India Worthy / Wednesday, October 10, 2018 / Categories: Items of Interest On “New” Personality Types Deniz S. Ones, University of Minnesota-Twin Cities; Brenton M. Wiernik, University of South Florida An Industrial, Work, and Organizational Psychology Perspective In September, Gerlach, Farb, Revelle, and Amaral published an article in Nature Human Behavior proclaiming they had identified four personality types that robustly replicated across 4 data sets. Its publication set off a media frenzy, with the authors’ university and major news outlets announcing this as a major breakthrough—for example, “Scientists identify four personality types”, “Comprehensive data analysis dispels established paradigms in psychology”[3,4], “Big data gives the ‘Big Five’ personality traits a makeover”, among dozens of others[e.g., 6–12]*. The Gerlach et al. article and ensuing media frenzy had an immediate impact on us. First, as personality scholars, we were interested in the authors’ methods and conclusions. Second, we were left fielding emails, text messages, and phone calls from our collaborators, colleagues, scientists and practitioners, organizations, and test publishers. In this article, we accessibly describe Gerlach et al.’s methods and findings and how they fit with existing personality research. We review the articles’ relevance and importance for the I–O psychology community and address some frequently asked questions. What were the goals of the article? Gerlach et al. sought to identify personality types in web-based self-report Big Five personality measures. They summarized their objective as gaining “insight into whether personality types truly exist” (p. 735). What methods were used? Gerlach et al. used four publicly-available datasets with responses from over 1.5 million individuals. They used an unsupervised machine learning clustering method (Gaussian mixture models) to identify clusters of people with similar Big Five scores. Traditional cluster-determination criteria suggested 13 clusters. However, clustering methods tend to produce spurious clusters, so Gerlach et al. compared the density of (proportion of people belonging to) each of the 13 clusters with that of a null model based on randomized data. Only four clusters survived this density constraint. Two of these clusters were relatively consistent across the four datasets; the other two were observed in only 3 datasets. What were the major findings? Gerlach et al. identified 4 major clusters in five-dimensional (Big Five) personality space. These clusters represent combinations of traits that are more likely to be observed than others. The Averagecluster includes people who are within ±1 standard deviation from the mean on all Big Five dimensions. The Role Modelcluster includes people low on Neuroticism, but high on the other Big Five. The Reserved cluster includes people low on Openness and Neuroticism. The Self-Centered cluster includes people high on Extraversion, but low on Agreeableness, Openness, and Conscientiousness. Were new personality types discovered? Personality’s multidimensional space is lumpy. The Big Five personality dimensions (and their narrower aspects/facets) are correlated to varying degrees[16–18]. The consequence of these intercorrelations is that some trait combinations are more likely than others—for example, more people will be high on both Conscientiousness and Agreeableness than high on Conscientiousness, but low on Agreeableness.+ That some trait configurations are more common is already well-known. In the industrial-organizational psychology literature, many measures have been developed that capture people’s similarity to these high-density configurations. For example, Integrity tests assess a configuration of high Conscientiousness, high Agreeableness, and low Neuroticism and Narcissism measures capture a configuration of high Extraversion, but low Agreeableness. These compound personality scales are among the most widely-applied personality measures in practice. Do personality types exist? Demonstrating that personality forms “types” requires identifying unambiguous characteristics that indicate differences in kind, in contrast to dimensional characteristics that describe differences in degree. For personality to function as “types”, members of different classes (e.g., Type A versus Type B) must have clearly distinct levels on a cluster of traits, with little overlap between groups. Decades of personality research have found that personality traits do not work this way—people’s personalities are best understood as profiles of high to low values on several dimensions (e.g., the Big Five). Indeed, the last bastions of types in personality theory have recently fallen (e.g., Self-Monitoring, Type A), with data clearly supporting dimensional interpretations over discrete types. Personality’s basic units are continuous traits, a fact undisputed by Gerlach et al. The density-clusters identified by Gerlach et al. overlap substantially in their Big Five trait levels and cannot be interpreted as discrete types—rather than “types”, they are better as described as “lumps in the batter” of personality traits. Any scheme that places individuals into a limited number of clusters (e.g., 16 types in the MBTI) necessarily breaks continuous personality dimensions into discrete units and therefore loses valuable individual differences information. What are implications for scientists who measure personality in their studies? There is no scientific support for abandoning dimensional personality measurement in favor of using types in research. The Pan-Hierarchical Five Factor Model provides an integrative taxonomy of traits at all levels of the personality hierarchy and can help scientists better select and use appropriate dimensional personality measures. What are implications for practitioners who use personality assessments in organizations? Many practitioners who contacted us wondered whether they should incorporate Gerlach et al.’s four “types” into their assessments. We don’t see much value in this—e.g., there is little to be gained by declaring a person is “average”—most people will fall into this cluster. Any decision based on personality density-cluster membership (“type”) is bound to lose highly valuable predictive information about the constituent traits. However, direct dimensional measures tapping compound personality traits may be useful for organizational applications (e.g., self-centeredness, a compound trait similar to one of the Gerlach et al. clusters). What are implications for personality publishers and consultancies? Most companies offering personality assessments have much larger test-taker databases than those used by Gerlach et al., with responses from hundreds of thousands, if not millions, of people. These data are a rich resource for identifying multidimensional personality density distributions. Publishers and consultancies can apply computational approaches similar to Gerlach et al. to explore measures’ nomological networks. If clusters like those described by Gerlach et al. emerge, explore whether cluster membership predicts work-relevant variables. In doing so, it is critical to remember that differences among individuals are ones of degree and not type! If readers have additional questions about Gerlach et al.’s research—or more generally personality types—please let us know. We hope to compile the above and other questions into an expanded FAQ resource. Deniz Ones may be reached at email@example.com, and Brenton Wiernik may be reached at firstname.lastname@example.org. References  Gerlach, M., Farb, B., Revelle, W., & Amaral, L. A. N. (2018). A robust data-driven approach identifies four personality types across four large data sets. Nature Human Behaviour, 2(10), 735–742. https://doi.org/10/gd8ds5  Guarino, B. (2018, September 17). Scientists identify four personality types. Washington Post. Retrieved from https://www.washingtonpost.com/science/2018/09/17/scientists-identify-four-personality-types/  Northwestern University. (2018, September 17). Scientists determine four personality types based on new data: Comprehensive data analysis dispels established paradigms in psychology [Press release]. Retrieved October 8, 2018, from https://news.northwestern.edu/for-journalists/press-kits/scientists-determine-four-personality-types-based-on-new-data/  Northwestern University. (2018, September 17). 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P., Haslam, N., Tian, J., & Ones, D. S. (2018). Direct and conceptual replications of the taxometric analysis of Type A behavior. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10/cp7n  Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality measures relevant to industrial, work, and organizational psychology. In D. S. Ones, N. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work and organizational psychology (2nd ed., Vol. 1, pp. 366–407). London, United Kingdom: Sage. https://doi.org/10.4135/9781473914940.n14 * For more nuanced or critical perspectives in popular outlets, see pieces published in Psychology Today, Live Science, or Ars Technica. + Density patterns become even more complex if we make finer attribute distinctions by considering more specific personality aspects and facets. 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