to which a person is focused on the past. | "I added value yesterday." `0.5` | | `focus_present` | The degree to which a person is focused on the present. | "Do it today!" `0.66` | | `external_focus` | The degree to which a person is focused on people other than themselves. | "We make her strong." `0.5` | | `self_focus` | The degree to which a person's language is focused on themselves. | "I do things for me, myself and me!" `0.5` | ## Specifications and Sample Use Cases[​](#specifications-and-sample-use-cases "Direct link to Specifications and Sample Use Cases") A score of `0` on any of the Temporal and Orientation measures implies that there was no language that suggests the person is focused on past, present or future. A score of >`0` implies that the person is using language that relates to the past, present, or future. A score of `0` on the `self_focus` measure implies that the person is not focused on themselves. A score >`0` implies that the person is using language that is focused on themselves. A score of `0` on the `external_focus` measure implies that a person is not focused on external entities. A score >`0 `implies that a person is using language that is focused on external entities. Let’s look at a couple of examples: > **Example 1:** *What can I do? I'm helpless.* ``` // partial response { "additional_indicators": { "focus_future": 0, "focus_past": 0, "focus_present": 0.42857142857142855, "self_focus": 0.2857142857142857 } } ``` The example sentence returns a score `0.28` for `self_focus`. This is because two out of a total seven words in the sentence indicate a focus on the self, specifically *I* and *I’m*. Similarly, a `focus_present` score of `0.42` indicates that three of a total seven words in the sentence show a focus on the present, specifically *can*, *do* and *am*. > **Example 2:** *What will I do? I'm helpless.* ``` // partial response { "additional_indicators": { "focus_future": 0.14285714285714285, "focus_past": 0, "focus_present": 0.2857142857142857, "self_focus": 0.2857142857142857 } } ``` By changing the word *can* to *will*, we see that `focus_future` now returns a score of `0.14` since one out of a total of seven words in the sentence indicate a focus on the future. --- # From Scores to Insights Once you have obtained language analysis scores, the next step is understanding how to use them effectively. This involves applying statistical methods and interpretation techniques to extract meaningful insights. Below are key approaches to consider: * **Standardization & Comparison** – Use methods like z-scores to compare results across different datasets or timeframes. * **Statistical Testing** – Apply t-tests, ANOVA, or rank means to determine significant differences between groups. * **Trends & Patterns** – Identify overarching trends in language use that correlate with behavioral or psychological traits. * **LLM Interpretation** – Leverage large language models to contextualize scores within broader linguistic and semantic frameworks. * **Actionable Insights** – Translate findings into strategic recommendations, whether for marketing, customer engagement, or decision-making. --- # I Scored My Dataset - Now What? Once you have used the Receptiviti API to analyze your language data, you can use a variety of statistical methods and tools to further explore and understand the nuances of your dataset. By applying techniques like z-scoring, rank norming, and statistical tests such as t-tests and ANOVAs, you can identify patterns, differences, and relationships within the data. Our Receptiviti UI allows you to craft visual representations such as graphs and charts to help make the data accessible and interpretable to a broader audience. Additionally, integrating these findings into your platform can make your insights actionable, providing real-time benefits. Each of these steps adds depth to your analysis, helping transform raw data into valuable insights. ## Z-Scoring[​](#z-scoring "Direct link to Z-Scoring") Z-scoring is a statistical method used to normalize data by converting raw values into standardized scores that represent how far a data point is from the mean, measured in standard deviations. For example, in language data analysis, z-scoring could be applied to psycholinguistic measures like word frequency or emotional tone to compare results across different consumer groups on a standardized scale. This ensures that differences are assessed relative to the variability within each dataset, rather than the raw values alone. We would use z-scoring in this case to enable fair comparisons across groups with different scales or variances, ensuring that insights are consistent and not distorted by differences in measurement units or data distribution. ## Rank Norming[​](#rank-norming "Direct link to Rank Norming") Rank norming methods are statistical techniques used to transform data into ranked values, making it easier to compare groups without being influenced by outliers or skewed distributions. For example, in language data analysis, rank norming could be applied to compare the frequency of certain psycholinguistic traits, such as emotional tone or cognitive processing, across multiple consumer groups. Unlike other normalization methods, rank norming ensures that the analysis focuses on the relative order of values rather than their absolute differences. We would apply rank norming in this case to highlight meaningful patterns in language use while minimizing the impact of extreme values, providing a fair and robust way to compare groups across diverse datasets. ## T-Tests[​](#t-tests "Direct link to T-Tests") T-tests are statistical methods used to compare the means of two groups to determine if the difference between them is statistically significant. For example, in analyzing language data, a t-test could be applied to compare the average level of authenticity in the language of two consumer groups, such as repeat buyers and one-time buyers. By examining the means, we can determine if the observed difference in authenticity is meaningful or likely due to chance. We would use a t-test in this case to confirm whether the difference in language patterns reflects a true psychological distinction between the two groups, ensuring that any insights are statistically robust and actionable. ## ANOVA[​](#anova "Direct link to ANOVA") Analysis of Variance (ANOVA) is a statistical method used to determine if there are statistically significant differences between the means of three or more groups. For example, in a study analyzing language data for three consumer groups, ANOVA could be used to compare psycholinguistic measures like agency, authenticity, or emotional tone across the groups. This allows us to identify which psychological traits differ significantly between the consumer groups. We would apply ANOVA in this case to uncover the specific psychological dimensions that set each consumer group apart, helping us better understand the unique psychologies driving their language patterns. ## Visualizations[​](#visualizations "Direct link to Visualizations") See our [Visualization UI section](https://docs.receptiviti.com/visualization-ui/.md). ## Large Language Models (LLMs)[​](#large-language-models-llms "Direct link to Large Language Models (LLMs)") Large language models (LLMs) are AI systems trained on vast amounts of text data to understand and generate language. However, on their own, LLMs cannot apply established psychological frameworks and produce psychological insights in a way that is credible, repeatable, scientifically rigorous, and rooted in measurement. Receptiviti's scientifically validated measures provide consistent, reliable, and objective psychological assessment from language. By integrating Receptiviti with LLMs, we can enable automatic summarization and interpretation of Receptiviti scores, making language-based psychological insights easily interpretable and actionable. We would apply this approach to enhance the scalability and accessibility of Receptiviti insights while ensuring the quality and reliability of results. ## Regression Analysis[​](#regression-analysis "Direct link to Regression Analysis") Regression analysis is a mathematical approach used to identify factors (independent variables) that are related to or predictive of a key outcome (dependent variable). Several types of regression analyses exist, including linear regression and multiple linear regression. For example, in language data analysis, a linear regression model could be used to determine whether a call center agent's communication style (measured through concrete language, emotional tone, and highly empathetic language) predicts customer's purchase behavior. We would apply regression analysis in this case to determine which linguistic factors have significant impact on the behavioral outcome we seek to understand. ## K-Means Cluster Analysis[​](#k-means-cluster-analysis "Direct link to K-Means Cluster Analysis") K-means cluster analysis is a machine learning technique used to segment datasets into clusters (groups) based on similarities and differences between data points. For example, in language analysis, K-means clustering could be applied to a dataset of target consumers to identify distinct market segments within the broader consumer group. We would apply K-means clustering to characterize the personas of each market segment, enabling marketers to optimize their strategies for better engagement and alignment with their audiences. ## Build a Machine Learning (ML) Model[​](#build-a-machine-learning-ml-model "Direct link to Build a Machine Learning (ML) Model") ML models are algorithms that learn patterns from training data to make predictions or classifications when analyzing new data. Receptiviti scores can be used as features in ML models to enhance predictive capability, accuracy, explainability, and interpretability by incorporating scientifically validated psychological insights from language. For example, an ML model could use Receptiviti scores such as anger, fear, affiliation drive, and cognitive processing to predict which employees may be experiencing impaired well-being and burnout. These insights could then inform targeted, proactive interventions to support employee engagement. We would apply this approach when building data-driven predictive models that benefit from psychological insights to enhance decision-making and strategic outcomes. ## Correlation Analysis[​](#correlation-analysis "Direct link to Correlation Analysis") Correlation analysis helps identify which linguistic measures are associated with outcome or metadata variables such as performance, satisfaction, or approval ratings. These correlations not only help identify which measures are associated with an outcome, but also clarify the direction of that relationship — whether an increase in the linguistic measure is linked to an increase (positive correlation) or a decrease (negative correlation) in the outcome variable. These relationships are typically measured using the Pearson correlation coefficient (r), which quantifies the strength and direction of a linear relationship between two variables. Positive values of r indicate that the measure increases with the outcome, while negative values suggest it decreases. The closer the value is to ±1, the stronger the relationship. Correlation analysis is especially useful when combined with other techniques like z-scoring, as it allows you to identify patterns without requiring large sample sizes or complex models, making it an accessible tool for initial exploration and insight generation. | Absolute r Value | Interpretation in Language-based Psychology Research | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | 0.10–0.19 | **Weak** - likely noise or context specific | | 0.20–0.29 | **Modest but reliable** – typical for single LIWC features | | 0.30–0.39 | **Strong** – notable for language-based traits | | 0.40+ | **Very Stong** – relatively rare when using single category correlations and generally less realistic when working with language data | info See our [Score Interpretation Guide](https://docs.receptiviti.com/from-scores-to-insights/score-interpretation-guide.md) for further information about score interpretation. --- # Score Interpretation Guide ## Methods for Normed Measures[​](#methods-for-normed-measures "Direct link to Methods for Normed Measures") ### Summarizing the Normed Measure Scale[​](#summarizing-the-normed-measure-scale "Direct link to Summarizing the Normed Measure Scale") Because normed measures are **baselined using a standard normal curve approach**, we can make the following assumptions: 1. A **score of 50** represents the **average** for the population defined by the norming context. 2. The **higher above 50**, the more above average the score. 3. The **lower below 50**, the further below average the score. *** ### Examples[​](#examples "Direct link to Examples") * A **leader’s earnings call transcript** scoring **50** on `big_5.empathetic` using a **custom norm** based on executive earnings call language would mean the leader comes across as **average on empathy** compared to peer executives. * A **consumer’s concatenated social media posts**, analyzed using the **Receptiviti Written Norming Context**, scores **75** on `drives.risk_seeking`. This means the consumer is **above average on risk-seeking** relative to the general population. *** ## Determining High or Low Normed Scores[​](#determining-high-or-low-normed-scores "Direct link to Determining High or Low Normed Scores") ### Interpreting Normed Scores – Bucket Approach[​](#interpreting-normed-scores--bucket-approach "Direct link to Interpreting Normed Scores – Bucket Approach") Since normed measures are **baselined using a standard normal curve**, we can use a **bucketing approach** to determine **high or low scores** relative to the norming context. This approach categorizes scores based on **the number of standard deviations away from the mean**. Below, we provide **buckets and labels** at specific standard deviation and score thresholds. ![bell-curve-range](/img/bell-curve-range.png) By applying this method, you can quickly identify unusual patterns, track behavioral shifts, and take action when necessary. | Label | Standard Deviation | Score Upper Bound | Score Lower Bound | | --------------- | ------------------ | ----------------- | ----------------- | | Extremely High | Over 3 | 100 | 87.5 | | Very High | +3 | 87.5 | 75 | | Moderately High | +2 | 75 | 62.5 | | Slightly High | +1 | 62.5 | 54.125 | | Average | +/- 0.33 | 54.125 | 45.875 | | Slightly Low | -1 | 45.875 | 37.5 | | Moderately Low | -2 | 37.5 | 25 | | Very Low | -3 | 25 | 12.5 | | Extremely Low | Below -3 | 12.5 | 0 | note Standard Deviation = 12.5 The bucket labels and thresholds guide is a general rule of thumb. However, given that each use case is different, it's not the only justifiable way to bucket scores. If you'd like to use different labels or boundaries, or are interested in what is likely the best fit for your cohort, we encourage you to reach out to our sales team at . ## Interpreting Normed Scores – Comparison Approach[​](#interpreting-normed-scores--comparison-approach "Direct link to Interpreting Normed Scores – Comparison Approach") Since scores range from **0 to 100**, they can be **compared** to determine what qualifies as a high or low score within a given group. ### Example[​](#example "Direct link to Example") Two speeches are analyzed using the **Receptiviti Spoken Norming Context**: * **Speech 1** scores **30** on **Big 5 Friendliness**. * **Speech 2** scores **10** on **Big 5 Friendliness**. Both **Speech 1 and Speech 2 come across as less friendly**, but **Speech 2 is the least friendly** of the two. *** ## Methods for Proportional Measures[​](#methods-for-proportional-measures "Direct link to Methods for Proportional Measures") ### Understanding Proportional Measures[​](#understanding-proportional-measures "Direct link to Understanding Proportional Measures") Unlike normed measures, **proportional measures are not baselined**. Each measure has a **unique mean, variance, and distribution**, making it important to **reference [base rates](https://docs.receptiviti.com/norming-and-base-rates/receptiviti-base-rates.md) and standard deviations** (either **Receptiviti curated** or **your own**). We can make the following assumptions: * The **higher** the score is above the **base rate**, the higher it is **relative to the context** the base rate was derived from. * The **lower** the score is below the **base rate**, the lower it is **relative to the context** the base rate was derived from. *** ## Interpreting Proportional Scores – Comparison Approach[​](#interpreting-proportional-scores--comparison-approach "Direct link to Interpreting Proportional Scores – Comparison Approach") Since **proportional scores range from 0 to 1** (or **-1 to 1** if Sentiment), they can be **compared** within a given sample group to determine relative differences. ### **Example**[​](#example-1 "Direct link to example-1") Two pieces of **marketing ad copy** are analyzed using **LIWC’s Analytical Thinking measure**: * **Ad 1** scores **0.71**. * **Ad 2** scores **0.30**. **Ad 1 comes across as more analytical than Ad 2**. ## Regarding Calculating Percentile Using Z-score[​](#regarding-calculating-percentile-using-z-score "Direct link to Regarding Calculating Percentile Using Z-score") When you compute a percentile from the z-score values supplied by our API, the result will differ slightly from the rank-normed percentile scores generated directly by the API (if that output format is enabled and specified in the query parameters). This is because our API (when using the rank-based norming method) provides the actual raw percentile based on the data in the norming dataset (i.e., the percentage of observations at or below the current score), without assuming a perfectly normal distribution. While the raw scores of our measures do fall into normal-shaped distributions, there will be slight variations from a perfect standard normal curve based on your choice of norming dataset. We default to the standard normal curve/z-score norming method, as we find that most people naturally interpret behaviour through the lens of a normal distribution. This also better reflects the way many traits are naturally distributed. In other words, most people fall near the average on a given trait, and it usually takes a truly extreme example for someone to be perceived as extreme or an outlier on something (e.g., even very extraverted individuals are rarely viewed as truly having fringe-level or unusual levels of extraversion.) Percentile scores can be very helpful if your goal is to understand rank rather than perception. info Please reach out to if you would like the ability to adjust your output from default z-scoring to rank normed percentiles. Once we enable rank-based norming on your account, this method can be requested by including an additional query parameter in each API call. --- # Knowledge Base The Knowledge Base explores the deeper scientific and methodological foundations behind our language-based measures and frameworks. Each topic addresses a focused question about interpretation, context, or methodology, pairing concise summaries with deeper discussions. These entries draw from psycholinguistic theory, statistical validation, and applied research to help you interpret results responsibly and confidently. Use this section as a companion to the main documentation when you need insight into the reasoning, assumptions, or science that underpins the frameworks. ### How do self-reported and peer-reported personality insights compare with language-based personality and psychology insights?[​](#how-do-self-reported-and-peer-reported-personality-insights-compare-with-language-based-personality-and-psychology-insights "Direct link to How do self-reported and peer-reported personality insights compare with language-based personality and psychology insights?") In general, Receptiviti's language measures will correlate more strongly with objective behavioural measures....(click to expand) In short Language-based personality insights correlate more strongly with behavioural outcomes than with self-reports, which are shaped by bias and self-perception. Because language is a direct behavioural signal, it offers a more objective view of how people actually think and interact — behaviour best predicts behaviour, while surveys best predict survey responses. #### Detailed explanation[​](#detailed-explanation "Direct link to Detailed explanation") In general, Receptiviti's language measures will correlate more strongly with objective behavioural measures (e.g., job performance) than self-report measures. This is due to the biases inherent in self-report and the methodological differences between behaviour measures (e.g., language measures and behavioural outcome data) and survey inventories (i.e., self-report). Language-based scores are behavioural data in and of themselves. Language is a core behavioural mechanism for interpersonal engagement that reflects how people think, communicate, and respond in the environments that matter. Behaviour is the best predictor of behaviour, and surveys are the best predictors of survey measures due to common method variance. For more information, the section [Correlations with Self-Reports](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#correlations-with-self-reports) in the Big 5 documentation describes the process of using survey-based self-reports and peer-reports as a method of validating Receptiviti personality measures. Regarding how survey-based psychological analysis and language-based psychological analysis complement each other, there are several concepts to keep in mind. Self-reported personality data collected through surveys is not ground truth data. In other words, self-reported personality assessments are not a perfect reflection of a person's “true” personality and, thus, are not a gold-standard that language-based assessments aim to replicate. Rather, self-reported and language-based assessments offer different perspectives on an individual's character, each with their own insights. Self-reported personality surveys reflect how a person sees themselves (i.e., their self-concept), and, thus, come with various biases and inaccuracies, including: * **Social Desirability Bias:** In order to portray themselves favorably, respondents tend to both consciously and subconsciously answer questions in a manner they perceive as socially acceptable rather than provide truthful responses. For instance, individuals often exaggerate positive traits such as friendliness or diligence, while minimizing negative traits like aggression or selfishness. * **Self-Other Knowledge Asymmetry:** Individuals have more information with which to judge their own internal traits or behavioural expressions (e.g., feelings, thoughts), compared to their own external traits or behavioural expressions (e.g., voice, gestures). On the other hand, external peers have less information about others’ internal thoughts and feelings, but they have more information about external-facing behaviours. This asymmetry in information between self and others causes an asymmetry in accuracy when assessing internal and external traits via surveys. Said differently, self-reported personality tends to be more accurate when individuals are reporting traits that are more internally-focused (like neuroticism) and less accurate when assessing externalized traits (like agreeableness). * **Reference Group Bias:** Individuals’ self-perception and reported personality can be skewed by the characteristics and norms of their immediate social circles. For instance, an individual surrounded by introverts may perceive themselves as extraverted because they are more sociable than those within their immediate social circle. However, when assessed against societal standards, it may be clear that they exhibit characteristics more aligned with introversion. Unlike self-reported personality, language-based personality insights describe how a person comes across to other people in a specific context (or if enough language data is collected, more generally) based on their verbal/written behaviour. Using language also comes with many benefits, including: * **Minimizing Response Bias:** Psycholinguistic analysis focuses on function word usage. Because function words are processed largely subconsciously, it makes it difficult for individuals to change their language to present themselves in a manner that is not aligned with their “true” personality. Also, when compared to individuals’ self-reported personalities, language-based personality can reveal what traits people are trying to mask or are less conscious of. * **Minimizing Observer Bias:** Using Receptiviti’s psycholinguistic dimensions to assess personality, you are measuring everyone with the same yardstick. By ensuring respondents are evaluated consistently, the insights derived from personality evaluations can be compared effectively. * **Ease of Scalability:** Given that language serves as the primary method of communication and social interaction, collecting large quantities of language data samples is straightforward and more reflective of how a person behaves in real-time/real-world contexts. Moreover, language processing is automated with Receptiviti’s API, so personality insights can be derived across hundreds of individuals in seconds. * Accounting for Self-Other Knowledge Asymmetry: In the development of our personality frameworks, we consider both peer and self-reported results. We are able to do this because (a) individuals express their more internally-focused personality traits through specific linguistic cues (b) others are able to infer personality traits through specific linguistic cues. Here are a few resources that provide further information about the validity of language-based personality assessment: * An academic book chapter that discusses language-based personality insights, ["Natural Language Use as a Marker of Personality"](https://www.researchgate.net/publication/310597242_Natural_language_use_as_a_marker_of_personality). * A research article entitled [Self-Other Knowledge Asymmetries in Personality Pathology.](https://www.researchgate.net/publication/224955928_Self-Other_Knowledge_Asymmetries_in_Personality_Pathology) * Our blog post entitled [Your Personality Assessment Isn’t Objective](https://www.receptiviti.com/post/your-personality-assessment-isnt-objective) overviews the differences between personality assessment via self report vs language. *** ### How does context impact scores? How should context be accounted for?[​](#how-does-context-impact-scores-how-should-context-be-accounted-for "Direct link to How does context impact scores? How should context be accounted for?") When building Receptiviti algorithms, we use ingredients that are found to be sufficiently stable...(click to expand) In short Receptiviti’s models balance trait-like stability with context-sensitive variability, identifying which language signals reflect enduring psychological patterns and which shift with situation. Context isn’t removed—it’s measured and normed—so insights remain reliable across domains while staying sensitive to setting, tone, and purpose. Adequate sample size (≈900 words or multiple shorter texts) and proper norming ensure results are stable, interpretable, and comparable across contexts. #### Detailed explanation[​](#detailed-explanation-1 "Direct link to Detailed explanation") When building Receptiviti algorithms, we use ingredients that are found to be sufficiently psychometrically stable across contexts. The goal is not to eliminate context, but to clarify which aspects of language reflect enduring psychological traits and which capture temporary states. Our psycholinguistic models and LIWC are grounded in decades of research showing that certain language patterns consistently reflect underlying traits, even as surface-level topics, settings, or populations vary. By balancing trait-like stability with context-sensitive (state-like) variability, insights remain both reliable in reflecting psychological patterns and responsive to the nuances of specific situations. This enables the ability to create personas and perform informative longitudinal analyses, such as monitoring behavior change, wellbeing, or group cohesion. In summary, over-controlling for context while developing language-based psychological measures risks stripping away the very signal the measures are designed to detect, since measures are designed to capture state-dependent responses as well as traits. Info Any behavioral indicator of personality is expected to vary across contexts while maintaining rank order stability (e.g., an extravert will be less chatty in a library versus a restaurant, but they’ll still be more talkative in the quiet setting than an introvert in the same context). Sample size plays an important role as well. The more language data collected from a person, the more representative and reliable the signal becomes, allowing trait-based patterns to emerge despite natural variation in context. For example, analyzing a minimum of 750 to 1,000 words in a job interview often provides a strong signal of how someone is likely to approach their work. Reviewing a few earnings call Q\&A sessions can give a meaningful picture of an executive’s leadership style and external communication approach. For example, the chart below illustrates an analysis that reviews excerpts from executives/leaders with a given word count and demonstrates how closely the excerpt corresponds to how the same people use language in general. This highlights that above a word count threshold of \~900 words, there is good stability in leadership profiles in that the excerpt is highly correlated to the baseline. ![Stability vs. word count chart](/img/stability-word-count-chart.png) For those who do not have samples of text with 900+ words per person, another way to build robust profiles is to collect text data across multiple interactions (moments) or contexts. [In this article](https://www.receptiviti.com/post/how-many-interactions-are-needed-to-capture-a-personality), we test and outline how many short text samples (100-200 words per sample) and medium-length texts (350-450 words per sample) collected across multiple communication contexts are required to determine a stable personality profile. Additionally, it is important to account for context through base rates, norming, and interpretation. Context, especially whether the data is written or spoken, can influence the expected base rates for proportion-based category measures. Base rates represent the mean scores of a proportion-based measure ([read more about base rates here](https://docs.receptiviti.com/norming-and-base-rates/receptiviti-base-rates.md)). They are important to understand for each specific use case or data source type. For instance: * People typically write more analytically than they speak (Written mean ≈ 0.6; Spoken mean ≈ 0.5). * CEOs tend to use significantly higher rates of first person plural and clout language than the general population. Base rates provide a primary method for interpreting proportion-based scores. A score higher than the base rate suggests elevated usage relative to the context the base rate was derived from. A lower score suggests reduced usage relative to that same context. Norming allows customers to baseline their normed measure scores against a dataset that is representative of a particular context. Through norming, customers are able to compare a language sample to others in similar contexts, helping clarify whether the language patterns captured within the sample are situational or indicative of underlying traits. Base rates, or means, are one of the core statistics used to create norming tables for normed measures. Scores for normed measures are standardized using Z-scoring, which transforms raw scores into values that reflect distance from the mean in standard deviation units. This creates a normal distribution, which is then projected onto a 0 to 100 scale. Customers can norm scores against their own data, which allows the norms to directly reflect their unique context, or they can use Receptiviti’s Spoken or Written norms. These norms are curated to represent how people typically express themselves across a variety of common spoken or written contexts (read more about norming options here). Consider this example: Click here for the examples | Text | LIWC Affiliation (Proportion-based) | Drives Affiliation (Normed) | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------- | --------------------------- | | Excerpt: What really impressed me was how naturally the phone fit into a night of hanging out…What really impressed me was how naturally the phone fit into a night of hanging out, talking, and just being with friends. We were all piled onto the couch, ordering food, catching up on each other’s lives, swapping stories, teasing each other, and drifting in and out of serious conversations. It felt easy and real, and somehow the phone actually added to that energy instead of pulling us out of it. Someone would grab it to show a photo from a recent trip, FaceTime someone who could not be there so they could still feel included, or pass it around to show a meme that had us all laughing. It flowed in and out of the group like it belonged there.The camera captured those little moments, even in bad lighting or when someone was caught mid-laugh, without needing anyone to stop and adjust settings. The sound was clear enough on speaker that it felt like our friend on the other end of the call was sitting right there with us. Even texting in the group chat, adding reactions, or sharing a random thought felt like part of the bigger conversation happening in the room.What I loved was how it never pulled focus. It supported what we were already doing: talking over each other, finishing each other’s sentences, listening, reconnecting, and staying present. The design made it easy to pass from one person to the next without it feeling like a disruption. It just blended in and kept things flowing.The performance held up without a hitch. No lag, no overheating, no weird glitches even after hours of use. Everything ran smoothly in the background, which let us stay focused on the moment instead of fiddling with settings or waiting for apps to load.In a time when tech can easily make people feel distant or distracted, this felt different. It helped us stay close, kept everyone in the loop, and made it easier to be together in a way that felt natural. For once, the phone felt like part of the group. Not something we were hiding behind or escaping into, but something that brought us closer and helped us stay connected to each other. | .065 | 96.53 | | Excerpt: What impressed me most was how consistently smooth and responsive the phone felt…What impressed me most was how consistently smooth and responsive the phone felt across everything I used it for. From the moment I turned it on, the setup was fast and intuitive. Within minutes, I had my apps, settings, and preferences in place, and I was able to jump right into my usual routines without any friction. There were no delays, no confusing steps, and no unnecessary prompts. It was refreshingly seamless.The display is one of the best I have used. It is bright, crisp, and incredibly sharp, with excellent color accuracy and clarity. The high refresh rate makes scrolling feel natural, almost like the content is moving with your hand. Whether I was reading articles, flipping through photos, or watching videos, everything looked smooth and felt fast.I put the phone through a full range of tasks, including streaming high-resolution video, using real-time navigation, editing and exporting images, and keeping multiple apps open in the background. Not once did it lag, crash, or freeze. The phone stayed cool the entire time, even during extended use. Battery life held up impressively well. I charged it overnight, used it heavily all day, and still had power left the next morning.During a group call with friends, the audio and video stayed sharp throughout, which made it easy to stay connected and feel part of the moment without technical hiccups getting in the way.The build quality deserves a mention too. The materials feel premium, the design is sleek, and the device has a solid, balanced weight that makes it comfortable to hold for long periods. It is not too heavy, not too light, and it fits naturally in your hand.The camera system performed well in all conditions, including low light and fast motion. Shots came out clear, colors were true to life, and processing was quick without needing extra adjustment.Overall, what stood out was the thoughtfulness behind each feature. Nothing felt tacked on for show. It felt like every part of the phone had a purpose, and that purpose was to make everyday use smoother, faster, and more enjoyable. The performance was reliable from start to finish, and everything just worked. | .005 | 32.79 | Given that the base rate for LIWC Affiliation in written data is approximately .02 (or 2%), and the average for all normed measures is 50 (due to the normal distribution), the author of Sample 1 demonstrated an extremely high level of affiliation drive, whether using proportion-based or normed scores. In contrast, the author of Sample 2 showed a moderately low affiliation drive. Since we used general written base rates and norms, the interpretation reflects a comparison to (or the context of) how people typically write. To close the loop, the context of the data does not affect the analysis process beyond informing target sample size and expected base rates or choice of norms, which serve as the reference points during interpretation. Interpretation is the best way to document context. *** ### What sample size is required to produce meaningful insights?[​](#what-sample-size-is-required-to-produce-meaningful-insights "Direct link to What sample size is required to produce meaningful insights?") It depends on what you consider meaningful. The ideal sample size...(click to expand) In short Meaningful sample size depends on your goals. Smaller datasets can still yield valuable insights when word counts are sufficient, revealing patterns within individuals or small groups. For more rigorous or quantitative work, larger samples naturally strengthen reliability and generalizability, though the ideal size ultimately depends on your analytical approach. #### Detailed explanation[​](#detailed-explanation-2 "Direct link to Detailed explanation") It depends on what you consider meaningful. The ideal sample size ultimately depends on your goals and how statistically rigorous any analysis conducted on top of the Receptiviti output needs to be. Smaller sample sizes can provide useful signals, especially in exploratory or qualitative phases. Receptiviti results can be interpreted meaningfully at the individual sample level, as long as there is sufficient word count. For example, analyzing a single person’s language can provide a reliable view into that individual’s psychological profile or persona. Receptiviti can also provide meaningful insight when used to analyze multiple samples in a small dataset. For example, when analyzing a dataset of language samples derived from 5 to 10 participants, you can: * Look at convergence and divergence in a group. Do people show a wide range of thinking styles, or are they consistently more intuitive than deliberative? * Compare predefined subgroups. How do the language profiles of consumers who rate a product highly differ from those who rate it poorly? ([see this analysis of Ozempic and Saxenda reviewers](https://www.receptiviti.com/post/a-psychological-analysis-of-ozempic-users-a-guide-for-pharmaceutical-marketers)) How do the dynamics of two teams with different performance outcomes differ? * Generate a group-level snapshot. On average, are participants in a focus group about Product X more people-focused or task-focused? Do members of a team display traits that support an innovative culture, such as being open to change and curious? If you are aiming to conduct statistical analysis on top of scores, recommended sample sizes are guided by the research methods commonly used in your field of work or for that particular data analysis approach. In general, more participants or samples support greater reliability and statistical power (and are more representative of a given population). This is not an aspect of Receptiviti, but rather a consideration of the statistical principles and methodological standards that guide data analysis practices more broadly. *** ### Should I collect or use any metadata?[​](#should-i-collect-or-use-any-metadata "Direct link to Should I collect or use any metadata?") The answer to this question is entirely dependent on the goal of your analysis...(click to expand ) #### Detailed explanation[​](#detailed-explanation-3 "Direct link to Detailed explanation") The answer to this question is entirely dependent on the goal of your analysis. If you are trying to, for example, understand the differences between the dynamics of two teams, then collecting a metadata variable like team name or id would be required. If you are doing a cluster analysis and would like to produce market segments based on both psychographic and demographic variables, then including metadata like age can be helpful. If metadata is not explicitly related to what you are interested in, then it is not something to include. Note If you are using more advanced statistical analysis methods to pull out statistically significant patterns in the data, it is also an option to include demographics as control variables in your models and analyses. --- # Norming and Base Rates This section provides guidance on understanding and applying norming and base rates effectively. Establishing your own norms allows for tailored comparisons that reflect the specific populations or contexts relevant to your analysis. Using different norming contexts—such as industry-specific language, cultural variations, or genre-based benchmarks—enhances the precision and relevance of your insights. --- # Custom Norming To gain meaningful insights from language analysis, it’s essential to interpret scores within the context that makes sense for the language source. While Receptiviti provides norming through the use of our extensive proprietary datasets, custom norming allows you to tailor the norms to your specific dataset or context. For instance, you can create norms based on a specific dataset to generate scores that are directly relevant to that particular data. Alternatively, you can establish norms that apply more broadly to a context, enabling analysis of new datasets within the same contextual framework even if they were not part of the original norming data. This flexibility ensures that normed measures better reflect either localized conditions or broader contextual trends, enhancing the relevance and accuracy of your insights. Visit our [API Reference](https://docs.receptiviti.com/api-reference) documentation to learn how to structure your API calls to perform custom norming. ## Getting Started[​](#getting-started "Direct link to Getting Started") Custom norming of a dataset of language samples is designed for users of the Receptiviti API who wish to establish a median base rate that accurately reflects the average of the dataset’s aggregate. People talk differently in different contexts. For example, C-suite executives tend to use language with lower frequencies of personal pronouns and first-person singular pronouns such as "I," "me," and "my" than individuals in lower positions and the general population. For reasons like this, it might not be particularly useful or even accurate to compare the way a group of CEOs speak with the patterns of the general population. ### Selecting the Right Norming Approach[​](#selecting-the-right-norming-approach "Direct link to Selecting the Right Norming Approach") To determine the best approach for norming your dataset, consider the following factors: 1. **Start by assessing your stored data**: If you do not have any stored data, the best option is to use Receptiviti's curated norming datasets, which are designed to provide reliable scores without requiring custom inputs. If you have stored data, proceed to evaluate its similarity to the data you want to analyze. 2. **Evaluate the similarity of your stored data**: If your dataset is identical—meaning it was collected under the same conditions, from the same population, and with the same linguistic characteristics as the data you want to analyze—you can confidently proceed with it for norming. However, if your data is only similar, meaning it shares some but not all key characteristics (such as data context, professional jargon, or formality), additional considerations are required to assess whether it provides a reliable baseline. If the stored data is significantly different from your analysis dataset, it is still recommended to use curated norming datasets, as they ensure consistency and validity in interpretation. 3. **Assess the size of your dataset**: We recommend working with a robust dataset of text samples for norming, especially when building toward high-confidence outputs. That said, even relatively small datasets may qualify for custom norming in certain contexts — particularly when the use case is well defined or highly domain-specific. Rather than enforce hard thresholds, we encourage you to reach out so we can help assess whether your dataset is a good candidate for custom norming or whether one of our curated norming sets may be more suitable. info Larger norming datasets tend to be more representative, making them a more generalizable baseline when analyzing new, similar samples. Smaller norming datasets may be better suited for internal comparisons, where all analyzed samples are also included in the norming dataset. However, when analyzing contexts that are very niche or have inherently small sample and population sizes, a small dataset can still provide meaningful norms, even for unrepresented samples. While custom norming can be valuable, it's not always necessary; if your data closely aligns with an existing norming context, curated norms may be a suitable option. Feel free to contact us at for help determining the best approach for your data. ### Selecting the Right Norming Context[​](#selecting-the-right-norming-context "Direct link to Selecting the Right Norming Context") Choosing the right norming context is necessary for ensuring accurate results. **If you have even one text sample, you can still use an existing custom norm to analyze your data with.** However, it’s important to note that single samples/small amounts of samples cannot be used to create new norms. Instead, they are best analyzed within a framework of an already created custom norm. ### Properly Creating a Custom Norm[​](#properly-creating-a-custom-norm "Direct link to Properly Creating a Custom Norm") Creating a custom norm requires a sufficiently large dataset to ensure the norming process accurately reflects the characteristics of your target context. By supplying a robust dataset during the custom norming process, you can ensure that the resulting norms are both meaningful and effective for subsequent analyses. Passing Text Samples to the Norming Endpoint The custom norming endpoint requires you to pass a JSON array of text samples as input. Currently, it does not support CSV uploads directly. To use this endpoint, you need to programmatically prepare your dataset by converting your text samples into a JSON array format. This format ensures the endpoint can process each text sample individually. If your data is stored in a CSV file, you will need to write a script to read the CSV, extract the relevant text, and convert it into the required JSON structure before making the API call. Visit our [API Reference](https://docs.receptiviti.com/api-reference) documentation to learn more about structuring your data. ### Should You Norm Your Dataset?[​](#should-you-norm-your-dataset "Direct link to Should You Norm Your Dataset?") Follow the guidelines below to get a better understanding of whether or not you should use custom norming for your analyses. ### Using the Developer Packages for Custom Norming[​](#using-the-developer-packages-for-custom-norming "Direct link to Using the Developer Packages for Custom Norming") The [Developer Resources](https://docs.receptiviti.com/developer-resources.md) provide a streamlined and efficient way to perform custom norming, making them an excellent choice even if you don’t plan to use the package for other API calls. By leveraging these packages, you can handle custom norming tasks without worrying about managing multiple norming passes or other complexities involved in direct API interactions. This simplicity allows for a smoother, faster setup and execution of norming processes, enabling you to focus on tailoring your data for accurate results. For R users, go [here](https://receptiviti.github.io/receptiviti-r/reference/receptiviti_norming.html), and for Python users, go [here](https://receptiviti.github.io/receptiviti-python/functions/norming/). If you're already familiar with using our R or Python packages, here's a basic guide to getting started with the norming tool: Developer package norming example To create or interact with a norming context, you'll need to provide some details, such as a name or text to establish the context. For example, you might decide to name your context `formal_tone` to analyze texts in a formal style later. Once you've set up a custom norming context, you can use it to analyze future texts by referencing its name in your request. Here's an example: `receptiviti.norming("formal_tone", "/path/to/file.csv", version="v2", text_column="Text")` In this case, `formal_tone` is the custom context you created earlier, and the system will apply it when analyzing your text. This tool helps you fine-tune your analysis to specific needs or styles and set specific benchmarks for analyzing future texts. ## Use Case Example[​](#use-case-example "Direct link to Use Case Example") Let's say we want to analyze a dataset that contains language from a C-suite executive. Instead of using Receptiviti's default population norms, it would make more sense to measure their language against a group of others who are much more likely to use similar language as the executive in our analysis. In this case, we could use a CEO earnings call dataset as a norming context to ensure that the scores produced by the analysis of the executive reflect a norming dataset that makes more sense contextually. Once you have your norming context dataset, (i.e., the earnings calls), and you have the language you want to analyze using that earnings calls norming context, you're ready to perform the norming process. **Custom norming is a 3-step process:** 1. Start with a `POST` call to the endpoint `https://api.receptiviti.com/v2/norming/custom`. Think of this like the setup phase where you give your norming context a unique name and some information about the word count and sentence structures: * `name` (required) gives the custom norming context a name. * `min_word_count` (optional) sets the minimum required word count for a text sample to be included in the norming context. Any samples not meeting that threshold are filtered out. * `max_punctuation` (optional) sets the maximum amount of punctuation allowed in a sample. If set to `0.5`, any sentence whose punctuation makes up 50% of the sentence would be excluded, e.g., `This. is. a. test.` 2. Send a `PATCH` call to the endpoint `/v2/norming/{context_name}/one`. This requires the entire dataset either in whole or in part (depending on its size). This establishes the norms from our base measures, LIWC and SALLEE. * The call to this endpoint requires the name of the norming context you created in step 1. If you named your norming context `earnings-calls-norms` it would be `https://api.receptiviti.com/v2/norming/earnings-calls-norms/one`. * Example payload: ``` [ { "text": "" }, { "text": "test" }, { "text": "This. is. a. test." }, { "text": "This is a test." }, { "text": "Hello world. Here goes nothing!" } ] ``` 3. Send a `PATCH` call to the endpoint `/v2/norming/{context_name}/two`. You must pass the exact same dataset here as you did for step 2. This takes the normed base measures from step 1 to enable us to establish the formulas that make up our normed scores, i.e., Big 5, Drives, etc. * The call to this endpoint would be `https://api.receptiviti.com/v2/norming/earnings-calls-norms/two`. At this point, your custom norming context is created. To check which custom norming contexts are present in your account, you can make a `GET` call to `/v2/norming`. This will list the contexts you have created as well as any available Receptiviti contexts. A `GET` call to `/v2/norming/custom` will list only the custom contexts, including word counts and other metadata. Now, to analyze the executive using their language against the custom norming context we created above, we would make a `POST` call to `https://api.receptiviti.com/v2/analyze?custom_context=earnings-calls-norms`. The response will be the scores for the measures included in our account, normed to the earnings calls dataset we created a norming context for. A `DELETE` call to `/v2/norming/custom/{context_name}` will remove the custom context. info See the [API Reference](https://docs.receptiviti.com/api-reference) section for complete reference documentation for the `v2/norming` endpoints. Alternatively, you can use our [Developer Resources](https://docs.receptiviti.com/developer-resources.md) to set custom norming contexts. For R users, go [here](https://receptiviti.github.io/receptiviti-r/reference/receptiviti_norming.html), and for Python users, go [here](https://receptiviti.github.io/receptiviti-python/functions/norming/). --- # Streamlined Custom Norming This guide provides a streamlined approach designed for users who want effective results without diving deep into code. While the [Developer Resources](https://docs.receptiviti.com/developer-resources.md) offer greater flexibility, this method balances ease of use with reliable outputs, making it an option for those looking to implement custom norming. ## Creating a Custom Norm Context[​](#creating-a-custom-norm-context "Direct link to Creating a Custom Norm Context") ``` import pandas as pd import receptiviti # pip install receptiviti import os from dotenv import load_dotenv # Load environment variables from .env file load_dotenv() # Read credentials from environment RECEPTIVITI_KEY = os.getenv("RECEPTIVITI_KEY") RECEPTIVITI_SECRET = os.getenv("RECEPTIVITI_SECRET") RECEPTIVITI_URL = os.getenv("RECEPTIVITI_URL") # Confirm that you're authenticated with Receptiviti receptiviti.status() # Set verbose logging, version, and other options for the norming function verbose = True version = "v2" options = { "min_word_count":100, "max_punctuation": 0.5 } # Specify the file and column to process file_path = "input.csv" # Ensure this is set to the actual CSV file text_column = "Text" # Ensure this matches the column containing language data norming_name = "my-norming-context" # Set your custom norming context # Ensure the file exists if not os.path.isfile(file_path): raise FileNotFoundError(f"File '{file_path}' not found.") # Execute the norming function with the provided parameters results = receptiviti.norming( key=os.getenv("RECEPTIVITI_KEY"), secret=os.getenv("RECEPTIVITI_SECRET"), url=os.getenv("RECEPTIVITI_URL"), verbose=verbose, version=version, options=options, text=file_path, text_column=text_column, name=norming_name ) # Print results print("Norming Analysis Results:") print(results) ``` info You can add the line `receptiviti.norming()` to your script after authenticating to see a list of custom norming contexts that exist in your account. ## Norming Your Data to Your Custom Context[​](#norming-your-data-to-your-custom-context "Direct link to Norming Your Data to Your Custom Context") ``` import pandas as pd import receptiviti # pip install receptiviti import os from dotenv import load_dotenv # Load environment variables from .env file load_dotenv() # Read credentials from environment RECEPTIVITI_KEY = os.getenv("RECEPTIVITI_KEY") RECEPTIVITI_SECRET = os.getenv("RECEPTIVITI_SECRET") RECEPTIVITI_URL = os.getenv("RECEPTIVITI_URL") # Ensure credentials are loaded correctly # Confirm that you're authenticated with Receptiviti receptiviti.status() dataset = pd.read_csv("your-dataset.csv") # Define required variables (ensure these are set correctly) version = "v2" # Example value; replace with actual version if needed custom_context = "my-norming-context" # Replace with your custom norming context verbose = True # Adjust if needed output_path = "output.csv" # Replace with desired output file path text_column = "Text" # Adjust to match actual column name retained_columns = dataset[["Speaker", "Text"]] # Assuming you want to retain all columns from input # Call Receptiviti API results = receptiviti.request( key=os.getenv("RECEPTIVITI_KEY"), secret=os.getenv("RECEPTIVITI_SECRET"), url=os.getenv("RECEPTIVITI_URL"), text="file_name.csv", version=version, custom_context=custom_context, verbose=verbose, output=output_path, text_column=text_column ) # Combine results with retained columns combined_results = pd.concat([retained_columns.reset_index(drop=True), results.reset_index(drop=True)], axis=1) # Display and save results print("Analysis Results:") print(combined_results) combined_results.to_csv(output_path, index=False) ``` --- # Norming ### Norming Options[​](#norming-options "Direct link to Norming Options") Normed measures such as those found in Receptiviti's [Big 5 Personality](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md), [Needs and Values](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md), [DISC](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-disc.md), and [Fast and Slow Thinking](https://docs.receptiviti.com/frameworks/normed-frameworks/thinking-fast-and-slow.md) frameworks produce scores that are baselined against large datasets. These norming datasets contextualize the scores so they are reflective of the language patterns that are typical within a particular context or for a specific language data source. The norming process results in scores that fall along a standard normal curve, as shown below: ![standard normal curve](/assets/images/standard-normal-curve-resized-f00228c902e3b8275819ddd642a33dfb.png) When scoring data with the Receptiviti API, it is important to make the right choice about what dataset to use for norming in order to produce accurate insights. There are three options: **1. Receptiviti's Written Norming Dataset:** The data that you score will be compared against written language from a wide variety of contexts, ranging from social media to emails to news articles to novels. This dataset would be applicable for use with social media posts, web copy, product reviews, and other data sources that involve written language. **2. Receptiviti's Spoken Norming Dataset:** The data that you score will be compared against transcribed language from a wide variety of contexts, ranging from interviews to group calls to speeches to earnings calls. This dataset would be applicable for use with executive speeches, transcripts of focus group interviews, and other data sources that involve spoken language. **3. A Custom Norming Dataset:** With custom norming, the scores for each data point are compared only against the data that you have provided for custom norming, making it reflective of your use case and data sources. This simplifies score interpretation, as the distribution usually allows for a clear distinction between higher and lower scores. When using Receptiviti's norming datasets, note that the average of scores within your data may be systematically high or low on different measures due to differences in the way that people speak or write in the particular context or platform where your data was collected. This can be informative in some cases, or may simply be a pattern you need to remain aware of. Custom norming makes the score distributions more predictable and easier to interpret. info Detailed information on using custom norming features, including when and why to apply them, is available [here](https://docs.receptiviti.com/norming-and-base-rates/custom-norming/.md). deciding whether or not to use the custom norming feature --- # Receptiviti Base Rates The table below outlines base rates for Receptiviti and LIWC API measures. Base rates are the mean scores of a measure in a specific context. Because language is context dependent, base rates are not universally applicable (e.g., base rates calculated using spoken language should not be used as reference points when interpreting analysis of written language). Each proportional measure has a unique base rate, meaning the psychologically relevant words captured by each proportional measure occur naturally in language at varying rates. Some measures have a higher base rate like `cognitive_processes` (base rate in natural conversation = 0.1227). Other measures like `discrepancy` have a lower base rate (base rate in natural conversation = 0.0145). Thus, for some measures, a change of .05 can be huge psychologically even though the score magnitude looks tiny if you're not familiar with the measure. If your analysis focuses on the psychology of a specific sub-population (i.e., executives) or context (i.e., work calls) that is not represented by our published base rates, we recommend calculating base rates based on your proprietary datasets to be able to understand what is a high or low score in the context of your data. This can be accomplished by either calculating the mean score of each Receptiviti measure for your dataset post-analysis or by using Receptiviti's custom norming tool. note The base rates provided for SALLEE Emotions are drawn from a very large and diverse language dataset and are intended solely as reference points. Please note that emotional scores can vary widely and are highly sensitive to context, tone, and circumstance. ## Spoken Norms Base Rates[​](#spoken-norms-base-rates "Direct link to Spoken Norms Base Rates") LIWC | Measure | Mean | Stdv | | ------------------------------- | ----------- | ----------- | | liwc15.achievement | 0.0156864 | 0.00896096 | | liwc15.adjectives | 0.039879 | 0.0108305 | | liwc15.adverbs | 0.0582961 | 0.0158574 | | liwc15.affective\_processes | 0.0440714 | 0.0164322 | | liwc15.affiliation | 0.0293051 | 0.0192892 | | liwc15.all\_punctuation | 0.149943 | 0.0605968 | | liwc15.analytical\_thinking | 0.505986 | 0.216155 | | liwc15.anger\_words | 0.00284224 | 0.00338075 | | liwc15.anxiety\_words | 0.00174824 | 0.00202782 | | liwc15.apostrophes | 0.034745 | 0.0149419 | | liwc15.articles | 0.0674661 | 0.015672 | | liwc15.assent | 0.00617692 | 0.00669608 | | liwc15.authentic | 0.430433 | 0.194159 | | liwc15.auxiliary\_verbs | 0.0969327 | 0.0193046 | | liwc15.biological\_processes | 0.00934392 | 0.00866691 | | liwc15.body | 0.00274842 | 0.00348921 | | liwc15.causation | 0.0162946 | 0.00644977 | | liwc15.certainty | 0.0145231 | 0.00555023 | | liwc15.clout | 0.68802 | 0.190865 | | liwc15.cognitive\_processes | 0.123115 | 0.0281735 | | liwc15.colons | 0.000182561 | 0.000716176 | | liwc15.commas | 0.0412558 | 0.0279754 | | liwc15.comparisons | 0.0259105 | 0.011387 | | liwc15.conjunctions | 0.066115 | 0.0137953 | | liwc15.dashes | 0.00823717 | 0.0146303 | | liwc15.death | 0.000895483 | 0.00193854 | | liwc15.dictionary\_words | 0.89548 | 0.0419544 | | liwc15.differentiation | 0.0336184 | 0.01088 | | liwc15.discrepancies | 0.0163186 | 0.00739854 | | liwc15.drives | 0.0808326 | 0.0258348 | | liwc15.emotional\_tone | 0.618772 | 0.225757 | | liwc15.exclamations | 0.000251008 | 0.00151587 | | liwc15.family | 0.00194952 | 0.00392999 | | liwc15.feel | 0.00388894 | 0.00340994 | | liwc15.female | 0.00385913 | 0.00815393 | | liwc15.filler\_words | 0.000305401 | 0.00139136 | | liwc15.focus\_future | 0.0146854 | 0.00628323 | | liwc15.focus\_past | 0.0368525 | 0.0190014 | | liwc15.focus\_present | 0.119793 | 0.0272751 | | liwc15.friends | 0.00216598 | 0.00267326 | | liwc15.function\_words | 0.546137 | 0.0477916 | | liwc15.health | 0.00411603 | 0.0050477 | | liwc15.hear | 0.00784896 | 0.0059247 | | liwc15.home | 0.00306113 | 0.00383459 | | liwc15.i | 0.0343926 | 0.0234175 | | liwc15.impersonal\_pronouns | 0.0706703 | 0.0202231 | | liwc15.informal\_language | 0.0128721 | 0.0115143 | | liwc15.ingestion | 0.00198586 | 0.00332097 | | liwc15.insight | 0.0275727 | 0.0108129 | | liwc15.interrogatives | 0.0162412 | 0.00596389 | | liwc15.leisure | 0.00957182 | 0.010114 | | liwc15.male | 0.0100239 | 0.0123938 | | liwc15.money | 0.0115466 | 0.012779 | | liwc15.motion | 0.0198376 | 0.00739901 | | liwc15.negations | 0.0161393 | 0.00795081 | | liwc15.negative\_emotion\_words | 0.010983 | 0.00706285 | | liwc15.netspeak | 0.00133404 | 0.00301138 | | liwc15.nonfluencies | 0.00458697 | 0.0045717 | | liwc15.numbers | 0.0236537 | 0.0171302 | | liwc15.other\_grammar | 0.278743 | 0.0341839 | | liwc15.other\_punctuation | 0.00769956 | 0.0095485 | | liwc15.parentheses | 0.000319097 | 0.00131115 | | liwc15.perceptual\_processes | 0.0213276 | 0.00911987 | | liwc15.periods | 0.0519266 | 0.0293854 | | liwc15.personal\_concerns | 0.0453083 | 0.0204955 | | liwc15.personal\_pronouns | 0.0945636 | 0.0275632 | | liwc15.positive\_emotion\_words | 0.0324209 | 0.0150219 | | liwc15.power | 0.0235424 | 0.0109927 | | liwc15.prepositions | 0.12877 | 0.0178209 | | liwc15.pronouns | 0.165322 | 0.0345094 | | liwc15.quantifiers | 0.0234709 | 0.00757616 | | liwc15.question\_marks | 0.00393125 | 0.0064933 | | liwc15.quotes | 0.00117106 | 0.00400123 | | liwc15.relativity | 0.129528 | 0.0255581 | | liwc15.religion | 0.00135238 | 0.00312144 | | liwc15.reward | 0.0165691 | 0.0090235 | | liwc15.risk | 0.00432279 | 0.00377794 | | liwc15.sad\_words | 0.0024095 | 0.00218905 | | liwc15.see | 0.00864022 | 0.00498853 | | liwc15.semicolons | 0.000224234 | 0.000892304 | | liwc15.sexual | 0.000433195 | 0.00143551 | | liwc15.she\_he | 0.0100448 | 0.0128678 | | liwc15.six\_plus\_words | 0.213883 | 0.0491734 | | liwc15.social\_processes | 0.0985366 | 0.0284974 | | liwc15.space | 0.0681477 | 0.0152513 | | liwc15.swear\_words | 0.000405211 | 0.00133469 | | liwc15.tentative | 0.0314505 | 0.0119751 | | liwc15.they | 0.00835238 | 0.00641713 | | liwc15.time | 0.0441597 | 0.0138875 | | liwc15.time\_orientation | 0.170983 | 0.0287364 | | liwc15.verbs | 0.175824 | 0.0309965 | | liwc15.we | 0.0206273 | 0.0177875 | | liwc15.work | 0.0253243 | 0.0154333 | | liwc15.you | 0.0211483 | 0.0133236 | LIWC Extension | Measure | Mean | Stdv | | ----------------------------------- | ----------- | ----------- | | liwc\_extension.absolutist | 0.011910653 | 0.006815584 | | liwc\_extension.abstract | 0.373594885 | 0.040132114 | | liwc\_extension.action | 0.117293713 | 0.024087227 | | liwc\_extension.agency\_language | 0.042703021 | 0.013792625 | | liwc\_extension.allure | 0.079683209 | 0.024762544 | | liwc\_extension.approach | 0.13611781 | 0.053651356 | | liwc\_extension.avoidance | 0.04698469 | 0.034485037 | | liwc\_extension.communion\_language | 0.014537257 | 0.010054909 | | liwc\_extension.concrete | 0.253482421 | 0.045057967 | | liwc\_extension.high\_empathy | 0.102846955 | 0.028140003 | | liwc\_extension.inaction | 0.007142594 | 0.00494518 | | liwc\_extension.low\_empathy | 0.26444749 | 0.040140552 | SALLEE Emotions | Measure | Mean | Stdv | | ------------------- | ------------------ | ------------------ | | sallee.sentiment | 0.516116366244894 | 0.0308410015619418 | | sallee.goodfeel | 0.100846381976162 | 0.0461579336600042 | | sallee.badfeel | 0.0686136494863709 | 0.0443063068886882 | | sallee.emotionality | 0.193751037989964 | 0.0700074042812606 | | sallee.ambifeel | 0.0279646485907223 | 0.0176830373773708 | | sallee.approach | 0.135223587903985 | 0.0514492821162878 | | sallee.avoidance | 0.0473144764580809 | 0.0331941991821287 | | sallee.admiration | 0.033332333054721 | 0.0202637946680387 | | sallee.amusement | 0.003137077615406 | 0.0062281909765347 | | sallee.excitement | 0.0121739908008766 | 0.0143022885520791 | | sallee.gratitude | 0.0181400072851303 | 0.0133443311424969 | | sallee.joy | 0.0209092586102479 | 0.0175296276726278 | | sallee.love | 0.0185555131804948 | 0.0143547306859791 | | sallee.anger | 0.025035084822676 | 0.0240320025304493 | | sallee.boredom | 0.0024636997335821 | 0.0047641173048394 | | sallee.disgust | 0.0107153259110113 | 0.011972027443823 | | sallee.fear | 0.0226570084046184 | 0.0204881400896081 | | sallee.sadness | 0.0162768315241016 | 0.0149143685946252 | | sallee.calmness | 0.0100927592657571 | 0.0085104296870682 | | sallee.curiosity | 0.0091978918529449 | 0.0112651402566057 | ## Written Norms Base Rates[​](#written-norms-base-rates "Direct link to Written Norms Base Rates") LIWC | Measure | Mean | Stdv | | ------------------------------- | ----------- | ----------- | | liwc15.achievement | 0.0157437 | 0.00956914 | | liwc15.adjectives | 0.0451384 | 0.0100299 | | liwc15.adverbs | 0.0477705 | 0.0178575 | | liwc15.affective\_processes | 0.0547714 | 0.0199929 | | liwc15.affiliation | 0.0215316 | 0.011642 | | liwc15.all\_punctuation | 0.164898 | 0.0468774 | | liwc15.analytical\_thinking | 0.621625 | 0.24443 | | liwc15.anger\_words | 0.0053888 | 0.00591107 | | liwc15.anxiety\_words | 0.00279604 | 0.00242396 | | liwc15.apostrophes | 0.0185493 | 0.015402 | | liwc15.articles | 0.0663611 | 0.0205136 | | liwc15.assent | 0.00384407 | 0.00542152 | | liwc15.authentic | 0.449034 | 0.248591 | | liwc15.auxiliary\_verbs | 0.08143 | 0.0237861 | | liwc15.biological\_processes | 0.0246586 | 0.0174967 | | liwc15.body | 0.00796412 | 0.00751632 | | liwc15.causation | 0.0148121 | 0.00643048 | | liwc15.certainty | 0.0139405 | 0.00589577 | | liwc15.clout | 0.581337 | 0.20804 | | liwc15.cognitive\_processes | 0.108311 | 0.0305972 | | liwc15.colons | 0.00244938 | 0.00407788 | | liwc15.commas | 0.0382497 | 0.0256828 | | liwc15.comparisons | 0.0222256 | 0.00695673 | | liwc15.conjunctions | 0.0600964 | 0.0142388 | | liwc15.dashes | 0.00860721 | 0.00944564 | | liwc15.death | 0.00148521 | 0.00237832 | | liwc15.dictionary\_words | 0.862015 | 0.0522411 | | liwc15.differentiation | 0.0293569 | 0.0118191 | | liwc15.discrepancies | 0.0169691 | 0.0083716 | | liwc15.drives | 0.0735027 | 0.0201745 | | liwc15.emotional\_tone | 0.575082 | 0.264847 | | liwc15.exclamations | 0.00720915 | 0.0148132 | | liwc15.family | 0.00467469 | 0.00606797 | | liwc15.feel | 0.00610012 | 0.00446765 | | liwc15.female | 0.0103722 | 0.0146321 | | liwc15.filler\_words | 0.000343825 | 0.000776028 | | liwc15.focus\_future | 0.0137743 | 0.0076538 | | liwc15.focus\_past | 0.0424332 | 0.0211448 | | liwc15.focus\_present | 0.0979263 | 0.0399397 | | liwc15.friends | 0.00315343 | 0.00338504 | | liwc15.function\_words | 0.508298 | 0.0578201 | | liwc15.health | 0.00957108 | 0.0130435 | | liwc15.hear | 0.00747993 | 0.00586397 | | liwc15.home | 0.0051454 | 0.00486593 | | liwc15.i | 0.0474989 | 0.0321653 | | liwc15.impersonal\_pronouns | 0.0493682 | 0.0158787 | | liwc15.informal\_language | 0.015609 | 0.0190321 | | liwc15.ingestion | 0.00504371 | 0.00660189 | | liwc15.insight | 0.0226022 | 0.00886695 | | liwc15.interrogatives | 0.0141309 | 0.00557117 | | liwc15.leisure | 0.0116361 | 0.00876493 | | liwc15.male | 0.0168055 | 0.0174231 | | liwc15.money | 0.00674614 | 0.00887423 | | liwc15.motion | 0.0206508 | 0.00718073 | | liwc15.negations | 0.015602 | 0.00841695 | | liwc15.negative\_emotion\_words | 0.0175574 | 0.0104995 | | liwc15.netspeak | 0.00616887 | 0.0119934 | | liwc15.nonfluencies | 0.00261875 | 0.00272702 | | liwc15.numbers | 0.0173492 | 0.0127054 | | liwc15.other\_grammar | 0.252522 | 0.0406932 | | liwc15.other\_punctuation | 0.00999886 | 0.016047 | | liwc15.parentheses | 0.00239922 | 0.00384123 | | liwc15.perceptual\_processes | 0.0262421 | 0.0123788 | | liwc15.periods | 0.0618122 | 0.0254979 | | liwc15.personal\_concerns | 0.0525336 | 0.0262659 | | liwc15.personal\_pronouns | 0.0961124 | 0.0342884 | | liwc15.positive\_emotion\_words | 0.036541 | 0.0169348 | | liwc15.power | 0.0242762 | 0.0120206 | | liwc15.prepositions | 0.130735 | 0.0220148 | | liwc15.pronouns | 0.145569 | 0.0420835 | | liwc15.quantifiers | 0.0202931 | 0.00692145 | | liwc15.question\_marks | 0.00654351 | 0.00751303 | | liwc15.quotes | 0.00733319 | 0.0106882 | | liwc15.relativity | 0.137027 | 0.0256214 | | liwc15.religion | 0.00256211 | 0.00581801 | | liwc15.reward | 0.015304 | 0.00724976 | | liwc15.risk | 0.00446074 | 0.00309604 | | liwc15.sad\_words | 0.0039024 | 0.00314168 | | liwc15.see | 0.0107275 | 0.00702211 | | liwc15.semicolons | 0.00174653 | 0.00330957 | | liwc15.sexual | 0.00154367 | 0.00301106 | | liwc15.she\_he | 0.0190523 | 0.021354 | | liwc15.six\_plus\_words | 0.246948 | 0.0694989 | | liwc15.social\_processes | 0.0967361 | 0.0314074 | | liwc15.space | 0.0668411 | 0.0171603 | | liwc15.swear\_words | 0.00290409 | 0.00650011 | | liwc15.tentative | 0.02471 | 0.011058 | | liwc15.they | 0.00765391 | 0.00568643 | | liwc15.time | 0.0519246 | 0.015068 | | liwc15.time\_orientation | 0.153437 | 0.0363552 | | liwc15.verbs | 0.15875 | 0.0364333 | | liwc15.we | 0.00818935 | 0.00884057 | | liwc15.work | 0.0293412 | 0.0245145 | | liwc15.you | 0.0137265 | 0.0134485 | LIWC Extension | Measure | Mean | Stdv | | ----------------------------------- | ----------- | ----------- | | liwc\_extension.absolutist | 0.012234899 | 0.00657061 | | liwc\_extension.abstract | 0.342406292 | 0.043050363 | | liwc\_extension.action | 0.10626774 | 0.023579965 | | liwc\_extension.agency\_language | 0.0373195 | 0.013089012 | | liwc\_extension.allure | 0.07183369 | 0.025819539 | | liwc\_extension.approach | 0.171096915 | 0.069510717 | | liwc\_extension.avoidance | 0.0686031 | 0.043534453 | | liwc\_extension.communion\_language | 0.018712394 | 0.013208766 | | liwc\_extension.concrete | 0.262689501 | 0.04051544 | | liwc\_extension.high\_empathy | 0.103731125 | 0.03283153 | | liwc\_extension.inaction | 0.009381738 | 0.00485507 | | liwc\_extension.low\_empathy | 0.230127943 | 0.034995246 | SALLEE Emotions | Measure | Mean | Stdv | | ------------------- | ------------------ | ------------------ | | sallee.sentiment | 0.516688452714283 | 0.0424318901218112 | | sallee.goodfeel | 0.133166923885296 | 0.06788146547697 | | sallee.badfeel | 0.0997900184567257 | 0.0592600038545297 | | sallee.emotionality | 0.262658816590926 | 0.100241996602172 | | sallee.ambifeel | 0.0350738402757094 | 0.0194911873157864 | | sallee.approach | 0.173838206616407 | 0.0688886267626769 | | sallee.avoidance | 0.0713486866993312 | 0.0430446403866653 | | sallee.admiration | 0.039281031595755 | 0.0254293684098989 | | sallee.amusement | 0.0088389990356598 | 0.0206273767356995 | | sallee.excitement | 0.0073894419268624 | 0.0098043977139411 | | sallee.gratitude | 0.0183016626291018 | 0.0126095811418309 | | sallee.joy | 0.0238211020218329 | 0.0206670819730906 | | sallee.love | 0.0352784064906563 | 0.028665435786645 | | sallee.anger | 0.0340660272906104 | 0.0296090534997463 | | sallee.boredom | 0.0062687852387848 | 0.0096810357422236 | | sallee.disgust | 0.0184388904897591 | 0.0196116796885501 | | sallee.fear | 0.0279191585191435 | 0.0201317584611499 | | sallee.sadness | 0.0262212691749449 | 0.0188325608608942 | | sallee.calmness | 0.0116217983733134 | 0.0089704647977253 | | sallee.curiosity | 0.0099420199743363 | 0.0102380551126441 | --- # Overview ## What is Receptiviti?[​](#what-is-receptiviti "Direct link to What is Receptiviti?") Receptiviti measures the psychological and behavioral signals embedded in language and communication. Receptiviti translates the measurement of the emotions, personality, motivations, thinking styles, interpersonal dynamics, communication styles, and other psychological characteristics expressed through communication into clear, easy-to-understand, highly actionable insights or recommendations. Receptiviti's goal is to make psychological and behavioral insights measurable at scale without sacrificing scientific rigor, transparency, consistency, or interpretability. Receptiviti combines scientifically validated, deterministic measurement models with scalable technology to produce insights and recommendations that organizations can confidently incorporate into their research, products, workflows, and business decision-making. ## What does Receptiviti measure?[​](#what-does-receptiviti-measure "Direct link to What does Receptiviti measure?") Receptiviti measures a wide range of scientifically validated psychological states and traits reflected in language, including personality, emotions, motivations, thinking styles, interpersonal dynamics, communication styles, behavioral tendencies, and other psychological constructs. Receptiviti measures are deterministic, meaning that given a particular input, they always produce the exact same output. Receptiviti measures are also interpretable, benchmarkable, and comparable across individuals, groups, organizations, brands, audiences, and other populations. Because language can be measured continuously, Receptiviti can also help organizations understand how these states and traits change over time. ## What makes Receptiviti different?[​](#what-makes-receptiviti-different "Direct link to What makes Receptiviti different?") Unlike opaque or black-box AI or machine learning systems, Receptiviti combines decades of peer-reviewed language psychology research with deterministic, transparent, interpretable measurement models that can be benchmarked, validated, and consistently reproduced. Rather than relying solely on self-report surveys or assessment sessions, Receptiviti measures the psychological and behavioral signals reflected in written or spoken and transcribed language from virtually any source, whether collected specifically for analysis or generated naturally through everyday communication. This enables organizations to measure psychology at scale and in the contexts that matter, monitor change over time, and apply the same scientific foundation consistently across individuals, teams, organizations, brands, audiences, and other populations. ## What is Receptiviti's origin?[​](#what-is-receptivitis-origin "Direct link to What is Receptiviti's origin?") Receptiviti's roots are in academia and a discipline commonly referred to as language psychology, or sometimes psycholinguistics. Language psychology is the scientific study of how language reflects psychological states, traits, motivations, and behavior. The founding science behind Receptiviti is Linguistic Inquiry and Word Count, or LIWC for short. LIWC established many of the scientific methods used to measure psychological signals embedded in language and, with more than 34,000 research citations, remains one of the most widely cited and scientifically validated approaches in psychology and computational social science. Building on that foundation, with innovation at the forefront, Receptiviti was created to transform decades of language psychology research into a scalable technology platform that helps organizations better understand the people and voices that matter to them, including customers, employees, leaders, brands, and audiences, through the language and communication they produce every day. ## The Relationship between LIWC and Receptiviti[​](#the-relationship-between-liwc-and-receptiviti "Direct link to The Relationship between LIWC and Receptiviti") Receptiviti operates as the commercial arm of LIWC (the academic offering). Members of our science and sales teams have worked in Pennebaker’s lab and contributed to various iterations of LIWC. Today, our science team continues to evolve and expand the science, while the customer-facing side of our company supports its application. The science team has developed over 2,600 additional categories using the same validated methodology established in LIWC's original design. Some of these are made available to customers as proportion-based measures (like LIWC Extension), while others are used internally. Receptiviti’s proportion-based measures, together with LIWC, serve as the foundational ingredients of Receptiviti’s normed algorithmic measures. The normed algorithmic measures are never based on “black box” machine learning models—an important distinction. Instead, they are designed by building formulas from the ground up, selecting and weighting ingredients using both theoretical and data-driven methods. Theory-based methods draw on more than 25,000 published studies that have used LIWC to predict traits, states, behaviors, and other outcomes. These findings inform how we construct each measure. Data-driven methods include statistical approaches such as principal component analysis, regression modeling, and embeddings to test performance and inform and validate design using our internal datasets. ## What kinds of language can Receptiviti analyze?[​](#what-kinds-of-language-can-receptiviti-analyze "Direct link to What kinds of language can Receptiviti analyze?") Receptiviti analyzes written or transcribed spoken language from virtually any source. This includes AI and chatbot conversations; marketing and public-facing content such as advertising copy, website copy, press releases, campaign copy, and product descriptions; business and organizational communications such as internal communications, emails, memos, employee engagement surveys, and performance reviews; customer interactions and market research data such as customer reviews, customer feedback, support or call center conversations, sales calls, open-ended survey responses, qualitative research interviews, and focus group interviews; meetings and spoken interactions such as team meetings, media interviews, earnings calls, presentations, town halls, and therapy sessions; talent selection and development data such as job interviews and coaching sessions; digital and social communications such as social media posts, chat messages, and online forums; reflective writing such as journals and diaries; and more. Though human language and psychology are the foundations of our science, Receptiviti measures can also be used to analyze LLM-generated language in order to predict and modulate how people perceive and respond to AI agents or generated content in general, including LLM-generated advertising copy and website text. ## What does Receptiviti return?[​](#what-does-receptiviti-return "Direct link to What does Receptiviti return?") Receptiviti uses scientifically validated psychological measures to generate scores as quantitative outputs that serve as the foundation for all Receptiviti products and services. Depending on how an organization works with Receptiviti, outputs may include API responses for integration into custom applications and analyses, interactive reports with integrated interpretations and recommendations, customizable dashboards and visualizations, presentations and written analyses, or custom research and advisory deliverables. These outputs help organizations better understand people and communication across a wide range of use cases. ## How do organizations use Receptiviti?[​](#how-do-organizations-use-receptiviti "Direct link to How do organizations use Receptiviti?") Organizations can access Receptiviti through our web-based API, containerized API deployments, interactive reports, dashboards, advisory services, and custom research or consulting engagements, depending on how they want to incorporate psychological insights into their products, workflows, research, or decision-making. Whether integrated directly into software or AI applications, deployed within an organization's own infrastructure, used to analyze large language datasets, incorporated into strategic consulting engagements, or used to generate interactive reports, dashboards, and recommendations, Receptiviti provides flexible ways to apply language psychology across a wide range of use cases. ## Who does Receptiviti help?[​](#who-does-receptiviti-help "Direct link to Who does Receptiviti help?") Receptiviti supports organizations across any industry where understanding people or communication creates value. Our customers span industries including AI, technology, finance and investing, marketing, communications, public relations, consumer research, human resources and people analytics, executive development, healthcare, sports, education, social work, government, and mental health support. Across these sectors, organizations use Receptiviti to better understand users, customers, employees, leaders, brands, audiences, patients, and other populations through the language and communication they produce every day. *** Click any of the links below to get started, find practical information on our science and technology, access step-by-step guides, or to find reference documentation on our products and services. ## [Frameworks](https://docs.receptiviti.com/frameworks/.md) [Integrate psychological insights into your products.](https://docs.receptiviti.com/frameworks/.md) ## [Developer Resources](https://docs.receptiviti.com/developer-resources.md) [Streamline integration with our platform.](https://docs.receptiviti.com/developer-resources.md) ## [Visualization UI](https://docs.receptiviti.com/visualization-ui/.md) [Generate charts and graphs from your data.](https://docs.receptiviti.com/visualization-ui/.md) ## [API Reference](https://docs.receptiviti.com/api-reference) [All the resources you need to integrate with our API.](https://docs.receptiviti.com/api-reference) ## [Data Preparation](https://docs.receptiviti.com/data-preparation/.md) [Language support and text cleaning guidance for best results.](https://docs.receptiviti.com/data-preparation/.md) ## [Recommendations](https://docs.receptiviti.com/selecting-measures-for-analysis/.md) [Tailored framework recommendations based on specific use cases.](https://docs.receptiviti.com/selecting-measures-for-analysis/.md) ## [Norming](https://docs.receptiviti.com/norming-and-base-rates/.md) [Ensure you're comparing apples to apples.](https://docs.receptiviti.com/norming-and-base-rates/.md) --- # Receptiviti API Dashboard The dashboard is the main user interface to find information about your account. ![dashboard overview](/assets/images/dashboard-overview-7ceb2e6208a77cbb6c189229ffdd531a.png) *** Below is a summary of each section displayed in the Receptiviti API dashboard. ### Plan Details[​](#plan-details "Direct link to Plan Details") This section outlines the specifics of your API subscription, including the start date of your plan, the total word limit allocated, whether overage usage is enabled, pricing for overages, and whether you have access to beta features, such as our [Visualization UI](https://docs.receptiviti.com/visualization-ui/.md). It also defines how many words are available for your Visualiation UI account under your current access level. *** ### API Key Pair[​](#api-key-pair "Direct link to API Key Pair") This area displays your unique API credentials. The API Key is visible and used to authenticate requests, while the API Secret is hidden by default but can be rotated. This is a critical section for integration security and should be handled carefully. *** ### API Usage[​](#api-usage "Direct link to API Usage") This section shows your current API usage within the billing cycle. It tracks how many words you've used and how many are remaining, giving you a clear snapshot of your consumption against your total limit. *** ### Beta Usage[​](#beta-usage "Direct link to Beta Usage") If you have access to the Visualization UI, this section tracks usage separately from the main API allocation. It functions similarly to the standard API usage tracker, displaying the percentage used and words remaining from your beta quota. *** ### Navigation Bar[​](#navigation-bar "Direct link to Navigation Bar") Located at the top of the dashboard, the navigation bar allows you to quickly switch between core features of the platform—such as CSV Upload, Custom Norming, Documentation, and more. The logged-in user's email is also displayed here for account context. *** ### Terms and Contact[​](#terms-and-contact "Direct link to Terms and Contact") Beneath the API Key section, you’ll find a note confirming your organization’s acceptance of the API Terms of Use, along with a link to the full terms and a contact email for support or questions. --- # CSV Upload Tool This is a basic user interface we provide where you can upload a CSV file of text to have it scored and returned as a separate CSV. The CSV upload tool is found in your account dashboard. You can initiate an account by going [here](https://dashboard.receptiviti.com/score). Below are instructions for each required field. *** ![csv-upload-ui](/assets/images/csv-upload-ui-8d5b90aa064a6c693952a3ab05293317.png) *** ### API Version[​](#api-version "Direct link to API Version") **Description:** This item reflects the version of the API that will be used for scoring. * Available options by default: `v2` * Available options for legacy users: `v1`, `v2` info Some features (like **Norming Context**) are only available in **API V2**. *** ### Norming Context (Required for API V2)[​](#norming-context-required-for-api-v2 "Direct link to Norming Context (Required for API V2)") **Description:** Choose the *norming context* to apply when calculating scores.
Norming adjusts scores based on reference groups or baselines. important This option is only available when using **API V2**. *** ### Text Column Index (Required)[​](#text-column-index-required "Direct link to Text Column Index (Required)") **Description:** Provide the 1-based index of the column that contains the **text samples** to be scored in your CSV. **Example:**
If your CSV format is like this: ``` id,text,speaker 1,Hello!,Joe ``` Then enter: `2` (because "text" is the second column). *** ### Contains Header Row?[​](#contains-header-row "Direct link to Contains Header Row?") **Description:** Indicate whether your CSV file contains a header row (column names). **Options:** * `yes` – CSV has headers (e.g., `id,text,speaker`) * `no` – First row is data *** ### Delimiter Type[​](#delimiter-type "Direct link to Delimiter Type") **Description:** Select the delimiter used in your CSV file. **Options:** * `comma` (default) * `tab` * `semicolon` * `pipe` *** ### Upload CSV File[​](#upload-csv-file "Direct link to Upload CSV File") **Instructions:** Drag and drop your CSV file into the upload area, or click to select it manually. **Limits:** * Maximum: 25,000 rows/10MB * If your word limit is exceeded, an error message will appear note If you surpass your word limit while a CSV is being scored, the output will include the rows after the threshold was reached but they will not contain any scores. Additionally, if you submit a file with more than 25,000 rows, only the first 25,000 will be included in the output. *** ### Text Column Preview[​](#text-column-preview "Direct link to Text Column Preview") After uploading your CSV and setting the **Text Column Index**, a preview of the extracted text will appear here for verification. *** ### Submit[​](#submit "Direct link to Submit") Click the **Submit** button once all fields are complete and your file is uploaded. *** ## CSV Formatting FAQ[​](#csv-formatting-faq "Direct link to CSV Formatting FAQ") This covers best practices for structuring your CSVs when uploading text data for scoring or analysis. *** ### What is the simplest valid CSV format?[​](#what-is-the-simplest-valid-csv-format "Direct link to What is the simplest valid CSV format?") The most basic structure includes: * A `Unique ID` column * A `Text` column containing the content you want to analyze See below for a basic example: ``` | Unique ID | Text | |-----------|----------------| | 1 | Text sample 1 | | 2 | Text sample 2 | | 3 | Text sample 3 | ``` Each row is treated as one **separate sample** of language. *** ### Can I include other metadata?[​](#can-i-include-other-metadata "Direct link to Can I include other metadata?") Yes — if you have additional metadata like: * **Speaker identity** * **Date** * **Demographics (age, gender, etc.)** * **Annotated labels (job role, conversation type, etc.)** ...you can include those as additional columns. See the two formats below for examples with metadata like: ``` | Unique ID | Text | Date | Exec or Analyst | |-----------|-------------------|------------|-----------------| | 1 | Text sample | 01-Aug-25 | Analyst | ``` and ``` | Unique ID | Text | Speaker | |-----------|-------------------|---------| | 1 | Text sample | Tom | ``` *** ### Should each row be one sentence? One conversation?[​](#should-each-row-be-one-sentence-one-conversation "Direct link to Should each row be one sentence? One conversation?") That depends on what you want to analyze. * If you want **conversation-level insights** (e.g., full transcripts), each row should be a full conversation. * If you want **individual-level insights** (e.g., per executive or staff member), each row should contain that person's full text. Each row = one unit of analysis. Visit [this page](https://docs.receptiviti.com/data-preparation/optimizing-outcomes-by-manipulating-text.md) for more in-depth information about organizing text for different outcomes. *** ### Can I upload multiple files?[​](#can-i-upload-multiple-files "Direct link to Can I upload multiple files?") No. The CSV upload tool only supports one document at a time. *** ### Summary of Best Practices[​](#summary-of-best-practices "Direct link to Summary of Best Practices") * Include a `Unique ID` column. * Organize your `Text` column according to your intended analysis outcome. * Add relevant metadata in extra columns. * Use one file for all samples if building a custom norm. --- # Custom Norming Contexts from Dashboard This guide walks you through creating and managing custom norming contexts from your user **Dashboard**. *** ## Creating a New Custom Norming Context[​](#creating-a-new-custom-norming-context "Direct link to Creating a New Custom Norming Context") ![custom-norm-dashboard](/assets/images/custom-norm-dashboard-cfebf9c8692ed43be5274dc341c9137a.png) Click **New Custom Norming Context** to open the upload form. Here’s what each field means: ### Required Fields[​](#required-fields "Direct link to Required Fields") * **Name** *(Required)*:
The label used to identify your norming context in the dashboard. * **Text Column Index** *(Required)*:
Use a 1-based index for the column in your CSV that contains the language samples. > Example: If your CSV's second column contains the text, enter `2`. * **Contains Header Row**:
Choose `Yes` if your CSV includes headers in the first row.
Choose `No` if the first row is data. * **Delimiter Type**:
Common choices: `comma`, `tab`, `pipe`, etc. Choose based on how your CSV is formatted. *** ### Optional Filters[​](#optional-filters "Direct link to Optional Filters") These filters refine which rows are used in calculating the custom norm: * **Minimum Word Count**:
Only rows with `word count >= minimum` will be used for norming.
*Defaults to 350 if left blank.* * **Maximum Punctuation**:
Only rows with `punctuation score <= maximum` will be used.
If left blank, no punctuation filtering is applied. *** ## Text Column Preview[​](#text-column-preview "Direct link to Text Column Preview") Once you've uploaded your CSV and selected the correct **Text Column Index**, a preview of your text data will appear below the form. Use this to confirm formatting before clicking **Create**. *** ## Viewing Norming Context Details[​](#viewing-norming-context-details "Direct link to Viewing Norming Context Details") Click on any custom norm name to open its **Context Details** view (Screenshot 8). This includes: | **Section** | **Details** | | ------------------- | ----------------------------------------------------------------------- | | Name | Norming context name | | Version Info | Major and Minor version numbers | | Minimum Word Count | Threshold for sample inclusion based on word count | | Maximum Punctuation | Threshold for sample inclusion based on punctuation score | | Pass One Results | Submitted vs. analyzed samples, word count, filtered blanks/punctuation | | Pass Two Results | Duplicate set of stats to validate consistency | | Timestamps | Created and Modified datetimes | Use this view to audit what was included and filtered in your norming process. *** ## Viewing Your Custom Norming Contexts[​](#viewing-your-custom-norming-contexts "Direct link to Viewing Your Custom Norming Contexts") Once you've created norming contexts, they appear in a list view: * **Name**: Clickable link to view details * **Submitted / Analyzed Samples**: Number of input and successfully processed text samples * **Analyzed Word Count**: Total words analyzed * **Average Word Count**: Per-sample word count average * **Status**: Should read `Completed` once processing is done * **Delete**: Red trash can icon lets you remove any context *** ## Custom Norming FAQ[​](#custom-norming-faq "Direct link to Custom Norming FAQ") This covers best practices for structuring your CSVs when uploading text data for custom norming. ### What is the simplest valid CSV format?[​](#what-is-the-simplest-valid-csv-format "Direct link to What is the simplest valid CSV format?") The most basic structure includes: * A `Unique ID` column * A `Text` column containing the content you want to analyze See below for a basic example: ``` | Unique ID | Text | |-----------|----------------| | 1 | Text sample 1 | | 2 | Text sample 2 | | 3 | Text sample 3 | ``` Each row is treated as one **separate sample** of language. *** ### Should each row be one sentence? One conversation?[​](#should-each-row-be-one-sentence-one-conversation "Direct link to Should each row be one sentence? One conversation?") That depends on the norming context you are trying to create. The dataset that will be used for norming should be organized in a way that aligns with the way you expect to organize insights later-on for scoring and analysis. For example: * If you plan to analyze transcripts for conversation-level insights, each row should be a full conversation. * If you plan to analyze individuals (e.g., per executive or staff member), each row should contain a single person's aggregated text. Each row = one unit of analysis. Visit [this page](https://docs.receptiviti.com/data-preparation/optimizing-outcomes-by-manipulating-text.md) for more in-depth information about organizing text for different outcomes. *** ### Can I upload multiple CSV files?​[​](#can-i-upload-multiple-csv-files "Direct link to Can I upload multiple CSV files?​") To create a custom norm, all language samples must be compiled into a single CSV file. The system does not support combining multiple CSV files into one custom norm. *** ### Will creating a custom norm subtract from my Receptiviti word count allotment?[​](#will-creating-a-custom-norm-subtract-from-my-receptiviti-word-count-allotment "Direct link to Will creating a custom norm subtract from my Receptiviti word count allotment?") The process to create a custom norm is separate from the standard process of scoring a dataset. So, the data uploaded to create a custom norm will not subtract from your total word bank. Your word bank usage will only be dedicated to data analyzed for the purpose of scoring. *** ### Can I delete a custom norm? Can I update a custom norm?​[​](#can-i-delete-a-custom-norm-can-i-update-a-custom-norm "Direct link to Can I delete a custom norm? Can I update a custom norm?​") Yes, you will have the ability to delete an old custom norm and create a new updated custom norm as needed. For example, you may start with a custom norm based on a dataset of 200 samples. As you collect more data, you may replace that custom norm with a custom norm based on a larger dataset. This would involve deleting the previously created custom norm and uploading a new CSV format custom norming dataset that contains the previously uploaded samples as well as any new samples as rows in the CSV. *** ## Important Notes[​](#important-notes "Direct link to Important Notes") * You can create as many norming contexts as needed. * Only samples that pass your filters will be used to compute custom norms. * Custom norming is available **only in API V2**, and the norm must be selected during CSV upload. --- # Getting Started ## Calling the API[​](#calling-the-api "Direct link to Calling the API") There are several ways you can make calls to the [Receptiviti API](https://dashboard.receptiviti.com/). How you choose to do so is dependent upon your experience and comfort level in the different tools available. Some of the most commonly used methods are found below. You can access our developer documentation through our **API Reference** page, found [here](https://docs.receptiviti.com/api-reference). ## The Receptiviti CSV Upload Tool[​](#the-receptiviti-csv-upload-tool "Direct link to The Receptiviti CSV Upload Tool") This is a basic user interface we provide where you can upload a CSV file of text to have it scored and returned as a separate CSV. This is an easy way for people with no programming experience to use the API. Note that you will need an account to use the CSV upload tool. You can initiate an account by going [here](https://dashboard.receptiviti.com/score). Further details about using the CSV upload tool can be found [here](https://docs.receptiviti.com/overview/dashboard/csv-upload-tool.md). ## Python[​](#python "Direct link to Python") To use Python to make a single text sample call to the Receptiviti API, you can use the following syntax in your Terminal, replacing `api_key` and `api_secret` with your actual credentials, as well as adding your text sample to the `"content"` section: ``` import json import requests url = 'https://api.receptiviti.com/v2/analyze/written' api_key = api_secret = data = json.dumps({ 'request_id': 'req-1', 'content': 'my text sample....' }) resp = requests.post(url, auth=(api_key, api_secret), data=data) print(json.dumps(resp.json(), indent=4, sort_keys=True)) ``` note The example above uses the default `written` norming context. The other default norming context is `spoken`. Alternatively, you can create your own norming contexts, which are available in your account once you create them. More information on norming and norming contexts is [here](https://docs.receptiviti.com/norming-and-base-rates/.md). ## Postman[​](#postman "Direct link to Postman") Postman is a popular API collaboration platform widely used by developers. It allows you to make calls to endpoints you specify by authorizing your credentials and using your own input data. You can use Postman with or without an account — the Scratch Pad feature allows you to make API calls without an account, but if you want to create a Workspace and save your API calls, you will need an account. Either way, you will need to [download the Postman desktop app](https://www.postman.com/). To get started using Postman (with an account): 1. In Postman, create a new Workspace, then open a new tab. 2. In the Postman input fields, configure the following: 1. From the drop-down menu, select **POST**. 2. In the URL field, enter `https://api.receptiviti.com/v2/analyze/written`. ![Postman Workspace](/assets/images/postman-workspace1-55aac66dc581bab5900e39b7a8590273.jpg) 3. Click the **Authorization** header, and in the **Type** drop-down, select **Basic Auth**. 4. To the right of the drop-down, in the **Username** field, paste your API Key, and in the **Password** field, paste your API Secret. Both are found in your [dashboard](https://dashboard.receptiviti.com/). ![Postman Workspace 2](/assets/images/postman-workspace2-85a3fc0c827e45d04a4b3501eb7be637.jpg) 5. Click the **Body** header, then click the **raw** button, and then select **JSON** from the drop-down menu. 6. In the content window below the settings you just configured, paste your text sample and be sure to have it formatted in JSON. (Note that these instructions specify a call with a single text sample.) ![Postman Workspace 3](/assets/images/postman-workspace3-65e85db2a62ab2c6f8dbd48fd58c8387.jpg) 7. Finally, click **Send**. You should see a successful response (Status: 200 OK) that lists the scores of the measures associated with your Receptiviti account in the **Body** tab of the **Response** window, which appears just below where you pasted your sample. If you are getting an error, be sure that you selected **POST** as the call method, that the body is properly formatted JSON, and that the endpoint is exactly as it appears above in step 2.2. For more in-depth information about the Receptiviti API endpoints, as well as code samples for calling the API programmitcally, see the [API Reference](https://docs.receptiviti.com/api-reference) section. For more information about interpreting the responses, see the [Frameworks](https://docs.receptiviti.com/frameworks/.md) section. note The example above uses the default `written` norming context. The other default norming context is `spoken`. Alternatively, you can create your own norming contexts, which are available in your account once you create them. More information on norming and norming contexts is [here](https://docs.receptiviti.com/norming-and-base-rates/.md). ## cURL[​](#curl "Direct link to cURL") To use cURL to make a single text sample call to the Receptiviti API, you can use the following syntax in your Terminal, replacing `:` with your actual credentials, as well as adding your text sample to the `"content"` section: ``` curl --location --request \ POST 'https://api.receptiviti.com/v2/analyze/written' \ -u : \ --header 'Content-Type: application/json' \ --data-raw '{ "request_id": "req-1", "content": "my text sample...." }' ``` For more in-depth information about making calls to the Receptiviti API and interpreting responses, see the [API Reference](https://docs.receptiviti.com/api-reference) section. note The example above uses the default `written` norming context. The other default norming context is `spoken`. Alternatively, you can create your own norming contexts, which are available in your account once you create them. More information on norming and norming contexts is [here](https://docs.receptiviti.com/norming-and-base-rates/.md). --- # Word Count Guidelines This table outlines the minimum word count requirements for generating reliable psycholinguistic insights across Receptiviti and LIWC measures. These word count thresholds reflect the amount of text needed to produce statistically valid and interpretable results. Shorter texts may yield noisy or incomplete outputs, while meeting or exceeding ideal counts enhances reliability, particularly for nuanced psychological interpretation. In general, more language yields more robust the analysis. Word count requirements exist in language analysis because there needs to be enough language to reliably measure how a person's linguistic choices reflect certain patterns, especially those that are less common. This is similar to behavioral analysis, where it takes fewer observations to measure something people do frequently, like smiling, than something rarer, like eye rolling. info At word counts below the minimum requirement (even as low as a single word), proportional scores remain accurate, reflecting the proportion of relevant words in the analyzed text, but offer limited interpretability. For psychologically meaningful insights, we recommend adhering to the minimum word count requirements—even when using proportional measures. | Framework | Bare Minimum Word Count Requirement | | ------------------------------ | ----------------------------------- | | Personality - Big 5 | 350 | | Drives | 350 | | Cognition | 350 | | Social Dynamics | 350 | | Needs and Values | 350 | | Personality - DISC | 350 | | Interpersonal Circumplex | 350 | | Fast and Slow Thinking | 350 | | LIWC (Summary Variables) | 50-200 | | LIWC (Linguistic Dimensions) | 50-200 | | LIWC (Other Grammar) | 50-200 | | LIWC (Psychological Processes) | 200 | | LIWC Extension | 200 | | Emotions (SALLEE) | 1 | | Temporal and Orientation | 200 | | Toxicity | 10 - 20 | --- # Selecting Frameworks and Measures This section provides tailored framework recommendations based on specific use cases to help you select the most appropriate tools for your project needs. * [**Measure Bundles**](https://docs.receptiviti.com/selecting-measures-for-analysis/measure-bundles.md): An overview of recommended groups of measures that each reflect different aspects of the same underlying psychological concept. * [**What's Your Receptiviti Use Case?**](https://docs.receptiviti.com/selecting-measures-for-analysis/whats-your-receptiviti-use-case.md): Explore framework suggestions categorized by their strengths and intended use cases, making it easier to align your goals with the right technologies. --- # Measure Bundles Measure Bundles are recommended groups of measures that each reflect different aspects of the same underlying psychological concept. The bundle approach is different from our frameworks, which usually involve algorithmic measures based on psychological models such as the `Big Five` or `DISC`. Bundles instead facilitate exploratory analyses by providing users with a menu of relevant measures that will help make sense of patterns of results. For users who prefer a theory-based or top-down strategy, the measures contained in each bundle may be the starting point for creating your own composite measures, algorithms, or models. For users taking a more data-driven approach, the information below can help make sense of patterns in exploratory data analyses and connect your findings with the psychological literature. Note that many of the linguistic predictors below are context-sensitive, so always keep the psychological context of the linguistic data in mind when interpreting results. Scroll down or click the following links to explore measure bundles related to [well-being](#well-being), [social orientation](#socially-oriented), [adaptability](#adaptability), [coachability](#coachability), and [self vs. other focus](#self-vs-other-focus). ## Well-Being[​](#well-being "Direct link to Well-Being") ### Highlights[​](#highlights "Direct link to Highlights") * Well-being can be measured holistically or subdivided into cognitive, emotional, and eudaimonic (i.e., meaningfulness) components; some models include positive social relationships, engagement, and achievement as additional elements of well-being. * The most common approach to assessing well-being in the social-behavioral sciences are single-item survey measures, though multidimensional models and language-based measurement have gained traction in recent decades. * Positive linguistic markers of well-being include words related to social engagement and specific positive emotions, such as words related to gratitude and love. * Negative correlates of well-being include verbal measures of stress, emotional volatility, depression, black-and-white thinking, self-consciousness, and rumination. ### Background Research[​](#background-research "Direct link to Background Research") Well-being is typically defined as a subjective sense of wellness or a positive state of being. Though well-being is often measured using single-item scales that ask people to rate their overall happiness or satisfaction with life (Jovanović & Lazić, 2020), there are also multidimensional approaches that are well-supported by research. Researchers tend to agree that there are at least three primary facets of well-being: satisfaction with life (cognitive judgments about life satisfaction), happiness (positive emotions such as joy and pleasure), and eudaimonia (a sense of meaning and purpose in life; OECD, 2013). In finer-grained models such as PERMA (Positivity, Engagement, Relationships, Meaning, and Achievement; Seligman, 2018), social well-being (strong and rewarding relationships), engagement (interest in and excitement about life), and accomplishment (including both private achievements and esteem from peers) are sometimes considered additional separate components. Though all components of well-being are correlated, it is possible to have high levels of one facet and moderate or at times low degrees of the others. Granular measures of well-being are therefore useful in cases where the aim is to specifically target one or more aspects of well-being for improvement through coaching, therapy, or employee support. The aspects of well-being that are targeted in language analysis depend on users’ research aims and preferred theoretical model. Users will also decide whether they want to focus on positive, negative, or all aspects of well-being. The norm in current academic research is that the absence of distress on its own isn’t sufficient for well-being, and accurate well-being assessment requires understanding both beneficial and harmful aspects of life (see Yiğit & Çakmak, 2024). However, there may be cases where some indicators are unclear or not present in the available text. For example, in professional or otherwise public conversations where negative emotional language tends to be suppressed (Moran et al., 2013), overall positive emotion and subtle indicators like pronouns may provide a stronger and more reliable signal. #### Applications[​](#applications "Direct link to Applications") Choosing which aspects of well-being to target in a language analysis will depend on the intended applications. For dynamic visualizations over time, it’s possible to create a longitudinal well-being dashboard that tracks the pulse of a large organization by analyzing open-ended survey responses or communications within a company. Such dashboards might focus specifically on language cues that are most relevant to the workplace, such as words related to stress and social engagement within a team. In mental health-specific applications, such as analyzing therapy transcripts to identify markers of well-being improvements during a patient’s treatment progress, it may make sense to focus on risk factors, such as rumination or black-and-white thinking, in addition to protective language patterns like references to love and gratitude. ### Main Facets of Well-Being[​](#main-facets-of-well-being "Direct link to Main Facets of Well-Being") #### Satisfaction with Life[​](#satisfaction-with-life "Direct link to Satisfaction with Life") Being satisfied with life (i.e., the cognitive component of well-being) typically involves comparing your life against what you know of others’ lives and your own personal standards. For example, a common ladder measure of life satisfaction asks people in a single survey item whether they are living their worst (lowest rung) or best (highest rung) life at the moment (Cantril,1966). These measures are considered cognitive in that they involve conscious judgment and self-evaluation rather than emotions or intuition. Given the relatively cold, cognitive nature of the measure, it is possible to believe that you’re objectively living your best life without feeling very joyful or content moment to moment (especially during busy or transitional times, e.g., when raising young children or starting a business). #### Happiness[​](#happiness "Direct link to Happiness") The emotional component of well-being involves feeling positive emotions such as joy and love. These can range from low-intensity contentment and a general positive mood to more intense delight, passionate love, or amusement. Research on happiness emphasizes the importance of not only alleviating distress but also increasing positive emotions such as joy in order to facilitate personal growth, mental health, and well-being (Frederickson, 2004). Indeed, interventions that focus on increasing positive emotions have downstream benefits for both alleviating distress and anxiety as well as improving satisfaction with life and happiness (Chakhssi et al., 2018). #### Meaning[​](#meaning "Direct link to Meaning") The meaning component of life satisfaction is often referred to as eudaimonia or eudaimonic well-being, using the Greek word for a holistic form of happiness encompassing both intellectual and emotional flourishing (i.e., living a good life in accordance with reason). In psychology, people who feel that they have a high degree of eudaimonia in their day-to-day lives feel that they are not only successful or happy but that their lives have meaning–which, for many, means flourishing through finding purpose in life and living up to their full potential through work, community engagement, and close relationships. The eudaimonic component of well-being is related to the highest-order transpersonal experiences in Maslow’s hierarchy needs, in which people transcend concerns related to the self and other people to focus on a higher meaning and purpose (Compton, 2018). ### Language Cues[​](#language-cues "Direct link to Language Cues") The linguistic measures of well-being are based on correlates of self-reports for overall well-being as well as its three facets individually (satisfaction with life, happiness, and meaning), including both the positive (e.g., positive emotion, flourishing) and negative (e.g., distress, poor mental health) ends of each spectrum. There are also both trait and state components in linguistic models of well-being. As with any predictors of well-being, it is critical to note that context matters. For example, being a forgiving person does not tend to promote happiness for people in relationships with friends and partners who fail to make amends or otherwise earn forgiveness after wrongdoing (McNulty & Fincham, 2012). Likewise, some of the linguistic predictors discussed below may perform best in neutral or healthy environments, as opposed to toxic or dysfunctional social contexts (e.g., workplaces, schools, or cultures). #### Cognition[​](#cognition "Direct link to Cognition") Though cognitive language can be beneficial in some contexts, such as diary entries or private writing where the explicit goal is to process complex thoughts and emotions, it tends to reflect cognitive load in everyday life. Cognitive load refers to the current demands on your working memory (active thought processes that require conscious effort). Multi-tasking, distractions, and rumination all add to such cognitive processing. Chronically having too many tasks occupying executive functioning can be stressful and lead to inefficient mental processes, which people may experience as mental torpor or “brain fog.” Thus, cognitive processing language in everyday work tasks or communications can reflect tentative, confused, or ruminative thinking styles that undermine well-being. Another cognitive tendency that erodes mental health and well-being is absolutist thinking, also known as black-and-white thinking (Al-Mosaiwi et al., 2018​​). All-or-nothing thinking is a common denominator of various maladaptive thought processes, including catastrophizing (viewing small stressors or setbacks as catastrophes) and fixed mindset (believing that people and relationships cannot change with effort; Dweck & Yeager, 2019). People who are vulnerable to depression or are in a depressive episode are more likely to engage in absolutist thinking, and this kind of thinking can be a barrier to recovery that therapists try to help clients learn to identify and avoid (Teasdale et al., 2001). #### Emotion[​](#emotion "Direct link to Emotion") Positivity isn’t identically correlated with all aspects of well-being. Specifically, language expressing positive emotion is more strongly related to happiness than the other facets of well-being; on the other hand, negative emotion is a stronger (negative) predictor of life satisfaction–the cognitive component of well-being–than positive affect (Jaidka et al., 2020). Within negative emotions, the more common emotions such as fear and anger tend to offer better signals for well-being prediction than lower-frequency emotions such as disgust or embarrassment, though negativity overall is a good indicator of distress or dissatisfaction with life. Under the positive emotion umbrella, gratitude and other kinds of positive social engagement such as love and affection (or the absence of loneliness) are particularly important to overall well-being (Wood et al., 2014). It is important to be aware, however, that display rules governing which emotions are appropriate to express differ across contexts. Always check your sample’s usage of a language measure before using it to predict an outcome. If an emotion category only appears in a small proportion of your total sample, that may mean that it’s considered inappropriate in that setting–meaning that the people who are using it All of the above emotion measures are available in SALLEE. #### Social Behavior[​](#social-behavior "Direct link to Social Behavior") Humans are social animals who, with rare exceptions, require at least one or a few deep human connections (close friends, family, or romantic partners) as well as a broader, shallower social support to thrive. It makes sense then that affiliation words relating to relationships, family, and friends tends to be associated with greater well-being in social communication, such as posts on social media (e.g., Pang et al., 2020). In professional contexts where references to friends, private lives, or leisure activities are taboo or uncommon, it’s still possible to use inward vs. outward focus as a marker of self-focus and attention to others. People who use more “we” and “you” are more likely to be happier and satisfied with life overall (Jaidka et al., 2020; Pang et al., 2020). However, keep in mind that those markers are confounded with clout, as higher status people tend to be happier and use more other-focused pronouns like “we” and “you” (Kacewicz et al., 2014). #### Traits[​](#traits "Direct link to Traits") Vulnerability to stress and anxiety (stress prone and anxiety prone) are facets of trait negative affectivity (also known as neuroticism), a personality trait that represents increased risk for mental and physical illness (Lahey, 2009). Each facet indicates that a person tends to be more distressed by both everyday turbulence and major life stressors than the average neurotypical person. That is, someone vulnerable to stress will experience setbacks (rejection or tight deadlines) as more distressing and less manageable than a more emotionally stable person, who may barely register the same setbacks or view them as opportunities for growth. Note that the stress and anxiety-prone measures do not necessarily indicate that a person is currently, at the moment of speaking or writing, experiencing distress. Rather, these measures reflect greater risk of experiencing distress and mental health setbacks when confronted with life stressors on average over time. It is also important to remember that traits are stable but not entirely immutable. Behavioral interventions through therapy involving cognitive-behavioral techniques (such as refocusing on others when starting to lapse into a ruminative, self-focused cycle) can shift personality traits (Roberts et al., 2017). Neuroticism also decreases developmentally over the lifespan as a function of experience and physical changes (especially in the brain) over time (Chopik & Kitayama, 2018). ### Well-Being Measures Collection[​](#well-being-measures-collection "Direct link to Well-Being Measures Collection") | Framework | Measure | | ------------------------- | --------------------------------------------------------------------------------------------------------------------- | | Cognition | [Cognitive Processes](https://docs.receptiviti.com/frameworks/normed-frameworks/cognition.md#measures) | | LIWC Extension | [Absolutist](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md#measures) | | SALLEE | [Goodfeel](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | SALLEE | [Badfeel](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | SALLEE | [Fear](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | SALLEE | [Anger](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | SALLEE | [Gratitude](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | SALLEE | [Love](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | SALLEE | [Affection](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | | Social Dynamics or Drives | [Affiliation](https://docs.receptiviti.com/frameworks/normed-frameworks/drives.md#measures) | | LIWC | [We](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md#measures) | | LIWC | [You](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md#measures) | | Big 5 | [Stress Prone](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#categories-and-facets) | | Big 5 | [Anxiety Prone](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#categories-and-facets) | References * Al-Mosaiwi, M., & Johnstone, T. (2018). In an absolute state: Elevated use of absolutist words is a marker specific to anxiety, depression, and suicidal ideation. Clinical Psychological Science, 6(4), 529-542. * Cantril, H. (1966). The pattern of human concerns. New Brunswick: Rutgers University Press. * Chakhssi, F., Kraiss, J. T., Sommers-Spijkerman, M., & Bohlmeijer, E. T. (2018). The effect of positive psychology interventions on well-being and distress in clinical samples with psychiatric or somatic disorders: A systematic review and meta-analysis. BMC psychiatry, 18, 1-17. * Chopik, W. J., & Kitayama, S. (2018). Personality change across the life span: Insights from a cross-cultural, longitudinal study. Journal of personality, 86(3), 508–521. * Compton, W. C. (2018). Self-actualization myths: What did Maslow really say? Journal of Humanistic Psychology. * Dweck, C. S., & Yeager, D. S. (2019). Mindsets: A view from two eras. Perspectives on Psychological Science, 14, 481-496. * Fredrickson, B. L. (2004). The broaden–and–build theory of positive emotions. Philosophical transactions of the royal society of London. Series B: Biological Sciences, 359(1449), 1367-1377. * Jaidka, K., Giorgi, S., Schwartz, H. A., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2020). Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods. Proceedings of the National Academy of Sciences, 117, 10165-10171. * Jovanović, V., & Lazić, M. (2020). Is longer always better? A comparison of the validity of single-item versus multiple-item measures of life satisfaction. Applied Research in Quality of Life, 15, 675-692. * Kacewicz, E., Pennebaker, J. W., Davis, M., Jeon, M., & Graesser, A. C. (2014). Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology, 33(2), 125-143. * Lahey, B. B. (2009). Public health significance of neuroticism. American Psychologist, 64(4), 241. * McNulty, J. K., & Fincham, F. D. (2012). Beyond positive psychology? Toward a contextual view of psychological processes and well-being. American Psychologist, 67(2), 101. * Moran, C. M., Diefendorff, J. M., & Greguras, G. J. (2013). Understanding emotional display rules at work and outside of work: The effects of country and gender. Motivation and Emotion, 37, 323-334. * Nielsen, K. S., Gwozdz, W., & De Ridder, D. (2019). Unraveling the relationship between trait self-control and subjective well-being: The mediating role of four self-control strategies. Frontiers in Psychology, 10, 432571. * OECD. (2013). OECD guidelines on measuring subjective well-being. OECD Publishing. * Pang, D., Eichstaedt, J. C., Buffone, A., Slaff, B., Ruch, W., & Ungar, L. H. (2020). The language of character strengths: Predicting morally valued traits on social media. Journal of Personality, 88(2), 287-306. doi: 10.1111/jopy.12491 * Roberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., & Hill, P. L. (2017). A systematic review of personality trait change through intervention. Psychological bulletin, 143(2), 117. doi: 10.1037/bul0000088. * Seligman, M. (2018). PERMA and the building blocks of well-being. The journal of positive psychology, 13(4), 333-335. * Teasdale, J. D., Scott, J., Moore, R. G., Hayhurst, H., Pope, M., & Paykel, E. S. (2001). How does cognitive therapy prevent relapse in residual depression? Evidence from a controlled trial. Journal of Consulting and Clinical Psychology, 69(3), 347. * Wood, A. M., Froh, J. J., & Geraghty, A. W. (2010). Gratitude and well-being: A review and theoretical integration. Clinical Psychology Review, 30(7), 890-905. doi: * Yiğit, B., & Çakmak, B. Y. (2024). Discovering Psychological Well-Being: A Bibliometric Review. Journal of Happiness Studies, 25, 1-24. ## Self vs. Other Focus[​](#self-vs-other-focus "Direct link to Self vs. Other Focus") ### Highlights[​](#highlights-1 "Direct link to Highlights") * Pronouns are face-valid markers of social attention that reflect mental health, social status, and personality. * Because personal pronouns are so frequently used, context is more critical than it is for other measures when interpreting these measures. * Self-focus is measured by first-person singular pronouns “I,” “me,” “my,” and contractions involving those words. * Other-focus is measured by second-person and first-person plural pronouns “you,” “your,” “we,” “our,” and contractions of those words. ### Background Research[​](#background-research-1 "Direct link to Background Research") Focusing on the self versus others is a simple attentional measure that is foundational to how people see their worlds and a lynchpin of mental health. The psychological meaning of self-focus is contextual. Though focusing on the self to a high degree (or to the exclusion of others) is riskier overall than the opposite pattern, there are times and places where each point of view is beneficial. The psychological meanings of self vs. other-directed pronouns are highly contextual (Mehl et al., 2012). Thinking about the self can be helpful in the context of self-improvement (e.g., through therapy, journaling, or affirmations), but self-focus in social situations often indicates anxious self-consciousness or lack of interest in other people, both of which are barriers to connecting with other people in positive, productive ways (e.g., making friends or learning new information). Self-focus can also be harmful when discussing negative events. Though shirking responsibility for mistakes (“mistakes were made” vs. “I messed up”) can come across as dishonest, distancing oneself from distress is natural for neurotypical people—especially those who score high on the personality trait of emotional stability or are otherwise not prone to depression or other emotional disorders (Kross & Ayduk, 2017). Thinking of distress from a more distant, less personal perspective is often healthier than internalizing distress and ruminating about the events that triggered it. Being focused on social aspects of a situation, on the other hand, makes people easier to collaborate and interact with for several reasons (see Abele & Wojciszke, 2014). Socially oriented people are more socially sensitive (aware and considerate of others’ points of view), which translates to kinder, friendlier behavior as well as better ability to learn from others (see the Coachability section). Some downsides of being focused on others can, at the extreme, include lack of self-awareness and at times (such as when other focus is combined with ambition and aggression) a ruthless approach to earning others’ approval. Whether self and other-focus are harmful ultimately depends on what a person is attending to in the internal and external social environments. If self-focus is negative—for example, thinking about personal mistakes, embarrassments, or flaws—then that is a harmful point of view that people should attempt to correct through thinking more about the world around them. Self-focus that has to do with affirmations, gratitude, or necessary self-change can be quite positive (though even in those cases, self-focus should not be unmitigated; Seih & Pennebaker, 2014). Along the same lines, if other-focus has a Machiavellian or callous bent (for example, thinking of others only to determine how to manipulate them or get ahead) then it may be better to step back from society and try to gain better self-awareness. On the other hand, other-focus that serves to amplify empathy and capitalize on collaboration opportunities leads to better relationships, creativity, and productivity. ### Applications[​](#applications-1 "Direct link to Applications") Assessing self vs. other focus is especially relevant for determining the degree to which a job candidate fits with the workplace culture or its core values. It can also be useful in personnel selection and promotion decisions such as when deciding whether someone who is self-focused, focused on the group, or balanced between the two approaches would be the best fit for the group and the immediate challenges. For example, if an organization is dealing with internal crises, it may be especially critical to have a leader who uses more other-focused pronouns and has little self-consciousness or concern for the self in isolation from the group. ### Language Measures[​](#language-measures "Direct link to Language Measures") Self versus other focus, as indicated by personal pronouns, is basic and universal enough to be useful across many diverse contexts—though, as always, the linguistic context, and especially its affordances and social norms, can affect how the relevant language cues should be interpreted. For example, is the person alone or with people? In a situation where self-disclosure is expected or unusual? Note that the fundamental meaning of various pronouns are essentially universal (e.g., “I” always refers to the self and involves some degree of self-focus; it is the psychological impact of attending to the self or other people that changes depending on the affordances of a situation.) #### Focusing on the Self[​](#focusing-on-the-self "Direct link to Focusing on the Self") Especially in social interactions or when writing or talking about distressing events, self-focus (measured using “I”-words) is less beneficial than focusing on the outside social world (using “we,” “s/he,” or “you” pronouns). In interactions, self-focus indicates self-consciousness or inattention to others, which can be perceived as lack of interest or social skills. People who are socially anxious also experience more self-focused attention (Vriends et al., 2017). In writing about distressing topics, such as past trauma or upsetting events, focusing on the self tends to be more distressing than self-distancing (Kross & Ayduk, 2017). The association between self-focus and psychological vulnerabilities like rumination, self-consciousness, rumination, and distress partly accounts for self-focus being viewed as a sign of general vulnerability to distress or negative affectivity (Tackman et al., 2019). Keep in mind, however, that a relatively high rate of “I”-words are appropriate and healthy in some contexts, such as writing a diary entry about everyday life events or taking credit for your own successes in presentations (e.g., “my project” or “I designed”; see Mehl et al., 2012). #### Focusing on Others[​](#focusing-on-others "Direct link to Focusing on Others") As noted earlier, self vs. other focus is intertwined with other psychological variables, including social status or clout. People who have more power over others and their own lives tend to use “I”-words less and “we” and “you”-words more. Part of this shift is practical, with leaders focusing on the people they are responsible for; subordinates, on the other hand, have no one working under them. People also use “we” more when they perceive a high degree of self-other overlap, especially with close friends or team members they see as an extension or part of themselves either in general or in a given situation (e.g., work or at home; Aron et al., 2004). When interpreting measures of self-versus other focus, always pay attention to the pragmatics of the situation: Based on each person’s role in the group or team, where does their attention need to be—on their own work, on others’, or an even mixture of both? Is the speaker responsible solely for themselves or their group? Pronoun use provides a sense of who and what people are attending to, which sometimes conflicts with their role in a group or can be a hindrance to job performance (e.g., in the case of a self-conscious leader). ### Self vs. Other Focus Measures Collection[​](#self-vs-other-focus-measures-collection "Direct link to Self vs. Other Focus Measures Collection") | Framework | Measure | | ------------------------ | ---------------------------------------------------------------------------------------------------------------------- | | LIWC | [I](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md#measures) | | LIWC | [We](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md#measures) | | LIWC | [You](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md#measures) | | Social Dynamics | [Inward Focus](https://docs.receptiviti.com/frameworks/normed-frameworks/social-dynamics.md#measures) | | Social Dynamics | [Outward Focus](https://docs.receptiviti.com/frameworks/normed-frameworks/social-dynamics.md#measures) | | Temporal and Orientation | [Self Focus](https://docs.receptiviti.com/frameworks/proportional-frameworks/temporal-and-orientation.md#measures) | | Temporal and Orientation | [External Focus](https://docs.receptiviti.com/frameworks/proportional-frameworks/temporal-and-orientation.md#measures) | References * Abele, A. E., & Wojciszke, B. (2014). Communal and agentic content in social cognition: A dual perspective model. In Advances in experimental social psychology (Vol. 50, pp. 195-255). Academic Press. * Aron, A., McLaughlin-Volpe, T., Mashek, D., Lewandowski, G., Wright, S. C., & Aron, E. N. (2004). Including others in the self. European Review of Social Psychology, 15, 101-132. * Kross, E., & Ayduk, O. (2017). Self-distancing: Theory, research, and current directions. In Advances in experimental social psychology (Vol. 55, pp. 81-136). Academic Press. * Mehl, M. R., Robbins, M. L., & Holleran, S. E. (2012). How taking a word for a word can be problematic: Context-dependent linguistic markers of extraversion and neuroticism. Journal of Methods and Measurement in the Social Sciences, 3(2), 30-50. doi: * Seih, Y. T., Chung, C. K., & Pennebaker, J. W. (2011). Experimental manipulations of perspective taking and perspective switching in expressive writing. Cognition & Emotion, 25(5), 926-938. * Tackman, A. M., Sbarra, D. A., Carey, A. L., Donnellan, M. B., Horn, A. B., Holtzman, N. S., Edwards, T. M. S., Pennebaker, J. W., & Mehl, M. R. (2019). Depression, negative emotionality, and self-referential language: A multi-lab, multi-measure, and multi-language-task research synthesis. Journal of Personality and Social Psychology, 116, 817. doi: 10.1037/pspp0000187 * Vriends, N., Meral, Y., Bargas-Avila, J. A., Stadler, C., & Bögels, S. M. (2017). How do I look? Self-focused attention during a video chat of women with social anxiety (disorder). Behaviour Research and Therapy, 92, 77-86. doi: 10.1016/j.brat.2017.02.008 ## Adaptability[​](#adaptability "Direct link to Adaptability") ### Highlights[​](#highlights-2 "Direct link to Highlights") * Adaptable people grow and improve in response to changes and challenges, both external (e.g., economic changes, rejection) and internal (e.g., illness, disability). * Measuring adaptability is especially relevant for predicting how people (customers, leaders, employees, and others) will respond to changes and novelty. * Positive correlates of adaptability include trait measures of openness and state measures of curiosity; negative predictors of adaptability include cautious and avoidant language. ### Background Research[​](#background-research-2 "Direct link to Background Research") Adaptability is the ability to change in response to situational constraints, especially challenges. These pressures can be momentary, but the more critical test of adaptability occurs when people are forced to either change or stagnate in response to fundamental tectonic shifts in their personal lives or the broader culture (e.g., technology, the economy, politics). Adaptability is correlated with flexibility, resilience, perseverance, and grit; though adaptability is not identical to those terms, it can be thought of as a necessary but not sufficient condition for them (Kashdan & Rottenberg, 2010). For a person to cope with the unexpected, ranging from trauma and adversity to new tools or information, they must be able to adapt to uncertain circumstances. Yet someone who is able to thrive under novel circumstances may not be especially resilient–that is, they may be masters of improvisation in a given moment while also having relatively low stamina for adapting to a long run of changes (Martin et al., 2012). In psychology, adaptability is often studied with respect to coping with distressing and unexpected circumstances, such as illness, unemployment, or disasters (e.g., Zhang et al., 2021). On the lighter side, everyday adaptability is less focused on coping and has more to do with being open to change, flexible, and willing to learn new things–characteristics that are strong predictors of success and fulfillment in one’s career (Zacher, 2014). The Receptiviti adaptability measure bundle includes variables that are relevant to both types of adaptability, though we focus primarily on behavioral manifestations of adaptability that you might find in everyday life in workplace communication or social interactions. ### Applications[​](#applications-2 "Direct link to Applications") Measures of adaptability are especially relevant in hiring or personnel selection and customer segmentation. When assessing the audience for any new product, it is important to be able to identify early adopters and assess the adaptability of target markets. In hiring, adaptability may be most relevant to leaders of large, dynamic organizations. In such roles, adaptability–in response to changing economic forces, trends, and cultural shifts–may be even more important for success than specific skills or competencies. ### Language Measures[​](#language-measures-1 "Direct link to Language Measures") #### Risk Assessment[​](#risk-assessment "Direct link to Risk Assessment") People who are adaptable are not entirely incautious or risk-prone, but they tend to be less cautious and avoidant when faced with potential risks than others (Johnsen et al., 1998). That is, rather than freezing and retreating in the face of stress or adversity, adaptable people approach and cope with stressors in flexible ways, often involving positive reframing (e.g., viewing a layoff as a chance to take a vacation, learn new skills, and change one’s career path; Munroe et al., 2022). Thus, lower rates of risky language and avoidant words are both indicators of adaptability. #### Exploring the Environment[​](#exploring-the-environment "Direct link to Exploring the Environment") Independent of how people deal with risks, adaptability generally means adjusting and thriving in response to new information, social norms, and opportunities. For many people, adopting and adapting to new technology is a common challenge in everyday life and in the workplace. This mindset is reflected in language related to openness (especially to novelty and change) and curiosity. People differ in both openness to new experiences and the degree to which they are curious about the world. Both characteristics are also related to happiness. When people are content or happy–or experiencing more positive than negative emotions on average–they are more likely to explore new ideas or experiences (Frederickson, 2013). The association between positivity and a “broaden and build” mindset is one reason that falling in love often leads to self-expansion–discovering new parts of the self, learning new skills or knowledge, and exploring the opportunities available (Sheets, 2014). Adaptability is a versatile and psychologically fundamental measure with relevance to a wide range of outcomes related to well-being and job performance. ### Adaptability Measure Collection[​](#adaptability-measure-collection "Direct link to Adaptability Measure Collection") | Framework | Measure | | ---------------- | ---------------------------------------------------------------------------------------------------------------- | | Big 5 | [Cautious](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#categories-and-facets) | | LIWC Extension | [Avoidance](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md#measures) | | Needs and Values | [Openness to Change](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md#measures) | | Needs and Values | [Curiosity](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md#measures) | References * Fredrickson, B. L. (2013). Positive emotions broaden and build. In Advances in experimental social psychology (Vol. 47, pp. 1-53). Academic Press. * Johnsen, B. H., Laberg, J. C., Eid J. (1998). Coping strategies and mental health problems in a military unit. Military Medicine, 163, 599–602. * Kashdan, T. B., & Rottenberg, J. (2010). Psychological flexibility as a fundamental aspect of health. Clinical psychology review, 30(7), 865-878. * Martin, A. J., Nejad, H., Colmar, S., & Liem, G. A. D. (2012). Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. Journal of Psychologists and Counsellors in Schools, 22(1), 58-81. DOI: 10.1017/jgc.2012.8 * Munroe, M., Al-Refae, M., Chan, H. W., & Ferrari, M. (2022). Using self-compassion to grow in the face of trauma: The role of positive reframing and problem-focused coping strategies. Psychological Trauma: Theory, Research, Practice, and Policy, 14(S1), S157. * Sheets, V. L. (2014). Passion for life: Self-expansion and passionate love across the life span. Journal of Social and Personal Relationships, 31, 958-974. * Zacher, H. (2014). Career adaptability predicts subjective career success above and beyond personality traits and core self-evaluations. Journal of vocational behavior, 84(1), 21-30. * Zhang, K., Wu, S., Xu, Y., Cao, W., Goetz, T., & Parks-Stamm, E. J. (2021). Adaptability promotes student engagement under COVID-19: the multiple mediating effects of academic emotion. Frontiers in psychology, 11, 633265. ## Coachability[​](#coachability "Direct link to Coachability") ### Highlights[​](#highlights-3 "Direct link to Highlights") * People who are highly coachable excel at paying attention to and learning from a mentor’s or coach’s advice, including criticism and praise. * Coachable people aren’t necessarily compliant or even subordinate, but they should be humble enough to admit they have room for improvement and be grateful for guidance. * They should also be ambitious, committed to working towards achievements, and confident that growth through coaching is possible. * Positive correlates of coachability include words related to gratitude, humility, and achievement. ### Background Research[​](#background-research-3 "Direct link to Background Research") Coachability refers broadly to the degree that someone can respond proactively and productively to others’ constructive feedback, including both praise and criticism. Being coachable entails internal and external aspects, including self-motivation (also referred to as intrinsic motivation; Fishbach & Woolley, 2022), a growth mindset (rather than believe that skills are fixed and immovable; Dweck & Yeager, 2019), commitment to their job or the position for which they are receiving coaching (Bozer et al., 2013), the emotion regulation skills to respond positively to setbacks or criticism (see also Adaptability), and the humility required to follow another person’s lead (Porter & Schumann, 2018). Coachability involves not merely being driven to succeed but specifically being able to work with others—people who not only encourage but also give critical feedback at times—to continually improve over time (Weiss & Merrigan, 2021). Though coachability is correlated with self-improvement and greatly helped by having a growth mindset, it should be noted that people are also able to improve over time (personally or professionally) even if they cope poorly with criticism. For example, people who score high in grandiose narcissism (i.e., arrogance, superiority, and entitlement) are highly sensitive to even constructive criticism regarding their competence, yet they can nevertheless go on to great accomplishments and success through more independent avenues of self-improvement (Miller et al., 2021). Thus, assessing coachability provides guidance on how rather than whether to foster a person’s growth. People are more coachable to the degree that they are able to control their behavior and emotions. A person cannot respond productively to setbacks if they aren’t able to dynamically regulate their thoughts and feelings in response to environmental opportunities and pressures (Duckworth & Steinberg, 2015). Self-control is a foundational building block of many aspects of well-being and mental health. In the context of coachability, self-control is especially useful for being able to follow behavioral advice that is difficult or doesn’t come naturally and down-regulating negative emotions about criticism. ### Applications[​](#applications-3 "Direct link to Applications") The most obvious use for a coachability measure is in sports management and athletic coaching, including helping with resource allocation and recruitment decisions. Outside of athletics, assessing a person’s coachability is also useful in determining whether and when to invest in on-the-job-training and coaching for existing employees. Coachability is also relevant when deciding whether to promote a junior employee to a role that involves expanding or changing their skillset. Being low in coachability does not mean that a person will not improve over time, but it does mean that investing heavily in coaching and mentorship for that person should not be as much of a priority as it would be for someone who is likely to be more receptive to such training. If someone is low on coachability, they may excel best in linear promotions rather than shifting laterally (e.g., from an individual contributor to a manager position) or taking on any new role that requires mentorship. ### Language Measures[​](#language-measures-2 "Direct link to Language Measures") **Self-control**, reflected in the disciplined measure, is foundational to every aspect of coachability. A person must be able to control their thoughts and regulate their emotions to maintain positive momentum and motivation, especially in the absence of obvious external rewards or in the face of failure. Self-discipline, for these reasons, is also associated with better well-being and relationship quality (Nielsen et al., 2019). **Intrinsic motivation** involves working or striving towards some goal because it feels good (i.e., is joyful or meaningful) personally rather than because of some expected external reward (e.g., money, rewards). People who are intrinsically motivated or enjoy doing something for its own sake tend to persist longer and attain greater expertise than people who are motivated primarily by extrinsic rewards, such as recognition, awards, or payment. That is, because the act of doing something is rewarding in and of itself for intrinsically motivated people, they are ironically more likely to achieve tangible mastery milestones and recognition relative to people who are extrinsically motivated (i.e., the means-ends fusion model; Woolley & Fishbach, 2012). **Responding to criticism** with growth rather than anger or giving up is made easier by a few characteristics. Being open to change and humble (both measures available in the Big Five framework) as a personality trait or habitual practice can make it easier to see setbacks and criticism as opportunities for growth. Indeed, trait humility is related to both openness to other viewpoints and growth mindset (i.e., the belief that people can change and improve with work; Porter & Schumann, 2018). **Gratitude** as a momentary emotion or regular practice also sets people up for adaptive responses to setbacks. Being grateful for opportunities and for the time other people have taken to provide feedback can help to positively reframe criticisms and failure as catalysts for self-improvement and growth (Armenta et al., 2017). Gratitude, as with many of the other measures listed in this document, is an important building block of well-being and mental health (Wood & Geraghty, 2010). ### Coachability Measures Collection[​](#coachability-measures-collection "Direct link to Coachability Measures Collection") | Framework | Measure | | ---------------- | ------------------------------------------------------------------------------------------------------------------- | | Big 5 | [Disciplined](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#categories-and-facets) | | Drives | [Achievement](https://docs.receptiviti.com/frameworks/normed-frameworks/drives.md#measures) | | Needs and Values | [Open to Change](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md#measures) | | Big 5 | [Humble](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#categories-and-facets) | | SALLEE | [Gratitude](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#measures) | References * Armenta, C. N., Fritz, M. M., & Lyubomirsky, S. (2017). Functions of positive emotions: Gratitude as a motivator of self-improvement and positive change. Emotion Review, 9(3), 183-190. * Bozer G., Sarros, J. C., Santora, J. C. (2013). The role of coachee characteristics in executive coaching for effective sustainability. Journal of Management Development, 32, No.3, pp.277-294. * Duckworth, A. L., & Steinberg, L. (2015). Unpacking self‐control. Child development perspectives, 9, 32-37. * Dweck, C. S., & Yeager, D. S. (2019). Mindsets: A view from two eras. Perspectives on Psychological Science, 14, 481-496. * Fishbach, A., & Woolley, K. (2022). The structure of intrinsic motivation. Annual Review of Organizational Psychology and Organizational Behavior, 9, 339-363. * Miller, J. D., Back, M. D., Lynam, D. R., & Wright, A. G. (2021). Narcissism today: What we know and what we need to learn. Current Directions in Psychological Science, 30, 519-525. doi: 10.1177/09637214211044109 * Nielsen, K. S., Gwozdz, W., & De Ridder, D. (2019). Unraveling the relationship between trait self-control and subjective well-being: The mediating role of four self-control strategies. Frontiers in Psychology, 10, 432571. * Porter, T., & Schumann, K. (2018). Intellectual humility and openness to the opposing view. Self and Identity, 17(2), 139-162. * Weiss, J. A., & Merrigan, M. (2021). Employee Coachability: New Insights to Increase Employee Adaptability, Performance, and Promotability in Organizations. International Journal of Evidence Based Coaching & Mentoring, 19, 121-136. * Wood, A. M., Froh, J. J., & Geraghty, A. W. (2010). Gratitude and well-being: A review and theoretical integration. Clinical Psychology Review, 30(7), 890-905. doi: * Woolley, K., & Fishbach, A. (2023). The means-ends fusion model of intrinsic motivation (p. 55). New York, NY: Oxford University Press. ## Socially Oriented[​](#socially-oriented "Direct link to Socially Oriented") ### Highlights[​](#highlights-4 "Direct link to Highlights") * Social orientation is an umbrella term that includes attention to other people, interest in others’ feelings and thoughts, and behavior that aims to preserve, promote, and protect social ties. * Though not sufficient on its own in most cases, some degree of social interest is often viewed as a necessary component of a functional business, team, or individual. * Words reflecting affiliation, extraversion, social behavior or categories, and DISC people focus all positively correlate with social orientation. ### Background Research[​](#background-research-4 "Direct link to Background Research") Social orientation refers to the degree to which people think about other people and prioritize relationships. Individual differences in the degree to which people are oriented toward people and social goals have been most famously studied in the context of the Big Two personality dimensions and the systemizing-empathizing theory of autism and gender. Both of these models frame social orientation as an enduring trait that is partly hardwired in the brain and partly related to upbringing, especially social norms related to gender. In research from both frameworks, a balanced point of view—that is, focusing to similar degrees on social and nonsocial aspects of the environment—seems to be best for getting along with people and living a good life. The same language variables that reflect social orientation as a trait can also be used as indicators of social orientation as a momentary mental state—an approach that may make more sense than the trait perspective in cases where users have a thin slice of behavior from each writer or speaker and cannot track people across multiple contexts over time. ### Applications[​](#applications-4 "Direct link to Applications") Social orientation measures can be used to assess people, groups, or cultures. Some applications of the social orientation measure include characterizing how a corporate culture is viewed from the perspective of potential customers or the general public. There are cases where an organization may wish to come across as people-centric and other situations where a more objective, task-focused voice may be more appropriate (given aspects of the economy, online buzz about a company, or current events). Likewise, leaders are often defined by their (dis)interest in people and personal relationships. Whether an organization is seeking a social or impersonal leader, it will be useful to be able to quickly and unobtrusively assess that trait. ### Language Measures[​](#language-measures-3 "Direct link to Language Measures") Some of the relevant psychological constructs indexed by the measures in this bundle are need for affiliation, empathy, communion (or communal orientation), and interest in other people’s mental states. As noted, each of these measures can be used to capture either mental states or traits, though all are more commonly researched in the context of traits or individual differences. For example, need for affiliation is typically construed as a chronic, stable motivation (Köllner & Schultheiss, 2014). They are collectively reflected in LIWC’s affiliation and social categories, including language referring to other people, close relationships, and affection, and the DISC people focus measure. Empathy with a given person’s feelings or thoughts varies from moment to moment (Depow et al., 2021), but the tendency to be interested in other people’s thoughts and feelings on average, across all situations, is also studied as a stable individual difference (contrasted with systemizing; Baron-Cohen, 2010; Greenberg et al., 2018) that may to some degree be hardwired in the structure of the brain (Banissy et al., 2012). Though it is most useful in contexts with affordances for empathy (e.g., responses to others’ bad news), the LIWC extension empathy measure can be used as an index of the degree to which a person is thinking in callous, socially distant (low empathy) or warm, sympathetic ways (high empathy). Caring about building and maintaining relationships—i.e., having a communal orientation—can be measured both as a momentary mental state tied to a specific context and a trait that is stable across contexts as well. Communal orientation, especially when combined with agency (e.g., being both friendly and outgoing), is associated with being more likable (Dufner & Krause, 2023). People also provide better social support to friends and partners when they care about and put resources into maintaining those relationships, and actively supporting people in close relationships is one of the routes through which communal and agentic behavior interact to facilitate more satisfying relationships and lives (Helgeson, 1994). Behavior that is both agentic and communal (e.g., outgoing, actively supportive) manifests as extraversion (available in Receptiviti’s Big Five framework) in social contexts. Note though that although communal behavior is associated with well-being, recent longitudinal findings suggest that communal connections may be better understood as consequences rather than causes of emotional wellness (Vella-Broderick et al., 2023). ### Socially Orientated Measures Collection[​](#socially-orientated-measures-collection "Direct link to Socially Orientated Measures Collection") | Framework | Measure | | ------------------------- | -------------------------------------------------------------------------------------------------------------------- | | Social Dynamics or Drives | [Affiliation](https://docs.receptiviti.com/frameworks/normed-frameworks/drives.md#measures) | | LIWC or Social Dynamics | [Social](https://docs.receptiviti.com/frameworks/normed-frameworks/social-dynamics.md#measures) | | DISC | [People Focus](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-disc.md#api-response-measures) | | LIWC Extension | [Empathy](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md#measures) | | Big Five | [Extraversion](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md#categories-and-facets) | References * Banissy, M. J., Kanai, R., Walsh, V., & Rees, G. (2012). Inter-individual differences in empathy are reflected in human brain structure. Neuroimage, 62(3), 2034-2039. * Baron-Cohen, S. (2010). Empathizing, systemizing, and the extreme male brain theory of autism. Progress in brain research, 186, 167-175. * Depow, G. J., Francis, Z., & Inzlicht, M. (2021). The experience of empathy in everyday life. Psychological Science, 32(8), 1198-1213. * Dufner, M., & Krause, S. (2023). On how to be liked in first encounters: The effects of agentic and communal behaviors on popularity and unique liking. Psychological Science, 34(4), 481-489. * Greenberg, D. M., Warrier, V., Allison, C., & Baron-Cohen, S. (2018). Testing the empathizing–systemizing theory of sex differences and the extreme male brain theory of autism in half a million people. Proceedings of the National Academy of Sciences, 115, 12152-12157. * Helgeson, V. S. (1994). Relation of agency and communion to well-being: Evidence and potential explanations. Psychological bulletin, 116(3), 412. * Köllner, M. G., & Schultheiss, O. C. (2014). Meta-analytic evidence of low convergence between implicit and explicit measures of the needs for achievement, affiliation, and power. Frontiers in psychology, 5. 98021. * Vella-Brodrick, D., Joshanloo, M., & Slemp, G. R. (2023). Longitudinal relationships between social connection, agency, and emotional well-being: a 13-year study. The Journal of Positive Psychology, 18(6), 883-893. --- # What's Your Receptiviti Use Case? Explore framework suggestions categorized by their strengths and intended use cases, making it easier to align your goals with the right technologies. *** Select Use Case: \-- Select --▼ --- # Getting Started The Receptiviti UI is a powerful tool that enables you to analyze language data and visuzalize it in a variety of ways. You can generate charts, graphs, and other visualizations to simplify interpretation, making it easy to tell stories from your data. You can also use the Receptiviti UI to easily score datasets by uploading them through the interface. Once the datasets have been processed, you can download the resulting scores in CSV format for further analysis or integration into other workflows. If you’d like to sign up for a UI account, contact us at . If you have a Receptiviti account with UI access, go to and sign in using your credentials. To get started with your UI project, head over to the next section, [Creating a Project](https://docs.receptiviti.com/visualization-ui/creating-a-project.md)! [Creating a Project](https://docs.receptiviti.com/visualization-ui/creating-a-project.md) [Visualizing with Charts and Graphs](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/.md) [Template Gallery](https://docs.receptiviti.com/visualization-ui/template-gallery.md) [Preparing Your Data](https://docs.receptiviti.com/data-preparation/.md)
Please be aware that the Receptiviti UI is in beta. While the UI is being continuously updated, some documentation pages or screenshots may reference older workflows. We greatly appreciate your patience while we work to get those replaced. --- # Creating a Project Once logged in, you can choose to either **score a language dataset** or start a **visualization project**. ![UI home](/assets/images/ui-homepage-4f6871301072ce0fbd0ee3492979da84.png) ## Scoring a Language Dataset[​](#scoring-a-language-dataset "Direct link to Scoring a Language Dataset") The Receptiviti UI allows you to not only upload datasets for visualization projects, but to score your data set via the Receptiviti API all within the user interface. From the [homepage](https://ui.receptiviti.com/), click either the **Score** tab in the top navigation bar or the **Score Language** button to access language scoring features. *** ### The Language Sources Page[​](#the-language-sources-page "Direct link to The Language Sources Page") When scoring a dataset through the Receptiviti UI, you can either add an existing Receptiviti data file (`.recd`) or score a new file. To create a new project from an existing scored language file, click the **Actions** button next to the file you'd like to use. To score a new file, click **Score New File**. ![Screenshot](/assets/images/language-sources-page-8a23720e2a399d8655bd92f45eb6e27c.png) important Receptiviti does not save any of your language source or visualization projects on its web servers. A copy of your scored language is saved to your web browser's local storage and is available only to you. It's good practice to regularly save your work manually to your hard drive as `.recd` files. These serve as backups of your scored language data and can be re-uploaded later if needed. *** ### The Select Data File Page[​](#the-select-data-file-page "Direct link to The Select Data File Page") Once you've clicked **Score New File**, this leads to the **Select Data File** page, where you can upload your file. The maximum allowable file size is **10 MB**. You must upload a **CSV** (comma-separated values) file with a **header row**. For transcripts, acceptable formats include: * VTT (from Zoom) * JSON (from Symbol.ai) * CSV with columns for timestamps, speaker name, and other metadata Click **Select File** to browse your system for your upload, and select the appropriate **Value delimiter** that matches your dataset: comma, semicolon, tab, or vertical bar. ![Screenshot](/assets/images/select-data-file-page-select-fd7419474c82fea3b000f2c02a85d362.png) Select the column you want to analyze. Only one column can be selected. If you wish to analyze multiple columns of text, use spreadsheet software of your choice to merge them into a single column and export a new CSV. tip For very large files, try a test run with a smaller subset of your data. Each column will be validated and classified as one of the following: * **Text** – input language eligible for analysis * **Categorical** – metadata for aggregation or filtering * **Numeric** – numerical metadata * **Date** – timestamps or date-based metadata If a column isn't classified correctly, it may be due to formatting issues in your file. Check the **Troubleshooting** section under **Learn More** for help. You'll also see: * The **word count** selected for analysis * The number of words available in your current billing cycle * The estimated **remaining words** after this analysis Click **Continue** to proceed to the **Confirm Submission** page. *** ### The Confirm Submission page[​](#the-confirm-submission-page "Direct link to The Confirm Submission page") Here, you can name your dataset, verify the selected input file and text column, and choose a **norming context**. ![confirm submission](/assets/images/confirm-submission-page-3f0e95a00f1a30ac312102105aeb821f.png) important If your language dataset contains written memos, emails, etc., choose the **written** context; for spoken transcripts, choose **spoken**. These contexts affect how normed scores like Personality, Social Dynamics, Drives, Needs, and Values are interpreted. See the [Norming and Base Rates](https://docs.receptiviti.com/norming-and-base-rates/.md) section for more in-depth information about our norming and custom norming options. You can also: * Toggle **Split by Sentence** (This option splits your text into individual sentences, allowing you to view insights for each sentence rather than treating the entire text as a single unit.) * Enable [**Sparse SALLEE**](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#calling-the-api-in-sparse-mode) * Choose to **auto-download a backup `.recd` file** When you're ready, click **Submit**. *** ### Scoring Complete[​](#scoring-complete "Direct link to Scoring Complete") ![scoring complete](/assets/images/scoring-complete-109bd6c786a40dc47e76e6fe285461a1.png) After submission, a banner will confirm that the analysis is complete. You can now: * View analysis results * Use the data in **Visualization Projects** * Access the analyzed files available in the **Language Sources** list There, you'll see: * Dataset name * Creation date * Input source Back in **Language Sources**, click **Actions** to: * View the full file (split by sentence if selected, with metadata and scores) * Toggle framework filters on and off * Start a new visualization project * **Export scores** to CSV * **Download the `.recd` file** * **Delete the dataset** ![analysis results](/assets/images/analysis-results-5386b907936d1214ea5f5070abf6e6cc.png) If you choose to export, the scored CSV will download to your hard drive. *** ## Visualizing a Language Dataset[​](#visualizing-a-language-dataset "Direct link to Visualizing a Language Dataset") Once your data source is analyzed and is complete, click the **Actions** button and select **Visualize → New Project** from the menu. ![action menu visualize](/assets/images/new-project-visualize-action-d2de2daeb86e64b13d64eab7d4fe744a.png) You will be taken to the **Visualize** tab, where various settings will appear based on the dataset you uploaded and scored. note Clicking the **Visualize** tab in the top navigation bar before any project has been saved opens the visualization menu, where you can begin creating a new project. If a project already exists, settings relevant to visualizing that dataset will be available. *** ### Template Selection[​](#template-selection "Direct link to Template Selection") At this stage, you have two options for setting up your project: * **Apply a template** to automatically pre-fill recommended settings based on common use cases. This is a good choice if you're working with standard data types or want to get started quickly. * **Continue without a template** to manually configure each setting. This gives you full control and is ideal for custom datasets or more advanced use cases. ![template selection](/assets/images/template-selection-18708a6f2d082ba94d17282dab3d8b05.png) Click **Continue Project Setup** to move on to pre-visualization dataset settings. *** ### Column and Data Type Editing[​](#column-and-data-type-editing "Direct link to Column and Data Type Editing") At this stage, you can edit the column types in your dataset.
For example, if **Text** was selected, it will be treated as a **Categorical** type (which is what was scored). ![column types](/assets/images/column-types-95c5410e1e05f1a7ce5a44f102e3c71a.png) You can also modify the type of any other columns as needed. The options available are: * **Text** – input language eligible for analysis * **Categorical** – metadata for aggregation or filtering * **Numeric** – numerical metadata * **Date** – timestamps or date-based metadata ![change column types](/assets/images/change-column-type-ecaf590fe7bce2c931710e73447e5071.png) *** ### Base Rate Selection[​](#base-rate-selection "Direct link to Base Rate Selection") Here, you can choose the **default base rate**. important **Base rates** represent average scores from a broader language dataset (including normed context sets) that help you interpret your own data. See the [Norming and Base Rates](https://docs.receptiviti.com/norming-and-base-rates/.md) section for more in-depth information about our base rates across frameworks. The selected base rate will be used as the default for all worksheets within this project. If available, a **Show Base Rates** toggle will appear in each worksheet when visualizing participating measures. ![choose base rate](/assets/images/default-base-rate-8b7a3bed001fb2f9ecd4e59bc06514f4.png) *** ### Norming Context Selection[​](#norming-context-selection "Direct link to Norming Context Selection") You can also select a **default norming context**, such as **Written**, **Spoken**, **Default**, or any custom norm to align interpretation with the type of language in your dataset. important If your language dataset contains written memos, emails, etc., choose the **written** context; for spoken transcripts, choose **spoken**. These contexts affect how normed scores like Personality, Social Dynamics, Drives, Needs, and Values are interpreted. See the [Norming and Base Rates](https://docs.receptiviti.com/norming-and-base-rates/.md) section for more in-depth information about our norming and custom norming options. ![choose norming context](/assets/images/choose-norm-context-a35df1f58a24f8f0036b5eef72851718.png) *** ### Continue to Worksheets or Dashboards[​](#continue-to-worksheets-or-dashboards "Direct link to Continue to Worksheets or Dashboards") Once settings are configured, you can proceed to explore your data using **Worksheets** or by creating **Dashboards**. See the [Analyzing Results](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/.md) section for more details about visualizations. *** ## File Types[​](#file-types "Direct link to File Types") The Receptiviti UI saves data and project files separately. Data is downloaded as a `.recd` file and a project is downloaded as a `.recp` file. Template files are saved as `.rect`. When you open a saved project and its associated data cannot be found in the browser cache, you will be prompted to upload the associated data file. In this case, you can upload the corresponding `.recd` file to restore the project. Using these files can help you avoid using further word count on your plan by reloading the data that the UI has already analyzed, such as if you were to move to a different project file and then come back to the previously loaded one. ## Opening an Existing Receptiviti UI Project[​](#opening-an-existing-receptiviti-ui-project "Direct link to Opening an Existing Receptiviti UI Project") Opening an existing UI project requires that you have a `.recp` file (UI project file) saved to your system. **To open an existing file:** 1. Click **Open Project** from the UI home page. 2. Select a `.recp` file from your system's hard drive to upload. 3. If your browser has not cached the corresponding `.recd` data file for your project, you will be prompted to retrieve it from your system. Once you have uploaded one or both of these files, you will be able to access the project and start building out visualizations. --- # Export to Highlights Emotions by Topic and Topics by Emotion worksheets allow you to generate worksheets based on the data in the original Emotions by Topic or Topics by Emotion worksheet you generated. This feature exports an overview, or *highlights*, of the language used to populate the Emotions by Topic or Topics by Emotion in a separate worksheet associated with the one it’s based on. The example below uses the **Return to Work** taxonomy to export its highlights by topic. To create an **Export to Highlights** worksheet: 1. Within a working project, click the **Worksheets** tab. 2. Create a new worksheet by clicking either of the **Create Worksheets** icons 3. Select either an **Emotions by Topic** or **Topics by Emotion** worksheet. ![select worksheets emotions](/assets/images/select-worksheets-emotions-7288b61375a7115033051bde9e6ce72a.png) 4. Choose a topic related to the selected taxonomy – in this case we’re using the **Management** topic found within the **Work-Related Topics** taxonomy in an **Emotions by Topic** worksheet. ![highlights in management](/assets/images/highlights-in-management1-6b58356abafa702ad68f9981ea540788.png) 5. Click the **Export to Highlights** button to the right of the graph, under **Save**. ![click export to highlights](/assets/images/click-export-to-highlights-31bd6cb42bd00088d1faa9815ec68d0d.png) 6. In the worksheet preview section at the left of the UI, you will see a new worksheet preview window populated under the existing Emotions by Topic or Topics by Emotion worksheet. ![highlights tile](/assets/images/highlights-tile-02d17148e15c992b5daae236257f71a4.png) 7. Click the new preview window to see the table containing the selected highlights of the topic – in this case, **Back to Normal**. The **Return to Work** taxonomy defaults to the SALLEE (Emotions) framework to filter on. You can see that SALLEE is selected in the **Sentence Framework** drop-down menu. ![see highlights](/assets/images/see-highlights-0d7b1baf531d1a4655ae6e051613a510.png) The table shows the specific Emotions that are highlighted, the number of sentences containing each emotion (**Sentence Count**), and examples of the highlights in text. note You can choose which emotions to display in the table by selecting them in the **Sentence Emotions** drop-down menu. note If you choose a subset of emotions or topics to display in an Emotions by Topic and Topics by Emotion worksheet and then export to highlights, the Highlights worksheet will also have those facets selected to display. ## Export to Highlights in a Dashboard[​](#export-to-highlights-in-a-dashboard "Direct link to Export to Highlights in a Dashboard") The same functionality to export an Emotions by Topic or Topics by Emotion worksheet to highlights is available in dashboards, as well. Follow the same steps as above for the worksheets, clicking the Export to Highlights button from the set of icons at the right of the worksheet frame in a dashboard. ![export to highlights dashboard](/assets/images/export-to-highlights-dashboard-be9026450f4a6f1ae0590374fc278baa.png) The exported worksheet will be created in the first available space after the original worksheet that is big enough to accommodate the default worksheet size. --- # Menu Click the menu icon at the top left of the UI to access the **Project** and **Template** menus, as well as the links to **Manage Storage**, **Documentation**, and **About** sections. ![menu icon](/assets/images/menu-icon-88fc6cb084209e59bd54c99f6724135c.png) ![menus](/assets/images/menus-e09049bab4643ce03155851fa8b8cd45.png) * Project Menu * Template Menu * Documentation * About | | | | --------- | ----------------------------------------------------------------------------------------------------- | | **New** | Navigates to the **Create Project** workflow. | | **Open** | Opens the file storage system on your computer, allowing you to select a previously-analyzed project. | | **Save** | Saves a copy of the `.recp` file in the state the project is in at the time of saving. | | **Close** | Closes the project but keeps the application open. Navigates back to the homepage. | | | | | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Save** | Saves the current project’s worksheets and dashboards to a `.rect` template file on your computer. If you choose to keep **Save a copy in browser** checked, it will also save the file to the UI. To retrieve the `.rect` template file from your computer to view with your dataset, you will have to create a new project. Note that you cannot apply a user template to a currently open project. | | **Manage Templates** | Opens the **User Created Templates** dialogue where you can upload, remove, rename, and search for user templates. Acts as a reference for existing user templates when viewing it while within an open project. | | | | | ----------------- | --------------------------------------------------------------------------------------------------------------------- | | **Documentation** | Links to the site for all of[Receptiviti’s API documentation.](https://docs.receptiviti.com/ "Visit Example") | | | | | --------- | ------------------------------------------- | | **About** | Displays version and copyright information. | ## Manage Storage[​](#manage-storage "Direct link to Manage Storage") The **Manage Storage** feature displays all datasets resident in the application, along with the parameters used during analysis. It allows you to upload datasets that may have been analyzed on a different computer or browser. **To access the Manage Storage feature:** 1. At the top left of the UI, click the menu icon. 2. Click **Manage Storage** from the list of options. 3. Locate the file(s) you want to download or delete using the buttons at the right side of a row from the list of your saved analyzed datasets. You cannot delete a project currently in use. ![manage stored data](/assets/images/manage-stored-data-750100b78ffee0cdc9900cc3545d9378.png) * The **Source File** column indicates the project. * The **Scored Field** column indicates which column of your dataset file was analyzed. * The **Split by Sentence** column indicated whether or not the data was split up by sentence. * The **SALLEE Mode** column indicated whether SALLEE scores are displayed using sparse or standard SALLEE. More information on the SALLEE modes can be found [here](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md#sallee-sparse-mode) * The **Word Count** column displays the number of words analyzed from the dataset. * The **Created On** and **Last Used** columns refer to the start and most recent use dates of the project. * The **Download analysis archive** button allows you to save the dataset to a file. This will recreate the `.recd` file that was created during the initial analysis. * The **Delete** button will clear the data from the browser but will not affect the downloaded file. The delete action has a confirmation step. * The **Upload analysis archive** button lets you add a previously-analyzed but not-currently-resident dataset to the application. This could be a dataset that you deleted and would like to use again, or possibly a dataset that was analyzed on a different browser or computer. Using this feature allows you to avoid re-analyzing a dataset and thus not accumulating word count on your account. This accepts a `recd` file, which is analyzed data, and not the original file (CSV, VTT, etc.) that you uploaded during project creation. note Opening a project that requires data not currently resident in the browser will prompt a dialog requesting that you locate file that contains the required data. --- # Saving Worksheets as Local Files You can download worksheets to your computer by clicking the **Save** icon present at the top right of each worksheet. Tables and highlights save as CSV files, while charts and graphs with visualization elements (all other worksheet types) save as PNG files. For charts and graphs that save to image files (PNG format), you will be prompted to choose an image size from the **Image Sizes** dialog box that appears. ![image sizes](/assets/images/image-sizes1-163cf20414d6cf2cfe48d1fbbc540e20.png) The same functionality is present when saving and downloading worksheets from dashboards by clicking the **Save** icon at the top right of a worksheet window. ![download dashboard](/assets/images/download-dashboard-383fb94ff050f2e262e5519d34a1293c.png) note Downloaded worksheets are read-only and can't be interacted with in the same way as in a project, or loaded into a project. --- # Template Gallery During project creation, the step that follows the selection of your language column to analyze is the the template selection process. The template gallery allows you to browse through the Receptiviti custom templates and any user-created custom templates. note Template options vary based on project type. The template offered in the template gallery step during project submission for one project type is not the same template as a different project template despite them sharing the same input type requirement. ## Receptiviti Templates[​](#receptiviti-templates "Direct link to Receptiviti Templates") We have designed a suite of templates that are useful for common project types. These will allow you to view a customized set of worksheets and/or dashboards without having to build them yourself. The template gallery lists the templates as individual worksheets, as well as having them arranged together in dashboard form. **To apply a Receptiviti template:** 1. Go through the steps to [create a project](https://docs.receptiviti.com/visualization-ui/creating-a-project.md). 2. When you reach the **Select Project Template** step, select the **Receptiviti Templates** tab. 3. Under the search box you will see **Survey Template** (in the case of survey data). Select it. 4. The **Preview** panel will appear on the right, in which you can scroll to browse the worksheets and dashboards included in the template. 5. Click **Apply**. ![template gallery with preview.png](/assets/images/template-gallery-preview-33f60f1085dbae331ac89690637faaf5.png) ### Default Survey Template[​](#default-survey-template "Direct link to Default Survey Template") This template is a set of worksheets and a dashboard designed for survey insights. The Template Gallery provides a preview of the available templates. The worksheets included are as follows: **Worksheets in Default Survey Template** 1. Positive vs. Negative Emotion: Bar Chart 2. Emotions Breakdown: Bar Chart 3. Admiration Topics: Topics by Emotion 4. Joy Topics: Topics by Emotion 5. Anger Topics: Topics by Emotion 6. Fear Topics: Topics by Emotion 7. Admiration Topics: Highlights 8. Joy Topics: Highlights 9. Anger Topics: Highlights 10. Fear Topics: Highlights 11. Drives Expressed by Respondents: Radar Chart 12. Schwartz Values Expressed by Respondents: Radar Chart 13. Psychological States Expressed by Respondents: Radar Chart **Dashboards in Default Survey Template** Organizational Culture
Worksheets in Organizational Culture Dashboard 1. Drives Expressed by Respondents: Radar Chart 2. Schwartz Values Expressed by Respondents: Radar Chart 3. Psychological States Expressed by Respondents: Radar Chart ### Default Transcript Template[​](#default-transcript-template "Direct link to Default Transcript Template") This template is a set of worksheets and a dashboard designed for transcript insights. The Template Gallery provides a preview of the available templates. The worksheets included are as follows: **Worksheets in Default Transcript Template** 1. Emotions Breakdown: Bar Chart 2. Word Count By Speaker: Bar Chart 3. Analytical Thinking Over Time By Speaker: Line Chart 4. Cognitive Load Over Time By Speaker: Line Chart 5. Authenticity Over Time By Speaker: Line Chart 6. Clout Over Time By Speaker: Line Chart 7. Language Style Matching (LSM): Heatmap 8. Group Drives: Radar Chart 9. Group Schwartz Values: Radar Chart 10. Drives By Speaker: Radar Chart 11. Shwartz Values by Speaker: Radar Chart 12. "I" Words Over Time By Speaker: Line Chart 13. "We" Words Over Time By Speaker: Line Chart 14. Certainty Over Time By Speaker: Line Chart 15. Tentative Over Time By Speaker: Line Chart 16. Positive Emotion Over Time By Speaker: Line Chart 17. Negative Emotion Over Time By Speaker: Line Chart 18. Group Time Orientation By Speaker: Bar Chart 19. Future Focus Over Time By Speaker: Line Chart 20. Group Curiosity and Openness: Radar Chart 21. Curiosity Over Time By Speaker: Line Chart 22. Open To Change Words Over Time By Speaker: Line Chart 23. Group DISC Profile: Radar Chart 24. Group DISC Profile: Bar Chart 25. DISC Profile By Speaker: Radar Chart **Dashboards in Default Transcript Template** Group Commitment Worksheets in Group Commitment Dashboard 1. Language Style Matching (LSM): Heatmap Group Drives and Values Worksheets in Group Drives and Values Dashboard 1. Group Drives: Radar Chart 2. Group Schwartz Values: Radar Chart 3. Drives By Speaker: Radar Chart 4. Shwartz Values by Speaker: Radar Chart Group Collaboration Worksheets in Group Collaboration Dashboard 1. "I" Words Over Time By Speaker: Line Chart 2. "We" Words Over Time By Speaker: Line Chart 3. Certainty Over Time By Speaker: Line Chart 4. Tentative Over Time By Speaker: Line Chart 5. Positive Emotion Over Time By Speaker: Line Chart 6. Negative Emotion Over Time By Speaker: Line Chart 7. Group Time Orientation By Speaker: Bar Chart 8. Future Focus Over Time By Speaker: Line Chart Group Curiosity and Openness Worksheets in Group Curiosity and Openness Dashboard 1. Group Curiosity and Openness: Radar Chart 2. Curiosity Over Time By Speaker: Line Chart 3. Open To Change Words Over Time By Speaker: Line Chart Group DISC Profile Worksheets in Group DISC Profile Dashboard 1. Group DISC Profile: Radar Chart 2. Group DISC Profile: Bar Chart 3. DISC Profile By Speaker: Radar Chart ### Default General Data Table Transcript[​](#default-general-data-table-transcript "Direct link to Default General Data Table Transcript") This template contains a Summary bar chart containing several prominent LIWC measures: Analytical Thinking, Clout, Authentic, and Emotional Tone. These measures provide an overall glimpse into the personalities of the people in question and compare them to each other side-by-side. note This template is a work in progress. ## User Templates[​](#user-templates "Direct link to User Templates") When you create a worksheet or a dashboard, you will likely find that you want to use the same graph types and frameworks and measures to apply to other datasets or parts of your dataset. By using the **User Templates** feature, you can upload templates that you have already saved. ![user template gallery](/assets/images/user-templates-b74f6d177be71eec9058b3fc3af90c6b.png) note If you save a template, it will save the current state of the project's worksheets and dashboards. Templates are not individual worksheets or dashboards to load into an existing project; they are projects themselves. **To create and save your own custom user template:** 1. Create one or more worksheets and/or dashboards of your liking that represent your data in a way that you wish to save as a template. 2. At the top left of the page, click the menu icon. ![open menu](/assets/images/open-menu-a2ffae703e2b876ec5ea574840b4b465.png); 3. Click **Save** under the **Template** heading. ![save user template](/assets/images/save-user-template-6e4aed1ff45cd6551ffb3dc3c00c0aa9.png); 4. Give your template a name in the **File Name** field, then click **Save File**. (You can also choose to stick with the default template name, which is the name of the project.) ![naming user template](/assets/images/naming-user-template-e2e24c495558df53e3d823b53474b850.png) This will save a `.rect` template file to your computer, and if you choose to keep `Save a copy in browser` checked, will also save the file to the UI. When retrieving the `.rect` template file from your computer to view with your dataset, you will have to create a new project and locate it within your file finder. info You cannot apply a user template to a currently open project — the **Manage Templates** menu acts as a reference for existing user templates when viewing it while within an open project. To open a template, you must either start a new project or load an existing template. **To load a user template (`.rect` template file) into a project if it is saved to the UI:** 1. In the Template Gallery, with the **User Templates** tab selected, click a template you would like to load. ![select user template](/assets/images/select-user-template-b0fa9171cb73c16e940ddec38b644572.png) 2. Click **Apply**. ![apply template](/assets/images/apply-template-6088d23efb6bb452de17383bb6c14046.png) 3. If the template contains worksheets, click the **Worksheets** tab. If it contains dashboards, click the **Dashboards** tab. ![select worksheets](/assets/images/select-worksheets-331d8f46c6175b37a035ea2dde48af00.png) * For worksheets: When you load a template, you can preview the worksheet(s) it contains on the left side of the interface. To view or edit a worksheet, click on it. ![select template in worksheet](/assets/images/select-template-in-worksheets-b8490e01217476550071552373ea53ef.png) * For dashboards: the template will load and you will see the dashboard template listed in the preview window at the left of the interface. To view or edit a worksheet, click on it. ![dashboard template](/assets/images/dashboard-template-b1dadb8dca3e5b61f13084959ef65db6.png) **To view a user template (`.rect` template file) from within the UI:** 1. At the top left of the page, click the menu icon. ![open menu](/assets/images/open-menu-a2ffae703e2b876ec5ea574840b4b465.png); 2. Click **Manage Templates**. ![select-manage-templates](/assets/images/select-manage-templates-ca8abc089bd5278bc12a107d24a08e6d.png) 3. Click any template listed to view details about it. You can use the editing icon to delete and rename the template(s). ![templates menu info](/assets/images/templates-menu-info-081e339084e834c1b8afcf068a546d44.png) ## Column Matching[​](#column-matching "Direct link to Column Matching") You may need to match columns of your dataset in cases where the default column naming convention within the template does not correspond verbatim to a column in your dataset. In the case of the image below, the dataset contains a column called `Speaker` that you must match to the `speaker_name` column, which is the naming convention within the chosen template. ![column mapping](/assets/images/match-columns-e1c523b3f10bf11580f249f16887f1dd.png) note **Partial Column Matching:** When you don't match all columns during the setup, you can still proceed to the analysis phase. However, please be aware that some dimensions will be missing, which may affect the functionality of certain charts. This can be useful for preliminary reviews, but for a comprehensive analysis, ensure all required columns are matched. info The `Hide matched columns` toggle button displays only the columns from your CSV file that do not match a column found within the template columns. --- # Visualizing with Charts and Graphs Click any of the chart and graph types below to learn more about the UI's visualization tools. ![Bar Chart Icon](/icons/icon_graph_bar_vert.svg)[Bar Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/bar-charts.md) [Circumplex Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/circumplex-chart.md) ![Emotions by Topic Icon](/icons/icon_graph_table.svg)[Emotions by Topic](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/emotions-by-topic.md) ![Heatmap Icon](/icons/icon_heatmap.svg)[Heatmap](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/heatmap.md) ![Highlights Icon](/icons/icon_highlights.svg)[Highlights](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/highlights.md) ![Line Chart Icon](/icons/icon_graph_line.svg)[Line Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/line-charts.md) ![Lollipop Chart Icon](/icons/icon_graph_radar.svg)[Lollipop Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/lollipop-chart.md) ![Radar Chart Icon](/icons/icon_graph_radar.svg)[Radar Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/radar-chart.md) ![Scatterplot Icon](/icons/icon_scatterplot.svg)[Scatterplot](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/scatterplot.md) ![Score Proportion Icon](/icons/icon_scoreproportion.svg)[Score Proportion](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/score-proportion.md) ![Table Icon](/icons/icon_graph_table.svg)[Table](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/table.md) ![Topics by Emotions Icon](/icons/icon_highlights.svg)[Topics by Emotions](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/topics-by-emotions.md) --- # Bar Chart This chart is standard for plotting several measures for multiple categories. It excels for use with categorical data variables such as `speaker_name`, `gender`, `office_location`, `age`, and `tenure`, among others. It works well when comparing how categorical variables score across framework measures. The bar chart allows you to gain nuanced insight into categorical groups of interest by helping to discern how varying geographic, demographic, or organizational groups within an organization feel about key themes, or compares how multiple speakers on a call score on a selection of Big 5 measures, for example. ![bar chart personality](/assets/images/bar-chart-personality-f4676d23349e5ca1567b2a4fdcccb7c2.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group Data By** buttons allow you to select among the following: * **Category:** Plots the bars based on categorical input data, such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. Lists the colour-coded measures below in the legend. * **Score:** Plots the bars based on the score(s) derived from the selected measure(s) and lists the colour-coded categorical inputs below in the legend. The **Score** option is useful for analyzing and visualizing the distribution of scores across different categories. * **Two Categories:** Plots any two input data categories filtered on a chosen measure. Note that a bar chart created using either **Category** or **Score** will be discarded to create a **Two Categories** chart, as seen in the dialogue that appears when clicking **Two Categories**: ![discard chart](/assets/images/discard-chart-423a8025f4079e7b65ea4be939d54716.png) In the example below, the **Two Categories** option is used to display the disciplined measure in the Big 5 Personality framework within age distributed by office location. ![bar chart disciplined](/assets/images/bar-chart-disciplined-3e291c61beae6090bae04b4f8989023f.png) * The **Cluster Category** drop-down menu allows you to select the input value to cluster the bars with. * The **Bar Category** drop-down menu allows you to select the input value to aggregate the chart’s data with. * The **Score** drop-down menu allows you to select the measures you want to use to get scores for. The measures are organized by framework and are listed alphabetically. Note that you can search for a measure by typing it in the field provided at the top of the drop-down menu. * The **Add Filter** option allows you to further filter by categorical input data. For example, if you wanted to filter by gender in the example above for the **Two Categories** bar chart, you would click **Add Filter**, then select a category to filter by, then select **Female** to display the same results but filtered by only female employees. ![add filter](data:image/png;base64,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) * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper and lower ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the y-axis range. ![custom axis ranges](/assets/images/custom-axis-range-c2929d69b956954c401dc468858d517f.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. --- # Circumplex Chart This chart visualizes the `agentic` and `communal` leadership qualities through the analysis of a person's language. It offers an effective visual representation that provides a clear and immediate understanding of the leadership style conveyed in the text, enabling a nuanced interpretation of a person's approach to leadership. The `agentic` and `communal` measures capture two primary ways of navigating social environments: `agentic` speakers emphasize pursuing personal goals; `communal` speakers emphasize building and maintaining relationships with other people. The two tendencies can operate together or alone, with all possible combinations together forming the Interpersonal Circumplex chart. Speakers who use highly `agentic` language are likely exerting willpower to pursue personal goals, indicating that they prioritize things individually and for personal motivations or desires. Using `communal` language suggests that a person is cooperating and connecting with others to improve social relationships, indicating that they are likely doing things with other people to help meet the group's goals. These two form the “Big Two” dimensions of social cognition. For further information about the Interpersonal Circumplex, including details related to scoring and interpretation, see [this page](https://docs.receptiviti.com/frameworks/normed-frameworks/interpersonal-circumplex.md#subfacets-of-the-interpersonal-circumplex) ![circumplex chart](/assets/images/circumplex-04c0e081084b69a69ee966a3d6887a89.jpg) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. Note that you can type in this field to search and select an existing category. Why can't I see my column in the Group By menu? The **Group By** drop-down menu only contains categorical data fields. You can modify the data type of a column in your dataset so that it becomes available for analysis in charts within the **Group By** drop-down menu. More information can be found [here](https://docs.receptiviti.com/visualization-ui/creating-a-project.md#column-and-data-type-editing). * The **Add Filter** tool allows you to filter by input values and select individual speakers/participants or smaller subsets of participants. To do so, select an input value in `Filter Field` such as `speaker_name` and choose the individual(s) from the list under `is one of`. --- # DISC Quadrant Chart This chart offers a comprehensive framework for understanding and mapping out various personality types and behavioural styles, allowing teams to better understand and facilitate group dynamics and personal development. It is often used in personality assessments and team-building exercises, visually representing different personality traits and behavioural styles by plotting nodes within a four-quadrant model. DISC is typically defined by four DISC styles, each representing a style of interacting with one’s environment: * **D** is normally referred to as dominant; D-type people tend to be ambitious, active, bold leaders; * **I** is alternately referred to as influence or inducement; I-type people lead through connections, creativity, and collaboration; * **S** is called stable, submissive, steady, or supportive; S-type people tend to be faithful, modest, methodical people who value relationships; and * **C** is labeled compliant, conscientious, or cautious; C-type people prefer to do their jobs accurately, unobtrusively, and impersonally. A person’s score on the four DISC styles is derived from their scores on two dimensions of the DISC axis: * **Bold** vs. **Reserved**; and * **People-focused** vs. **Task-focused** For in-depth information surrounding the history of DISC and Receptiviti's DISC framework, see the [DISC page](https://docs.receptiviti.com/frameworks/personality-disc) on our API documentation site. ![disc chart](/assets/images/disc-chart-969b482b82d77ac18f55a00ee07cac6b.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. Note that you can type in this field to search and select an existing category. * The **Subjects** drop-down menu allows you to select the data points to feature on the chart, such as the individual speaker or speakers. * The **Add Filter** option allows you to further filter by categorical input data. For example, if you had many speakers in a meeting and wanted to view the data for a smaller subset or single individual, you could filter them here by clicking **Add Filter** and selecting your desired input value(s). note When using **Add Filter** you can only filter by categorical variable(s) that you haven’t already used for your **Group By** field. For example, if you have selected all the speakers in your dataset to display on the chart, you will not have anything else to filter by, so there will be no option in the menu. Further filtering beyond speakers is more applicable to datasets that contain more metadata columns to filter by. --- # Emotions by Topic This table measures emotions found within text about a taxonomy topic. Emotions display in descending order of occurrence and are optionally filtered by categories such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. Emotional valence and intensity are represented by color. This chart works best with a dataset that includes references to key topics of interest, such as a survey about remote work or a meeting about employee wellbeing. It can help to identify the emotions expressed when a topic is mentioned and compare how often emotions are expressed when a topic is mentioned. ![emotions by topic](/assets/images/emotions-by-topic-b4a2bf9e3b39f5b97b27592239104cf1.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Taxonomy** drop-down menu allows you to select the taxonomy (dictionary) that contains the topics to filter by. * The **Topic** drop-down menu allows you to select the topic within the taxonomy to display in the chart. * The **Emotions** drop-down menu allows you to select any emotion(s) you want to display in the chart. note If no emotions are selected, this chart displays the top 10 emotions ordered by occurrence. You can view the text samples in a dataset that expressed a SALLEE emotion and discussed a topic in the chosen taxonomy. Simply click the horizontal bar associated with the topic you want to see the sample for, and the sample will display below the bar chart, sorted by decreasing relevance. In the image below, `Anger` is selected, as indicated by the shadow spanning the its horizontal bar. The chart populates the emotions that were most present in descending order of emotional intensity. ![emotions about structure](/assets/images/emotions-about-structure-f9fb8ce11222878ddcf7b75778f043a1.png) * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the x-axis range. ![custom axis ranges](/assets/images/custom-x-axis-0b65c4917be63034f569a9cd828a8369.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. note Negative values are disallowed for this chart type. --- # Heatmap This chart allows you to visualize the magnitude of Language Style Matching (LSM) between groups for any selected category, most typically between three or more verbose speakers. Magnitudes are represented by a range of colored cells. This chart is an effective way to provide an overview of rapport/alignment between individuals or groups and can help to highlight misaligned individuals. It can also be used to represent multiple groups of people, such as for comparing different departments across organizations, or different office locations, ages, tenures, or gender, among others. ![heatmap](/assets/images/heatmap-bf8c52578d2617e23ad268d1b6965924.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Category** drop-down menu allows you to select the input category whose members you want to compare. * The **Relative/Absolute** toggle button allows y ou to choose between the two separate colour scaling options. Absolute uses the absolute value of each data point to determine its colour, while relative colour scaling uses the value of each data point relative to the other values in the heatmap to determine its colour. * The **Add Filter** tool allows you to filter by input categories (`city`, `age`, `gender`, etc.). In the example above from the UI image, the age ranges within an organization are measured for their linguistic alignment, and it’s filtered down specifically to the Toronto office instead of displaying the alignment of the company as a whole. --- # Highlights This chart allows you to analyze emotions exhibited in text about a chosen topic, or topics relevant to text exhibiting an emotion by comparing their sentence counts and most significant sentences in a table. It works well for datasets that include references to key topics of interest such as a survey about remote work or a meeting about employee wellbeing. This chart will help you if you are interested in: * understanding how people feel about key topics, * identifying and comparing the frequency of topics mentioned when an emotion is expressed, or the frequency of the emotions expressed when a topic is mentioned, * identifying examples of sentences in a dataset that represent key topics and emotions. ![table excitement](/assets/images/table-excitement-3ee8e5f06099c7aee57f630275a05902.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Find sentences expressing** drop-down menu allows you to select any [SALLEE emotion](https://docs.receptiviti.com/frameworks/emotions) This converts to the Find sentences discussing drop-down menu when selecting any topic from a taxonomy. * The **Sentence Framework** drop-down menu allows you to choose the framework or taxonomy from which the emotions or topics will be displayed in the table. These co-occur with the chosen emotion or topic in the above drop-down menu. * The **Sentence Emotions** drop-down menu allows you to choose any emotion(s) you want to display in the chart. If you choose SALLEE in the Sentence Framework drop-down menu, this will be the Sentence Emotions menu. If you choose a taxonomy from the Sentence Framework drop-down menu, this will be the Sentence Topics menu. note All emotions/topics are displayed if none are chosen in this drop-down menu. ## Selecting Highlights[​](#selecting-highlights "Direct link to Selecting Highlights") You can control which highlights to display in the table. Highlights found in both the table cell and in the **Select sentences to highlight** dialog list are sorted by decreasing relevance, and the top two highlights are displayed by default. To enable highlight selection: 1. Click the **Enable highlight** selection icon at the far right corner of the UI. ![enable highlight](/assets/images/enable-highlight-cf7ec600de719229f51ee211757cb17c.png) 2. Click the **Select highlights** icon that appears next to the highlight sentences. ![select highlights](/assets/images/select-highlights-03f641e7ac016c5a5e9a339e5013ab60.png) 3. In the **Select sentences to highlight** dialog box, uncheck any of the highlights to prevent them from displaying in the **Highlights** column. (You can also add more highlights to display — it depends on how many sentences express language that are considered highlights. These sentences will be available to select or deselect in the **Select sentences to highlight** dialog box.) ![highlight sentences](/assets/images/highlight-sentences-69bec10e7a2c68507349ea7163217a05.png) 4. Click Confirm. ![confirm sentence highlights](/assets/images/confirm-sentence-highlights-3681de2733ba31208a354c1a1223124c.png) --- # Language Style Matching Chart This chart allows you to measure the magnitude of language style matching (LSM) that occurs between two or more people from a conversation transcript. Language style matching is highly predictive of a wide range of interpersonal outcomes and behaviors. When people in a conversation are paying attention to one another, their language tends to mirror one another’s. This process is similar to that which occurs with physical mirroring, but is more subconscious. LSM occurs at the grammatical level, and is calculated by measuring the similarity between the use of function words between two or more people. LSM can be measured in a simple conversation between two people, or in a setting as varied as an entire subreddit community. ![LSM chart](/assets/images/LSM-chart-01959b7b1407784a8f0b58b5e91a8f07.png) info The [Heatmap](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/heatmap.md) chart also features LSM between two or more people. It provides a visual sense of what the mean LSM is for a particular dataset and how variant the pairwise scores are, though because it does not require a datetime column in the dataset it will not display LSM data related time segments of a transcript. It looks at the whole dataset as one snapshot. The LSM chart allows you to explore in further detail the dynamic aspects of LSM occuring throughout a conversation, and allows you to compare trends. You can split a transcript and look at data a slice at a time, giving you the ability to see the highs and lows of people's LSM over time. Some LSM insights can be drawn from patterns rather than just overall scores. For example, if a speaker says something divisive that alienates other speakers, there may be a widening LSM for a period of time between the group or between individuals in the group. Or, if all speakers are coming to a consensus, there may be a convergence over time. In terms of measuring LSM, the line graph excels in these scenarios where more time-based and inter-group comparisons are needed. note The LSM chart requires that an uploaded dataset contain a datetime component so that it can map LSM to time segments on the chart. ## When to Use Pairwise LSM vs. One-to-Many LSM[​](#when-to-use-pairwise-lsm-vs-one-to-many-lsm "Direct link to When to Use Pairwise LSM vs. One-to-Many LSM") **Pairwise:** Use this when measuring the LSM between individual people within a group. For example, Person 1 vs. Person 2, Person 1 vs. Person 3, or Person 2 vs. Person 3, etc. **One-to-Many:** Use this when measuring the fit of each individual within a group. For example, Person 1 vs. the average of Person 2 and Person 3, or Person 2 vs. the average of Person 1 and Person 3, etc. ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. * The **X-Axis** drop-down allows you to select the column from your dataset to use to populate the x-axis of the chart. * The **Comparison Type** drop-down allows you to select between the two methods of calculating language style matching: * **Pairwise** charts LSM in relationships between individual people within the transcript. * **One-to-Many** charts LSM related to how each person fits within the whole group. * The **Subjects** drop-down allows you to select a category to compare to the rest of the dataset, such as `speaker_name`. In the case of speakers in a transcript, selecting a speaker in this menu will plot that speaker on the y-axis, with low LSM at the bottom of the axis and high LSM at the top. The speaker(s) chosen in the **Partners** drop-down below will be plotted with their LSM scores in relation to the Subject throughout the timeframe of the transcript. This menu also contains the the **Averages** option, which computes and displays the LSM Group Average. * The **Segment By** drop-down allows you to break out the x-axis into a number of segments to display an average value. The Receptiviti UI calculates the beginning and end measurements within the featured dataset to determine which series of segments to display. * The **Segment Interval** drop-down allows you to adjust the division and granularity of the segmentation. * The **Add Filter** tool allows you to filter by input values and select individual speakers/participants or smaller subsets of participants. To do so, select an input value in `Filter Field` such as `speaker_name` and choose the individual(s) from the list under `is one of`. * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper and lower ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the y-axis range. ![custom axis ranges](/assets/images/custom-axis-range-c2929d69b956954c401dc468858d517f.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. --- # Line Chart This chart works well for call transcripts as it plots single measures over time for multiple speakers, allowing you to gain insights into how a person or group of people varied emotionally and psychologically throughout the duration of a conversation. The line chart below uses the [SALLEE measure](https://docs.receptiviti.com/frameworks/emotions) `Goodfeel` which tracks overall positive emotion. Emotion scores (including scores `Goodfeel`, `Badfeel`, and `Ambifeel`) will always fall between `0.0` and `1.0`. `Goodfeel`, `Badfeel`, and `Ambifeel` will allow you to differentiate between degrees of negative, neutral, or positive emotions based on the scoring. ![goodfeel linechart](/assets/images/goodfeel-linechart-3cf8bfd43490407a98cc309121824328.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. Note that you can type in this field to search and select an existing category. * The **X-Axis** drop-down menu allows you to set the time along the x-axis. The values are pulled from columns in the input dataset in which the types are date-based. * The **Y-Axis** drop-down menu allows you to select a measure from the available frameworks so you can see how the different speakers varied emotionally and psychologically throughout the duration of the conversation. You can type in this field to search and select an existing measure. * The **Segments** slider allows you to set the number of segments by which to cluster the data. * The **Add Filter** tool allows you to filter by input values and select individual speakers/participants or smaller subsets of participants. To do so, select an input value in `Filter Field` such as `speaker_name` and choose the individual(s) from the list under `is one of`. ![line chart add filter](/assets/images/line-chart-add-filter-daf5b1c9cbf563ad6a957b660976879b.png) info If you filter by **Overall Dataset** in the **Group By** field, you will be able to select multiple measures to display. The Y axis plots the average score of the chosen measures across the entire dataset over the timeframe. * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper and lower ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the y-axis range. ![custom axis ranges](/assets/images/custom-axis-range-c2929d69b956954c401dc468858d517f.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. --- # Lollipop Chart This chart plots normed measures from a single framework to generate an individual or group profile, defaulting to display aggregate scores for the overall dataset. It works well with categorical variables such as `speaker`, `gender`, `office location`, `age`, and `tenure`, among others. You can use the filter to narrow down to a single category. Each data point is represented by a dot at the end of a thin vertical line and the length of the stick represents the value of the data point. The position of the dot indicates its location on the horizontal axis. In the image below, the Personality measures are filtered on the overall dataset of language from members of the Austin office location of an organization. ![lolliop chart](/assets/images/lollipop-chart-3f02ca750f4828e37e847ad03423b68b.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Framework** drop-down allows you to select among frameworks that contain normed measures and plots scores for each measure in the framework on the chart. * The **Add Filter** option allows you to further filter by categorical input data. For example, if you had many speakers in a meeting and wanted to view the data for a smaller subset or single individual, you could filter them here by clicking **Add Filter** and selecting your desired input value(s). --- # Radar Chart This chart compares three or more normed measures from multiple categories and displays them on radially arranged axes. It works well with categorical variables such as `speaker`, `gender`, `office location`, `age`, and `tenure`, among others. It also works well with datasets that represent a group — for example, in creating a profile for an executive team by grouping by overall dataset from a transcript using Big 5 and/or DISC Personality profiling. note Radar charts only allow normed scores, so datasets with 350+ word samples for categorical variables/overall are the minimum requirement. ![custom axis ranges](/assets/images/radar-chart-92404c0a0a82bd70ae83dc68641fd5c1.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `city`, or any category your dataset is labeled with. Note that you can type in this field to search and select an existing category. * The **Axes** drop-down menu allows you to select three or more measures from the available frameworks so you can plot the different frameworks radially on their axes. The closer the point is to the outside of the radius, the higher the score and vice-versa. You can type in this field to search and select an existing measure. * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the chart's range. ![custom axis ranges](/assets/images/radar-axes-84e1afca6eb85a535f07006acad98b26.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. note Negative values are disallowed for this chart type. --- # Scatterplot This chart works well for combining two measures on the two axes for multiple categorical data points such as participants in a meeting. The dots represent a comparison of the aggregates of the two selected measures. By using the scatterplot, you can compare the average score of the mean of the two measures, identify how correlated two measures are (positive correlation, negative correlation, uncorrelated), and identify outliers in the dataset. Other useful categorical data variables to filter by with the scatterplot are `gender`, `office` `location`, `age`, and `tenure`, among others. ![scatterplot leadership](/assets/images/scatterplot-leadership-4e298f6d26d7be3268190eed6ee06e17.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **X-Axis** and **Y-Axis** drop-down menus allow you to select a measure from the available frameworks so you can see how the different speakers varied emotionally and psychologically throughout the duration of the conversation. The dots represent a comparison of the aggregates of the two selected measures. You can type in this field to search and select an existing measure. * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. You can type in this field to search and select an existing category. * The **Add Filter** option allows you to further filter by categorical input data. For example, if you had many speakers in a meeting and wanted to view the data for a smaller subset or single individual, you could filter them here by clicking **Add Filter** and selecting your desired input value(s). note The dots will be empty circles for participants whose word counts are too low for valid normed scores. * Under **Additional Configurations**, you will find **Override Axis Ranges**. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper and lower ranges of the data plus a small buffer. Choosing **Custom** enables you to customize both the x-axis and y-axis ranges. ![custom axis ranges scatterplot](/assets/images/custom-axis-scatter-d6861d13b39ab0ff2c193b8ffa327351.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. --- # Score Proportion This chart allows you to compare the percentage of samples within several categories that express multiple emotions or topics. It works well with categorical data variables such as `speaker`, `gender`, `office` `location`, `age`, and `tenure`, among others, i.e., those found in a survey with demographic data. It also works well for datasets that include references to key topics of interest, such as surveys about remote work or meetings about employee wellbeing. The score proportion chart is useful to compare the proportion (percentage) of demographic groups (or other categorical variables) that express emotions or mention key topics, as well as to compare the frequency (count) at which demographic groups (or other categorical variables) express emotions or key topics. ![score proportion](/assets/images/score-proportion-4c1a2a8de336c0c48e75a40153cce43e.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either the overall dataset or by a category such as `speaker_name`, `gender`, or any category your dataset is labeled with. Note that you can type in this field to search and select an existing category. * The **Framework** drop-down menu allows you to choose an entire framework or a taxonomy, making available all of their respective measures or topics. It selects a set of options rather than filtering. * The **Emotions** drop-down menu allows you to choose any emotion(s) you want to display in the chart. Note that this drop-down menu converts to the **Topics** drop-down menu when you select a taxonomy in the **Framework** menu. * The **Proportion** and **Count** buttons allow you to toggle between displaying either the percentage of grouped samples or the number of grouped samples expressing emotion. * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the x-axis range. ![custom axis ranges](/assets/images/custom-x-axis-0b65c4917be63034f569a9cd828a8369.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. note Negative values are disallowed for this chart type. --- # Table The table allows you to compare several measures from datasets with categorical variables for multiple categories such as `speaker`, `gender`, `office_location`, etc. Categories are represented by rows in the table. The table is helpful in comparing how categorical variables score across frameworks and measures and can help identify relatively high or low scores. ![personality table](/assets/images/personality-table-b5851e3ce5d1c173fee4d7ee8a3ce81d.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Group By** drop-down menu allows you to set the column to use to group the chart’s data by selecting either a category such as `speaker_name`, `gender`, or any category your dataset is labeled with. You can also select a taxonomy topic. Note that you can type in this field to search and select an existing category. * The **Columns** drop-down menu allows you to populate columns in the table by selecting among the measures in frameworks or the topics in taxonomies. ## Average, Count, and Percentage[​](#average-count-and-percentage "Direct link to Average, Count, and Percentage") In a table where columns display dictionary-counted measures (excluding Cognition and Additional Indicators), you will see an italicized label under the name of the measure, as well as the eye icon to the right of the measure label. Clicking the icon allows you to toggle between displaying: * **Average:** the average of the measure per the metadata category row. * **Count:** the number of instances of the chosen measure per the metadata category row. Non-zero scores only. * **Percentage:** the percentage of the measure per the metadata category row. Non-zero scores only. ![display columns](/assets/images/display-columns-a923487ab422ff1dd5d87701d9467351.png) Toggling off the selection will hide the column, while toggling the selection on will produce a new column containing the selection to the right of the current column. In the image below, all three options are toggled on. ![count and avg and percent](/assets/images/table-count-avg-18fc7bdfd6b86bf7a18df6df781abea1.png) --- # Topics by Emotion This chart plots topics that are relevant to text that are relevant to text exhibiting an emotion . Topics display in descending order of occurrence and are optionally filtered by categories such as `speaker_name`, `speaker_id`, or any category your dataset is labeled with. Emotional valence and intensity are represented by color. This chart works best with datasets that include references to key topics of interest, such as a survey about remote work or a meeting about employee wellbeing. It can help to identify the highest frequency topics mentioned when an emotion is expressed and allows you to compare the frequency of topics mentioned when an emotion is expressed. ![topics by emotion fear](/assets/images/topics-by-emotion-fear-722600939a8a97b0628167aed0480aed.png) ## Parameters[​](#parameters "Direct link to Parameters") * The **Taxonomy** drop-down menu allows you to select the taxonomy (dictionary) that contains the topics to filter by. * The **Emotion** drop-down menu allows you to select an emotion you want to display in the chart. * The **Topics** drop-down menu allows you to select the topic(s) within the taxonomy to display in the chart. note If no emotions are selected, this chart displays the top 10 topics ordered by occurrence. You can view the text samples in a dataset that trigger the topic classification within a selected taxonomy. Simply click the horizontal bar associated with the topic you want to see the sample for, and the sample will display below the bar chart, sorted by decreasing relevance. In the image below, `Teamwork` is selected, as indicated by the shadow spanning the its horizontal bar. ![topics by emotion teamwork](/assets/images/topics-by-emotion-teamwork-d0c940c8efc5b671fe5ab5fc5042e00b.png) * Under `Additional Configurations`, you will find `Override Axis Ranges`. This tool allows you to toggle between **Fit to Data** and **Custom**. By choosing **Fit to Data** (the default setting), the chart will scale to the upper ranges of the data plus a small buffer. Choosing **Custom** enables you to customize the x-axis range. ![custom axis ranges](/assets/images/custom-x-axis-0b65c4917be63034f569a9cd828a8369.png) Customizable axis ranges provide flexibility to zoom in and view your data in greater detail, making it easier to analyze patterns that might otherwise be missed. Zooming out allows you to standardize the scale across multiple charts, which is especially useful in views such as dashboards where you want to compare different datasets on a consistent scale. This feature gives you control over how your data is presented, ensuring that key insights are clear and accessible. note Negative values are disallowed for this chart type. --- # Worksheets and Dashboards Both the worksheets and dashboards allow you to view data by creating different chart types and filtering the data and measures in different ways. Worksheets are simply a place to create one large chart, table, or graph, while dashboards provide a space to create and combine different worksheets. ## Worksheets[​](#worksheets "Direct link to Worksheets") **To access the chart, table, and graphing tools for worksheets:** 1. In an active project, click the **Worksheets** tab. ![click worksheets tab](/assets/images/click-worksheets-tab-f47320185b957b0a3c4ae1f499a52b89.png) 2. In the new blank page, click the **Create Worksheet** icon. ![create worksheet icon](data:image/png;base64,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) 3. Choose a chart, table, or graph you’d like to visualize your data on. ![worksheet visualizations](/assets/images/worksheet-visualizations-a1b99922e4147a2fa08eaebc2183399e.png) ## Dashboards[​](#dashboards "Direct link to Dashboards") **To access the chart, table, and graphing tools for dashboards:** 1. In an active project, click the **Dashboards** tab. ![click dashboard tab](/assets/images/click-dashboard-tab-0bc384dd53880753c4f8c752dc0d05e7.png) 2. Click the **Create Dashboard** icon ![create dashboard icon](data:image/png;base64,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) 3. After naming your dashboard, click **Add**. ![add-dashboard](/assets/images/add-new-dashboard-d42c1aa6d6a11df47ec75e1c9777a85a.png) 4. In the new worksheet placeholder, click the **Add Worksheet** button. ![add new dashboard tile](/assets/images/add-new-dashboard-tile-13298567440ab7e065665bced87897b8.png) 5. In the **Add to Dashboard** menu, select one that you want to use. ![add to dashboard menu](/assets/images/add-to-dashboard-menu-56b003370960692018c27c3108f25397.png) 6. The tools to analyze and filter results will display on the left of the UI for the chart-type you selected. ![design chart](/assets/images/design-chart-bd3f511da58338fa4da5a992d3ecd4a5.png) ## Dashboard Features[​](#dashboard-features "Direct link to Dashboard Features") The dashboard contains the same set of tools and features as found in the worksheets — the difference being that you can view and arrange several different worksheets on the same page in several different configurations. The width of a worksheet can be sized to be small, medium, or large. The largest width takes an entire row, while the smallest allows for three worksheets to fit in a row. ### Worksheet tools[​](#worksheet-tools "Direct link to Worksheet tools") 1. Edit worksheet content and settings. 2. Rename worksheet. 3. Delete worksheet. 4. Save worksheet as image file to your computer. 5. Resize worksheet by clicking and dragging them left or right. 6. Rearrange worksheet on the dashboard by dragging and dropping them laterally or vertically. ![dashboard-tile](/assets/images/dashboard-tile-with-numbers-0ce7723b1ef3dcea6ce766b395d33215.png) ### Text Tool for Dashboard Descriptors[​](#text-tool-for-dashboard-descriptors "Direct link to Text Tool for Dashboard Descriptors") By adding a text window to your dashboard, you can include customized text descriptions that provide additional context and insights for yourelf and your viewers. In the **Add to Dashboard** dialog, simply click **Tools**, then click **Text** and your text window will appear in your dashboard. ![dashboard tools](/assets/images/dashboard-tools-87064c5babac2989bb46f4ad6f37fdfe.png) ![text in dashboard](/assets/images/text-in-dashboard-4814cb42df027343c7c01ead2fb88024.png) ---