Personality - Big 5

Overview

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).

Note: 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.

Note: The Personality Package is a bundled subscription that includes all scores from each of the Big 5 Personality, Social Dynamics, and Drives frameworks.

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          "words_per_sentence": 3,
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          "six_plus_words": 0.6666666666666666,
          "capitals": 0.043478260869565216,
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          "emoticons": 0,
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        "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

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

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

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

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

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

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

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.

Note: For 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

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

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

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.

Specs and Examples

Scores in the Big 5 Personality framework are 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 language sample being analyzed.

Note: Results will be most reliable when your text sample is >350 words in length.

Following are examples of how to interpret the Personality scores derived from sample language:

Example 1: 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 up skill 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. I wouldn't call myself overly ambitious - not like my friends in investment banking. 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!

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 78.6 for self_assured. This means that 78.6% 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": 78.66211090753443
  }
}

Example 2: 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 - I was and am tired! Life as a developer means needing to continuously up skill oneself or risk becoming irrelevant. As someone who enjoyed a laid-back life, this frustrated me. I worked in the same company for 12 years because I didn't know what else to do with my life. When the company had to shut down due to financial woes, I found myself lost - not because this was my calling, but because i hate change! Eventually, I found a new job - urgh - another dev shop. I'm definitely not ambitious - i don't find my career to be my calling - I believe my problem is that I don't really know what I would do otherwise. I don't know what I want.

// partial response
{
  "personality": {
    "self_assured": 24.222933286132587
  }
}

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 24.2. This means that only 24.2% of text samples in our baseline dataset score less than what this sample returned for self-assured.

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

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.

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