personality types research paper

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Research Article

Personality types revisited–a literature-informed and data-driven approach to an integration of prototypical and dimensional constructs of personality description

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Psychology, Freie Universität Berlin, Berlin, Germany

Roles Conceptualization, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Affiliation Department of Psychology, University of Duisburg-Essen, Duisburg Germany

Affiliation Personality Psychology and Psychological Assessment Unit, Helmut Schmidt University of the Federal Armed Forces Hamburg, Hamburg, Germany

• André Kerber, 
• Marcus Roth, 
• Philipp Yorck Herzberg

• Published: January 7, 2021
• https://doi.org/10.1371/journal.pone.0244849
• Peer Review
• Reader Comments

A new algorithmic approach to personality prototyping based on Big Five traits was applied to a large representative and longitudinal German dataset (N = 22,820) including behavior, personality and health correlates. We applied three different clustering techniques, latent profile analysis, the k-means method and spectral clustering algorithms. The resulting cluster centers, i.e. the personality prototypes, were evaluated using a large number of internal and external validity criteria including health, locus of control, self-esteem, impulsivity, risk-taking and wellbeing. The best-fitting prototypical personality profiles were labeled according to their Euclidean distances to averaged personality type profiles identified in a review of previous studies on personality types. This procedure yielded a five-cluster solution: resilient, overcontroller, undercontroller, reserved and vulnerable-resilient. Reliability and construct validity could be confirmed. We discuss wether personality types could comprise a bridge between personality and clinical psychology as well as between developmental psychology and resilience research.

Citation: Kerber A, Roth M, Herzberg PY (2021) Personality types revisited–a literature-informed and data-driven approach to an integration of prototypical and dimensional constructs of personality description. PLoS ONE 16(1): e0244849. https://doi.org/10.1371/journal.pone.0244849

Editor: Stephan Doering, Medical University of Vienna, AUSTRIA

Received: January 5, 2020; Accepted: December 17, 2020; Published: January 7, 2021

Copyright: © 2021 Kerber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this article were made available by the German Socio-Economic Panel (SOEP, Data for years 1984-2015) at the German Institute for Economic Research, Berlin, Germany. To ensure the confidentiality of respondents’ information, the SOEP adheres to strict security standards in the provision of SOEP data. The data are reserved exclusively for research use, that is, they are provided only to the scientific community. To require full access to the data used in this study, it is required to sign a data distribution contract. All contact informations and the procedure to request the data can be obtained at: https://www.diw.de/en/diw_02.c.222829.en/access_and_ordering.html .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Although documented theories about personality types reach back more than 2000 years (i.e. Hippocrates’ humoral pathology), and stereotypes for describing human personality are also widely used in everyday psychology, the descriptive and variable-oriented assessment of personality, i.e. the description of personality on five or six trait domains, has nowadays consolidated its position in modern personality psychology.

In recent years, however, the person-oriented approach, i.e. the description of an individual personality by its similarity to frequently occurring prototypical expressions, has amended the variable-oriented approach with the addition of valuable insights into the description of personality and the prediction of behavior. Focusing on the trait configurations, the person-oriented approach aims to identify personality types that share the same typical personality profile [ 1 ].

Nevertheless, the direct comparison of the utility of person-oriented vs. variable-oriented approaches to personality description yielded mixed results. For example Costa, Herbst, McCrae, Samuels and Ozer [ 2 ] found a higher amount of explained variance in predicting global functioning, geriatric depression or personality disorders for the variable-centered approach using Big Five personality dimensions. But these results also reflect a methodological caveat of this approach, as the categorical simplification of dimensionally assessed variables logically explains less variance. Despite this, the person-centered approach was found to heighten the predictability of a person’s behavior [ 3 , 4 ] or the development of adolescents in terms of internalizing and externalizing symptoms or academic success [ 5 , 6 ], problem behavior, delinquency and depression [ 7 ] or anxiety symptoms [ 8 ], as well as stress responses [ 9 ] and social attitudes [ 10 ]. It has also led to new insights into the function of personality in the context of other constructs such as adjustment [ 2 ], coping behavior [ 11 ], behavioral activation and inhibition [ 12 ], subjective and objective health [ 13 ] or political orientation [ 14 ], and has greater predictive power in explaining longitudinally measured individual differences in more temperamental outcomes such as aggressiveness [ 15 ].

However, there is an ongoing debate about the appropriate number and characteristics of personality prototypes and whether they perhaps constitute an methodological artifact [ 16 ].

With the present paper, we would like to make a substantial contribution to this debate. In the following, we first provide a short review of the personality type literature to identify personality types that were frequently replicated and calculate averaged prototypical profiles based on these previous findings. We then apply multiple clustering algorithms on a large German dataset and use those prototypical profiles generated in the first step to match the results of our cluster analysis to previously found personality types by their Euclidean distance in the 5-dimensional space defined by the Big Five traits. This procedure allows us to reliably link the personality prototypes found in our study to previous empirical evidence, an important analysis step lacking in most previous studies on this topic.

The empirical ground of personality types

The early studies applying modern psychological statistics to investigate personality types worked with the Q-sort procedure [ 1 , 15 , 17 ], and differed in the number of Q-factors. With the Q-Sort method, statements about a target person must be brought in an order depending on how characteristic they are for this person. Based on this Q-Sort data, prototypes can be generated using Q-Factor Analysis, also called inverse factor analysis. As inverse factor analysis is basically interchanging variables and persons in the data matrix, the resulting factors of a Q-factor analysis are prototypical personality profiles and not hypothetical or latent variable dimensions. On this basis, personality types (groups of people with similar personalities) can be formed in a second step by assigning each person to the prototype with whose profile his or her profile correlates most closely. All of these early studies determined at least three prototypes, which were labeled resilient, overcontroler and undercontroler grounded in Block`s theory of ego-control and ego-resiliency [ 18 ]. According to Jack and Jeanne Block’s decade long research, individuals high in ego-control (i.e. the overcontroler type) tend to appear constrained and inhibited in their actions and emotional expressivity. They may have difficulty making decisions and thus be non-impulsive or unnecessarily deny themselves pleasure or gratification. Children classified with this type in the studies by Block tend towards internalizing behavior. Individuals low in ego-control (i.e. the undercontroler type), on the other hand, are characterized by higher expressivity, a limited ability to delay gratification, being relatively unattached to social standards or customs, and having a higher propensity to risky behavior. Children classified with this type in the studies by Block tend towards externalizing behavior.

Individuals high in Ego-resiliency (i.e. the resilient type) are postulated to be able to resourcefully adapt to changing situations and circumstances, to tend to show a diverse repertoire of behavioral reactions and to be able to have a good and objective representation of the “goodness of fit” of their behavior to the situations/people they encounter. This good adjustment may result in high levels of self-confidence and a higher possibility to experience positive affect.

Another widely used approach to find prototypes within a dataset is cluster analysis. In the field of personality type research, one of the first studies based on this method was conducted by Caspi and Silva [ 19 ], who applied the SPSS Quick Cluster algorithm to behavioral ratings of 3-year-olds, yielding five prototypes: undercontrolled, inhibited, confident, reserved, and well-adjusted.

While the inhibited type was quite similar to Block`s overcontrolled type [ 18 ] and the well-adjusted type was very similar to the resilient type, two further prototypes were added: confident and reserved. The confident type was described as easy and responsive in social interaction, eager to do exercises and as having no or few problems to be separated from the parents. The reserved type showed shyness and discomfort in test situations but without decreased reaction speed compared to the inhibited type. In a follow-up measurement as part of the Dunedin Study in 2003 [ 20 ], the children who were classified into one of the five types at age 3 were administered the MPQ at age 26, including the assessment of their individual Big Five profile. Well-adjusteds and confidents had almost the same profiles (below-average neuroticism and above average on all other scales except for extraversion, which was higher for the confident type); undercontrollers had low levels of openness, conscientiousness and openness to experience; reserveds and inhibiteds had below-average extraversion and openness to experience, whereas inhibiteds additionally had high levels of conscientiousness and above-average neuroticism.

Following these studies, a series of studies based on cluster analysis, using the Ward’s followed by K-means algorithm, according to Blashfield & Aldenderfer [ 21 ], on Big Five data were published. The majority of the studies examining samples with N < 1000 [ 5 , 7 , 22 – 26 ] found that three-cluster solutions, namely resilients, overcontrollers and undercontrollers, fitted the data the best. Based on internal and external fit indices, Barbaranelli [ 27 ] found that a three-cluster and a four-cluster solution were equally suitable, while Gramzow [ 28 ] found a four-cluster solution with the addition of the reserved type already published by Caspi et al. [ 19 , 20 ]. Roth and Collani [ 10 ] found that a five-cluster solution fitted the data the best. Using the method of latent profile analysis, Merz and Roesch [ 29 ] found a 3-cluster, Favini et al. [ 6 ] found a 4-cluster solution and Kinnunen et al. [ 13 ] found a 5-cluster solution to be most appropriate.

Studies examining larger samples of N > 1000 reveal a different picture. Several favor a five-cluster solution [ 30 – 34 ] while others favor three clusters [ 8 , 35 ]. Specht et al. [ 36 ] examined large German and Australian samples and found a three-cluster solution to be suitable for the German sample and a four-cluster solution to be suitable for the Australian sample. Four cluster solutions were also found to be most suitable to Australian [ 37 ] and Chinese [ 38 ] samples. In a recent publication, the authors cluster-analysed very large datasets on Big Five personality comprising more than 1,5 million online participants using Gaussian mixture models [ 39 ]. Albeit their results “provide compelling evidence, both quantitatively and qualitatively, for at least four distinct personality types”, two of the four personality types in their study had trait profiles not found previously and all four types were given labels unrelated to previous findings and theory. Another recent publication [ 40 ] cluster-analysing data of over 270,000 participants on HEXACO personality “provided evidence that a five-profile solution was optimal”. Despite limitations concerning the comparability of HEXACO trait profiles with FFM personality type profiles, the authors again decided to label their personality types unrelated to previous findings instead using agency-communion and attachment theories.

We did not include studies in this literature review, which had fewer than 199 participants or those which restricted the number of types a priori and did not use any method to compare different clustering solutions. We have made these decisions because a too low sample size increases the probability of the clustering results being artefacts. Further, a priori limitation of the clustering results to a certain number of personality types is not well reasonable on the base of previous empirical evidence and again may produce artefacts, if the a priori assumed number of clusters does not fit the data well.

To gain a better overview, we extracted all available z-scores from all samples of the above-described studies. Fig 1 shows the averaged z-scores extracted from the results of FFM clustering solutions for all personality prototypes that occurred in more than one study. The error bars represent the standard deviation of the distribution of the z-scores of the respective trait within the same personality type throughout the different studies. Taken together the resilient type was replicated in all 19 of the mentioned studies, the overcontroler type in 16, the undercontroler personality type in 17 studies, the reserved personality type was replicated in 6 different studies, the confident personality type in 4 and the non-desirable type was replicated twice.

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Average Big Five z-scores of personality types based on clustering of FFM datasets with N ≥ 199 that were replicated at least once. Error bars indicate the standard deviation of the repective trait within the respective personality type found in the literature [ 5 , 6 , 10 , 22 – 25 , 27 – 31 , 33 – 36 , 38 , 39 , 41 ].

https://doi.org/10.1371/journal.pone.0244849.g001

Three implications can be drawn from this figure. First, although the results of 19 studies on 26 samples with a total N of 1,560,418 were aggregated, the Big Five profiles for all types can still be clearly distinguished. In other words, personality types seem to be a phenomenon that survives the aggregation of data from different sources. Second, there are more than three replicable personality types, as there are other replicated personality types that seem to have a distinct Big Five profile, at least regarding the reserved and confident personality types. Third and lastly, the non-desirable type seems to constitute the opposite of the resilient type. Looking at two-cluster solutions on Big Five data personality types in the above-mentioned literature yields the resilient opposed to the non-desirable type. This and the fact that it was only replicated twice in the above mentioned studies points to the notion that it seems not to be a distinct type but rather a combined cluster of the over- and undercontroller personality types. Further, both studies with this type in the results did not find either the undercontroller or the overcontroller cluster or both. Taken together, five distinct personality types were consistently replicated in the literature, namely resilient, overcontroller, undercontroller, reserved and confident. However, inferring from the partly large error margin for some traits within some prototypes, not all personality traits seem to contribute evenly to the occurrence of the different prototypes. While for the overcontroler type, above average neuroticism, below average extraversion and openness seem to be distinctive, only below average conscientiousness and agreeableness seemed to be most characteristic for the undercontroler type. The reserved prototype was mostly characterized by below average openness and neuroticism with above average conscientiousness. Above average extraversion, openness and agreeableness seemed to be most distinctive for the confident type. Only for the resilient type, distinct expressions of all Big Five traits seemed to be equally significant, more precisely below average neuroticism and above average extraversion, openness, agreeableness and conscientiousness.

Research gap and novelty of this study

The cluster methods used in most of the mentioned papers were the Ward’s followed by K-means method or latent profile analysis. With the exception of Herzberg and Roth [ 30 ], Herzberg [ 33 ], Barbaranelli [ 27 ] and Steca et. al. [ 25 ], none of the studies used internal or external validity indices other than those which their respective algorithm (in most cases the SPSS software package) had already included. Gerlach et al. [ 39 ] used Gaussian mixture models in combination with density measures and likelihood measures.

The bias towards a smaller amount of clusters resulting from the utilization of just one replication index, e.g. Cohen's Kappa calculated by split-half cross-validation, which was ascertained by Breckenridge [ 42 ] and Overall & Magee [ 43 ], is probably the reason why a three-cluster solution is preferred in most studies. Herzberg and Roth [ 30 ] pointed to the study by Milligan and Cooper [ 44 ], which proved the superiority of the Rand index over Cohen's Kappa and also suggested a variety of validity metrics for internal consistency to examine the construct validity of the cluster solutions.

Only a part of the cited studies had a large representative sample of N > 2000 and none of the studies used more than one clustering algorithm. Moreover, with the exception of Herzberg and Roth [ 30 ] and Herzberg [ 33 ], none of the studies used a large variety of metrics for assessing internal and external consistency other than those provided by the respective clustering program they used. This limitation further adds up to the above mentioned bias towards smaller amounts of clusters although the field of cluster analysis and algorithms has developed a vast amount of internal and external validity algorithms and criteria to tackle this issue. Further, most of the studies had few or no other assessments or constructs than the Big Five to assess construct validity of the resulting personality types. Herzberg and Roth [ 30 ] and Herzberg [ 33 ] as well, though using a diverse variety of validity criteria only used one clustering algorithm on a medium-sized dataset with N < 2000.

Most of these limitations also apply to the study by Specht et. al. [ 36 ], which investigated two measurement occasions of the Big Five traits in the SOEP data sample. They used only one clustering algorithm (latent profile analysis), no other algorithmic validity criteria than the Bayesian information criterion and did not utilize any of the external constructs also assessed in the SOEP sample, such as mental health, locus of control or risk propensity for construct validation.

The largest sample and most advanced clustering algorithm was used in the recent study by Gerlach et al. [ 39 ]. But they also used only one clustering algorithm, and had no other variables except Big Five trait data to assess construct validity of the resulting personality types.

The aim of the present study was therefore to combine different methodological approaches while rectifying the shortcomings in several of the studies mentioned above in order to answer the following exploratory research questions: Are there replicable personality types, and if so, how many types are appropriate and in which constellations are they more (or less) useful than simple Big Five dimensions in the prediction of related constructs?

Three conceptually different clustering algorithms were used on a large representative dataset. The different solutions of the different clustering algorithms were compared using methodologically different internal and external validity criteria, in addition to those already used by the respective clustering algorithm.

To further examine the construct validity of the resulting personality types, their predictive validity in relation to physical and mental health, wellbeing, locus of control, self-esteem, impulsivity, risk-taking and patience were assessed.

Mental health and wellbeing seem to be associated mostly with neuroticism on the variable-oriented level [ 45 ], but on a person-oriented level, there seem to be large differences between the resilient and the overcontrolled personality type concerning perceived health and well-being beyond mean differences in neuroticism [ 33 ]. This seems also to be the case for locus of control and self-esteem, which is associated with neuroticism [ 46 ] and significantly differs between resilient and overcontrolled personality type [ 33 ]. On the other hand, impulsivity and risk taking seem to be associated with all five personality traits [ 47 ] and e.g. risky driving or sexual behavior seem to occur more often in the undercontrolled personality type [ 33 , 48 ].

We chose these measures because of their empirically known differential associations to Big Five traits as well as to the above described personality types. So this both offers the opportunity to have an integrative comparison of the variable- and person-centered descriptions of personality and to assess construct validity of the personality types resulting from our analyses.

Materials and methods

The acquisition of the data this study bases on was carried out in accordance with the principles of the Basel Declaration and recommendations of the “Principles of Ethical Research and Procedures for Dealing with Scientific Misconduct at DIW Berlin”. The protocol was approved by the Deutsches Institut für Wirtschaftsforschung (DIW).

The data used in this study were provided by the German Socio-Economic Panel Study (SOEP) of the German institute for economic research [ 49 ]. Sample characteristics are shown in Table 1 . The overall sample size of the SOEP data used in this study, comprising all individuals who answered at least one of the Big-Five personality items in 2005 and 2009, was 25,821. Excluding all members with more than one missing answers on the Big Five assessment or intradimensional answer variance more than four times higher than the sample average resulted in a total Big Five sample of N = 22,820, which was used for the cluster analyses. 14,048 of these individuals completed, in addition to the Big Five, items relevant to further constructs examined in this study that were assessed in other years. The 2013 SOEP data Big Five assessment was used as a test sample to examine stability and consistency of the final cluster solution.

https://doi.org/10.1371/journal.pone.0244849.t001

The Big Five were assessed in 2005 2009 and 2013 using the short version of the Big Five inventory (BFI-S). It consists of 15 items, with internal consistencies (Cronbach’s alpha) of the scales ranging from .5 for openness to .73 for openness [ 50 ]. Further explorations showed strong robustness across different assessment methods [ 51 ].

To measure the predictive validity, several other measures assessed in the SOEP were included in the analyses. In detail, these were:

Patience was assessed in 2008 with one item: “Are you generally an impatient person, or someone who always shows great patience?”

Risk taking.

Risk-taking propensity was assessed in 2009 by six items asking about the willingness to take risks while driving, in financial matters, in leisure and sports, in one’s occupation (career), in trusting unknown people and the willingness to take health risks, using a scale from 0 (risk aversion) to 10 (fully prepared to take risks). Cronbach’s alpha was .82 for this scale in the current sample.

Impulsivity/Spontaneity.

Impulsivity/spontaneity was assessed in 2008 with one item: Do you generally think things over for a long time before acting–in other words, are you not impulsive at all? Or do you generally act without thinking things over for long time–in other words, are you very impulsive?

Affective and cognitive wellbeing.

Affect was assessed in 2008 by four items asking about the amount of anxiety, anger, happiness or sadness experienced in the last four weeks on a scale from 1 (very rare) to 5 (very often). Cronbach’s alpha for this scale was .66. The cognitive satisfaction with life was assessed by 10 items asking about satisfaction with work, health, sleep, income, leisure time, household income, household duties, family life, education and housing, with a Cronbach’s alpha of .67. The distinction between cognitive and affective wellbeing stems from sociological research based on constructs by Schimmack et al. [ 50 ].

Locus of control.

The individual attitude concerning the locus of control, the degree to which people believe in having control over the outcome of events in their lives opposed to being exposed to external forces beyond their control, was assessed in 2010 with 10 items, comprising four positively worded items such as “My life’s course depends on me” and six negatively worded items such as “Others make the crucial decisions in my life”. Items were rated on a 7-point scale ranging from “does not apply” to “does apply”. Cronbach’s alpha in the present sample for locus of control was .57.

Self-esteem.

Global self-esteem–a person’s overall evaluation or appraisal of his or her worth–was measured in 2010 with one item: “To what degree does the following statement apply to you personally?: I have a positive attitude toward myself”.

To assess subjective health, the 12-Item Short Form Health Survey (SF-12) was integrated into the SOEP questionnaire and assessed in 2002, 2004, 2006, 2008 and 2010. In the present study, we used the data from 2008 and 2010. The SF-12 is a short form of the SF-36, a self-report questionnaire to assess the non-disease-specific health status [ 52 ]. Within the SF-12, items can be grouped onto two subscales, namely the physical component summary scale, with items asking about physical health correlates such as how exhausting it is to climb stairs, and the mental component summary scale, with items asking about mental health correlates such as feeling sad and blue. The literature on health measures often distinguishes between subjective and objective health measures (e.g., BMI, blood pressure). From this perspective, the SF-12 would count as a subjective health measure. In the present sample, Cronbach’s alpha for the SF-12 items was .77.

Derivation of the prototypes

The first step was to administer three different clustering methods on the Big Five data of the SOEP sample: First, the conventional linear clustering method used by Asendorpf [ 15 , 35 , 53 ] and also Herzberg and Roth [ 30 ] combines the hierarchical clustering method of Ward [ 54 ] with the k-means algorithm [ 55 ]. This algorithm generates a first guess of personality types based on hierarchical clustering, and then uses this first guess as starting points for the k-means-method, which iteratively adjusts the personality profiles, i.e. the cluster means to minimize the error of allocation, i.e. participants with Big Five profiles that are allocated to two or more personality types. The second algorithm we used was latent profile analysis with Mclust in R [ 56 ], an algorithm based on probabilistic finite mixture modeling, which assumes that there are latent classes/profiles/mixture components underlying the manifest observed variables. This algorithm generates personality profiles and iteratively calculates the probability of every participant in the data to be allocated to one of the personality types and tries to minimize an error term using maximum likelihood method. The third algorithm was spectral clustering, an algorithm which initially computes eigenvectors of graph Laplacians of the similarity graph constructed on the input data to discover the number of connected components in the graph, and then uses the k-means algorithm on the eigenvectors transposed in a k-dimensional space to compute the desired k clusters [ 57 ]. As it is an approach similar to the kernel k-means algorithm [ 58 ], spectral clustering can discover non-linearly separable cluster formations. Thus, this algorithm is able, in contrast to the standard k-means procedure, to discover personality types having unequal or non-linear distributions within the Big-Five traits, e.g. having a small SD on neuroticism while having a larger SD on conscientiousness or a personality type having high extraversion and either high or low agreeableness.

Within the last 50 years, a large variety of clustering algorithms have been established, and several attempts have been made to group them. In their book about cluster analysis, Bacher et al. [ 59 ] group cluster algorithms into incomplete clustering algorithms, e.g. Q-Sort or multidimensional scaling, deterministic clustering, e.g. k-means or nearest-neighbor algorithms, and probabilistic clustering, e.g. latent class and latent profile analysis. According to Jain [ 60 ], cluster algorithms can be grouped by their objective function, probabilistic generative models and heuristics. In his overview of the current landscape of clustering, he begins with the group of density-based algorithms with linear similarity functions, e.g. DBSCAN, or probabilistic models of density functions, e.g. in the expectation-maximation (EM) algorithm. The EM algorithm itself also belongs to the large group of clustering algorithms with an information theoretic formulation. Another large group according to Jain is graph theoretic clustering, which includes several variants of spectral clustering. Despite the fact that it is now 50 years old, Jain states that k-means is still a good general-purpose algorithm that can provide reasonable clustering results.

The clustering algorithms chosen for the current study are therefore representatives of the deterministic vs. probabilistic grouping according to Bacher et. al. [ 59 ], as well as representatives of the density-based, information theoretic and graph theoretic grouping according to Jain [ 60 ].

Determining the number of clusters

There are two principle ways to determine cluster validity: external or relative criteria and internal validity indices.

External validity criteria.

External validity criteria measure the extent to which cluster labels match externally supplied class labels. If these external class labels originate from another clustering algorithm used on the same data sample, the resulting value of the external cluster validity index is relative. Another method, which is used in the majority of the cited papers in section 1, is to randomly split the data in two halves, apply a clustering algorithm on both halves, calculate the cluster means and allocate members of one half to the calculated clusters of the opposite half by choosing the cluster mean with the shortest Euclidean distance to the data member in charge. If the cluster algorithm allocation of one half is then compared with the shortest Euclidean distance allocation of the same half by means of an external cluster validity index, this results in a value for the reliability of the clustering method on the data sample.

As allocating data points/members by Euclidean distances always yields spherical and evenly shaped clusters, it will favor clustering methods that also yield spherical and evenly shaped clusters, as it is the case with standard k-means. The cluster solutions obtained with spectral clustering as well as latent profile analysis (LPA) are not (necessarily) spherical or evenly shaped; thus, allocating members of a dataset by their Euclidean distances to cluster means found by LPA or spectral clustering does not reliably represent the structure of the found cluster solution. This is apparent in Cohen’s kappa values <1 if one uses the Euclidean external cluster assignment method comparing a spectral cluster solution with itself. Though by definition, Cohen’s kappa should be 1 if the two ratings/assignments compared are identical, which is the case when comparing a cluster solution (assigning every data point to a cluster) with itself. This problem can be bypassed by allocating the members of the test dataset to the respective clusters by training a support vector machine classifier for each cluster. Support vector machines (SVM) are algorithms to construct non-linear “hyperplanes” to classify data given their class membership [ 61 ]. They can be used very well to categorize members of a dataset by an SVM-classifier trained on a different dataset. Following the rationale not to disadvantage LPA and spectral clustering in the calculation of the external validity, we used an SVM classifier to calculate the external validity criteria for all clustering algorithms in this study.

To account for the above mentioned bias to smaller numbers of clusters we applied three external validity criteria: Cohen’s kappa, the Rand index [ 62 ] and the Hubert-Arabie adjusted Rand index [ 63 ].

Internal validity criteria.

Again, to account for the bias to smaller numbers of clusters, we also applied multiple internal validity criteria selected in line with the the following reasoning: According to Lam and Yan [ 64 ], the internal validity criteria fall into three classes: Class one includes cost-function-based indices, e.g. AIC or BIC [ 65 ], whereas class two comprises cluster-density-based indices, e.g. the S_Dbw index [ 66 ]. Class three is grounded on geometric assumptions concerning the ratio of the distances within clusters compared to the distances between the clusters. This class has the most members, which differ in their underlying mathematics. One way of assessing geometric cluster properties is to calculate the within- and/or between-group scatter, which both rely on summing up distances of the data points to their barycenters (cluster means). As already explained in the section on external criteria, calculating distances to cluster means will always favor spherical and evenly shaped cluster solutions without noise, i.e. personality types with equal and linear distributions on the Big Five trait dimensions, which one will rarely encounter with natural data.

Another way not solely relying on distances to barycenters or cluster means is to calculate directly with the ratio of distances of the data points within-cluster and between-cluster. According to Desgraupes [ 67 ], this applies to the following indices: the C-index, the Baker & Hubert Gamma index, the G(+) index, Dunn and Generalized Dunn indices, the McClain-Rao index, the Point-Biserial index and the Silhouette index. As the Gamma and G(+) indices rely on the same mathematical construct, one can declare them as redundant. According to Bezdek [ 68 ], the Dunn index is very sensitive to noise, even if there are only very few outliers in the data. Instead, the authors propose several ways to compute a Generalized Dunn index, some of which also rely on the calculation of barycenters. The best-performing GDI algorithm outlined by Bezdek and Pal [ 68 ] which does not make use of cluster barycenters is a ratio of the mean distance of every point between clusters to the maximum distance between points within the cluster, henceforth called GDI31. According to Vendramin et al. [ 69 ], the Gamma, C-, and Silhouette indices are the best-performing (over 80% correct hit rate), while the worst-performing are the Point-Biserial and the McClain-Rao indices (73% and 51% correct hit rate, respectively).

Fig 2 shows a schematic overview of the procedure we used to determine the personality types Big Five profiles, i.e. the cluster centers. To determine the best fitting cluster solution, we adopted the two-step procedure proposed by Blashfield and Aldenfelder [ 21 ] and subsequently used by Asendorpf [ 15 , 35 , 53 ] Boehm [ 41 ], Schnabel [ 24 ], Gramzow [ 28 ], and Herzberg and Roth [ 30 ], with a few adjustments concerning the clustering algorithms and the validity criteria.

LPA = latent profile analysis, SVM = Support Vector Machine.

https://doi.org/10.1371/journal.pone.0244849.g002

First, we drew 20 random samples of the full sample comprising all individuals who answered the Big-Five personality items in 2005 and 2009 with N = 22,820 and split every sample randomly into two halves. Second, all three clustering algorithms described above were performed on each half, saving the 3-, 4-,…,9- and 10-cluster solution. Third, participants of each half were reclassified based on the clustering of the other half of the same sample, again for every clustering algorithm and for all cluster solutions from three to 10 clusters. In contrast to Asendorpf [ 35 ], this was implemented not by calculating Euclidean distances, but by training a support vector machine classifier for every cluster of a cluster solution of one half-sample and reclassifying the members of the other half of the same sample by the SVM classifier. The advantages of this method are explained in the section on external criteria. This resulted in 20 samples x 2 halves per sample x 8 cluster solutions x 3 clustering algorithms, equaling 960 clustering solutions to be compared.

The fourth step was to compute the external criteria comparing each Ward followed by k-means, spectral, or probabilistic clustering solution of each half-sample to the clustering by the SVM classifier trained on the opposite half of the same sample, respectively. The external calculated in this step were Cohen's kappa, Rand’s index [ 62 ] and the Hubert & Arabie adjusted Rand index [ 63 ]. The fifth step consisted of averaging: We first averaged the external criteria values per sample (one value for each half), and then averaged the 20x4 external criteria values for each of the 3-,4-…, 10-cluster solutions for each algorithm.

The sixth step was to temporarily average the external criteria values for the 3-,4-,… 10-cluster solution over the three clustering algorithms and discard the cluster solutions that had a total average kappa below 0.6.

As proposed by Herzberg and Roth [ 30 ], we then calculated several internal cluster validity indices for all remaining cluster solutions. The internal validity indices which we used were, in particular, the C-index [ 70 ], the Baker-Hubert Gamma index [ 71 ], the G + index [ 72 ], the Generalized Dunn index 31 [ 68 ], the Point-Biserial index [ 44 ], the Silhouette index [ 73 ], AIC and BIC [ 65 ] and the S_Dbw index [ 66 ]. Using all of these criteria, it is possible to determine the best clustering solution in a mathematical/algorithmic manner.

The resulting clusters where then assigned names by calculating Euclidean distances to the clusters/personality types found in the literature, taking the nearest type within the 5-dimensional space defined by the respective Big Five values.

To examine the stability and consistency of the final cluster solution, in a last step, we then used the 2013 SOEP data sample to calculate a cluster solution using the algorithm and parameters which generated the solution with the best validity criteria for the 2005 and 2009 SOEP data sample. The 2013 personality prototypes were allocated to the personality types of the solution from the previous steps by their profile similarity measure D. Stability then was assessed by calculation of Rand-index, adjusted Rand-index and Cohen’s Kappa for the complete solution and for every single personality type. To generate the cluster allocations between the different cluster solutions, again we used SVM classifier as described above.

To assess the predictive and the construct validity of the resulting personality types, the inversed Euclidean distance for every participant to every personality prototype (averaged Big Five profile in one cluster) in the 5-dimensional Big-Five space was calculated and correlated with further personality, behavior and health measures mentioned above. To ensure that longitudinal reliability was assessed in this step, Big Five data assessed in 2005 were used to predict measures which where assessed three, four or five years later. The selection of participants with available data in 2005 and 2008 or later reduced the sample size in this step to N = 14,048.

Internal and external cluster fit indices

Table 2 shows the mean Cohen’s kappa values, averaged over all clustering algorithms and all 20 bootstrapped data permutations.

https://doi.org/10.1371/journal.pone.0244849.t002

Whereas the LPA and spectral cluster solutions seem to have better kappa values for fewer clusters, the kappa values of the k-means clustering solutions have a peak at five clusters, which is even higher than the kappa values of the three-cluster solutions of the other two algorithms.

Considering that these values are averaged over 20 independent computations, there is very low possibility that this result is an artefact. As the solutions with more than five clusters had an average kappa below .60, they were discarded in the following calculations.

Table 3 shows the calculated external and internal validity indices for the three- to five-cluster solutions, ordered by the clustering algorithm. Comparing the validity criterion values within the clustering algorithms reveals a clear preference for the five-cluster solution in the spectral as well as the Ward followed by k-means algorithm.

https://doi.org/10.1371/journal.pone.0244849.t003

Looking solely at the cluster validity results of the latent profile models, they seem to favor the three-cluster model. Yet, in a global comparison, only the S_Dbw index continues to favor the three-cluster LPA model, whereas the results of all other 12 validity indices support five-cluster solutions. The best clustering solution in terms of the most cluster validity index votes is the five-cluster Ward followed by k-means solution, and second best is the five-cluster spectral solution. It is particularly noteworthy that the five-cluster K-means solution has higher values on all external validity criteria than all other solutions. As these values are averaged over 20 independent cluster computations on random data permutations, and still have better values than solutions with fewer clusters despite the fact that these indices have a bias towards solutions with fewer clusters [ 42 ], there seems to be a substantial, replicable five-component structure in the Big Five Data of the German SOEP sample.

Description of the prototypes

The mean z-scores on the Big Five factors of the five-cluster k-means as well as the spectral solution are depicted in Fig 2 . Also depicted is the five-cluster LPA solution, which is, despite having poor internal and external validity values compared to the other two solutions, more complicated to interpret. To find the appropriate label for the cluster partitions, the respective mean z-scores on the Big Five factors were compared with the mean z-scores found in the literature, both visually and by the Euclidean distance.

The spectral and the Ward followed by k-means solution overlap by 81.3%; the LPA solution only overlaps with the other two solutions by 21% and 23%, respectively. As the Ward followed by k-means solution has the best values both for external and internal validity criteria, we will focus on this solution in the following.

The first cluster has low neuroticism and high values on all other scales and includes on average 14.4% of the participants (53.2% female; mean age 53.3, SD = 17.3). Although the similarity to the often replicated resilient personality type is already very clear merely by looking at the z-scores, a very strong congruence is also revealed by computing the Euclidean distance (0.61). The second cluster is mainly characterized by high neuroticism, low extraversion and low openness and includes on average 17.3% of the participants (54.4% female; mean age 57.6, SD = 18.2). It clearly resembles the overcontroller type, to which it also has the shortest Euclidean distance (0.58). The fourth cluster shows below-average values on the factors neuroticism, extraversion and openness, as opposed to above-average values on openness and conscientiousness. It includes on average 22.5% of the participants (45% female; mean age 56.8, SD = 17.6). Its mean z-scores closely resemble the reserved personality type, to which it has the smallest Euclidean distance (0.36). The third cluster is mainly characterized by low conscientiousness and low openness, although in the spectral clustering solution, it also has above-average extraversion and openness values. Computing the Euclidean distance (0.86) yields the closest proximity to the undercontroller personality type. This cluster includes on average 24.6% of the participants (41.3% female; mean age 50.8, SD = 18.3). The fifth cluster exhibits high z-scores on every Big Five trait, including a high value for neuroticism. Computing the Euclidean distances to the previously found types summed up in Fig 1 reveals the closest resemblance with the confident type (Euclidean distance = 0.81). Considering the average scores of the Big Five traits, it resembles the confident type from Herzberg and Roth [ 30 ] and Collani and Roth [ 10 ] as well as the resilient type, with the exception of the high neuroticism score. Having above average values on the more adaptive traits while having also above average neuroticism values reminded a reviewer from a previous version of this paper of the vulnerable but invincible children of the Kauai-study [ 74 ]. Despite having been exposed to several risk factors in their childhood, they were well adapted in their adulthood except for low coping efficiency in specific stressful situations. Taken together with the lower percentage of participants in the resilient cluster in this study, compared to previous studies, we decided to name the 5 th cluster vulnerable-resilient. Consequently, only above or below average neuroticism values divided between resilient and vulnerable resilient. On average, 21.2% of the participants were allocated to this cluster (68.3% female; mean age 54.9, SD = 17.4).

Summarizing the descriptive statistics, undercontrollers were the “youngest” cluster whereas overcontrollers were the “oldest”. The mean age differed significantly between clusters ( F [4, 22820] = 116.485, p <0.001), although the effect size was small ( f = 0.14). The distribution of men and women between clusters differed significantly (c 2 [ 4 ] = 880.556, p <0.001). With regard to sex differences, it was particularly notable that the vulnerable-resilient cluster comprised only 31.7% men. This might be explained by general sex differences on the Big Five scales. According to Schmitt et al. [ 75 ], compared to men, European women show a general bias to higher neuroticism (d = 0.5), higher conscientiousness (d = 0.3) and higher extraversion and openness (d = 0.2). As the vulnerable-resilient personality type is mainly characterized by high neuroticism and above-average z-scores on the other scales, it is therefore more likely to include women. In turn, this implies that men are more likely to have a personality profile characterized mainly by low conscientiousness and low openness, which is also supported by our findings, as only 41.3% of the undercontrollers were female.

Concerning the prototypicality of the five-cluster solution compared to the mean values extracted from previous studies, it is apparent that the resilient, the reserved and the overcontroller type are merely exact replications. In contrast to previous findings, the undercontrollers differed from the previous findings cited above in terms of average neuroticism, whereas the vulnerable-resilient type differed from the previously found type (labeled confident) in terms of high neuroticism.

Stability and consistency

Inspecting the five cluster solution using the k-means algorithm on the Big Five data of the 2013 SOEP sample seemed to depict a replication of the above described personality types. This first impression was confirmed by the calculation of the profile similarity measure D between the 2005/2009 and 2013 SOEP sample cluster solutions, which yielded highest similarity for the undercontroler (D = 0.27) and reserved (D = 0.36) personality types, followed by the vulnerable-resilient (D = 0.37), overcontroler (D = 0.44) and resilient (D = 0.50) personality types. Substantial agreement was confirmed by the values of the Rand index (.84) and Cohen’ Kappa (.70) whereas the Hubert Arabie adjusted Rand Index (.58) indicated moderate agreement for the comparison between the kmeans cluster solution for the 2013 SOEP sample and the cluster allocation with an SVM classifier trained on the 2005 and 2009 kmeans cluster solution.

Predictive validity

In view of the aforementioned criticisms that (a) predicting dimensional variables will mathematically favor dimensional personality description models, and (b) using dichotomous predictors will necessarily provide less explanation of variance than a model using five continuous predictors, we used the profile similarity measure D [ 76 ] instead of dichotomous dummy variables accounting for the prototype membership. Correlations between the inversed Euclidean similarity measure D to the personality types and patience, risk-taking, spontaneity/impulsivity, locus of control, affective wellbeing, self-esteem and health are depicted in Table 4 .

https://doi.org/10.1371/journal.pone.0244849.t004

Patience had the highest association with the reserved personality type (r = .19, p < .001). The propensity to risky behavior, e.g. while driving (r = .17, p < .001), in financial matters (r = .17, p < .001) or in health decisions (r = .13, p < .001) was most highly correlated with the undercontroller personality type. This means that the more similar the Big-Five profile to the above-depicted undercontroller personality prototype, the higher the propensity for risky behavior. The average correlation across all three risk propensity scales with the undercontroller personality type is r = .21, with p < .001. This is in line with the postulations by Block and Block and subsequent replications by Caspi et al. [ 19 , 48 ], Robins et al. [ 1 ] and Herzberg [ 33 ] about the undercontroller personality type. Spontaneity/impulsivity showed the highest correlation with the overcontroller personality type (r = -.18, p<0.001). This is also in accordance with Block and Block, who described this type as being non-impulsive and appearing constrained and inhibited in actions and emotional expressivity.

Concerning locus of control, proximity to the resilient personality profile had the highest correlation with internal locus of control (r = .25, p < .001), and in contrast, the more similar the individual Big-Five profile was to the overcontroller personality type, the higher the propensity for external allocation of control (r = .22, p < .001). This is not only in line with Block and Block’s postulations that the resilient personality type has a good repertoire of coping behavior and therefore perceives most situations as “manageable” as well as with the findings by [ 33 ], but is also in accordance with findings regarding the construct and development of resilience [ 77 , 78 ].

Also in line with the predictions of Block and Block and replicating the findings of Herzberg [ 33 ], self-esteem was correlated the highest with the resilient personality profile similarity (r = .33, p < .001), second highest with the reserved personality profile proximity (r = .15, p < .001), and negatively correlated with the overcontroller personality type (r = -.27, p < .001).

This pattern also applies to affective and cognitive wellbeing as well as physical and mental health measured by the SF-12. Affective wellbeing was correlated the highest with similarity to the resilient personality type (r = .27, p < .001), and second highest with the reserved personality type (r = .23, p < .001). The overcontroller personality type, in contrast, showed a negative correlation with affective (r = -.16, p < .001) and cognitive (r = -21, p < .001) wellbeing. Concerning health, a remarkable finding is that lack of physical health impairment correlated the highest with the resilient personality profile similarity (p = -.23, p < .001) but lack of mental health impairment correlated the highest with the reserved personality type (r = -.15, p < .001). The highest correlation with mental health impairments (r = .11, p < .001), as well as physical health impairments (r = .16, p < .001) was with the overcontroller personality profile similarity. It is striking that although the undercontroller personality profile similarity was associated with risky health behavior, it had a negative association with health impairment measures, in contrast to the overcontroller personality type, which in turn had no association with risky health behavior. This result is in line with the link of internalizing and externalizing behavior with the overcontroller and undercontroller types [ 79 ], respectively. Moreover, it is also in accordance with the association of internalizing problems with somatic symptoms and/or symptoms of depressiveness and anxiety [ 80 ].

A further noteworthy finding is that these associations cannot be solely explained by the high neuroticism of the overcontroller personality type, as the vulnerable-resilient type showed a similar level of neuroticism but no correlation with self-esteem, the opposite correlation with impulsivity, and far lower correlations with health measures or locus of control. The vulnerable-resilient type showed also a remarkable distinction to the other types concerning the correlations to wellbeing. While for all other types, the direction and significance of the correlations to affective and cognitive measures of wellbeing were alike, the vulnerable-resilient type only had a significant negative correlation to affective wellbeing while having no significant correlation to measures of cognitive wellbeing.

To provide an overview of the particular associations of the Big Five values with all of the above-mentioned behavior and personality measures, Table 5 shows the bivariate correlations.

https://doi.org/10.1371/journal.pone.0244849.t005

Investigating the direction of the correlation and the relativity of each value to each other row-wise reveals, to some extent, a clear resemblance with the z-scores of the personality types shown in Fig 3 . Correlation profiles of risk taking, especially the facet risk-taking in health issues and locus of control, clearly resemble the undercontroller personality profile (negative correlations with openness and conscientiousness, positive but lower correlations with extraversion and openness). Patience had negative correlations with neuroticism and extraversion, and positive correlations with openness and conscientiousness, which in turn resembles the z-score profile of the reserved personality profile. Spontaneity/impulsivity had moderate to high positive correlations with extraversion and openness, and low negative correlations with openness and neuroticism, which resembles the inverse of the overcontroller personality profile. Self-esteem as well as affective and cognitive wellbeing correlations with the Big Five clearly resemble the resilient personality profile: negative correlations with neuroticism, and positive correlations with extraversion, openness, openness and conscientiousness. Inspecting the SF-12 health correlation, in terms of both physical and mental health, reveals a resemblance to the inversed resilient personality profile (high correlation with neuroticism, low correlation with extraversion, openness, openness and conscientiousness, as well as a resemblance with the overcontroller profile (positive correlation with neuroticism, negative correlation with extraversion).

https://doi.org/10.1371/journal.pone.0244849.g003

On the variable level, neuroticism had the highest associations with almost all of the predicted variables, with the exception of impulsivity, which was mainly correlated with extraversion and openness. It is also evident that all variables in question here are correlated with three or more Big Five traits. This can be seen as support for hypothesis that the concept of personality prototypes has greater utility than the variable-centered approach in understanding or predicting more complex psychological constructs that are linked to two or more Big Five traits.

The goal of this study was to combine different methodological approaches while overcoming the shortcomings of previous studies in order to answer the questions whether there are replicable personality types, how many of them there are, and how they relate to Big Five traits and other psychological and health-related constructs. The results revealed a robust five personality type model, which was able to significantly predict all of the psychological constructs in question longitudinally. Predictions from previous findings connecting the predicted variables to the particular Big Five dimensions underlying the personality type model were confirmed. Apparently, the person-centered approach to personality description has the most practical utility when predicting behavior or personality correlates that are connected to more than one or two of the Big Five traits such as self-esteem, locus of control and wellbeing.

This study fulfils all three criteria specified by von Eye & Bogat [ 81 ] regarding person-oriented research and considers the recommendations regarding sample size and composition by Herzberg and Roth [ 30 ]. The representative and large sample was analyzed under the assumption that it was drawn from more than one population (distinct personality types). Moreover, several external and internal cluster validity criteria were taken into account in order to validate the groupings generated by three different cluster algorithms, which were chosen to represent broad ranges of clustering techniques [ 60 , 82 ]. The Ward followed by K-means procedure covers hierarchical as well as divisive partitioning (crisp) clustering, the latent profile algorithm covers density-based clustering with probabilistic models and information theoretic validation (AIC, BIC), and spectral clustering represents graph theoretic as well as kernel-based non-linear clustering techniques. The results showed a clear superiority of the five-cluster solution. Interpreting this grouping based on theory revealed a strong concordance with personality types found in previous studies, which we could ascertain both in absolute mean values and in the Euclidean distances to mean cluster z-scores extracted from 19 previous studies. As no previous study on personality types used that many external and internal cluster validity indices and different clustering algorithms on a large data set of this size, the present study provides substantial support for the personality type theory postulating the existence of resilient, undercontroller, overcontroller, vulnerable-resilient and reserved personality types, which we will refer to with RUO-VR subsequently. Further, our findings concerning lower validity of the LPA cluster solutions compared to the k-means and spectral cluster solutions suggest that clustering techniques based on latent models are less suited for the BFI-S data of the SOEP sample than iterative and deterministic methods based on the k-means procedure or non-linear kernel or graph-based methods. Consequently, the substance of the clustering results by Specht et. al. [ 36 ], which applied latent profile analysis on the SOEP sample, may therefore be limited.

But the question, if the better validity values of the k-means and spectral clustering techniques compared to the LPA indicate a general superiority of these algorithms, a superiority in the field of personality trait clustering or only a superiority in clustering this specific personality trait assessment (BFI-S) in this specific sample (SOEP), remains subject to further studies on personality trait clustering.

When determining the longitudinal predictive validity, the objections raised by Asendorpf [ 53 ] concerning the direct comparison of person-oriented vs. variable-oriented personality descriptions were incorporated by using continuous personality type profile similarity based on Cronbach and Gleser [ 75 ] instead of dichotomous dummy variables as well as by predicting long-term instead of cross-sectionally assessed variables. Using continuous profile similarity variables also resolves the problem that potentially important information about members of the same class is lost in categorical personality descriptions [ 15 , 53 , 83 ]. Predictions regarding the association of the personality types with the assessed personality and behavior correlates, including risk propensity, impulsivity, self-esteem, locus of control, patience, cognitive and affective wellbeing as well as health measures, were confirmed.

Overcontrollers showed associations with lower spontaneity/impulsivity, with lower mental and physical health, and lower cognitive as well as affective wellbeing. Undercontrollers were mainly associated with higher risk propensity and higher impulsive behavior. These results can be explained through the connection of internalizing and externalizing behavior with the overcontroller and undercontroller types [ 5 – 7 , 78 ] and further with the connection of internalizing problems with somatic symptoms and/or symptoms of depressiveness and anxiety [ 79 ]. The dimensions or categories of internalizing and externalizing psychopathology have a long tradition in child psychopathology [ 84 , 85 ] and have been subsequently replicated in adult psychopathology [ 86 , 87 ] and are now basis of contemporary approaches to general psychopathology [ 88 ]. A central proceeding in this development is the integration of (maladaptive) personality traits into the taxonomy of general psychopathology. In the current approach, maladaptive personality traits are allocated to psychopathology spectra, such as the maladaptive trait domain negative affectivity to the spectrum of internalizing disorders. However, the findings of this study suggests that not specific personality traits are intertwined with the development or the occurrence of psychopathology but specific constellations of personality traits, in other words, personality profiles. This hypothesis is also supported by the findings of Meeus et al. [ 8 ], which investigated longitudinal transitions from one personality type to another with respect to symptoms of generalized anxiety disorder. Transitions from resilient to overcontroller personality profiles significantly predicted higher anxiety symptoms while the opposite was found for transitions from overcontroller to resilient personality profiles.

The resilient personality type had the strongest associations with external locus of control, higher patience, good health and positive wellbeing. This not only confirms the characteristics of the resilient type already described by Block & Block [ 18 ] and subsequently replicated, but also conveys the main characteristics of the construct of resilience itself. While the development of resiliency depends on the quality of attachment experiences in childhood and youth [ 89 ], resiliency in adulthood seems to be closely linked to internal locus of control, self-efficacy and self-esteem. In other words, the link between secure attachment experiences in childhood and resiliency in adulthood seems to be the development of a resilient personality trait profile. Seen the other way around, the link between traumatic attachment experiences or destructive environmental factors and low resiliency in adulthood may be, besides genetic risk factors, the development of personality disorders [ 90 ] or internalizing or externalizing psychopathology [ 91 ]. Following this thought, the p-factor [ 92 ], i.e. a general factor of psychopathology, may be an index of insufficient resilience. Although from the viewpoint of personality pathology, having a trait profile close to the resilient personality type may be an index of stable or good personality structure [ 93 ], i.e. personality functioning [ 94 ], which, though being consistently associated with general psychopathology and psychosocial functioning, should not be confused with it [ 95 ].

The reserved personality type had the strongest associations with higher patience as well as better mental health. The vulnerable-resilient personality type showed low positive correlations with spontaneity/impulsivity and low negative correlations with patience as well as health and affective wellbeing.

Analyzing the correlations of the dimensional Big Five values with the predicted variables revealed patterns similar to the mean z-scores of the personality types resilient, overcontrollers, undercontrollers and reserved. Most variables had a low to moderate correlation with just one personality profile similarity, while having at least two or three low to moderate correlations with the Big Five measures. This can be seen as support for the argument of Chapman [ 82 ] and Asendorpf [ 15 , 53 ] that personality types have more practical meaning in the prediction of more complex correlates of human behavior and personality such as mental and physical health, wellbeing, risk-taking, locus of control, self-esteem and impulsivity. Our findings further underline that the person-oritented approach may better be suited than variable-oriented personality descriptions to detect complex trait interactions [ 40 ]. E.g. the vulnerable-resilient and the overcontroller type did not differ in their high average neuroticism values, while differing in their correlations to mental and somatic health self-report measures. It seems that high neuroticism is far stronger associated to lower mental and physical health as well as wellbeing if it occurs together with low extraversion and low openness as seen in the overcontroller type. This differential association between the Big-Five traits also affects the correlation between neuroticism and self-esteem or locus of control. Not differing in their average neuroticism value, the overcontroller personality profile had moderate associations with low self-esteem and external locus of control while the vulnerable-resilient personality profile did only show very low or no association. Further remarkable is that the vulnerable-resilient profile similarity had no significant correlation with measures of cognitive wellbeing while being negatively correlated with affective wellbeing. This suggests that individuals with a Big-Five personality profile similar to the vulnerable-resilient prototype seem not to perceive impairments in their wellbeing, at least on a cognitive layer, although having high z-values in neuroticism. Another explanation for this discrepancy as well as for the lack of association of the vulnerable-resilient personality profile to low self-esteem and external locus of control though having high values in neuroticism could be found in the research on the construct of resilience. Personalities with high neuroticism values but stable self-esteem, internal locus of control and above average agreeableness and extraversion values may be the result of the interplay of multiple protective factors (e.g. close bond with primary caregiver, supportive teachers) with risk factors (e.g. parental mental illness, poverty). The development of a resilient personality profile with below average neuroticism values, on the other hand, may be facilitated if protective factors outweigh the risk factors by a higher ratio.

An interesting future research question therefore concerns to what extent personality types found in this study may be replicated using maladaptive trait assessments according to DSM-5, section III [ 96 ] or the ICD-11 personality disorder section [ 97 ] (for a comprehensive overview on that topic see e.g. [ 98 ]). As previous studies showed that both DSM-5 [ 99 ] and ICD-11 [ 100 ] maladaptive personality trait domains may be, to a large extent, conceptualized as maladaptive variants of Big Five traits, it is highly likely that also maladaptive personality trait domains align around personality prototypes and that the person-oriented approach may amend the research field of personality pathology [ 101 ].

Taken together, the findings of this study connect the variable centered approach of personality description, more precisely the Big Five traits, through the concept of personality types to constructs of developmental psychology (resiliency, internalizing and externalizing behavior and/or problems) as well as clinical psychology (mental health) and general health assessed by the SF-12. We could show that the distribution of Big Five personality profiles, at least in the large representative German sample of this study, aggregates around five prototypes, which in turn have distinct associations to other psychological constructs, most prominently resilience, internalizing and externalizing behavior, subjective health, patience and wellbeing.

Limitations

Several limitations of the present study need to be considered: One problem concerns the assessment of patience, self-esteem and impulsivity. From a methodological perspective, these are not suitable for the assessment of construct validity as they were assessed with only one item. A further weakness is the short Big Five inventory with just 15 items. Though showing acceptable reliability, 15 items are more prone to measurement errors than measures with more items and only allow a very broad assessment of the 5 trait domains, without information on individual facet expressions. A more big picture question is if the Big Five model is the best way to assess personality in the first place. A further limitation concerns the interpretation of the subjective health measures, as high neuroticism is known to bias subjective health ratings. But the fact that the vulnerable-resilient and the overcontroler type had similar average neuroticism values but different associations with the subjective health measures speaks against a solely neuroticism-based bias driven interpretation of the associations of the self-reported health measures with the found personality clusters. Another limitation is the correlation between the personality type similarities: As they are based on Euclidean distances and the cluster algorithms try to maximize the distances between the cluster centers, proximity to one personality type (that is the cluster mean) logically implies distance from the others. In the case of the vulnerable-resilient and the resilient type, the correlation of the profile similarities is positive, as they mainly differ on only one dimension (neuroticism). These high correlations between the profile similarities prevents or diminishes, due to the emerging high collinearity, the applicability of general linear models, i.e. regression to calculate the exact amount of variance explained by the profile similarities.

The latter issue could be bypassed by assessing types and dimensions with different questionnaires, i.e. as in Asendorpf [ 15 ] with the California Child Q-set to determine the personality type and the NEO-FFI for the Big Five dimensions. Another possibility is to design a new questionnaire based on the various psychological constructs that are distinctly associated with each personality type, which is probably a subject for future person-centered research.

Acknowledgments

The data used in this article were made available by the German Socio-Economic Panel (SOEP, Data for years 1984–2015) at the German Institute for Economic Research, Berlin, Germany. However, the findings and views reported in this article are those of the authors. To ensure the confidentiality of respondents’ information, the SOEP adheres to strict security standards in the provision of SOEP data. The data are reserved exclusively for research use, that is, they are provided only to the scientific community. All users, both within the EEA (and Switzerland) and outside these countries, are required to sign a data distribution contract.

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• Published: 05 May 2020

Personality traits, emotional intelligence and decision-making styles in Lebanese universities medical students

• Radwan El Othman 1 ,
• Rola El Othman 2 ,
• Rabih Hallit 1 , 3 , 4   na1 ,
• Sahar Obeid 5 , 6 , 7   na1 &
• Souheil Hallit 1 , 5 , 7   na1  

BMC Psychology volume  8 , Article number:  46 ( 2020 ) Cite this article

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This study aims to assess the impact of personality traits on emotional intelligence (EI) and decision-making among medical students in Lebanese Universities and to evaluate the potential mediating role-played by emotional intelligence between personality traits and decision-making styles in this population.

This cross-sectional study was conducted between June and December 2019 on 296 general medicine students.

Higher extroversion was associated with lower rational decision-making style, whereas higher agreeableness and conscientiousness were significantly associated with a higher rational decision-making style. More extroversion and openness to experience were significantly associated with a higher intuitive style, whereas higher agreeableness and conscientiousness were significantly associated with lower intuitive style. More agreeableness and conscientiousness were significantly associated with a higher dependent decision-making style, whereas more openness to experience was significantly associated with less dependent decision-making style. More agreeableness, conscientiousness, and neuroticism were significantly associated with less spontaneous decision-making style. None of the personality traits was significantly associated with the avoidant decision-making style. Emotional intelligence seemed to fully mediate the association between conscientiousness and intuitive decision-making style by 38% and partially mediate the association between extroversion and openness to experience with intuitive decision-making style by 49.82 and 57.93% respectively.

Our study suggests an association between personality traits and decision-making styles. The results suggest that EI showed a significant positive effect on intuitive decision-making style and a negative effect on avoidant and dependent decision-making styles. Additionally, our study underlined the role of emotional intelligence as a mediator factor between personality traits (namely conscientiousness, openness, and extroversion) and decision-making styles.

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Decision-making is a central part of daily interactions; it was defined by Scott and Bruce in 1995 as «the learned habitual response pattern exhibited by an individual when confronted with a decision situation. It is not a personality trait, but a habit-based propensity to react in a certain way in a specific decision context» [ 1 ]. Understanding how people make decisions within the moral domain is of great importance theoretically and practically. Its theoretical value is related to the importance of understanding the moral mind to further deepen our knowledge on how the mind works, thus understanding the role of moral considerations in our cognitive life. Practically, this understanding is important because we are highly influenced by the moral decisions of people around us [ 2 ]. According to Scott and Bruce (1995), there are five distinct decision-making styles (dependent, avoidant, spontaneous, rational, intuitive) [ 1 ] and each individuals’ decision-making style has traits from these different styles with one dominant style [ 3 ].

The dependent decision-making style can be regarded as requiring support, advice, and guidance from others when making decisions. Avoidant style is characterized by its tendency to procrastinate and postpone decisions if possible. On the other hand, spontaneous decision-making style is hallmarked by making snap and impulsive decisions as a way to quickly bypass the decision-making process. In other words, spontaneous decision-makers are characterized by the feeling of immediacy favoring to bypass the decision-making process rapidly without employing much effort in considering their options analytically or relying on their instinct. Rational decision-making style is characterized by the use of a structured rational approach to analyze information and options to make decision [ 1 ]. In contrast, intuitive style is highly dependent upon premonitions, instinct, and feelings when it comes to making decisions driving focus toward the flow of information rather than systematic procession and analysis of information, thus relying on hunches and gut feelings. Several studies have evaluated the factors that would influence an individual’s intuition and judgment. Rand et al. (2016) discussed the social heuristics theory and showed that women and not men tend to internalize altruism _ the selfless concern for the well-being of others_ in their intuition and thus in their intuitive decision-making process [ 4 ]. Additionally, intuitive behavior honesty is influenced by the degree of social relationships with individuals affected by the outcome of our decision: when dishonesty harms abstract others, intuition promotion causes more dishonesty. On the contrary, when dishonesty harms concrete others, intuition promotion has no significant effect on dishonesty. Hence, the intuitive appeal of pro-sociality may cancel out the intuitive selfish appeal of dishonesty [ 5 ]. Moreover, the decision-making process and styles have been largely evaluated in previous literature. Greene et al. (2008) and Rand (2016) showed that utilitarian moral judgments aiming to minimize cost and maximize benefits across concerned individuals are driven by controlled cognitive process (i.e. rational); whereas, deontological moral judgments _where rights and duties supersede utilitarian considerations_ are dictated by an automatic emotional response (e.g. spontaneous decision-making) [ 6 , 7 ]. Trémolière et al. (2012) found that mortality salience makes people less utilitarian [ 8 ].

Another valuable element influencing our relationships and career success [ 9 ] is emotional intelligence (EI) a cardinal factor to positive patient experience in the medical field [ 10 ]. EI was defined by Goleman as «the capacity of recognizing our feelings and those of others, for motivating ourselves, and for managing emotions both in us and in our relationships» [ 11 ]. Hence, an important part of our success in life nowadays is dependent on our ability to develop and preserve social relationships, depict ourselves positively, and control the way people descry us rather than our cognitive abilities and traditional intelligence measured by IQ tests [ 12 ]. In other words, emotional intelligence is a subtype of social intelligence involving observation and analyses of emotions to guide thoughts and actions. Communication is a pillar of modern medicine; thus, emotional intelligence should be a cornerstone in the education and evaluation of medical students’ communication and interpersonal skills.

An important predictor of EI is personality [ 13 ] defined as individual differences in characteristic patterns of thinking, feeling and behaving [ 14 ]. An important property of personality traits is being stable across time [ 15 ] and situations [ 16 ], which makes it characteristic of each individual. One of the most widely used assessment tools for personality traits is the Five-Factor model referring to «extroversion, openness to experience, agreeableness, conscientiousness, neuroticism». In fact, personality traits have an important impact on individuals’ life, students’ academic performance [ 17 ] and decision-making [ 18 ].

Extroversion is characterized by higher levels of self-confidence, positive emotions, enthusiasm, energy, excitement seeking, and social interactions. Openness to experience individuals are creative, imaginative, intellectually curious, impulsive, and original, open to new experiences and ideas [ 19 ]. Agreeableness is characterized by cooperation, morality, sympathy, low self-confidence, high levels of trust in others, and tend to be happy and satisfied because of their close interrelationships [ 19 ]. Conscientiousness is characterized by competence, hard work, self-discipline, organization, strive for achievement and goal orientation [ 20 ] with a high level of deliberation making conscientious individuals capable of analyzing the pros and cons of a given situation [ 21 ]. Neuroticism is characterized by anxiety, anger, insecurity, impulsiveness, self-consciousness,and vulnerability [ 20 ]. High neurotic individuals have higher levels of negative affect, are easily irritated, and more likely to turn to inappropriate coping responses, such as interpersonal hostility [ 22 ].

Multiple studies have evaluated the impact of personality traits on decision-making styles. Narooi and Karazee (2015) studied personality traits, attitude to life, and decision-making styles among university students in Iran [ 23 ]. They deduced the presence of a strong relationship between personality traits and decision-making styles [ 23 ]. Riaz and Batool (2012) evaluated the relationship between personality traits and decision-making among a group of university students (Fig. 1 ). They concluded that «15.4 to 28.1% variance in decision-making styles is related to personality traits» [ 24 ]. Similarly, Bajwa et al. (2016) studied the relationship between personality traits and decision-making among students. They concluded that conscientiousness personality trait is associated with rational decision-making style [ 25 ]. Bayram and Aydemir (2017) studied the relationship between personality traits and decision-making styles among a group of university students in Turkey [ 26 ]. Their work yielded to multiple conclusion namely a significant association between rational and intuitive decision-making styles and extroversion, openness to experience, conscientiousness, and agreeableness personality traits [ 26 ]. The dependent decision-making style had a positive relation with both neuroticism and agreeableness. The spontaneous style had a positive relation with neuroticism and significant negative relation with agreeableness and conscientiousness. Extroversion personality traits had a positive effect on spontaneous style. Agreeableness personality had a positive effect on the intuitive and dependent decision-making style. Conscientiousness personality had a negative effect on avoidant and spontaneous decision-making style and a positive effect on rational style. Neuroticism trait had a positive effect on intuitive, dependent and spontaneous decision-making style. Openness to experience personality traits had a positive effect on rational style [ 26 ].

Schematic representation of the effect of the big five personality types on decision-making styles [ 24 ]

Furthermore, several studies have evaluated the relationship between personality traits and emotional intelligence. Dawda and Hart (2000) found a significant relationship between emotional intelligence and all Big Five personality traits [ 27 ]. Day and al. (2005) found a high correlation between emotional intelligence and extroversion and conscientiousness personality traits [ 28 ]. A study realized by Avsec and al. (2009) revealed that emotional intelligence is a predictor of the Big Five personality traits [ 29 ]. Alghamdi and al. (2017) investigated the predictive role of EI on personality traits among university advisors in Saudi Arabia. They found that extroversion, agreeableness, and openness to experience emerged as significant predictors of EI. The study also concluded that conscientiousness and neuroticism have no impact on EI [ 13 ].

Nonetheless, decision-making is highly influenced by emotion making it an emotional process. The degree of emotional involvement in a decision may influence our choices [ 30 ] especially that emotions serve as a motivational process for decision-making [ 31 ]. For instance, patients suffering from bilateral lesions of the ventromedial prefrontal cortex (interfering with normal processing of emotional signals) develop severe impairments in personal and social decision-making despite normal cognitive capabilities (intelligence and creativity); highlighting the guidance role played by emotions in the decision-making process [ 32 ]. Furthermore, EI affects attention, memory, and cognitive intelligence [ 33 , 34 ] with higher levels of EI indicating a more efficient decision-making [ 33 ]. In one study, Khan and al. concluded that EI had a significant positive effect on rational and intuitive decision-making styles and negative effect on dependent and spontaneous decision-making styles among a group of university students in Pakistan [ 35 ].

This study aims to assess the impact of personality traits on both emotional intelligence and decision-making among medical students in Lebanese Universities and to test the potential mediating role played by emotional intelligence between personality and decision-making styles in this yet unstudied population to our knowledge. The goal of the present research is to evaluate the usefulness of implementing such tools in the selection process of future physicians. It also aimed at assessing the need for developing targeted measures, aiming to ameliorate the psychosocial profile of Lebanese medical students, in order to have a positive impact on patients experience and on medical students’ career success.

Study design

This cross-sectional study was conducted between June and December 2019. A total of 296 participants were recruited from all the 7 faculties of medicine in Lebanon. Data collection was done through filling an anonymous online or paper-based self-administered English questionnaire upon the participant choice. All participants were aware of the purpose of the study, the quality of data collected and gave prior informed consent. Participation in this study was voluntary and no incentive was given to the participants. All participants were General medicine students registered as full-time students in one of the 7 national schools of medicine aged 18 years and above regardless of their nationality. The questionnaire was only available in English since the 7 faculties of medicine in Lebanon require a minimum level of good English knowledge in their admission criteria. A pilot test was conducted on 15 students to check the clarity of the questionnaire. To note that these 15 questionnaires related data was not entered in the final database. The methodology used in similar to the one used in a previous paper [ 36 ]

Questionnaire and variables

The questionnaire assessed demographic and health characteristics of participants, including age, gender, region, university, current year in medical education, academic performance (assessed using the current cumulative GPA), parental highest level of education, and health questions regarding the personal history of somatic, and psychiatric illnesses.

The personality traits were evaluated using the Big Five Personality Test, a commonly used test in clinical psychology. Since its creation by John, Donahue, and Kentle (1991) [ 37 ], the five factor model was widely used in different countries including Lebanon [ 38 ]; it describes personality in terms of five board factors: extroversion, openness to experience, agreeableness, conscientiousness and neuroticism according to an individual’s response to a set of 50 questions on a 5-point Likert scale: 1 (disagree) to 5 (agree). A score for each personality trait is calculated in order to determine the major trait(s) in an individual personality (i.e. the trait with the highest score). The Cronbach’s alpha values were as follows: total scale (0.885), extroversion (0.880), openness to experience (0.718), agreeableness (0.668), conscientiousness (0.640), and neuroticism (0.761).

Emotional intelligence was assessed using the Quick Emotional Intelligence Self-Assessment scale [ 38 ]. The scale is divided into four domains: «emotional alertness, emotional control, social-emotional awareness, and relationship management». Each domain is composed of 10 questions, with answers measured on a 5-point Likert scale: 0 (never) to 4 (always). Higher scores indicate higher emotional intelligence [ 38 ] (α Cronbach  = 0.950).

The decision-making style was assessed using the Scott and Bruce General Decision-Making Style Inventory commonly used worldwide since its creation in 1995 for this purpose [ 1 ]. The inventory consists of 25 questions answered according to a 5-point Likert scale: 1 (strongly disagree) to 5 (strongly agree) intended to evaluate the importance of each decision-making style among the 5 styles proposed by Scott and Bruce: dependent, avoidant, spontaneous, rational and intuitive. The score for each decision-making style is computed in order to determine the major style for each responder (α Cronbach total scale  = 0.744; α Cronbach dependent style  = 0.925; α Cronbach avoidant style  = 0.927; α Cronbach spontaneous style  = 0.935; α Cronbach rational style  = 0.933; α Cronbach intuitive style  = 0.919).

Sample size calculation

The Epi info program (Centers for Disease Control and Prevention (CDC), Epi Info™) was employed for the calculation of the minimal sample size needed for our study, with an acceptable margin of error of 5% and an expected variance of decision-making styles that is related to personality types estimated by 15.4 to 28.1% [ 24 ] for 5531 general medicine student in Lebanon [ 39 ]. The result showed that 294 participants are needed.

Statistical analysis

Statistical Package for Social Science (SPSS) version 23 was used for the statistical analysis. The Student t-test and ANOVA test were used to assess the association between each continuous independent variable (decision-making style scores) and dichotomous and categorical variables respectively. The Pearson correlation test was used to evaluate the association between two continuous variables. Reliability of all scales and subscales was assessed using Cronbach’s alpha.

Mediation analysis

The PROCESS SPSS Macro version 3.4, model four [ 40 ] was used to calculate five pathways (Fig.  2 ). Pathway A determined the regression coefficient for the effect of each personality trait on emotional intelligence, Pathway B examined the association between EI and each decision-making style, independent of the personality trait, and Pathway C′ estimated the total and direct effect of each personality trait on each decision-making style respectively. Pathway AB calculated the indirect intervention effects. To test the significance of the indirect effect, the macro generated bias-corrected bootstrapped 95% confidence intervals (CI) [ 40 ]. A significant mediation was determined if the CI around the indirect effect did not include zero [ 40 ]. The covariates that were included in the mediation model were those that showed significant associations with each decision-making style in the bivariate analysis.

Summary of the pathways followed during the mediation analysis

Sociodemographic and other characteristics of the participants

The mean age of the participants was 22.41 ± 2.20 years, with 166 (56.1%) females. The mean scores of the scales used were as follows: emotional intelligence (108.27 ± 24.90), decision-making: rationale style (13.07 ± 3.17), intuitive style (16.04 ± 3.94), dependent style (15.53 ± 4.26), spontaneous style (13.52 ± 4.22), avoidant style (12.44 ± 4.11), personality trait: extroversion (21.18 ± 8.96), agreeableness (28.01 ± 7.48), conscientiousness (25.20 ± 7.06), neuroticism (19.29 ± 8.94) and openness (27.36 ± 7.81). Other characteristics of the participants are summarized in Table  1 .

Bivariate analysis

Males vs females, having chronic pain compared to not, originating from South Lebanon compared to other governorates, having an intermediate income compared to other categories, those whose mothers had a primary/complementary education level and those whose fathers had an undergraduate diploma vs all other categories had higher mean rationale style scores. Those fathers, who had a postgraduate diploma, had a higher mean intuitive style scores compared to all other education levels. Those who have chronic pain compared to not and living in South Lebanon compared to other governorates had higher dependent style scores. Those who have chronic pain compared to not, those who take medications for a mental illness whose mothers had a primary/complementary education level vs all other categories and those whose fathers had a postgraduate diploma vs all other categories had higher spontaneous style scores (Table  2 ).

Higher agreeableness and conscientiousness scores were significantly associated with higher rational style scores, whereas higher extroversion and neuroticism scores were significantly associated with lower rational style scores. Higher extroversion, openness and emotional intelligence scores were significantly associated with higher intuitive scores, whereas higher agreeableness, conscientiousness and neuroticism scores were significantly associated with lower intuitive style scores. Higher agreeableness and conscientiousness were associated with higher dependent style scores, whereas higher openness and emotional intelligence scores were significantly associated with lower dependent styles scores. Higher agreeableness, conscientiousness, neuroticism, and emotional intelligence scores were significantly associated with lower spontaneous style scores. Finally, higher extroversion, neuroticism and emotional intelligence scores were significantly associated with lower avoidant style scores (Table  3 ).

Post hoc analysis: rationale style: governorate (Beirut vs Mount Lebanon p  = 0.022; Beirut vs South p  < 0.001; Mount Lebanon vs South p  = 0.004; South vs North p  = 0.001; South vs Bekaa p  = 0.047); monthly income (intermediate vs high p  = 0.024); mother’s educational level (high school vs undergraduate diploma p  = 0.048); father’s education level (undergraduate vs graduate diploma p = 0.01).

Intuitive style: father’s education level (high school vs postgraduate diploma p  = 0.046).

Dependent style: governorate (Beirut vs Mount Lebanon p  = 0.006; Beirut vs South p  = 0.003);

Avoidant style: mother’s educational level (high school vs undergraduate diploma p  = 0.008; undergraduate vs graduate diploma p  = 0.004; undergraduate vs postgraduate diploma p  = 0.001).

Mediation analysis was run to check if emotional intelligence would have a mediating role between each personality trait and each decision-making style, after adjusting overall covariates that showed a p  < 0.05 with each decision-making style in the bivariate analysis.

Rational decision-making style (Table  4 , model 1)

Higher extroversion was significantly associated with higher EI, b = 0.91, 95% BCa CI [0.60, 1.23], t = 5.71, p  < 0.001 (R2 = 0.31). Higher extroversion was significantly associated with lower rational decision-making even with EI in the model, b = − 0.06, 95% BCa CI [− 0.11, − 0.02], t = − 2.81, p  = 0.003; EI was not significantly associated with rational decision-making, b = 0.02, 95% BCa CI [− 0.0003, 0.03], t = 1.93, p  = 0.054 (R2 = 0.29). When EI was not in the model, higher extroversion was significantly associated with lower rational decision-making, b = − 0.05, 95% BCa CI [− 0.09, − 0.01], t = − 2.43, p  = 0.015 (R2 = 0.28). The mediating effect of EI was 21.22%.

Higher agreeableness was not significantly associated with EI, b = − 0.05, 95% BCa CI [− 0.40, 0.31], t = − 0.26, p  = 0.798 (R2 = 0.31). Higher agreeableness was significantly associated with higher rational decision-making style even with EI in the model, b = 0.07, 95% BCa CI [0.02, 0.11], t = 2.89, p  = 0.004; EI was not significantly associated with the rational decision-making, b = 0.01, 95% BCa CI [− 0.0003, 0.03], t = 1.92, p  = 0.055 (R2 = 0.29). When EI was not in the model, higher agreeableness was significantly associated with higher rational decision-making, b = 0.07, 95% BCa CI [0.02, 0.11], t = 2.86, p = 0.004 (R2 = 0.28). The mediating effect of EI was 0.10%.

Higher conscientiousness was significantly associated with higher EI, b = 1.40, 95% BCa CI [1.04, 1.76], t = 7.62, p  < 0.001 (R2 = 0.31). Higher conscientiousness was significantly associated with the rational decision-making style even with EI in the model, b = 0.09, 95% BCa CI [0.04, 0.14], t = 3.55, p < 0.001; EI was not significantly associated with the rational decision-making, b = 0.01, 95% BCa CI [− 0.0003, 0.03], t = 1.93, p  = 0.055 (R2 = 0.29). When EI was not in the model, conscientiousness was significantly associated with the rational decision-making style, b = 0.11, 95% BCa CI [0.07, 0.16], t = 4.76, p < 0.001 (R2 = 0.28). The mediating effect of EI was 22.47%.

Higher neuroticism was significantly associated with lower EI, b = − 0.50, 95% BCa CI [− 0.80, − 0.20], t = − 3.26, p  = 0.001 (R2 = 0.31). Neuroticism was not significantly associated with rational decision-making style with EI in the model, b = − 0.09, 95% BCa CI [− 0.05, 0.03], t = − 0.43, p  = 0.668; EI was not significantly associated with rational decision-making, b = 0.01, 95% BCa CI [− 0.0003, 0.03], t = 1.93, p  = 0.055 (R2 = 0.29). When EI was not in the model, neuroticism was not significantly associated with the rational decision-making style, b = − 0.02, 95% BCa CI [− 0.06, 0.02], t = − 0.81, p  = 0.418 (R2 = 0.28).

No calculations were done for the openness to experience personality traits since it was not significantly associated with the rational decision-making style in the bivariate analysis.

Intuitive decision-making style (Table 4 , model 2)

Higher extroversion was significantly associated with higher EI, b = 0.86, 95% BCa CI [0.59, 1.13], t = 6.28, p  < 0.001 (R2 = 0.41). Higher extroversion was significantly associated with higher intuitive decision-making even with EI in the model, b = 0.05, 95% BCa CI [0.002, 0.11], t = 2.03, p  = 0.043; EI was significantly associated with intuitive decision-making style, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.003 (R2 = 0.21). When EI was not in the model, higher extroversion was significantly associated with higher intuitive decision-making, b = 0.08, 95% BCa CI [0.03, 0.13], t = 3.21, p  = 0.001 (R2 = 0.18). The mediating effect of EI was 49.82%.

Higher agreeableness was significantly associated with EI, b = − 0.33, 95% BCa CI [− 0.65, − 0.02], t = − 2.06, p  = 0.039 (R2 = 0.41). Higher agreeableness was significantly associated with lower intuitive decision-making style even with EI in the model, b = − 0.15, 95% BCa CI [− 0.21, − 0.10], t = − 5.16, p  < 0.001; higher EI was significantly associated with higher intuitive decision-making, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.004 (R2 = 0.21). When EI was not in the model, higher agreeableness was significantly associated with lower intuitive decision-making, b = − 0.17, 95% BCa CI [− 0.22, − 0.11], t = − 5.48, p < 0.001 (R2 = 0.18). The mediating effect of EI was 6.80%.

Higher conscientiousness was significantly associated with higher EI, b = 1.18, 95% BCa CI [0.85, 1.51], t = 7.06, p < 0.001 (R2 = 0.41). Higher conscientiousness was significantly associated with lower intuitive decision-making style even with EI in the model, b = − 0.10, 95% BCa CI [− 0.16, − 0.03], t = − 2.95, p  = 0.003; higher EI was also significantly associated with higher intuitive decision-making, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.004 (R2 = 0.21). When EI was not in the model, conscientiousness was not significantly associated with the intuitive decision-making style, b = − 0.06, 95% BCa CI [− 0.12, 0.0004], t = − 1.95, p  = 0.051 (R2 = 0.18). The mediating effect of EI was 38%.

Higher openness to experience was significantly associated with higher EI, b = 1.44, 95% BCa CI [1.13, 1.75], t = 9.11, p  < 0.001 (R2 = 0.41). Higher openness to experience was significantly associated with higher intuitive decision-making style with EI in the model, b = 0.08, 95% BCa CI [0.01, 0.14], t = 2.38, p  = 0.017; higher EI was also significantly associated with intuitive decision-making style, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.004 (R2 = 0.21). When EI was not in the model, higher openness to experience was significantly associated with intuitive decision-making style, b = 0.12, 95% BCa CI [0.06, 0.18], t = 4.22, p  < 0.001 (R2 = 0.18). The mediating effect of EI was 57.93%.

No calculations were done for neuroticism personality trait since it was not significantly associated with the intuitive decision-making style in the bivariate analysis.

Dependent decision-making style (Table 4 , model 3)

Agreeableness was not significantly associated with EI, b = − 0.15, 95% BCa CI [− 0.49, 0.17], t = − 0.94, p  = 0.345 (R2 = 0.32). Higher agreeableness was significantly associated with higher dependent decision-making style even with EI in the model, b = 0.29, 95% BCa CI [0.23, 0.34], t = 10.51, p  < 0.001; higher EI was significantly associated with lower dependent decision-making, b = − 0.04, 95% BCa CI [− 0.06, − 0.02], t = − 4.50, p  < 0.001 (R2 = 0.40). When EI was not in the model, higher agreeableness was significantly associated with higher dependent decision-making, b = 0.29, 95% BCa CI [0.24, 0.35], t = 10.44, p  < 0.001 (R2 = 0.18). The mediating effect of EI was 2.38%.

Higher conscientiousness was significantly associated with higher EI, b = 1.04, 95% BCa CI [0.69, 1.38], t = 5.93, p  < 0.001 (R2 = 0.32). Higher conscientiousness was significantly associated with higher dependent decision-making style even with EI in the model, b = 0.15, 95% BCa CI [0.09, 0.20], t = 4.88, p  < 0.001; higher EI was also significantly associated with lower dependent decision-making, b = − 0.04, 95% BCa CI [− 0.06, − 0.02], t = − 4.50, p  < 0.001 (R2 = 0.40). When EI was not in the model, higher conscientiousness was significantly associated with a higher dependent decision-making style, b = 0.10, 95% BCa CI [0.04, 0.16], t = 3.49, p  < 0.001 (R2 = 0.36). The mediating effect of EI was 30.25%.

Higher openness to experience was significantly associated with higher EI, b = 1.37, 95% BCa CI [1.05, 1.69], t = 8.41, p  < 0.001 (R2 = 0.32). Higher openness to experience was significantly associated with lower dependent decision-making style even with EI in the model, b = − 0.13, 95% BCa CI [− 0.19, − 0.08], t = − 4.55, p < 0.001; higher EI was also significantly associated with dependent decision-making style, b = − 0.04, 95% BCa CI [− 0.19, − 0.08], t = − 4.50, p < 0.001 (R2 = 0.40). When EI was not in the model, higher openness to experience was significantly associated with lower dependent decision-making style, b = − 0.19, 95% BCa CI [− 0.24, − 0.14], t = − 7.06, p < 0.001 (R2 = 0.36). The mediating effect of EI was 43.69%.

No calculations were done for neuroticism and extroversion personality traits since they were not significantly associated with the dependent decision-making style in the bivariate analysis.

Spontaneous decision-making style (Table 4 , model 4)

Agreeableness was not significantly associated with EI, b = 0.17, 95% BCa CI [− 0.19, 0.53], t = 0.91, p  = 0.364 (R2 = 0.17). Higher agreeableness was significantly associated with lower spontaneous decision-making style even with EI in the model, b = − 0.10, 95% BCa CI [− 0.16, − 0.03], t = − 3.07, p  = 0.002; EI was not significantly associated with spontaneous decision-making, b = − 0.01, 95% BCa CI [− 0.03, 0.01], t = − 0.71, p  = 0.476 (R2 = 0.15). When EI was not in the model, higher agreeableness was significantly associated with lower spontaneous decision-making, b = − 0.10, 95% BCa CI [− 0.16, − 0.04], t = − 3.11, p = 0.002 (R2 = 0.15). The mediating effect of EI was 1.25%.

Higher conscientiousness was significantly associated with higher EI, b = 1.26, 95% BCa CI [0.88, 1.64], t = 6.56, p  < 0.001 (R2 = 0.17). Higher conscientiousness was significantly associated with lower spontaneous decision-making style even with EI in the model, b = − 0.16, 95% BCa CI [− 0.23, − 0.09], t = − 4.51, p  < 0.001; EI was not significantly associated with spontaneous decision-making style, b = − 0.01, 95% BCa CI [− 0.03, 0.01], t = − 0.71, p  = 0.476 (R2 = 0.15). When EI was not in the model, higher conscientiousness was significantly associated with lower spontaneous decision-making style, b = − 0.17, 95% BCa CI [− 0.23, − 0.10], t = − 5.11, p  < 0.001 (R2 = 0.15). The mediating effect of EI was 5.64%.

Neuroticism was not significantly associated with EI, b = − 0.22, 95% BCa CI [− 0.53, 0.08], t = − 1.43, p  = 0.153 (R2 = 0.17). Higher neuroticism was significantly associated with lower spontaneous decision-making style even with EI in the model, b = − 0.11, 95% BCa CI [− 0.16, − 0.06], t = − 4.05, p  < 0.001; EI was not significantly associated with spontaneous decision-making style, b = − 0.01, 95% BCa CI [− 0.03, 0.01], t = − 0.71, p = 0.476 (R2 = 0.15). When EI was not in the model, higher neuroticism was significantly associated with lower spontaneous decision-making style, b = − 0.11, 95% BCa CI [− 0.16, − 0.05], t = − 4.01, p  < 0.001 (R2 = 0.15). The mediating effect of EI was 1.49%.

No calculations were done for openness to experience and extroversion personality traits since they were not significantly associated with the spontaneous decision-making style in the bivariate analysis .

Avoidant decision-making style (Table 4 , model 5)

Higher extroversion was significantly associated with higher EI, b = 0.88, 95% BCa CI [0.54, 1.21], t = 5.18, p  < 0.001 (R2 = 0.15). Extroversion was not significantly associated with avoidant decision-making style even with EI in the model, b = − 0.01, 95% BCa CI [− 0.06, 0.05], t = − 0.27, p  = 0.790; higher EI was significantly associated with avoidant decision-making style, b = − 0.04, 95% BCa CI [− 0.06, 0.03], t = − 4.79, p  < 0.001 (R2 = 0.25). When EI was not in the model, extroversion was not significantly associated with avoidant decision-making style, b = − 0.05, 95% BCa CI [− 0.1, 0.08], t = − 1.69, p  = 0.092 (R2 = 0.19).

Higher neuroticism was significantly associated with lower EI, b = − 0.59, 95% BCa CI [− 0.91, − 0.27], t = − 3.60, p < 0.001 (R2 = 0.15). Neuroticism was not significantly associated with avoidant decision-making style even with EI in the model, b = − 0.03, 95% BCa CI [− 0.09, 0.02], t = − 1.34, p  = 0.182; higher EI was significantly associated with lower avoidant decision-making style, b = − 0.04, 95% BCa CI [− 0.06, − 0.03], t = − 4.79, p < 0.001 (R2 = 0.25). When EI was not in the model, neuroticism was not significantly associated with avoidant decision-making style, b = − 0.09, 95% BCa CI [− 0.06, 0.04], t = − 0.33, p  = 0.739 (R2 = 0.19).

No calculations were done for openness to experience, agreeableness, and conscientiousness personality traits since they were not significantly associated with the avoidant decision-making style in the bivariate analysis.

This study examined the relationship between personality traits and decision-making styles, and the mediation role of emotional intelligence in a sample of general medicine students from different medical schools in Lebanon.

Agreeableness is characterized by cooperation, morality, sympathy, low self-confidence, high levels of trust in others and agreeable individuals tend to be happy and satisfied because of their close interrelationships [ 19 , 20 ]. Likewise, dependent decision-making style is characterized by extreme dependence on others when it comes to making decisions [ 1 ]. Our study confirmed this relationship similarly to Wood (2012) [ 41 ] and Bayram and Aydemir (2017) [ 26 ] findings of a positive relationship between dependent decision-making style and agreeableness personality trait and a negative correlation between this same personality trait and spontaneous decision-making style. In fact, this negative correlation can be explained by the reliance and trust accorded by agreeable individuals to their surroundings, making them highly influenced by others opinions when it comes to making a decision; hence, avoiding making rapid and snap decisions on the spur of the moment (i.e. spontaneous decision-making style); in order to explore the point of view of their surrounding before deciding on their own.

Conscientiousness is characterized by competence, hard work, self-discipline, organization, strive for achievement, and goal orientation [ 20 ]. Besides, conscientious individuals have a high level of deliberation making them capable of analyzing the pros and cons of a given situation [ 21 ]. Similarly, rational decision-makers strive for achievements by searching for information and logically evaluating alternatives before making decisions; making them high achievement-oriented [ 20 , 42 ]. This positive relationship between rational decision-making style and conscientiousness was established by Nygren and White (2005) [ 43 ] and Bajwa et al. (2016) [ 25 ]; thus, solidifying our current findings. Furthermore, we found that conscientiousness was positively associated with dependent decision-making; this relationship was not described in previous literature to our knowledge and remained statistically significant after adding EI to the analysis model. This relationship may be explained by the fact that conscientious individuals tend to take into consideration the opinions of their surrounding in their efforts to analyze the pros and cons of a situation. Further investigations in similar populations should be conducted in order to confirm this association. Moreover, we found a positive relationship between conscientiousness and intuitive decision-making that lost significance when EI was removed from the model. Thus, solidifying evidence of the mediating role played by EI between personality trait and decision-making style with an estimated mediation effect of 38%.

Extroversion is characterized by higher levels of self-confidence, positive emotions, enthusiasm, energy, excitement seeking, and social interactions. Similarly, intuitive decision-making is highly influenced by emotions and instinct. The positive relationship between extroversion and intuitive decision-making style was supported by Wood (2012) [ 41 ], Riaz et al. (2012) [ 24 ] and Narooi and Karazee (2015) [ 23 ] findings and by our present study.

Neuroticism is characterized by anxiety, anger, self-consciousness, and vulnerability [ 20 ]. High neurotic individuals have higher levels of negative affect, depression, are easily irritated, and more likely to turn to inappropriate coping responses, such as interpersonal hostility [ 22 ]. Our study results showed a negative relationship between neuroticism and spontaneous decision-making style.

Openness to experience individuals are creative, imaginative, intellectually curious, impulsive and original, open to new experiences and ideas [ 19 , 20 ]. One important characteristic of intuitive decision-making style is tolerance for ambiguity and the ability to picture the problem and its potential solution [ 44 ]. The positive relationship between openness to experience and intuitive decision-making style was established by Riaz and Batool (2012) [ 24 ] and came in concordance with our study findings. Additionally, our results suggest that openness personality trait is negatively associated with dependent decision-making style similar to previous findings [ 23 ]. Openness to experience individuals are impulsive and continuously seek intellectual pursuits and new experiences; hence, they tend to depend to a lesser extent on others’ opinions when making decisions since they consider the decision-making process a way to uncover new experiences and opportunities.

Our study results showed that EI had a significant positive effect on intuitive decision-making style. Intuition can be regarded as an interplay between cognitive and affective processes highly influenced by tactic knowledge [ 45 ]; hence, intuitive decision-making style is the result of personal and environmental awareness [ 46 , 47 , 48 ] in which individuals rely on the overall context without much concentration on details. In other words, they depend on premonitions, instinct, and predications of possibilities focusing on designing the overall plan [ 49 ] and take responsibility for their decisions [ 46 ]. Our study finding supports the results of Khan and al. (2016) who concluded that EI and intuitive decision-making had a positive relationship [ 35 ]. On the other hand, our study showed a negative relationship between EI and avoidant and dependent decision-making styles. Avoidant decision-making style is defined as a continuous attempt to avoid decision-making when possible [ 1 ] since they find it difficult to act upon their intentions and lack personal and environmental awareness [ 50 ]. Similarly to our findings, Khan and al. (2016) found that avoidant style is negatively influenced by EI [ 35 ]. The dependent decision-making style can be regarded as requiring support, advice, and guidance from others when making decisions. In other words, it can be described as an avoidance of responsibility and adherence to cultural norms; thus, dependent decision-makers tend to be less influenced by their EI in the decision-making process. Our conclusion supports Avsec’s (2012) findings [ 51 ] on the negative relationship between EI and dependent decision-making style.

Practical implications

The present study helps in determining which sort of decision is made by which type of people. This study also represents a valuable contribution to the Lebanese medical society in order to implement such variables in the selection methods of future physicians thus recruiting individuals with positively evaluated decision-making styles and higher levels of emotional intelligence; implying better communication skills and positively impacting patients’ experience. Also, the present study may serve as a valuable tool for the medical school administration to develop targeted measures to improve students’ interpersonal skills.

Limitations

Even though the current study is an important tool in order to understand the complex relationship between personality traits, decision-making styles and emotional intelligence among medical students; however, it still carries some limitations. This study is a descriptive cross-sectional study thus having a lower internal validity in comparison with experimental studies. The Scott and Bruce General Decision-Making Style Inventory has been widely used internationally for assessing decision-making styles since 1995 but has not been previously validated in the Lebanese population. In addition, the questionnaire was only available in English taking into consideration the mandatory good English knowledge in all the Lebanese medical schools; however, translation, and cross-language validation should be conducted in other categories of Lebanese population. Furthermore, self-reported measures were employed in the present research where participants self-reported themselves on personality types, decision-making styles and emotional intelligence. Although, all used scales are intended to be self-administered; however, this caries risk of common method variance; hence, cross-ratings may be employed in the future researches in order to limit this variance.

The results suggest that EI showed a significant positive effect on intuitive decision-making style and a negative effect on avoidant and dependent decision-making styles. In addition, our study showed a positive relationship between agreeableness and dependent decision-making style and a negative correlation with spontaneous decision-making style. Furthermore, conscientiousness had a positive relationship with rational and dependent decision-making style and extroversion showed a positive relationship with intuitive decision-making style. Neuroticism had a negative relationship with spontaneous style and openness to experience showed a positive relationship with intuitive decision-making style and a negative relationship with dependent style. Additionally, our study underlined the role of emotional intelligence as a mediation factor between personality traits and decision-making styles namely openness to experience, extroversion, and conscientiousness personality traits with intuitive decision-making style. Personality traits are universal [ 20 ]; beginning in adulthood and remaining stable with time [ 52 ]. Comparably, decision-making styles are stable across situations [ 1 ]. The present findings further solidify a previously established relationship between personality traits and decision-making and describes the effect of emotional intelligence on this relationship.

Availability of data and materials

All data generated or analyzed during this study are not publicly available to maintain the privacy of the individuals’ identities. The dataset supporting the conclusions is available upon request to the corresponding author.

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Acknowledgements

We would like to thank all students who agreed to participate in this study.

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Radwan El Othman, Rabih Hallit & Souheil Hallit

Department of Pediatrics, Bahman Hospital, Beirut, Lebanon

Rola El Othman

Department of Infectious Disease, Bellevue Medical Center, Mansourieh, Lebanon

Rabih Hallit

Department of Infectious Disease, Notre Dame des Secours University Hospital Center, Byblos, Lebanon

Research and Psychology departments, Psychiatric Hospital of the Cross, P.O. Box 60096, Jal Eddib, Lebanon

Sahar Obeid & Souheil Hallit

Faculty of Arts and Sciences, Holy Spirit University of Kaslik (USEK), Jounieh, Lebanon

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REO and REO were responsible for the data collection and entry and drafted the manuscript. SH and SO designed the study; SH carried out the analysis and interpreted the results; RH assisted in drafting and reviewing the manuscript; All authors reviewed the final manuscript and gave their consent; SO, SH and RH were the project supervisors.

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The main objective of this chapter is to describe the concept of personality and approaches to researching it. For this reason, first a view on outlining the field of personality psychology in its present form, then the key term—personality—is discussed. The next section contains a synopsis of the main approaches to the study of personality, including psychoanalytic, learning and humanistic perspectives. The objective of the second part is to present the main theoretical directions in personality studies, which are divided into two basic trends. The first one is represented by type theories that focus on qualitative differences and discrete categories. The other direction is composed of trait theories that aim to formulate the latent structure of personality on the basis of statistical procedures, this has led to the development of the trait model adopted as the groundwork of this volume—the Big Five. The last section of this chapter is devoted to a general description of the most important theories exploring the development of personality across a lifespan (psychosexual, psychosocial, cognitive, and social cognitive).

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ORIGINAL RESEARCH article

Personality traits and career role enactment: career role preferences as a mediator.

• 1 Department of Psychology, University of Groningen, Groningen, Netherlands
• 2 Durham University Business School, Durham, United Kingdom
• 3 Faculty of Social Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

It has been argued that how a person’s career unfolds is increasingly affected by his or her own values, personality characteristics, goals and preferences. The current study addresses the issue of how we can explain that personality traits are associated with the enactment of certain career roles. Two survey studies (e.g., a two wave worker sample and a cross-sectional worker sample) were conducted to investigate the relationships between personality traits, career role preferences and career role enactment. As expected, results indicate that peoples’ personality traits predicted the preference for certain roles in the work context which, in turn, predicted the career roles they actually occupy. Specifically, our findings show that Extraversion, Conscientiousness and Openness to experience influence various career role preferences (i.e., Maker, Expert, Presenter, Guide, Director, and Inspirer role preferences) and, subsequently, the enactment of these career roles. Other traits, such as Neuroticism and Agreeableness, seem less important in predicting role preferences and subsequent role enactment. These results underline the importance of acknowledging not only individual trait differences but especially role preferences in explaining how careers develop over time. Further implications, limitations and research ideas are discussed.

Introduction

Nowadays, employees often can autonomously change, adapt, modify and tailor their jobs or the way in which they perform their jobs ( Parker, 2000 ; Organ et al., 2006 ; Oldham and Hackman, 2010 ). The question as to what determines how individuals customize their jobs, and ultimately their careers, has received increasing research attention ( Parker and Bindl, 2017 ). Several scholars have argued that how a person’s career unfolds is strongly affected by his or her own values, personality characteristics, goals and preferences ( Hall, 2004 ; Wille et al., 2012 ; Savickas, 2013 ). The Big Five trait taxonomy ( McCrae and Costa, 1996 , 1999 ; also see the Five Factor Model [FFM], Goldberg, 1990 ) appears to offer a particularly promising approach to the application of personality constructs to career related outcomes. Indeed, the Big Five is an empirically validated classification of the structure and nature of personality traits. Its usefulness for predicting job crafting behavior and career role enactment is apparent from several studies that show personality traits do indeed affect the way in which people perform their jobs over time ( Wille et al., 2010 , 2012 ; Bakker et al., 2012 ).

The current study also explores the relationship between personality traits and career role enactment, but includes a potential mediating mechanism. Indeed, previous research has left the question as to how we can explain that personality traits are associated with the enactment of certain career roles largely unanswered. In line with the functionalist approach to personality ( Wood et al., 2015 ), we argue that each personality trait engenders a preference for certain career roles. These preferences, in turn, will affect individuals’ behaviors and thus also the likelihood that certain career roles will eventually be enacted. Therefore, we propose that career role preferences will function as a mediating mechanism in the relationship between the Big Five personality traits and career role enactment (see Figure 1 ).

Figure 1. Conceptual model of how personality traits relate to career role preferences and career role enactment.

With this study, we hope to contribute to the existing literature in several ways. First, this study answers the call for more research to explain the personality traits – behavior at work relationship (see Barrick, 2005 ). Understanding the underlying mechanisms that clarify the relationship between personality traits and career role enactment may not only contribute to the development of personality theory, but may also help us to identify factors that more directly influence career role enactment (i.e., the proposed mediators). Second, we hope to contribute to the growing body of research that acknowledges that employees are not passive recipients of job characteristics who walk fixed career paths, but instead can be seen as active agents in the construction of their work and career ( Savickas, 2013 ; Wrzesniewski et al., 2013 ). By investigating how traits and career role preferences affect career role enactment, this study highlights how employees have a determining role in their own in career development. Insights into these matters can contribute to employee perceptions of control over the work environment and perceptions of self-efficacy and competence ( Spreitzer and Doneson, 2005 ). Finally, we hope that the insights derived from this study may offer some tentative practical suggestions to employees who want to plan their career, as well as to HRM-practitioners, coaches and others who are interested in providing guidance and support to individual employees.

Career Role Enactment and Career Role Preferences

In order to understand individual career development, the Career Roles Model was developed ( Hoekstra, 2011 ). This model builds on the notion that nowadays jobs can’t easily be defined by a set list of specific tasks. Instead, jobs have become more complex and can often be better described by work roles ( Huckvale and Ould, 1995 ). Work roles include tasks, but are more broad and also incorporate processes, responsibilities and functions that may change based on needs and opportunities that arise ( Huckvale and Ould, 1995 ). The Career Roles Model states that people enact different work roles in their jobs. Over time, these work roles may grow into enduring career roles ( Hall, 1976 ; Hoekstra, 2011 ). Career roles can be defined as stable and repetitive patterns of functioning in the work context that are independent of specific jobs and levels of functioning. Carrying out a role has been referred to as role enactment ( Hoekstra, 2011 ; De Jong et al., 2014 ). Career role enactment can thus be seen as the behavioral manifestation of occupying certain career roles (i.e., the actual engaging in these roles).

The Career Roles Model (see Supplementary Table A1 ) identifies six different career roles. These roles are based on the systematic combination of three classes of individual motives that drive people in their work and two organizational themes that guide organizations. The classes of individual motives, derived from Hogan (2007) , are distinction (e.g., autonomy and agency), integration (e.g., connectedness and belonging) and structure (e.g., collective meaning and cohesion). These classes are crossed with two organizational themes: exploitation (e.g., processes focused on stability) and exploration (e.g., processes aimed at innovation and change) derived from March (1999) . The six resulting roles are (1) the Maker role; (2) the Expert role; (3) the Presenter role; (4) the Guide role; (5) the Director role; and (6) the Inspirer role. According to the Career Roles Model, these six roles are the building blocks of individual careers and potentially attainable in most jobs with at least some employee autonomy ( Hoekstra, 2011 ).

Career role preferences are defined as “the mental act of identifying with the career role as part of the self” ( De Jong et al., 2014 . p. 201). A career role that is preferred is seen as more fitting to the self and more attractive and desirable than a career role that is not preferred. Thus, whereas a career role preference concerns the extent to which individuals like to see themselves in a certain light, career role enactment concerns the performing of acts that are associated with that role. We posit that career role preferences will serve as a mediating mechanism in the personality traits – career role enactment relationship. Before we turn to more specific hypotheses, we will elaborate on insights from personality theory that substantiate this general proposition.

Personality Traits and Their Relationship With Preferences and Behavior

Personality traits are aspects of personality that are relatively stable over time, differ across individuals and are relatively consistent over situations ( Anusic and Schimmack, 2016 ). Probably the most common framework to the study of personality traits is the Big Five. The Big Five trait taxonomy is a hierarchical model of personality traits with five broad factors, which represent personality at the broadest level of abstraction. These factors are Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Each factor describes a broad domain of psychological functioning that is composed from a set of more specific and narrow traits ( Roberts et al., 2006 ). Several scholars have argued that personality traits, processes and behaviors should be separated from each other in order to increase our understanding of how personality explains behaviors (see Baumert et al., 2017 ; Zeigler-Hill et al., 2019 ). In this perspective, personality traits can be seen as basic tendencies or general predispositions, largely controlled by biological influences ( McCrae and Costa, 2008 ; McCrae, 2018 ). In contrast, motivational processes, such as preferences, as well as subsequent behaviors, represent the interaction between personality traits and the specifics of the social context. As such, processes and behaviors give contextualized form to what it means to possess certain relatively broad and abstract personality traits (e.g., Cantor, 1990 ; McAdams and Pals, 2006 ; McCrae and Costa, 2008 ; Wood et al., 2015 ; McCrae, 2018 ). Preferences, therefore, can be seen as being a consequence of inherent personality traits. These preferences will, in turn, affect a person’s behavior. Specifically, an individual’s behavioral displays are expected to follow the law of effect : a certain behavior increases when it satisfies a person’s needs and desires, and a certain behavior decreases when it does not (see Wood et al., 2015 ). That is, a certain behavior will be displayed more often if that behavior is congruent with a person’s preferences (c.f., Trait activation theory; Christiansen and Tett, 2008 ). All in all, one could argue that personality traits affect a person’s preferences, and that these preferences will guide that person’s behavior in such a way that it will be beneficial and satisfying to the person.

Notably, several studies support the perspective that motivational processes (e.g., preferences, goals, motives) mediate the associations between Big Five personality traits and behavior relevant to organizational functioning, such as counterproductive work behavior ( Mount et al., 2006 ), job performance ( Barrick et al., 2002 ), creative achievement ( Prahbu et al., 2008 ), volunteering ( Carlo et al., 2005 ), career decision making ( Shafer, 2000 ), and training performance ( Major et al., 2006 ).

The Influence of Personality Traits in the Context of Career Roles

If we translate the above theoretical insights to our current research, it suggests that, depending on a person’s traits, some career roles will seem more attractive and desirable than others. If a certain career role is preferred, people will start to behave in a way that will allow them to engage in that role. Engaging in the role is likely to be intrinsically satisfying, because people are likely to feel good about being able to express their traits in their work environments. Arguably, certain roles allow people to express their traits more than others. Therefore, although restrictions by external demands or expectations can always occur, in general people’s preferences will influence their role-taking behavior or their role enactment ( Parker et al., 2010 ; De Jong et al., 2014 ).

So far, one of the few studies to investigate the link between personality traits and career roles was conducted by Wille et al. (2012) . Wille et al. (2012) specifically investigated the relationship between the Big Five personality traits and career role enactment. A sample of college alumni provided self-reports on their Big Five personality traits 3 months prior to graduation and 15 years later when their career had unfolded. Results indicated significant positive associations between several personality traits and (changes in) career role engagement. Individuals scoring high on Conscientiousness reported stronger engagement in the Expert role, Extraverts scored higher on the Presenter, Guide, Director and Inspirer roles. Agreeableness was predictive of stronger Guide role engagement and Openness was (unexpectedly) positively related to Presenter role engagement. Interestingly, Neuroticism did not relate significantly to engagement in any of the career roles. This study provides an excellent starting point for additional research on the matter. For instance, in the Wille et al. (2012) study, respondents were asked to retrospectively report on the importance of certain career roles over time. Confirmation of results from studies using different designs would bolster confidence in the findings. Moreover, in the Wille et al. (2012) study, potential mediating mechanisms between personality and career role enactment were not investigated, although they argue that such endeavors would be welcome (see p. 319). The current study addresses both issues by performing two studies with a different design (one two-wave and one cross-sectional design) and by examining the potential mediating role of career role preferences in the relationship between personality characteristics and career roles. In sum, the current study focuses on how specific personality characteristics relate to career role preferences and subsequently result in career role enactment (see Figure 1 ). In the following, we will discuss each of the career roles outlined in the Career Roles Model ( Hoekstra, 2011 ) and how these are expected to be related to individual preferences and the Big Five personality traits.

First, the Maker role can be characterized by the striving for personal goals such as individual mastery and success. The role has a strong emphasis on autonomy and independence ( Hoekstra, 2011 ). People that occupy the Maker role do well in an environment with clear guidelines and task descriptions. We argue that those who score high on Conscientiousness may be more likely to end up in the Maker role, because this personality trait engenders in people a preference for tasks in which they may demonstrate the will to achieve ( Digman and Inouye, 1986 ), to work hard, and to be responsible well organized ( Wille et al., 2013 ). Indeed, Conscientiousness is expected to be related to a preference for tasks in which people can show that they are agentic and responsible ( Chiaburu et al., 2011 ), which in turn will foster enactment of the Maker role.

The Expert role is also characterized by personal goal setting, with a strong emphasis on independent agency. Additionally, the Expert role is considered to be accompanied by a natural eagerness to explore. People in the Expert role therefore typically engage in problem solving behavior ( Hoekstra, 2011 ). Because Conscientiousness stimulates in people a preference for tasks in which they may demonstrate the will to achieve, we expect that Conscientiousness will also related to a preference for and subsequent enactment of the Expert role ( Wille et al., 2012 ). Additionally, both people who score high on Conscientiousness, as well as people who score high on Openness to experience, score relatively high on problem solving ability ( D’Zurilla et al., 2011 ) which may boost their preference for such role. Openness to experience is also often associated with the ability to think outside the box and with being curious and unconventional ( Barrick et al., 2003 ; Fuller and Marler, 2009 ), as well with a growth tendency and the ability to adapt ( Digman, 1997 ; Lepine et al., 2000 ), which may stimulate their preference for the Expert role, and therefore their subsequent of the Expert role.

Hypothesis 1 The positive relationship between Conscientiousness and career role enactment of the Maker role is mediated by preference for the Maker role.

Hypothesis 2a The positive relationship between Conscientiousness and career role enactment of the Expert role is mediated by preference for the Expert role.

Hypothesis 2b The positive relationship between Openness to experience and career role enactment of the Expert role is mediated by preference for the Expert role.

The Presenter role can be characterized by the focus on social interactions. Typically, people in this role engage in activities in which they influence others, for example as a sales person or a lawyer ( Hoekstra, 2011 ). In line with Wille et al. (2012) , we expect people who score high on Extraversion to be attracted to the Presenter role, because people scoring high on Extraversion are often dominant, active and assertive ( Wiggins and Broughton, 1985 ; Barrick et al., 2003 ) which matches nicely with the social influence aspect of the Presenter role. Thus, Extraversion is expected to be related to a preference for roles in which one may persuade and influence others ( Oh and Berry, 2009 ), which in turn is likely to be positively related to enactment of the Presenter role.

Characteristic for the Guide role is that behaviors take place in social settings and revolve around social interactions (e.g., connecting and cooperating with colleagues). However, in the Guide role the focus is not so much on influence and persuasion (as it is in the Presenter role), but on helping and guiding others while maintaining focus on others’ perspectives ( Hoekstra, 2011 ). Based on these characteristics of the Guide role, and in line with Wille et al. (2012) , we believe that people scoring high on Agreeableness/Friendliness will end up in the Guide role. Agreeableness/Friendliness is characterized by a tendency to be warm, kind and unselfish ( Costa and McCrae, 1997 ; Barrick et al., 2003 ). Furthermore, agreeable individuals will have a preference for harmonious interpersonal environments ( Barrick et al., 2002 ). Therefore, people who score high on Agreeableness/Friendliness will show a preference for seeing oneself as someone who is directed toward helping others and being cooperative, which in turn will enhance Guide role enactment.

Hypothesis 3 The positive relationship between Extraversion and career role enactment of the Presenter role is mediated by preference for the Presenter role.

Hypothesis 4 The positive relationship between Agreeableness/friendliness and career role enactment of the Guide role is mediated by preference for the Guide role.

The Director role is typified by activities focused on optimizing strategy and by minding the overarching structure of groups and organizations ( Hoekstra, 2011 ). In line with Wille et al. (2012) , we believe that especially those people who score high on Extraversion will prefer the Director role, as they have the opportunity to realize, establish and choose long-term goals from a dominant position ( Paulhus and John, 1998 ; Hogan and Holland, 2003 ). Indeed, as previous research has demonstrated, Extraversion is positively associated with a sensitivity to potential rewards ( Lucas et al., 2000 ) and dominance ( Barrick et al., 2003 ). Therefore, it is expected that Extraversion is positively associated with a preference for the Director role, which in turn will result in Director role enactment.

Finally, similar to the Director role, the Inspirer role is also characterized by the tendency to focus on optimizing strategy. However, people in the Inspirer role are predominantly concerned with initiating strategic change away from current strategic programs, (often) without formal authority ( Hoekstra, 2011 ). We believe that people who score high on Openness to experience are more likely to a preference for tasks that involve non-conformity and abstraction ( Barrick et al., 2003 ). Moreover, Openness to experience comes with the ability to be unconventional and think outside the box ( Costa and McCrae, 1992 ), both of which are important for initiating change in new directions. Furthermore, especially when lacking formal authority, inspiring others to embrace change initiatives is more likely when it is done in an energetic, assertive fashion and with the display of positive emotions ( Bono and Judge, 2004 ). As mentioned, extraverted people are relatively dominant, active and assertive ( Wiggins and Broughton, 1985 ; Barrick et al., 2003 ). Therefore, we believe that both Openness to experience and Extraversion will result in a preference for the Inspirer role, which in turn will foster enactment of the Inspirer role.

Hypothesis 5 The positive relationship between Extraversion and career role enactment of the Director role is mediated by preference for the Director role.

Hypothesis 6a The positive relationship between Extraversion and career role enactment of the Inspirer role is mediated by preference for the Inspirer role.

Hypothesis 6b The positive relationship between Openness to experience and career role enactment of the Inspirer role is mediated by preference for the Inspirer role.

Overview of the Studies

To test our hypotheses, we conducted two studies. Study 1 is a two-wave survey of US workers. In Wave 1, we assessed employees’ Big Five personality traits using the Big Five Inventory (BFI, John et al., 1991 , see also Benet-Martinez and John, 1998 ; John and Srivastava, 1999 ). In Wave 2, we assessed employees’ career role preferences using vignettes that were based on the CRIQ 1.0 ( De Jong et al., 2014 ) and career role enactment using the VLR-30 ( Hoekstra, 2011 ). Study 2 is a cross-sectional survey of Dutch workers. In this study, we assessed employees’ Big Five personality traits using the G5short ( Hiemstra et al., 2011 ), their career role preferences with the CRIQ 1.0, and their career role enactment with the VLR-30. The advantage of the two-wave study is that it may be less subjected to problems related to multicollinearity. Moreover, the studies sampled from different populations and used instruments to assess personality traits that fitted that specific population (developed for people from English and Dutch speaking populations, respectively). To measure career role preferences, we used in both studies the CRIQ 1.0. However, whereas in Study 1 we grouped items and wrote them into vignettes (arguably making it easier for respondents to differentiate among the various preferences), we used separate items in Study 2. All in all, by replicating our findings over studies with different research methods and samples, we aimed to bolster the confidence in our results ( Shadish et al., 2002 ).

Method Study 1

Respondents and procedure.

A two-wave online survey study with employees from the United States was conducted. In total, 279 employees 1 completed both waves ( M age = 39.11, SD age = 10.80, 49% female). Employees working a minimum of 24 (payed) hours a week were allowed to participate in the survey. Of the employees, 1.1% completed primary school, 18.3% completed secondary school, 18.3% completed technical secondary school, 48% completed a bachelor’s program, and 14.3% completed a master’s program or higher. Furthermore, for their current job 14.3% of the participants required little to no training, 19.7% required a few months to a year training, 28% required 1–2 years training, 31.2% required a considerable amount of training including several years of work-related experience, and 6.8% required extensive skill, knowledge and more than 5 years of experience. Average employment in the labor market was 18.38 years ( SD = 11.23).

After having received study approval from the ethics committee of the University, we recruited employees via the online platform Amazon Mechanical Turk. Previous studies have shown that Mechanical Turk data are as reliable as traditional survey samples, specifically when measures to increase data quality are taken into account ( Cheung et al., 2017 ; Keith et al., 2017 ; Buhrmester et al., 2018 ). Participants were briefed about the content of the study, the voluntary nature of the study and confidentiality before giving their informed consent. In Wave 1, employees completed a questionnaire that assessed demographic variables and personality traits. After 3 weeks, in Wave 2, career role preferences and career role enactment data were collected. Participation in each of the waves took approximately 15 min and employees received 1.75 US$ upon completion of both studies.

Personality Traits

Personality traits were measured using the Big Five Inventory (BFI, John and Srivastava, 1999 ). The BFI contains 44-items and assesses Neuroticism, Extraversion, Conscientiousness, Agreeableness, and Openness to experience 2 . Respondents were asked to indicate to what extent they agreed (1 = strongly disagree , 5 = strongly agree ) to statements as: “I see myself as someone who is depressed, blue” (Neuroticism, eight items, α = 0.92), “ I see myself as someone who is talkative” (Extraversion, eight items, α = 0.91), “ I see myself as someone who does a thorough job” (Conscientiousness, nine items, α = 0.89), “ I see myself as someone who is helpful and unselfish with others” (Agreeableness, nine items, α = 0.87) and “ I see myself as someone who is original, comes up with new ideas” (Openness to experience, ten items, α = 0.81).

Career Role Preferences

To assess the extent to which employees had a preference for certain career roles, we first made vignettes describing the six career roles and we presented those, in random order, to the respondents. The different vignettes were based on the items of the CRIQ 1.0 ( De Jong et al., 2014 ). An example of a vignette is “ You want to realize your goals and you want to get concrete results. You work hard and thorough on assignments and you like to get the process going. You are often the one who takes care of the concrete realization of a project. You take action when there is work to do. In addition, you want to organize things yourself to achieve good results. You focus on routine tasks and you can perform independently of others .” (Maker role) 3 .

Subsequently, for all vignettes, career role preferences were measured using an adaptation of the 7-item Self-brand connections questionnaire (with α’s ranging from α = 0.95 to α = 0.98, Escalas and Bettman, 2003 ). Employees were asked to indicate their agreement (1 = strongly disagree , 7 = strongly agree ) with statements like “ This role reflects who I am,” “ I feel a personal connection to this role,” and “I can identify with this role.”

Career Role Enactment

Career role enactment was measured with the VLR-30 ( Hoekstra, 2011 ). One advantage of the VLR-30 is that people’s enactment of multiple roles can be assessed. Each item of the 30-item questionnaire (five items per career role) gives an example of behavior that would fit specifically with one career role. Respondents were asked to indicate how well the item described what they would typically do at work (1 = not at all , 7 = very well ) during the last year. Examples of items are “I …” “… organize many things personally to get good results ” (Maker role, α = 0.66), “… analyze a problem that others find complicated” (Expert role, α = 0.82), “… frame a plan carefully to get broad acceptance ” (Presenter role, α = 0.86), “… gain someone’s confidence in a difficult relationship ” (Guide role, α = 0.87), “… take the lead in confusing situations” (Director role, α = 0.89) and “… stimulate others’ minds with creative ideas” (Inspirer role, α = 0.81).

Control Variables

Demographic variables (age, sex [0 = men ; 1 = women ], education [1 = preliminary school , 2 = high school , 3 = intermediate vocational education , 4 = higher vocational education , 5 = university degree ]) were included as control variables in the analyses. Moreover, we also added years of employment in the labor market and job zone [ranging from 1 = no to little preparation or education is needed to 5 = extensive preparation and education is needed ] as control variables ( Becker, 2005 ) to guard against job complexity affecting the relationship between personality traits and career role enactment. To assess job zone, we used the classifications as provided by an online tool for career exploration and analyses ( O * net, 2019 ; for similar use see Baughman et al., 2015 ).

Results Study 1

Preliminary analyses.

Correlations, means, and standard deviations of the study variables are presented in Supplementary Table A2 . Note that the correlations indicate that personality traits are associated with career role preferences and career role enactment, as hypothesized. Furthermore, these results are in line with previous findings on personality and career roles (e.g., Wille et al., 2012 ).

Mediation Analyses

To investigate the proposed mediating role of career role preferences in the relation between personality traits and career roles, the PROCESS macro for SPSS by Hayes (2013) was used (see Supplementary Tables A3 – A8 ; first columns). In each analysis, the enactment of one of the six roles was added as the dependent variable, the preference for that role was added as a mediator variable and the five personality variables were added as predictor variables 4 . Furthermore, age, sex, education, years of employment in the labor market and job zone were included as a covariate in the mediation model. In general, our overarching model that specific personality traits relate to career role preferences, subsequently resulting in career role enactment (see Figure 1 ), is confirmed. Below, we describe the results for each of the hypotheses.

Preference for the Maker role was, as expected, positively related to Maker role enactment (see Supplementary Table A3 ). In addition, we found a positive relation between Conscientiousness and preference for the Maker role, and between Conscientiousness and Maker role enactment. Furthermore, in line with Hypothesis 1, we found that the indirect effect of Conscientiousness via preference for the Maker role on perceived enactment of the Maker role was significant (Effect = 0.11, SE = 0.04, CI = [0.04; 0.20]). Other personality traits did not predict Maker role enactment.

Expert Role

We found a significant positive relationship between preference for the Expert role and Expert role enactment. However, we did not find a significant positive relationship between Conscientiousness and preference for the Expert role and Conscientiousness and Expert role enactment (see Supplementary Table A4 ). Hypothesis 2a could therefore not be confirmed.

Second, we found a significant positive relationship between Openness to experience and preference for the Expert role, and between Openness to experience and Expert role enactment. Furthermore, in line with Hypothesis 2b, we found that the indirect effect of Openness to experience via preference for the Expert role on enactment of the Expert role was significant (Effect = 0.23, SE = 0.06, CI = [0.12; 0.36]).

Presenter Role

For the Presenter role, we found a significant positive relationship between preference for this role and Presenter role enactment, between Extraversion and preference for the Presenter role, and between Extraversion and Presenter role enactment (see Supplementary Table A5 ). Furthermore, in line with Hypothesis 3, we found that the indirect effect of Extraversion via preference for the Presenter role on enactment of the Presenter role was significant (Effect = 0.10, SE = 0.04, CI = [0.02; 0.18]). Other personality traits did not predict Presenter role enactment.

For the Guide role, we found a significant positive relationship between preference for this role and Guide role enactment and between Agreeableness and preference for the Guide role (see Supplementary Table A6 ). Furthermore, in line with Hypothesis 4, we found that the indirect effect of Agreeableness via preference for the Guide role on enactment of the Guide role was significant (Effect = 0.40, SE = 0.09, CI = [0.25; 0.59]).

Unexpectedly, we found that Neuroticism and Extraversion were positively related to preference for the Guide role and that the indirect effects of Neuroticism and Extraversion via preference for the Guide role on enactment of the Guide role were significant as well (Effect = 0.15, SE = 0.06, CI = [0.05; 0.27]; Effect = 0.12, SE = 0.05, CI = [0.03; 0.23], respectively).

Director Role

We found a significant positive relationship between preference for this role and Director role enactment, between Extraversion and preference for the Director role and between Extraversion and enactment of the Director role (see Supplementary Table A7 ). Furthermore, in line with Hypothesis 5, we found that the indirect effect of Extraversion via preference for the Director role on perceived enactment of the Director role was significant (Effect = 0.11, SE = 0.05, CI = [0.01; 0.21]). Other personality traits did not predict Director role enactment.

Inspirer Role

For the Inspirer role, we found a significant positive relationship between preference for this role and Inspirer role enactment (see Supplementary Table A8 ). Moreover, we found a significant positive relationship between Extraversion and preference for the Inspirer role and between Extraversion and Inspirer role enactment. Furthermore, in line with Hypothesis 6a, we found that the indirect effect via preference for the Inspirer role on enactment of the Inspirer role was significant (Effect = 0.11, SE = 0.04, CI = [0.05; 0.20]).

Second, we found a significant positive relationship between Openness to experience and preference for the Inspirer role, and between Openness to experience and Inspirer role enactment. Furthermore, in line with Hypothesis 6b, we found that the indirect effect via preference for the Inspirer role was significant (Effect = 0.13, SE = 0.06, CI = [0.03; 0.26]).

Method Study 2

The second study was part of a survey on career role development and employability of a Dutch worker sample. Respondents were a random sample of 285 employees from different organizations (46.1% female, M age = 40.7, SD age = 9.5). Of the employees, 0.7% completed primary school, 5.6% completed secondary school, 13.7% completed technical secondary school, 50.4% completed a higher vocational program and 29.6% completed a bachelor or master’s program. Furthermore, for their current job 3.2% of the participants required a few months to a year training, 15.8% required 1–2 years training, 76.7% required a considerable amount of training including several years of work-related experience, and 4.3% required extensive skill, knowledge and more than 5 years of experience. Employees’ average organizational tenure was 7.68 years ( SD = 6.6).

Various companies (in different sectors) in the Netherlands were contacted after having received study approval from the ethics committee of the University. When organizations gave their permission, employees were invited via their work e-mail to participate in an online portal study. Participation was voluntary, not part of company policy, individual results were not shared with representatives of the participating organizations, and anonymity was guaranteed. The final sample consisted of employees from multiple organizations located in the Netherlands, representing a wide range of professions (e.g., technicians, nurses, doctors, policy makers). During the data collection phase, the respondents received multiple (e-mail) reminders. To provide an incentive for participation, respondents received a feedback report on their personality traits and career roles profile after completion of the study ( Kühne and Kroh, 2016 ).

Personality was measured with the Dutch version of the G5short, a 60-item questionnaire that has shown reliability and validity as a measure of the Big Five personality dimensions ( Hiemstra et al., 2011 , max. 12 items per subscale). Respondents were asked to indicate the extent to which each statement was descriptive of them by moving the slider to the left (0 = NO! ) or right (100 = YES! ). The sliders show textual captions, not the accompanying score. Translated examples of items are: “ Enjoys meeting new people ” (Extraversion, 12 items, α = 0.91), “ Stays calm under all circumstances ” (Stability, 12 items, α = 0.87), “ Is open to the values of others ” (Openness to experience, six items, α = 0.73), “ Works systematically ” (Conscientiousness, 11 items, α = 0.87), “ Has trust in others ” (Agreeableness/Friendliness, 9 items, α = 0.77).

Career role preferences were measured by the Career Role Identification Questionnaire (CRIQ 1.0, De Jong et al., 2014 ), a 40-item-set questionnaire (six scales, 20 item-words per scale). Each item-set contains three word-items from different career role scales. Thus, word-items referring to the same career role scale were never used in one item-set. For every word-item in the item-set we asked participants to rate on a 7-point scale: “ To what extent do the following words relate to you as a person ” ranging from 1 ( I do not relate to this word ) to 7 ( I strongly relate to this word ). Translated examples of word-items are “Make” (Maker role, α = 0.97), “Know” (Expert role, α = 0.96), “Show” (Presenter role, α = 0.94), “Connect” (Guide role, α = 0.94), “Control” (Director role, α = 0.96), and “Stimulate” (Inspirer role, α = 0.94). All Likert rating combinations are possible in every item-set (for example, 2-2-2, 5-3-1, or 7-5-3). We calculated the score for the preference for a role by adding up all responses from the word-items belonging to one career role scale.

Similar to Study 1, career role enactment was measured with the VLR-30 ( Hoekstra, 2011 ). Respondents indicated how well each of the 30 statements described the role they typically enacted in their work using a slider scale (1 = not at all , 100 = very well ). The sliders only showed the caption, not the accompanying score. We calculated the mean score for each of the career roles. Alpha’s from the different scales were α = 0.75 (Maker role), α = 0.76 (Expert role), α = 0.78 (Presenter role), α = 0.82 (Guide role), α = 0.83 (Director role) and α = 0.75 (Inspirer role).

As in Study 1, demographic variables (age, sex, education) as well as work related variables (organizational tenure and job zone) were included as control variables in the analyses ( Becker, 2005 ). Using the classification as provided by O * net (2019) , job zone scores were obtained by having an independent rater assessing all jobs of respondents on the extent to which they needed experience and job training for job performance, using education level and the job description given by the respondents (1 = no to little preparation or education is needed to 5 = extensive preparation and education is needed ). To calculate interrater reliability a second rater independently assessed 100 of the 285 jobs. Cohen’s kappa ( k = 0.86, SD = 0.044) was excellent.

Results Study 2

Supplementary Table A9 presents the correlations, means and standard deviations of all study variables. Note that similar to Study 1, correlations indicate that personality traits are associated with career roles preferences and enactment in the expected direction (also see Wille et al., 2012 ).

To investigate the proposed mediating role of career role preferences in the relation between personality traits and career roles, we again used the PROCESS macro ( Hayes, 2013 ). In each analysis the experienced enactment of one of the six roles was added as the dependent variable, the preference for that role was added as a mediator variable and the five personality variables were added as predictor variables 4 . Furthermore, age, sex, education, organizational tenure and job zone were included as a covariate in the mediation model (see Supplementary Tables A3 – A8 , last columns). As in Study 1, the findings support our overarching model that specific personality traits relate to career role preferences, subsequently resulting in career role enactment (see Figure 1 ). Below, we again describe the results for each of the hypotheses.

Preference for the Maker role was positively related to Maker role enactment (see Supplementary Table A3 ). In addition, we found a positive relation between Conscientiousness and preference for the Maker role, and between Conscientiousness and perceived Maker role enactment. Furthermore, in line with Hypothesis 1, we found that the indirect effect of Conscientiousness via preference for the Maker role on perceived enactment of the Maker role was significant (Effect = 0.06, SE = 0.02, CI = [0.02; 0.12]). Other personality traits did not predict Maker role enactment.

First, we found a significant positive relationship between preference for the Expert role and Expert role enactment (see Supplementary Table A4 ). Moreover, we found a significant positive relationship between Conscientiousness and preference for the Expert role. Furthermore, in line with Hypothesis 2a, we found that the indirect effect of Conscientiousness via preference for the Expert role on enactment of the Expert role was significant (Effect = 0.12, SE = 0.04, CI = [0.04; 0.21]).

Second, we found a significant positive relationship between Openness to experience and preference for the Expert role, and between Openness to experience and Expert role enactment. Furthermore, in line with Hypothesis 2b, we found that the indirect effect of Openness to experience via preference for the Expert role on enactment of the Expert role was significant (Effect = 0.20, SE = 0.06, CI = [0.10; 0.33]).

Unexpectedly, we found a negative relationship between Extraversion and preference for the Expert role. Furthermore, we found that the indirect effect of Extraversion via preference for the Expert role on enactment of the Expert role was significant (Effect = −0.10, SE = 0.03, CI = [−0.16;−0.05]).

For the Presenter role, we found a significant positive relationship between preference for this role and Presenter role enactment, between Extraversion and preference for the Presenter role, and between Extraversion and enactment of the Presenter role (see Supplementary Table A5 ). Furthermore, in line with Hypothesis 3, we found that the indirect effect of Extraversion via preference for the Presenter role on enactment of the Presenter role was significant (Effect = 0.10, SE = 0.03, CI = [0.04; 0.17]). Other personality traits did not predict Presenter role enactment.

For the Guide role, we found a significant positive relationship between preference for this role and Guide role enactment, and between Agreeableness and preference for the Guide role, and between Agreeableness and enactment of the Guide role (see Supplementary Table A6 ). Furthermore, in line with Hypothesis 4, we found that the indirect effect of Agreeableness via preference for the Guide role on perceived enactment of the Guide role was significant (Effect = 0.12, SE = 0.04, CI = [0.06; 0.21]).

Unexpectedly, we found a significant positive relationship between Extraversion and preference for the Guide role, and between Extraversion and Guide role enactment. Furthermore, we found that the indirect effect of Extraversion via preference for the Guide role on enactment of the Guide role was significant (Effect = 0.04, SE = 0.02, CI = [0.02; 0.08]).

For the Director role, we found a significant positive relationship between preference for this role and Director role enactment, between Extraversion and preference for the Director role, and between Extraversion and Director role enactment (see Supplementary Table A7 ). Furthermore, in line with Hypothesis 5, we found that the indirect effect via preference for the Director role on enactment of the Director role was significant (Effect = 0.11, SE = 0.03, CI = [0.05; 0.18]). Other personality traits did not predict Director role enactment.

First, we found a significant positive relationship between preference for the Inspirer role and Inspirer role enactment (see Supplementary Table A8 ). Moreover we found a significant positive relationship between Extraversion and preference for the Inspirer role, and between Extraversion and perceived Inspirer role enactment. Furthermore, in line with Hypothesis 6a, we found that the indirect effect via preference for the Inspirer role on enactment of the Inspirer role was significant (Effect = 0.07, SE = 0.02, CI = [0.03; 0.12]).

Second, we found a significant relationship between Openness to experience and preference for the Inspirer role, and between Openness to experience and Inspirer role enactment. Furthermore, in line with Hypothesis 6b, we found that the indirect effect via preference for the Inspirer role was significant (Effect = 0.11, SE = 0.04, CI = [0.04; 0.21]).

At work, employees have become increasingly responsible for shaping their careers ( Savickas, 2013 ). The results of the present studies extend the knowledge of the relationship between individual personality traits and career role enactment by focusing on the mediating role of career role preferences. Results for the specific career roles are mostly in line with previous research findings and our hypotheses ( Wille et al., 2012 ). The specific findings will be elaborated on below.

Career Roles, Preferences, and Personality

In line with our expectations, we found the following effects in both studies. First, Conscientiousness was found to be the related to a preference for the Maker role, which, in turn predicted Maker role enactment ( Hypothesis 1 ). This suggests that the possibility to pursue goals, an activity that aligns with the Conscientiousness domain, makes the Maker role more attractive ( Denissen and Penke, 2008 ) because in the Maker role there is a strong focus on goals and mastery. Interestingly, although Wille et al. (2012) also expected Conscientiousness and engagement in the Maker role to be related, they did not find this relationship in their study. Second, Openness to experience ( Hypothesis 2b ) was a positive predictor of preference for and subsequent enactment of the Expert role. Apparently, the eagerness to explore, a sense of curiosity and the ability to think outside the box are important determinants of the willingness to and likelihood that someone will take on an Expert role. Third, Extraversion ( Hypothesis 3 ) predicted enactment of the Presenter role via a preference for the Presenter role. This suggests that the ability and willingness to interact and connect with others, enhances peoples’ attraction to the Presenter role, because it allows them to engage in such activities. As a consequence, they are more likely to eventually end up in the Presenter role. Fourth, Agreeableness/Friendliness ( Hypothesis 4 ) was positively related to preference for the Guide role, which, in turn, predicted Guide role enactment. This indicates that agreeable individuals will prefer and have a higher likelihood to end up in the Guide role, arguably because this allows them to interact with others and develop meaningful relationships ( Judge et al., 2002 ). Fifth, Extraversion ( Hypothesis 5 ) was positively related to Director role enactment, via a preference for this role. Apparently, characteristics such as expressiveness and assertiveness make roles that combine influencing others and gaining status particularly attractive ( DeNeve and Cooper, 1998 ). Last, both Extraversion ( Hypothesis 6a ) and Openness to experience ( Hypothesis 6b ) were positively related to preference for and engagement in the Inspirer role. The Inspirer role may thus not only attract people who like to influence others, but also those who like to develop visions from a position of wonder, curiosity and exploration ( Benoliel and Somech, 2014 ). We also expected Conscientiousness to be a positive predictor of preference for and subsequent enactment of the Expert role ( Hypothesis 2a ). However, we only find support for this hypothesis in Study 2. As this result only emerged in one study, caution with interpreting this result is warranted.

There were also some unexpected findings that emerged from our studies. First, results in both studies show that extraversion was positively related to a preference for, and subsequently enactment of the Guide role. Extraversion has been argued to be important for establishing interpersonal connections with others ( Wiggins and Broughton, 1985 ; Wille et al., 2012 ). Because the Guide role entails forming connections and relationships with others it seems that, extraverted people – who are eager to connect with others-, prefer and end up in the Guide role. Second, some findings emerge from only one of the studies. Results from Study 1 show that Neuroticism was positively related to preference for the Guide role, which in turn predicted enactment of the Guide role. This finding is interesting, given the fact that previous research did not find strong links between Neuroticism and helping behavior ( Barrick et al., 1992 ) or between Neuroticism and prosocial behavior ( Habashi et al., 2016 ). However, recently, Guo et al. (2018) argued that in situations in which helping others requires less social skills, or when the social interaction is less anxiety-provoking, the negative association between Neuroticism and helping behavior may disappear. Instead, in these situations, other people’s suffering may also elicit more compassion and concern for others’ distress, which may promote neurotic individuals to behave prosocially. Perhaps this can explain why people high on Neuroticism ultimately ended up in the Guide Role in Study 1. Moreover, results from Study 2 show that Extraversion was negatively related to preference for, and subsequent enactment of the Expert role. Perhaps Extraverts find the Expert role less appealing because the Expert role requires little interpersonal contact and people in this role work mostly autonomous ( Hurtz and Donovan, 2000 ; Hoekstra, 2011 ). Notably, although overall the two studies show very similar findings, these last results only appeared in one of the studies. One reason for these differential findings may be that the studies differed in the sample that was used. For Study 1, US workers were surveyed, whereas for Study 2 Dutch workers were surveyed. Although the used personality questionnaires were constructed to assess the personality of English and Dutch speaking respondents, respectively, research has shown that trait answering patterns can differ depending on culture or geographic location ( Allik and McCrae, 2004 : Melchers et al., 2016 ). In addition, although the convergent validity of both scales has been assessed ( John et al., 2008 ; Hiemstra et al., 2011 ), the two scales may yield some differential relationships with outcome variables because of their differences. It remains unclear how the dissimilarities between the results of Study 1 and Study 2 should be interpreted as they could be the result from trait differences, response style differences or both ( Melchers et al., 2016 ). Therefore, caution with interpreting these results is warranted.

Taken together, although there are some unexpected results, the overall pattern of our findings indicates that individual personality traits and resulting personal role preferences indeed play an important part in career role enactment. First, our results seem to show that especially Extraversion, Conscientiousness, and Openness to experience influence a broader range of role preferences, and subsequently career role enactment. Other traits, like Neuroticism and Agreeableness are less strongly related to role preferences and subsequent role enactment. This is in line with previous research showing the relative importance of specific personality traits over others ( Wille et al., 2012 ). More importantly, our findings highlight the moderating mechanism through which personality traits can influence career role enactment. These findings testify to the importance of motivational processes for employee work behaviors (see Barrick et al., 2002 ). Indeed, peoples’ interest in certain roles is a good predictor of their actual enactment of these roles. As such, our findings underscore the importance of personal aspirations in how people shape their career. To understand career role enactment and long-term development, subsequent research may thus benefit, besides a focus on personality characteristics, from a focus on specific motivational processes such as personal preferences, goals and motives at work.

Strengths, Limitations, and Future Research

The present research has both strengths and limitations. One strength is that our studies include both a US and a Dutch sample. That we find similar patterns of results points to generalizability of the study findings. Of course, we should be cautious generalizing the results to other populations ( Bello et al., 2009 ), and more research with different populations would be welcome.

A limitation of our research is that Study 2 employed a cross-sectional design. Research has shown that the use of cross-sectional approaches to establish mediation effects can distort results ( Maxwell and Cole, 2007 ). Specifically, such designs are often criticized for the risk of a common method bias and inability to infer causal relations ( Podsakoff et al., 2003 ). Nonetheless, we opted for a cross-sectional design because we wanted to focus on peoples’ present knowledge of the self. That is, we were specifically interested in peoples own perceptions of their personality, preferences, and current role enactment. An alternative, as previously used by Wille et al. (2012) , would have been retrospective research. However, retrospectively reporting on a career path (e.g., preferences and roles in the past) may be quite difficult and prone to bias ( Miller et al., 1997 ). Moreover, for Study 1 we employed a 2-Wave research design and this study yielded similar results, which strengthens confidence in our findings. Nonetheless, future research could consider a number of options.

First, if time is not an issue, future research may consider testing the mediation hypotheses using a 3-Wave longitudinal design. Separating role preferences from the career role enactment measure and choosing longer time intervals would be beneficial as this provides opportunities to understand long-term effects of personality traits and role preferences for work outcomes. Moreover, longitudinal studies have been agued to allow for causal relationship analysis in complex designs ( MacKinnon et al., 2002 ). Second, future studies could consider obtaining measures from different sources (e.g., collecting data from both employees and their supervisors, Van der Heijden et al., 2015 ) in order to reduce common method bias. This may be especially valuable because it has been argued that under the influence of self-enhancement or self-protection tactics people sometimes portray their actual role enactment less accurately ( De Jong et al., 2014 ). Moreover, career roles are defined as “a coherent and enduring set of characteristics of the perceived effects of the way a person is doing his or her work” ( Hoekstra, 2011 , p. 165). This implies that it is not only self-perception that is important; others’ perceptions can be important as well. Notably, self-presentation tactics may also affect peoples’ reported career role preferences. For assessing career role preferences, future research may therefore also explore the usefulness of including measures of other peoples’ perceptions or use implicit measures of career role preferences as these may be less susceptible to social desirability (see Gadassi and Gati, 2009 ).

Notably, career development can be understood in terms of dynamic reciprocity ( Rounds and Tracey, 1990 ), where environments are influenced by individuals and individuals are influenced by environments ( Wood and Roberts, 2006 ; Wille et al., 2012 ). Specifically, career development can be seen as a gradual, interactive process that is the result of two simultaneous forces: role pressure and granting on the one hand, and role taking on the other ( Hoekstra, 2011 ; Wille et al., 2012 ; De Jong et al., 2014 ). The current study focused solely on role taking processes (e.g., selecting fitting roles based on personal preferences), whereas career role enactment is also influenced by role pressure processes ( Wille et al., 2012 ; De Jong et al., 2014 ). That is, external demands and expectations and environmental influences play a role in which career roles a person is expected to enact ( Hoekstra, 2011 ). For example, employees may feel pressured into certain roles due to expectations from others about which roles people should take based on perceived personality characteristics. Notably, people’s perception of certain personality traits in others, are often biased and not necessarily correct ( Zimmermann et al., 2018 ). We therefore would welcome longitudinal studies that investigate both role taking and role pressure processes, because this could help gain a better understanding of how employees integrate their own preferences with external pressures, by selecting, innovating and (re-) negotiating their roles ( Parker, 2007 ).

In terms of career role enactment, and in line with the trait activation theory, it seems to be that because we feel good about expressing certain (preferred) traits that the role taking process is activated ( Christiansen and Tett, 2008 ). Employees that are situated in a working environment that allows for the expression of individual interest and motives are thus rewarded for their behavior ( Tett et al., 2013 ; Judge and Zapata, 2015 ). As such, a positive feedback loop may be created that will sustain the behavior through which employees end up in certain roles. However, although we have studied personality traits and career role preferences in career role enactment, we did not include the role of affect in our study. Future research could focus on the role of affective forecasting or mood in career role development in order to investigate if people indeed expect to feel better or actually feel better when they have the opportunity to enact the roles that are fitting to their personality.

Last, as mentioned previously it may be worthwhile to investigate differences between cultures ( Cox et al., 1991 ; Melchers et al., 2016 ). For example, cultural background has been shown to affect peoples’ self-construal ( Pekerti and Kwantes, 2011 ). In individualistic cultures (most Western countries), the emphasis lies on personal welfare and personal goals. In comparison, in collectivistic cultures (mostly non-Western and Asian countries) the focus is more on collective well-being and group goals ( Markus and Kitayama, 1991 ). In both our studies, we used samples coming from individualistic cultures. Consequently, respondents in our studies may have been more inclined to behave according to their personal preferences and goals than respondents coming from more collectivistic cultures would have been. That is, it may be that the role of personality in career role enactment is greater in individualistic than in collectivistic countries. In collectivistic cultures, individuals are more likely to make an effort to fit into society and to pay heed to collective needs ( Triandis, 1995 ). In such cultures, the influence of personality and personal preferences on the enactment of career roles may be relatively small. Up to date, these cultural differences have not yet been included in career role research. Thus, future research could include differences between cultures in career role enactment in order to gain more insight in career role theory.

Practical Implications

As the current research suggests, personality traits and individual role preferences may influence the way that people behave in their work environment. Our findings have some tentative implications for organizations, HR professionals and employees. For example, HR-practices that acknowledge the fact that a certain job can be performed in multiple ways – in which different workers take on different roles- may benefit employees. In addition, the current study showed that peoples’ preferences are a good predictor of career role enactment. Organizations may consider tapping employees’ preferences in order to support role acquisition processes. Supporting environments that enable employees to perform work according to their preferences may lead to fulfilling career role enactment and prevent people from winding up in jobs that they do not like because they have to do the job in a certain way ( Roberts and Caspi, 2003 ; Roberts, 2006 ). Practices that strengthen employees’ perception that there is a fit between themselves and the environment (for instance because that environment is supportive of employees’ personal preferences and strengths) may play an important role in increasing commitment and diminishing burnout or turnover amongst employees ( Kristof-Brown et al., 2005 ; Pee and Min, 2017 ). Also important to note is that for most organizations to function effectively, it is not required that all employees can fulfill all roles. Likewise, there is not necessarily the need for all career roles to be equally distributed among the various jobs within the organization. This may provide employees some leeway in deciding on the career roles they want to take on. If given the chance, employees themselves are often motivated to select, optimize and develop their jobs and careers over time ( Roberts and DelVecchio, 2000 ). Yet, not all employees may know how exactly they can do so. Organizations may consider to support their employees through counseling, coaching and job-crafting training in order to provide employees with the skills and strategies needed for change and personal development ( Wrzesniewski and Dutton, 2001 ).

There has been a growing recognition that individual characteristics shape and influence behaviors at work. In this article, we investigated the role of personality traits and preferences in career role enactment at work. By introducing the importance of personal preferences as a mediating mechanism between personality and career role enactment, we hope to contribute to a better understanding of how people come to occupy certain career roles. Having a clear understanding of the self, and ones preferences at work may help employees to select those roles that are congruent with their interests.

Data Availability

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

This research was carried out in accordance with the recommendations of the Heymans Institute Ethical Guidelines. The protocol was approved by the Ethical Committee of Psychology, University of Groningen. Online informed consent was obtained from all participants.

Author Contributions

NdJ, BW, and KvdZ designed the study. NdJ collected and analyzed the data. NdJ and BW drafted the manuscript with NdJ taking the lead. JH and KvdZ engaged in several rounds of the critical feedback.

This research was supported by the University of Groningen, ISW, and GITP International BV.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.01720/full#supplementary-material

• ^ At Time 1, a total of 505 respondents completed the online questionnaire. We contacted these respondents again to participate at Time 2, and 302 respondents did so (of which five individuals did not participate at Time 1). From the initial sample of 297 workers that participated in both waves we deleted a number of respondents who indicated either not to use their data or who showed unusual response patterns ( N = 18). We continued the analyses with the remaining 279 workers. To test for the selective dropout at Time 2, control variables (age, gender, education, employment, and job complexity) were used to compare continuers ( N = 297) to dropouts ( N = 208). However, the results showed no significant differences between both groups in terms of the control variables [gender: F (1,503) = 2.14, p = 0.14, age: F (1,503) = 1.21, p = 0.27, education: F (1,503) = 1.34, p = 0.25, employment: F (1,503) = 2.14, p = 0.27 and job complexity: F (1,503) = 0.037, p = 0.85].
• ^ Although all questionnaires have been validated in previous research we performed confirmatory factor analyses. Fit indices for the personality measurements were initially RMSEA = 0.092, CFI = 0.93 for Study 1 and RMSEA = 0.096, CFI = 0.83 for Study 2. Therefore, additional EFA analyses were conducted. Consequently, several items in the personality questionnaire (10 items for Study 2) were deleted, which resulted in an adequate fit structure for both personality measurements. We found adequate fit indices for career role enactment (RMSEA = 0.091, CFI = 0.96 for Study 1 and RMSEA = 0.082, CFI = 0.93 for Study 2) and for career role preferences (RMSEA = 0.10, CFI = 0.94 for Study 2). Notably, if we would have included all items for both personality questionnaires the conclusion of our analyses would have been the same as they are now.
• ^ The study scenarios are available by contacting the first author.
• ^ We conducted multiple mediation analysis to account for the multiple predictor variables. To add multiple predictor variables we used the seed = option in PROCESS. When using this option, mathematically all resulting paths, direct and indirect effects are as if they had all been estimated simultaneously ( Hayes, 2013 ). Similar to previous research on personality characteristics and work outcomes we added all five personality traits at once ( Akhtar et al., 2015 ). However, because we expected a specific career role preference to influence role enactment only the preference for the specific role was added as a mediator in the analyses. Note that we also conducted mediation analyses with a more complex model, including all different career role preferences as mediators. This did not change the pattern of the results, therefore we continued our analyses using the least complicated model.

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Keywords : personality, career role preferences, career roles, career role enactment, job crafting

Citation: de Jong N, Wisse B, Heesink JAM and van der Zee KI (2019) Personality Traits and Career Role Enactment: Career Role Preferences as a Mediator. Front. Psychol. 10:1720. doi: 10.3389/fpsyg.2019.01720

Received: 23 March 2019; Accepted: 10 July 2019; Published: 25 July 2019.

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*Correspondence: Nicole de Jong, [email protected]

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• Published: 12 August 2024

A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology

• Priya Gupta 1 , 2 ,
• Marco Galimberti   ORCID: orcid.org/0000-0001-6052-156X 1 , 2 ,
• Yue Liu 3 ,
• Sarah Beck   ORCID: orcid.org/0000-0003-4176-2936 1 , 2 ,
• Aliza Wingo   ORCID: orcid.org/0000-0002-6360-6726 4 , 5 ,
• Thomas Wingo   ORCID: orcid.org/0000-0002-7679-6282 3 ,
• Keyrun Adhikari   ORCID: orcid.org/0000-0001-9129-1699 1 , 2 ,
• Henry R. Kranzler   ORCID: orcid.org/0000-0002-1018-0450 6 , 7 ,
• VA Million Veteran Program ,
• Murray B. Stein   ORCID: orcid.org/0000-0001-9564-2871 8 , 9 ,
• Joel Gelernter   ORCID: orcid.org/0000-0002-4067-1859 1 , 2 &
• Daniel F. Levey   ORCID: orcid.org/0000-0001-8431-9569 1 , 2  

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Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The ‘big five’ personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses, including depression, anxiety and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Here, utilizing the Million Veteran Program cohort, we conducted a genome-wide association study in individuals of European and African ancestry. Adding other published data, we performed genome-wide association study meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 208, 14, 3, 2 and 7 independent genome-wide significant loci associated with neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. These findings represent 62 novel loci for neuroticism, as well as the first genome-wide significant loci discovered for agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT and STX4 . Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety, while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.

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Identification of pleiotropy at the gene level between psychiatric disorders and related traits

Gene-based association analysis identifies 190 genes affecting neuroticism

Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Personality dimensions influence behaviour, thoughts, feelings and reactions to different situations. A valuable construct within the field of psychological research has converged on five different dimensions to characterize human personality: neuroticism, extraversion, agreeableness, conscientiousness and openness 1 , 2 . Personality dimensions could be playing an important role in the susceptibility and resilience to diagnosis of psychiatric disorders and their relationship with other health-related traits and responses to treatment.

The last decade has seen an increasing interest in understanding the dimensions of human personality through the lens of genetics. Depression is one mental disorder that has been studied with respect to its relationship to personality traits, with a large portion of genetic risk for depression being captured by neuroticism 3 . The same study found a modest negative association of genetic depression risk with conscientiousness, with small contributions from openness, agreeableness and extraversion. Neuroticism is one of the most studied dimensions of the ‘big five’ personality traits and numerous studies have found positive correlations with depression, anxiety and other mental illnesses 3 , 4 , 5 . Schizophrenia has also been associated with personality traits, especially neuroticism, which has been shown to increase risk for diagnosis 6 . A study using data from the Psychiatric Genomics Consortium (PGC) and personal genomics company 23andMe found two genomic loci to be common between neuroticism and schizophrenia. This study also reported six loci shared between schizophrenia and openness 7 .

The past 15 years have seen an explosion in the use of the genome-wide association study (GWAS). In 2010, Marleen de moor et al. from the Genetics of Personality Consortium (GPC) published a GWAS of the ‘big five’ personality traits conducted with 17,375 adults from 15 different samples of European ancestry (EUR) 8 . This study found two genome-wide significant (GWS) variants near the RASA1 gene on 5q14.3 for openness and one near KATNAL2 on 18q21.1 for conscientiousness but no significant associations for other personality traits. GPC then conducted studies on extraversion and neuroticism in their second phase and meta-analyses were performed. A GWAS of neuroticism that was conducted on approximately 73,000 subjects identified rs35855737 in the MAG1 gene as a GWS variant 9 . Although the sample size was increased substantially to 63,030 subjects in phase II, no GWS variants were detected for extraversion in that study 10 . In 2016, Lo et al. identified six loci associated with different personality traits, including loci for extraversion 11 . A paper that investigated neuroticism along with subjective well-being and depressive symptoms leveraging the UK Biobank (UKB) and other published data 12 was published this same year. A more detailed picture of neuroticism genetics was presented by Nagel et al. 2018 13 , where the authors collected neuroticism genotype data of 372,903 individuals from the UKB and performed a meta-analysis by combining the summary statistics from this UKB sample, 23andMe and GPC phase 1 samples, increasing the total sample size to 449,484. They identified a total of 136 loci and 599 genes showing GWS associations to neuroticism. In 2021, Becker et al. conducted a polygenic index study and created a resource with GWAS meta-analysis summary statistics combining different data cohorts for a large number of traits, including neuroticism, thus increasing the total sample size of neuroticism meta-analysis to 484,560 and increasing the number of novel GWS loci (although this was not the focus of this work) 14 . They also identified six genomic loci for extraversion.

In this work, we conducted GWAS of each of the ‘big five’ personality traits in a sample of ~224,000 individuals with genotype data available from the Million Veteran Program (MVP). Using linkage disequilibrium score regression (LDSC), we estimated the single-nucleotide polymorphism (SNP)-based heritability of each of the five personality traits. We then combined the MVP data with other sources of personality GWAS summary statistics from GPC and UKB and performed meta-analyses for each of the five personality traits, including as many as ~680,000 participants for the largest meta-analysis of neuroticism so far. To gain insights into the biology of these traits, we performed transcriptome-wide association studies (TWAS) and proteome-wide association studies (PWAS) followed by pathway and drug perturbation analyses and variant fine-mapping. We also studied the overlap of these personality traits with anxiety and other complex traits through phenome-wide genetic correlations and conditional analyses. We performed drug perturbation analyses with genes associated with neuroticism and found convergence on drugs for major depressive disorder (MDD). Finally, we conducted Mendelian randomization (MR) experiments to investigate the causal relationship of neuroticism and agreeableness, the two most genetically divergent traits, with depression and anxiety.

In the EUR GWAS in the MVP cohort, we identified in total 34 unique independent genomic loci significantly associated ( P value <5 × 10 −8 ) with at least one of the five personality traits (Table 1 ). The highest numbers of loci were found for extraversion and neuroticism (11 for each) while conscientiousness showed only two loci. In the MVP we identified 4,036 GWS variants ( P  < 5 × 10 −8 ) for neuroticism across 7 independent genomic loci harbouring genes including MAD1L1 , MAP3K14 , CRHR1 , CRHR1-IT1 and VK2 ( P  < 5 × 10 −8 ). Of these seven loci, two ( rs2717043 and rs4757136 ) were also reported to be GWS in Nagel et al. 13 . We identified 11 GWS loci for extraversion, the largest number of GWS loci to be identified for this trait. Associations for extraversion were found near several genes, including CRHR1 , MAPT and METTL15 (total 90 genes). For the two conscientiousness loci, the first locus maps to a region near the genes FOXP2 , PPP1R3A and MDFIC and the second locus maps to the ZNF704 gene, all of which are protein coding genes. For openness, 7 loci were identified spanning over 39 genes, including BRMS1 , RIN1 and B3GNT1 . For agreeableness, 3 loci were identified spanning 19 genes, including SOX7 , PINX1 and FOXP2 . The Manhattan plots for all five traits are shown in Supplementary Fig. 1 .

Two GWS variants were found for agreeableness in the African ancestry (AFR) sample. Variants rs2393573 (effect size, −0.106; standard error of the mean (s.e.m.), 0.018; 95% confidence interval (CI) −0.071, 0.141; P  = 7.502 × 10 −9 ) and rs112726823 (effect, −0.720; s.e.m., 0.130; 95% CI 0.465, 0.975; P 3.268 × 10 −8 ) mapped near CCDC6 and ARHGAP24 . We did not find any GWS variants for any of the other four personality traits in the AFR sample; the multiple subthreshold findings from this analysis may reach the GWS threshold in a larger sample. A list of lead independent SNPs found in the AFR sample for each trait is provided in Supplementary Tables 1 – 5 .

Meta-analysis in EUR populations

The meta-analysis for neuroticism showed associations with 208 independent GWS loci. The increased power due to the inclusion of MVP data resulted in the identification of 79 additional GWS loci, which were not significant in the previous study 13 . Only five loci identified previously ( rs1763839 , rs2295094 , rs11184985 , rs579017 and rs76923064 ) were no longer significant in our meta-analysis. A total of 17 loci of these 79 have also been discovered in the polygenic index study (Supplementary Table 6 ). Thus, we found 62 novel loci associated with neuroticism in our meta-analysis. SNPs and loci were mapped to genes based on chromosomal position, expression quantitative trait loci (eQTL) and chromatic interaction 15 . A total of 231 genes were found significant in the MAGMA (Multi-marker Analysis of GenoMic Annotation) gene-based test 16 . NSF , KANSL1 , FMNL1 , PLEKHM1 and CRHR1 ( P  < 2.850 × 10 −40 ) were among the top significant hits. The largest number of significant loci are located on chromosome 11, followed by chromosome 1. The GWS associations also include two loci with variants rs7818437 (effect, −0.021; s.e.m., 0.002; 95% CI −0.017, 0.025; P  = 7.599 × 10 −17 ) and rs76761706 (effect, −0.035; s.e.m., 0.002; 95% CI −0.031, 0.039; P  = 2.850 × 10 −40 ) located in inversion regions on chromosome 8 and 17, respectively. Variants in these two inversion regions were also previously reported to be significantly associated with neuroticism in the study by Okbay et al. 12 .

For extraversion, after meta-analysing the MVP and GPC data, the number of significant loci increased to 14. The lead signals were located on chromosomes 1–6,11,12, 17 and 19. The most significant locus harbours genes in/near WSCD2 ( P  < 3.449 × 10 −11 ) located on chromosome 12.

Chromosome 11 contains significant variant associations from three traits, namely neuroticism, extraversion and agreeableness, with neuroticism and extraversion both having findings near the ‘basic helix-loop-helix ARNT like 1’ ( ARNTL1 , also known as BMAL1 ) gene, with opposing and significant direction of effect at common variants. Complete information of all identified significant loci for each of the five traits with full statistics is provided in Supplementary Tables 6 – 10 . The cohorts used in meta-analysis are depicted in Fig. 1a . Manhattan plots for meta-analyses of each of the five traits are depicted in Fig. 2 .

a , Data collection of the five personality traits. b , Genetic correlation matrix among the five personality traits (meta-data). The heritability value of the respective trait is written in parenthesis. c , A karyogram showing the regions with significant local genetic correlation ( r G  > 0.3) between different personality traits.

The GWS variants in light-green colour. Reported P values are two-sided and not corrected for multiple testing. GWS threshold ( P  = 5 × 10 −8 ) is used to define significant variants and depicted by red line.

Trans-ancestry analysis

We performed trans-ancestry meta-analysis of the five personality traits combining EUR and AFR GWAS for each of the five traits using inverse variance weighing in METAL 17 . For neuroticism, the trans-ancestry analysis identified a total of 216 GWS loci, of which 16 are novel, that is, they were not GWS in the EUR meta-analysis (Supplementary Tables 11 – 15 ). Of the 208 GWS loci for neuroticism in the EUR meta-analysis, 200 remained GWS in trans-ancestry analysis, while the remaining 8 showed a marginally higher P value and thus do not pass the threshold for being GWS in trans-ancestry. For agreeableness and conscientiousness, in addition to the loci that were shown to be GWS in their respective EUR meta-analysis, two more novel loci ( rs140242735 located on chromosome 8 and rs10864876 located on chromosome 2 for agreeableness and conscientiousness, respectively) were identified as GWS in the trans-ancestry analysis. In case of openness, two loci out of the three that identified as GWS in EUR remained GWS in the trans-ancestry analysis. For extraversion, in total 13 were identified as GWS in the trans-ancestry analysis, of which 10 were also GWS in the EUR meta-analysis and 3 were newly identified.

We performed TWAS for each of the ‘big five’ personality traits in EUR (meta-analysis) using FUSION 18 and the GWAS summary statistics. We performed a multi-tissue TWAS in 13 different brain subtissues and blood using their respective expression profiles from Genotype Tissue-Expression project (GTEx v8) 19 . From a total 10,386 genes tested, we identified a total 175, 24, 5, 1 and 11 genes showing significant gene–trait associations across the 13 subtissues in neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively, after Bonferroni correction for 135,018 tests (10,386 genes across 13 tissues) (Fig. 3a ). Figure 3a shows the distribution of associations found across the 13 tissues for each trait. The highest number of gene–trait associations were found in brain caudate basal ganglia, cerebellum, cerebral hemisphere and frontal cortex regions for neuroticism and extraversion, while fewer TWAS gene–trait associations were identified for the other three personality traits, presumably owing to the comparatively lower power of their respective GWAS datasets.

a , A bar chart showing the number of significant TWAS genes per transcripts found of four personality traits with significant findings in respective subtissues. Scatter plots of neuroticism ( b ), agreeableness ( c ), extraversion ( d ) and openness ( e ) with TWAS z -scores of each gene transcript plotted on the y axis and its respective chromosomal location plotted on the x axis. The significant hits are shown in red circles with mapped gene names as labels. The blue horizontal line indicates the significance threshold of the z -score corresponding to the Bonferroni-corrected, two-sided P value. Conscientiousness data is reported in Supplementary Table 22 .

CRHR1, KANSLI1-AS1 and MAP-IT1 are among the top TWAS gene associations ( P  < 1.32 × 10 −23 ) for neuroticism (Fig. 3b ). The strong association of CRHR1 (encoding corticotropic-releasing hormone receptor), which in some prior work has been shown to be associated with treatment response to depression 20 , may suggest some common underlying elements regulating both neuroticism and depression. Extraversion also shows strong gene–trait associations with CRHR1, KANSL1-AS1 and MAPT-IT1 but with an opposite direction of effect to neuroticism. This may indicate some common genetic components whose differential behaviour regulates neuroticism and extraversion. There are nine such genes showing opposite direction of effect in neuroticism and extraversion (Supplementary Table 3 ).

LOC10271024064 and LRFN4 showed the strongest associations with openness and LINCR-0001 and FAM167A showed the strong associations with agreeableness, while only one gene, AP1G1 , showed association with conscientiousness in the 13 tissues considered. The complete list of all GWS TWAS gene hits for the five personality traits is provided in Supplementary Table 22 .

We investigated the association of personality traits with protein expression using PWAS. Based on the availability of protein profiles and the observed TWAS signal, dorsolateral prefrontal cortex brain protein profiles were chosen for the PWAS analysis. The PWAS identified 47 proteins to be significantly associated with neuroticism. Next, we checked the colocalization signal for these PWAS lead genes. Out of 47 PWAS lead genes, 35 genes showed a colocalization signal (H4 probability >0.5).

Five, two, two and four proteins were discovered for extraversion, agreeableness, conscientiousness and openness, respectively (Fig. 4 ). A complete list of all PWAS lead genes is provided in Supplementary Table 23 .

A Manhattan plot is displayed showing the significant protein associations observed for neuroticism. The red line in the plot depicts the Bonferroni-corrected, two-sided P value threshold at 5% FDR. The boxes on the right show the significant proteins found for the respective four personality traits.

We first used LDSC to calculate SNP-based heritability of each of the five personality traits within the MVP EUR cohort. The intercepts of the LDSC indicated no evidence for population stratification, with observed values of 1.01, 1.02, 0.99, 1.02 and 1.00 for neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. The SNP heritability ranges from 4% to 7% (Supplementary Fig. 2 ), with extraversion showing the highest heritability point estimate of all traits (neuroticism h 2  = 0.0655; s.e.m., 0.004; 95% CI 0.058, 0.073; agreeableness h 2  = 0.042; s.e.m., 0.003; 95% CI 0.036, 0.048; extraversion h 2  = 0.071; s.e.m., 0.003; 95% CI 0.065, 0.077; openness h 2  = 0.048; s.e.m., 0.003; 95% CI 0.042, 0.054; and conscientiousness h 2  = 0.047; s.e.m., 0.003; 95% CI 0.041, 0.053).

For the MVP AFR cohort, cov-LDSC was utilized to estimate personality heritabilities ( Methods ) 21 . Relative to the MVP EUR cohort, neuroticism and extraversion showed lower heritability (4.47% and 3.30%, respectively) in the AFR cohort, while for agreeableness, the heritability was similar (4.24%) (Supplementary Table 1 ). The values were not significant for conscientiousness and openness in AFR.

Before combining the MVP cohort-derived summary statistics with other data sources, we calculated the genetic correlation between the MVP personality summary statistics and other respective sources (Supplementary Table 2 ). A correlation coefficient value of 0.80 (s.e.m., 0.02) observed for the neuroticism summary statistics from the MVP cohort and Nagel et al. study 13 suggests that there is limited heterogeneity between the two datasets and supports their use in a meta-analysis. As shown in Supplementary Table 2 , the genetic correlations were high for all other four traits across data sources as well.

LDSC was used to estimate SNP-based heritability in the EUR participants for each personality trait in the meta-analysis. The SNP heritability values in the meta-analyses were similar to what was observed in the MVP-only cohort for the different traits in the EUR, with a decrease in heritability of extraversion from 7.1% to 5.1% (Fig. 1b ).

Genetic correlation estimates were also obtained between the meta-analysis summary statistics for the five personality traits. We found a significant degree of varying genetic overlap among the five personality traits. The genetic correlations are presented in Fig. 1b . The highest correlation is observed between neuroticism and agreeableness with a r G  = −0.51 (s.e.m., 0.030; P  = 3.813 × 10 −64 ).

Next, we estimated the genetic correlations of 1,437 traits listed in the Complex Traits Genetics Virtual Lab 22 summary statistics record to find other traits related to the five personality traits (Supplementary Tables 16 – 20 ). A total of 325 traits showed significant genetic correlation following multiple testing correction to one or more personality traits. We found MDD and anxiety showed varying degrees of significant correlations to different personality traits as shown in Fig. 5 . The highest genetic correlation is between neuroticism and anxiety ( r G  = 0.80). Neuroticism and agreeableness both show high genetic correlations to these traits, but in opposite directions with MDD (neuroticism r G  = 0.68; s.em. 0.02; P  < 5.00 × 10 −100 and agreeableness r G  = −0.35; s.e.m. 0.04; P  = 1.53 × 10 −22 ), manic behaviour (neuroticism r G  = 0.44; s.e.m. 0.08; 95% CI 0.641, 0.719; P  = 1.11 × 10 −8 and agreeableness r G  = −0.35; s.e.m. 0.11; 95% CI −0.134, 0.566; P  = 1.556 × 10 −3 ), anxiety (neuroticism r G  = 0.80; s.e.m. 0.06; 95% CI 0.682, 0.918; P  = 1.54×10 −46 and agreeableness r G  = −0.32; s.e.m. 0.08; 95% CI −0.163, 0.477; P  = 7.28 × 10 −5 ) and irritability (neuroticism r G  = 0.70; s.e.m. 0.02; 95% CI 0.661; 0.739, P  < 5.00 × 10 −100 and agreeableness r G  = −0.62; s.e.m. 0.04; 95% CI −0.542, 0.698; P  = 9.76 × 10 −61 ).

The y axis is the genetic correlation. Error bars (in black) indicate the 95% CIs of the estimated genetic correlation. Anxiety indicates substances taken for anxiety; medication is prescribed for at least 2 weeks. Heavy DIY activities describes the types of physical activity in last 4 weeks; for example, weeding, lawn mowing, carpentry and digging. Manic behaviour describes manic/hyper behaviour for 2 days. Detailed results for all traits, including the sample size of each of the traits, is presented in the Supplementary Tables 16 – 20 .

Local genetic correlations

Global genetic correlations use the average squared signal over the entire genome, which may sometimes mask opposing local correlations in different genomic regions. To counter that, we also calculated the local genetic correlations among the five personality trait pairs using Local Analysis of [co]Variant Association (LAVA) 23 . All personality pairs showed varying degree of correlation in different genomic regions except for the neuroticism–openness pair, which showed negligible global ( r G  = −0.01) and no local genetic correlation between the two. The highest number of correlated genomic chunks were found for neuroticism–extraversion and neuroticism–openness pairs (Fig. 1c and Supplementary Table 21 ).

Variant fine-mapping

To identify well-supported possible causal variants from the large list of SNPs showing associations with the personality traits, we performed genome-wide variant fine-mapping using PolyFun 24 . In total, 166 unique variants were fine-mapped across the five personality traits. The number of variants fine-mapped for neuroticism, extraversion, agreeableness, conscientiousness and openness were 155, 8, 4, 7 and 3, respectively. The complete list of variants fine-mapped for each of the personality traits is provided in the Supplementary Tables 24 – 28 .

Relationship between personality and psychiatric disorders

We performed additional analyses to help understand the significant differential genetic correlation observed between neuroticism and agreeableness with different psychiatric disorders such as MDD and anxiety.

Conditional analysis

Because the genetic correlation between anxiety and neuroticism was so high, we performed multi-trait-based conditional and joint analysis of neuroticism summary statistics conditioned on anxiety and MDD summary statistics individually. The anxiety and MDD summary statistic used is based on data from UKB, MVP and PGC with individuals of EUR ancestry (see Methods for details). We performed a similar analysis with agreeableness, which had a negative correlation with both MDD and anxiety, as a negative control.

After conditioning on MDD, the SNP heritability of the conditioned neuroticism summary statistic reduced significantly from 7.8% to 3% (Table 2 ). Out of the original 208 GWS leads, only 42 remained significant after conditioning, indicating there is substantial genetic overlap between neuroticism and MDD, which gets removed after conditioning. In case of conditioning on anxiety, again there is a decrease in neuroticism heritability, but to a lesser extent (Table 2 ). On conditioning agreeableness on MDD and anxiety, no significant reduction in heritability was observed. However, loss of one genomic locus, rs7240986 (18:53195249:A:G), was observed after conditioning on either anxiety or MDD for agreeableness.

Drug perturbation analysis

We performed a drug perturbation analysis to find drug candidates for neuroticism-enriched genes using gene2drug software 25 . Gene2drug utilizes the Connectivity Map transcriptomics data of ~13,000 cell lines exposed to different drugs, and based on these gene expression profiles and then pathway expression profiles (PEPs), it first matches the query gene to its pathway and then to its potential candidate drug. This analysis predicted 298 unique drugs to correspond to the 231 significantly associated neuroticism genes. The top-scoring drug was found to be desipramine, which is a tricylic antidepressant. Some of the other drugs predicted are flupenthixol (anti-psychotic), tetryzoline (α-adrenergic agonist), doxorubicin (anthracycline/chemotherapy) and digitoxigenin (cardenolide). Based on these results, we repeated the drug perturbation analysis with depression-enriched genes. While there were only 51 genes common between neuroticism and depression gene sets, there was a convergence on drugs in the perturbation analysis. Out of 286 and 298 drugs predicted for depression and neuroticism, respectively, 167 drugs were common to both. The complete list of drugs is presented in Supplementary Tables 29 and 30 .

After establishing genetic overlap of neuroticism with MDD and anxiety, we carried out an MR analysis to explore the possibility of a causal relationship between genetic risk for neuroticism and MDD or anxiety. The results of the MR analysis using different methods are presented in Table 3 . The results of MR indicate a bidirectional causal effect, with the exposure of MDD on neuroticism outcome showing an inverse variance weighting (IVW) effect value of 0.429 at a significant P value (2.072 × 10 −85 ). The exposure of neuroticism on MDD shows a higher causal effect value of 0.834 with a significant P value (6.413 × 10 −103 ). We performed sensitivity analysis of MR using MRlap, which corrects for different sources of bias, including sample overlap, because there are overlapping participants between the exposure and outcome datasets 26 . With MRlap, we observe similar results with positive significant corrected β values in MRlap performed between MDD and neuroticism in both directions (Supplementary Table 4 ).

We also investigated the casual relationship of neuroticism with anxiety. On performing MR with anxiety exposure on neuroticism, we found a β value of 0.179 ( P  = 1.248 × 10 −15 ) and a corrected β value with MRlap of 0.531 ( P  = 7.781 × 10 −14 ) showing evidence of causality. On reversing the direction, the causality effect was stronger as seen by higher β value of 0.70 ( P  = 5.767 × 10 −61 ) with MR and corrected β value of 0.548 ( P  = 1.129 × 10 −40 ) with MRlap. This suggests that there is stronger evidence of causal effect of neuroticism on anxiety as compared with the reverse based on the genetic susceptibility. GWAS of anxiety and anxiety disorders are still relatively underpowered compared with neuroticism, limiting the number of available genetic instruments available for testing as exposures.

We investigated the causal effect of agreeableness on MDD and anxiety and vice versa. In the case of MR of MDD exposure on agreeableness outcome, a β value of −0.284 ( P  = 5.775 × 10 −13 ) was observed indicating negative causal effect of MDD on agreeableness (Table 3 and Supplementary Table 4 ). The causal effect is bidirectional with similar values observed in the opposite direction as well. The results are consistent with genetic correlation findings where negative correlation was observed between agreeableness and MDD. MR analysis of agreeableness and anxiety also indicated bidirectional causal effect. However, here both the traits have limited instruments available.

Out-sample polygenic risk score prediction

We conducted polygenic prediction analysis to validate our findings using the Yale–Penn cohort 27 , which had NEO Personality Inventory (NEO PI-R) scores and genotype information available for 4,532 EUR individuals, and used those data to predict PRS for each of the big five personality traits ( Methods ). We found modest but significant r 2 values in line with previous reports for all personality traits 14 : neuroticism of 2%, extraversion of 2%, openness of 2%, agreeableness of 3% and conscientiousness of 1%.

We conducted a GWAS meta-analysis study of each of the ‘big five’ personality traits in a sample size of up to 682,688 participants. We combined original GWAS results from the MVP (available for all five traits) with summary statistics from the UKB (neuroticism only) and GPC (all traits except neuroticism) cohorts to perform a well-powered meta-analysis for EUR GWAS in each trait. We identified 468 independent significant SNPs associations mapping to 208 independent genomic loci, of which one-third are novel. We identified 231 significant gene associations with neuroticism in the gene-based analysis. The current study was also successful in identifying 23 significant genomic locus associations for the four other personality traits studied, for which prior knowledge in the literature was very limited. In AFR, we found lower heritabilities for neuroticism and extraversion and no significant results for conscientiousness and openness. We identified two GWS variants for agreeableness in AFR. This is probably a reflection of low power and underlines the critical need to increase recruitment in underrepresented groups. Our work provides new data to inform the underlying genetic architecture of personality traits.

Neuroticism, the trait with the largest available sample size in this study, is characterized by emotional instability, increased anxiousness and low resilience to stressful events. As such, it has been the focus of previous efforts in GWAS. As seen previously, neuroticism overlaps substantially with psychopathology, where it is usually viewed as a precursor or risk factor for depressive and anxiety symptoms. Extraversion had the second largest sample size and had the highest SNP-based heritability in the MVP. In our data, scoring high on extraversion was genetically correlated with risk-taking behaviours and had the second strongest negative genetic correlation with neuroticism. Agreeableness assays show how someone relates with other people, that is, how trusting one is or how likely to find fault in others. This trait was the most negatively correlated with neuroticism and irritability as well as MDD, anxiety and manic symptoms. Conscientiousness items relate to discipline and thoroughness, with specific questions being ‘are you lazy’ and ‘does a thorough job’. This trait was most closely associated with ‘types of physical activity in last 4 weeks: ‘heavy do-it-yourself (DIY)’. Finally, openness 10-item Big Five Inventory (BFI-10) items assay imagination and artistic interest. Openness was positively associated with extraversion and risk taking in our data. Educational attainment was positively correlated with openness and negatively associated with neuroticism, while the other three personality traits showed essentially no such overlap (Fig. 5 ). Since these are self-reported items, they naturally reflect one’s own assessment of one’s personality traits, which might filter actual traits and behaviour through a lens of how one wishes to appear or be perceived.

Using these GWAS summary statistics, with excellent power for neuroticism and moderate power for the other traits, we investigated the heritability of the different personality traits and studied genetic correlations among them using LDSC. SNP-based heritability for all five personality traits in EUR were statistically significant. Out of all the personality pairs studied, the strongest relationship was a negative genetic correlation observed between neuroticism and agreeableness ( r G  = −0.51, Fig. 1b ). Examining the genetic correlations of the five personality traits with 1,437 external traits including depression (neuroticism r G  = 0.68 and agreeableness r G  = −0.35), manic behaviour (neuroticism r G  = 0.44 and agreeableness r G  = −0.35), anxiety (neuroticism r G  = 080 and agreeableness r G  = −0.33) and irritability (neuroticism r G  = 0.70 and agreeableness r G  = −0.62) further reflected a pattern of opposing relationships between these traits (Fig. 5 and Supplementary Tables 16 – 20 ). We also calculated local genetic correlations between personality pairs using LAVA, which helped in identifying the genomic regions playing roles in differential overlap in the genetic architecture of personality. This analysis identified several regions where the effect direction differed from the whole genome genetic correlation.

The MVP, our discovery dataset, is one of the world’s largest biobanks and is a valuable resource for genetic studies. Some previously published personality trait studies had significant contribution from UKB data. It is important to quantify the heterogeneity in these independent cohorts and the different definitions of personality phenotype within each. We investigated the genetic correlation between traits defined on the basis of different inventories (BFI-10, EPQ-RS and NEO-FFI) of personality ascertainment with different cohorts, namely MVP, UKB (part of Nagel et al. study) and GPC, respectively. For neuroticism, Nagel et al. and MVP studies showed a high r G value of 0.80 making these two independent cohorts suitable for meta-analysis (Supplementary Table 1 ). Similarly, for extraversion, NEO-FFI and two-item inventories showed high r G of 0.89 in the extraversion data of GPC and MVP studies. While for agreeableness, openness and conscientiousness, the r G s between MVP and GPC cohort were lower (0.63–0.72); this may be due to the small size of the GPC dataset for these traits and the correspondingly large standard errors around the point estimate. The point estimate is not necessarily biased in any particular direction, we only mean there is uncertainty. This limitation will be addressed by future GPC studies with larger sample sizes. No novel loci were identified in the meta-analysis with GPC for these traits.

TWAS revealed common genes with changes in gene expression but with opposite direction of effect for some personality traits. A study by Ward et al. in 2020 reported five of these genes (Supplementary Table 3 ) as eQTLs showing significant associations with mood instability 28 . This is further supported by the local genetic correlation studies (Supplementary Sheet 5 ) where we found genomic region 45883902-47516224 on chromosome 17, which harbours genes KANSL1-AS1 , MAPT and MAPT-IT1 , showing negative local genetic correlation between neuroticism and extraversion with a ρ value of −0.57 and r 2 value of 0.32.

rs1876829 , which maps to CRHR-Intronic Transcript 1, emerged as the lead SNP ( P  = 7.872 × 10 −39 ) for neuroticism in the GWAS analysis. We also found multiple eQTL SNPs in this genomic region ( rs8072451 , rs17689471 , rs173365 and rs11012 ) for the CRHR1 gene to be significantly associated ( P value ranging from 1 × 10 −5 to 1 × 10 −37 ). The TWAS analysis showed significant association of this gene with neuroticism in nervous system tissues including caudate basal ganglia, frontal cortex, hippocampus and spinal cord cervical region. CRHR1 encodes the receptor of corticotropin-releasing hormone family, which are major regulators of the hypothalamic–pituitary–adrenal pathway 29 . Genetic variation in the corticotropin-releasing hormone system has been linked to several psychiatric illnesses 30 . Another study reported hypermethylation at corticotropin-releasing hormone-associated CpG site, cg19035496, in individuals with high general psychiatric risk score for disorders such as depression, anxiety, post-traumatic stress disorder and obsessive compulsive disorder 31 . Further, a study by Gelernter et al. found that CRHR1 significantly associated with re-experiencing post-traaumatic stress disorder symptoms 32 and also maximum habitual alcohol intake 33 . This gene is also involved in hippocampal neurogenesis 30 , while reduced hippocampal activation is associated with elevated neuroticism 34 . This makes CRHR1 a good lead candidate to be followed in future studies to understand the molecular processes impacted by genetic variation underlying a range of psychiatric traits including neuroticism.

While gene expression associations give a wide array of information on the involvement of different genes regulating the different biological processes underlying the biology of traits, searching protein expression associations confers several advantages, as proteins are the final implementers in the functioning of all cells for many biological processes. Through PWAS studies, we found 47 proteins showing significant association with neuroticism in the dorsolateral prefrontal cortex. The PWAS analysis also identified leucine-rich repeat and fibronectin type III domain-containing 5 (LRFN5) protein association with neuroticism, and this protein is also involved in synapse formation. This protein has shown higher levels in patients with MDD and has been suggested as a potential MDD biomarker 35 .

Examples of genes for which we found converging evidence in neuroticism for transcript and protein-level associations with neuroticism include low-density lipoprotein receptor-related protein 4 (LRP4), syntaxin 4 (STX4) and metabolism of cobalamin associated B (MMAB) (Supplementary Table 31 ). LRP4 has diverse roles in neuromuscular junctions and in disorders of the nervous system, including Alzheimer’s disease and amyotrophic lateral sclerosis 36 , STX4 is implicated in synaptic growth and plasticity 37 , and MMAB, which catalyses the final step in the conversion of cobalamin (vitamin B12) into adenosylcobalamin (biologically active coenzyme B12), all of which have broad implications for brain function, including those in relation to methylmalonic acidaemia 38 . Low levels of plasma vitamin B12 have been found to be associated with higher depression cases in multiple studies 39 .

We investigated the relationship of these personality traits with other psychiatric traits, cognitive functions and disorders in a broad phenome-wide scan of genetic correlations with 1,437 traits. A total of 325 traits showed significant genetic correlations with at least one of the five personality traits following multiple testing correction. Two important traits that had some of the strongest associations were MDD and anxiety. Whereas the association of neuroticism with depression and anxiety has been previously considered 4 , 13 , our analysis revealed that another personality trait, agreeableness, is also strongly associated with both anxiety and depression but in the opposite direction to neuroticism, showing a potential protective relationship. MR indicated a strong bidirectional causal relationship between neuroticism with anxiety and depression, while showing a bidirectional protective relationship for agreeableness for both traits. Variance explained for neuroticism was attenuated upon conditioning for MDD but remained significant, indicating some independent genetic component for neuroticism despite the strong overlap. Similar, but with a less strong effect, was seen of anxiety on neuroticism, which may be partly due to lower power of available anxiety summary statistics. Larger studies of anxiety disorders are needed to better understand this relationship. Conversely, when we conditioned on agreeableness, for MDD and anxiety we observed a nominal but non-significant change in SNP-based heritability. We conducted MR to further discern these patterns and it showed bidirectional causal effects with neuroticism, confirming a high degree of inter-relatedness between the traits. Given the high degree of genetic overlap between trait neuroticism and the expectation of personality trait expression preceding age of onset for MDD, a high trait neuroticism may be considered an early risk factor for anxiety, depressive and related psychopathology. Indeed, studies have shown persistent elevated neuroticism through adolescence is a risk factor for later susceptibility to anxiety and MDD diagnosis 40 .

Personality phenotyping in The MVP sample were done using self-report for the short BFI-10 inventory. As such, data are relatively sparse compared with more robust instruments and do not have more in-depth features such as facets found in the NEO inventory. The nature of large biobank studies such as the MVP comes with a crucial advantage in recruitment and sample size, but comes with the sacrifice of deep phenotyping. Future studies that compare findings from more deeply phenotyped samples to more sparse phenotyping used by the MVP would be valuable to address this limitation. Additionally, while we greatly expand on the amount of data available for agreeableness, conscientiousness, openness and extraversion, they still lag behind what has been accomplished for neuroticism. This means genetic instruments defined for the other four traits may lack the precision available for neuroticism. Larger samples still need to be collected to better understand these other traits.

Personality traits are known to have complex interactions with other human behaviours. In this work we have conducted comprehensive genomic studies of personality traits. We performed a GWAS in the MVP sample, the largest and most diverse biobank in the world, in both EUR and AFR to better understand genetic factors underlying personality traits. We combined this information with previously published results in a large meta-analysis, identifying novel genetic associations with five personality traits studied. We identified interactions in a phenome-wide genetic correlation analysis, finding novel relationships between complex traits. We used in silico analysis techniques to identify genetic overlap and causal relationships with depression and anxiety disorders. We also characterized underlying biology using predicted changes in gene and protein expression, biological pathway enrichment and drug perturbation analysis. These results substantially enhance our knowledge of the genetic basis of personality traits and their relationship to psychopathology.

Inclusion and ethics statement

This research was not restricted or prohibited in the setting of any of the included researchers. All studies were approved by local institutional research boards and ethics review committees. MVP was approved by the Veterans Affairs central institutional research board. We do not believe our results will result in stigmatization, incrimination, discrimination or personal risk to participants.

Cohort and phenotype

We used data release version 4 of the MVP 41 . The BFI-10 was included as part of a self-report Lifestyle survey provided to MVP participants, with two items for each of the personality traits (Supplementary Fig. 3 ). For the MVP EUR participants, the mean age was ~65.5 years for each of the five traits and 8% of the sample was female. For MVP AFR, the mean age was ~60.6 years for each trait while 14.0% of the sample was female.

Genotyping and imputation

Genotyping and imputation of MVP subjects has been described previously 41 , 42 . A customized Affymetrix Axiom Array was used for genotyping. MVP genotype data for biallelic SNPs were imputed using Minimac4 43 and a reference panel from the African Genome Resources panel by the Sanger Institute. Indels and complex variants were imputed independently using the 1000 Genomes phase 3 panel 44 and merged in an approach similar to that employed by the UKB. Ancestry group assignment within the MVP has been previously described 45 . Briefly, designation of broad ancestries was based on genetic assignment with comparison to 1000 Genomes reference panels 44 . Principal components to be used as covariates were generated within each assigned broad ancestral group.

GWAS and meta-analysis

We performed individual GWAS for each of the five personality traits in the MVP cohort 41 . The personality information along with genotype data were available for a total of 270,000 individuals with 240,000 EUR and 30,000 AFR. The GWAS was performed separately for each of the traits in the EUR and AFR datasets and the effect values were computed using linear regression.

MVP GWAS was conducted using linear regression in PLINK 2.0 using the first ten principal components, sex and age as covariates 46 . Variants were excluded if call missingness in the best-guess genotype exceeded 20%. Alleles with minor allele frequency (MAF) <0.1% were excluded. Additionally, only variants with an imputation accuracy of ≥0.6 were retained. After applying all filters, genotype data from 233,204, 235,742, 235,374, 234,880 and 220,015 participants were included for neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively.

For meta-analysis, summary statistics generated in this study (referred to as MVP study) were combined using METAL 17 with that from Nagel et al. and GPC phase I and II studies (Fig. 1a ) based on the availability of data for respective traits. The z -scores of variants provided in the summary statistics were converted into β scores 47 . The inverse variance weighing scheme of METAL was applied to weight the effect sizes of SNPs from the different source studies. For neuroticism, summary statistics from MVP and Nagel et al. studies 13 (excluding 23 and Me) were combined, increasing the total sample size to 682,688. For extraversion, summary statistics from MVP and GPC phase II study 10 were combined, while summary statistics from MVP and GPC phase I study 8 were combined for the respective meta-analysis of agreeableness, openness and conscientiousness. GPC data were already included in the neuroticism meta-analysis of Nagel et al.

The independent GWS loci for each of the personality traits were identified by clumping all SNPs using PLINK v1.9 software 48 . P value cut-off of 5 × 10 −8 , MAF >0.0001, distance cut-off of 1 MB and r 2  < 0.1 were used to define the lead SNPs using the 1000 Genomes phase 3 European reference panel 44 . The genes are mapped for the identified lead SNPs using biomaRt package in R 49 . The same parameters were used to define novel independent loci for comparison from the Nagel et al. 13 and Becker et al. 14 summary statistics (excluding 23 and Me).

Trans-ancestry analysis for each of the five personality phenotypes was performed by combining their respective summary statistics from AFR and EUR analyses using METAL 17 . As with the EUR-only meta-analysis, the inverse variance weighing scheme of METAL was applied to weight the effect sizes of SNPs from the two ancestries. We identified independent SNPs in the same manner as described above for the ancestry-stratified GWAS.

LDSC and SNP heritability

LDSC was performed based on the linkage disequilibrium reference from the 1000 Genomes data for all EUR cohorts and SNP heritability for each of the five personality traits was calculated 50 . To investigate the relation among the different personality traits, the LDSC-based correlation was also calculated between each pair of traits 51 . LDSC was also used to calculate genetic correlation of the personality traits with multiple other phenotypes (1,437 traits) with the Complex Traits Genetics Virtual Lab webtool 22 . A P value cut-off of 6.9 × 10 −6 (0.05/(1437 × 5)) was applied to filter the significant correlating pair of traits after multiple test correction.

For MVP AFR, linkage disequilibrium scores were computed from the approximately 123,000 AFR individuals’ genotype data in the MVP cohort using covariant LDSC software 21 . This linkage disequilibrium reference panel was then utilized to calculate SNP heritability in the MVP AFR cohort using LDSC.

We used LAVA 23 to calculate local heritability for the five personality traits and local genetic correlations for each pair. The genome was divided into 2,495 genomic chunks/loci to attain minimum linkage disequilibrium between them and maintain an approximate equal size of around 1 MB. The local heritability of each of the five personality traits was calculated for each of the 2,495 loci. For a given personality trait pair, local genetic correlations were calculated only for pairs that had significant local heritability (Bonferroni-corrected P value at 5% false discovery rate (FDR)) for both traits of the pair. Bonferroni multiple testing correction was also applied to genetic correlated P value to consider significant correlated pairs.

FUSION software 18 was used to perform TWAS. FUSION first estimates the SNP heritability of steady-state gene and uses the nominally significant ( P  < 0.05) genes for training the predictive models. The predictive model with significant out-of-sample R 2 (>0.01) and nominal P  < 0.05 in the five-fold cross-validation was then used for the predictions in the GWAS data. The process is performed for all five personality EUR GWAS data with 10,386 unique genes spanning over the 13 selected tissues. The expression weight panels for 13 a priori selected tissues were taken from GTEx v8 19 . We selected the different available brain tissues and whole blood as the tissues of interest, where Bonferroni corrections at FDR <0.05 were applied with the 10,386 genes test for the 13 tissues to find the genes with significant hits (P < 3.703 × 10 −7 ).

We performed PWAS to test the association between genetically regulated protein expression and different personality traits individually using FUSION software 18 . The weights for genetic effect on protein expression for the PWAS were from the Wingo et al. study 52 . In the PWAS, we integrated the protein weights with the summary statistics from the GWAS of each of the personality traits, respectively. Next, to decrease the probability of linkage contributing to the significant association in the PWAS, we performed colocalization analysis using COLOC 53 . In COLOC, we determined if the genetic variants that regulate protein expression colocalize with the GWAS variants for the personality trait. Significant proteins in the PWAS that also have COLOC posterior probability of hypothesis 4 (PP4) >50% have a higher probability of being consistent with a causal role in the personality trait of interest.

Fine-mapping

To identify likely causal variants, we performed variant fine-mapping using Polyfun software 24 . Since the fine-mapping was performed on the same EUR data, SNP-specific prior causal probabilities were taken directly from the pre-computed causal probabilities of 19 million imputed UKB SNPs with MAF >0.01 based on 15 UKB traits analysis. The fine-mapping was performed on the GWAS sumstats for each of the five personality traits. SuSiE 54 was used to map the posterior causal probabilities of the SNPs. The SNPs with posterior inclusion probability (PIP) value >0.95 were considered as significant for neuroticism, while a more relaxed cut-off of PIP >0.80 was used for other four personality traits to avoid loss of causal variant information due to the relatively less power in their respective datasets.

Conditional analysis was performed to investigate the possible mediating effects between depression or anxiety and neuroticism or agreeableness. Neuroticism meta-data GWAS summary statistics were used and conditioned on MDD and anxiety in individual runs. The MDD summary statistics were from Levey et al. study 55 and include a meta-analysis from the MVP, UKB, PGC and FinnGen. The anxiety summary statistics were taken from Levey et al. study 42 . With depression/anxiety studies as covariate traits, the conditional analysis of neuroticism (target trait) was carried out using multi-trait-based conditional and joint analysis utility of genome-wide complex trait analysis 56 . Similarly, the same method was used to perform conditional analysis of agreeableness on MDD and anxiety.

FUMA was used to carry out the MAGMA-based gene-association tests to find significantly associated genes for a trait from its GWAS summary data 15 . Drugs were searched for both neuroticism and MDD individually using their respective significantly associated genes derived from neuroticism meta-analysis summary statistics and MDD GWAS summary statistics from the Levey et al. summary statistics. To predict drug candidates for a given trait, significant genes associated with neuroticism/depression were given as input to gene2drug R-package 25 . Pre-computed Pathway Expression Profiles of the Connectivity Map data were taken from Drug Set Enrichment Analysis (DSEA) website. For each query gene, a maximum of five predicted drugs were predicted. Further, the drugs showing an E s core >0.5 and a P value less than 1 × 10 −6 were considered significant. The process was repeated for MDD.

MR was performed to study the causal relationship between four pairs of traits: neuroticism and MDD, neuroticism and anxiety, agreeableness and MDD, and agreeableness and anxiety. These traits had the highest genetic correlation. The summary statistics described previously for conditional analysis for all four traits were used for carrying out MR analysis as well. TwoSample MR package was used to perform the MR 57 . For each pair of traits, the TwoSample MR was run twice to see the effect of exposure of each of the two traits on the outcome of the other. After harmonizing the exposure and outcome instruments sets, clumping of SNPs (distance of 500 kb, r 2  = 0.05) was performed before conducting the MR analysis. Because some of our samples included in the analysis of personality overlap with our outcomes and exposures of interest, and a TwoSample MR is not robust to sample overlap, we also performed a sensitivity analysis for each trait pair using the MRlap package 26 . MRlap is specifically designed to account for many assumptions of MR, including sample overlap. It first calculates observed MR-based effect values and then a corrected effect value by using the genetic covariance calculated by LDSC.

The Yale–Penn cohort includes participants recruited from sites in the eastern United States 58 . A total of 11,705 participants completed the 240-item revised NEO PI-R, which assesses the domains of the five-factor model of personality: neuroticism, extraversion, openness to experience, agreeableness and conscientiousness 59 . Each domain has six facets. For example, the facets of neuroticism are anxiety, angry hostility, depression, self-consciousness, impulsiveness and vulnerability. Each item is rated on a five-point scale. Of the Yale–Penn participants with a NEO score, 4,582 were assigned to the broadly defined EUR group using the same methods as in the MVP sample and were unrelated. We used PRS-CS, Python software that uses Bayesian regression and continuous shrinkage priors, to calculate posterior effect sizes per SNP 60 . The 1000 Genomes linkage disequilibrium reference panel was used. The training datasets were summary statistics from the EUR meta-analysis for each of the five personality factors. The target dataset was a PLINK-formatted binary file set containing genotype information from the Yale–Penn participants 48 . Once score per SNP was generated by PRS-CS and PLINK was used to generate a score for each individual by summing SNP effect 48 . The lm (linear model) function in R was used to regress NEO PI-R scores on PRS, using age, sex and the first ten within-ancestry principal components as covariates 61 .

Ethics oversight

Research involving MVP in general is approved by the Veterans Affairs Central institutional research board; the current project was also approved by institutional research boards in West Haven, CT.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All MVP summary statistics are made available through dbGAP request at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v11.p1 . Meta-analysis summary statistics are available through the Levey lab website at https://medicine.yale.edu/lab/leveylab/data/ . Meta-analysis data will also be made available via the Complex Trait Genetics Virtual Lab at https://vl.genoma.io/ .

Code availability

No custom code was developed for analyses in this manuscript. All code used is cited and described in the methods. Software versions are accessible via PLINK v1.9 at https://www.cog-genomics.org/plink/1.9/ , PLINK v2.0 at https://www.cog-genomics.org/plink/2.0/ and Polyfun: version 1.0.0 SuSiE package version: 0.11.92.

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Acknowledgements

This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award no. 5IK2BX005058. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. A.W. was supported by a BLRD CDT award from the US Department of Veterans Affairs no. 1IK4BX005219 and grant I01 BX005686. A.W. and T.W. were supported by R01 grant no. AG072120. J.G. was supported by US Department of Veterans Affairs grant 5I01CX001849-04 and NIH grants R01DA037974 and R01DA058862. H.K. was supported by US Department of Veterans Affairs grant I01 BX004820 and the VISN4 Mental Illness Research, Education and Clinical Center of the Crescenz VAMC. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Detailed MVP Core team acknowledgements are included in the supplement.

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Priya Gupta, Marco Galimberti, Sarah Beck, Keyrun Adhikari, Joel Gelernter & Daniel F. Levey

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• Priya Gupta
• , Marco Galimberti
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• , Joel Gelernter
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Contributions

D.F.L. and P.G. designed the study. P.G. and D.F.L. drafted the manuscript. J.G. and M.B.S. provided ongoing feedback and refinement of the analytical plan, as well as early feedback on the drafted manuscript. D.F.L. and P.G. conducted GWAS on included cohorts. D.F.L. and P.G. discussed, created and refined the phenotype in the MVP. P.G. and M.G. discussed and refined MVP analytic plans. P.G. and Y.L. conducted TWAS and PWAS analysis with guidance from A.W., T.W. and D.F.L. S.B conducted out-sample PRS into the Yale–Penn cohorts with guidance from J.G. and H.R.K. P.G. D.F.L., M.G., S.B., Y.L., A.W., T.W. and K.A. conducted original analyses. D.F.L., T.W. and A.W. supervised original analyses. All authors critically evaluated and revised the manuscript.

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Correspondence to Daniel F. Levey .

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H.R.K. is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, Clearmind Medicine and Enthion Pharmaceuticals; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes; and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the past 3 years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi and Otsuka. J.G. and H.R.K. are holders of US patent 10,900,082 titled: ‘Genotype-guided dosing of opioid agonists’, issued 26 January 2021. J.G. is paid for editorial work on the journal Complex Psychiatry. The remaining authors declare no competing interests. J.G. is named as an inventor on PCT patent application no. 15/878,640 entitled ‘Genotype-guided dosing of opioid agonists’, filed 24 January 2018, and issued on 26 January 2021, as US patent no. 10900082. M.B.S. has stock options in Oxeia Biopharmaceuticals and EpiVario. He has been paid for his editorial work on Depression and Anxiety (Editor-in-Chief), Biological Psychiatry (Deputy Editor) and UpToDate (Co-Editor-in-Chief for Psychiatry). No other authors report competing interests.

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Gupta, P., Galimberti, M., Liu, Y. et al. A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01951-3

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The Power of Personality

Brent w. roberts.

University of Illinois

Nathan R. Kuncel

University of Minnesota

Rebecca Shiner

Colgate University

Avshalom Caspi

Institute of Psychiatry at Kings College, London, United Kingdom

Duke University

Lewis R. Goldberg

Oregon Research Institute

The ability of personality traits to predict important life outcomes has traditionally been questioned because of the putative small effects of personality. In this article, we compare the predictive validity of personality traits with that of socioeconomic status (SES) and cognitive ability to test the relative contribution of personality traits to predictions of three critical outcomes: mortality, divorce, and occupational attainment. Only evidence from prospective longitudinal studies was considered. In addition, an attempt was made to limit the review to studies that controlled for important background factors. Results showed that the magnitude of the effects of personality traits on mortality, divorce, and occupational attainment was indistinguishable from the effects of SES and cognitive ability on these outcomes. These results demonstrate the influence of personality traits on important life outcomes, highlight the need to more routinely incorporate measures of personality into quality of life surveys, and encourage further research about the developmental origins of personality traits and the processes by which these traits influence diverse life outcomes.

Starting in the 1980s, personality psychology began a profound renaissance and has now become an extraordinarily diverse and intellectually stimulating field ( Pervin & John, 1999 ). However, just because a field of inquiry is vibrant does not mean it is practical or useful—one would need to show that personality traits predict important life outcomes, such as health and longevity, marital success, and educational and occupational attainment. In fact, two recent reviews have shown that different personality traits are associated with outcomes in each of these domains ( Caspi, Roberts, & Shiner, 2005 ; Ozer & Benet-Martinez, 2006 ). But simply showing that personality traits are related to health, love, and attainment is not a stringent test of the utility of personality traits. These associations could be the result of “third” variables, such as socioeconomic status (SES), that account for the patterns but have not been controlled for in the studies reviewed. In addition, many of the studies reviewed were cross-sectional and therefore lacked the methodological rigor to show the predictive validity of personality traits. A more stringent test of the importance of personality traits can be found in prospective longitudinal studies that show the incremental validity of personality traits over and above other factors.

The analyses reported in this article test whether personality traits are important, practical predictors of significant life outcomes. We focus on three domains: longevity/mortality, divorce, and occupational attainment in work. Within each domain, we evaluate empirical evidence using the gold standard of prospective longitudinal studies—that is, those studies that can provide data about whether personality traits predict life outcomes above and beyond well-known factors such as SES and cognitive abilities. To guide the interpretation drawn from the results of these prospective longitudinal studies, we provide benchmark relations of SES and cognitive ability with outcomes from these three domains. The review proceeds in three sections. First, we address some misperceptions about personality traits that are, in part, responsible for the idea that personality does not predict important life outcomes. Second, we present a review of the evidence for the predictive validity of personality traits. Third, we conclude with a discussion of the implications of our findings and recommendations for future work in this area.

THE “PERSONALITY COEFFICIENT”: AN UNFORTUNATE LEGACY OF THE PERSON-SITUATION DEBATE

Before we embark on our review, it is necessary to lay to rest a myth perpetrated by the 1960s manifestation of the person–situation debate; this myth is often at the root of the perspective that personality traits do not predict outcomes well, if at all. Specifically, in his highly influential book, Walter Mischel (1968) argued that personality traits had limited utility in predicting behavior because their correlational upper limit appeared to be about .30. Subsequently, this .30 value became derided as the “personality coefficient.” Two conclusions were inferred from this argument. First, personality traits have little predictive validity. Second, if personality traits do not predict much, then other factors, such as the situation, must be responsible for the vast amounts of variance that are left unaccounted for. The idea that personality traits are the validity weaklings of the predictive panoply has been reiterated in unmitigated form to this day (e.g., Bandura, 1999 ; Lewis, 2001 ; Paul, 2004 ; Ross & Nisbett, 1991 ). In fact, this position is so widely accepted that personality psychologists often apologize for correlations in the range of .20 to .30 (e.g., Bornstein, 1999 ).

Should personality psychologists be apologetic for their modest validity coefficients? Apparently not, according to Meyer and his colleagues ( Meyer et al., 2001 ), who did psychological science a service by tabling the effect sizes for a wide variety of psychological investigations and placing them side-by-side with comparable effect sizes from medicine and everyday life. These investigators made several important points. First, the modal effect size on a correlational scale for psychology as a whole is between .10 and .40, including that seen in experimental investigations (see also Hemphill, 2003 ). It appears that the .30 barrier applies to most phenomena in psychology and not just to those in the realm of personality psychology. Second, the very largest effects for any variables in psychology are in the .50 to .60 range, and these are quite rare (e.g., the effect of increasing age on declining speed of information processing in adults). Third, effect sizes for assessment measures and therapeutic interventions in psychology are similar to those found in medicine. It is sobering to see that the effect sizes for many medical interventions—like consuming aspirin to treat heart disease or using chemotherapy to treat breast cancer—translate into correlations of .02 or .03. Taken together, the data presented by Meyer and colleagues make clear that our standards for effect sizes need to be established in light of what is typical for psychology and for other fields concerned with human functioning.

In the decades since Mischel’s (1968) critique, researchers have also directly addressed the claim that situations have a stronger influence on behavior than they do on personality traits. Social psychological research on the effects of situations typically involves experimental manipulation of the situation, and the results are analyzed to establish whether the situational manipulation has yielded a statistically significant difference in the outcome. When the effects of situations are converted into the same metric as that used in personality research (typically the correlation coefficient, which conveys both the direction and the size of an effect), the effects of personality traits are generally as strong as the effects of situations ( Funder & Ozer, 1983 ; Sarason, Smith, & Diener, 1975 ). Overall, it is the moderate position that is correct: Both the person and the situation are necessary for explaining human behavior, given that both have comparable relations with important outcomes.

As research on the relative magnitude of effects has documented, personality psychologists should not apologize for correlations between .10 and .30, given that the effect sizes found in personality psychology are no different than those found in other fields of inquiry. In addition, the importance of a predictor lies not only in the magnitude of its association with the outcome, but also in the nature of the outcome being predicted. A large association between two self-report measures of extraversion and positive affect may be theoretically interesting but may not offer much solace to the researcher searching for proof that extraversion is an important predictor for outcomes that society values. In contrast, a modest correlation between a personality trait and mortality or some other medical outcome, such as Alzheimer’s disease, would be quite important. Moreover, when attempting to predict these critical life outcomes, even relatively small effects can be important because of their pragmatic effects and because of their cumulative effects across a person’s life ( Abelson, 1985 ; Funder, 2004 ; Rosenthal, 1990 ). In terms of practicality, the −.03 association between taking aspirin and reducing heart attacks provides an excellent example. In one study, this surprisingly small association resulted in 85 fewer heart attacks among the patients of 10,845 physicians ( Rosenthal, 2000 ). Because of its practical significance, this type of association should not be ignored because of the small effect size. In terms of cumulative effects, a seemingly small effect that moves a person away from pursuing his or her education early in life can have monumental consequences for that person’s health and well-being later in life ( Hardarson et al., 2001 ). In other words, psychological processes with a statistically small or moderate effect can have important effects on individuals’ lives depending on the outcomes with which they are associated and depending on whether those effects get cumulated across a person’s life.

PERSONALITY EFFECTS ON MORTALITY, DIVORCE, AND OCCUPATIONAL ATTAINMENT

Selection of predictors, outcomes, and studies for this review.

To provide the most stringent test of the predictive validity of personality traits, we chose to focus on three objective outcomes: mortality, divorce, and occupational attainment. Although we could have chosen many different outcomes to examine, we selected these three because they are socially valued; they are measured in similar ways across studies; and they have been assessed as outcomes in studies of SES, cognitive ability, and personality traits. Mortality needs little justification as an outcome, as most individuals value a long life. Divorce and marital stability are important outcomes for several reasons. Divorce is a significant source of depression and distress for many individuals and can have negative consequences for children, whereas a happy marriage is one of the most important predictors of life satisfaction ( Myers, 2000 ). Divorce is also linked to disproportionate drops in economic status, especially for women ( Kuh & Maclean, 1990 ), and it can undermine men’s health (e.g., Lund, Holstein, & Osler, 2004 ). An intact marriage can also preserve cognitive function into old age for both men and women, particularly for those married to a high-ability spouse ( Schaie, 1994 ).

Educational and occupational attainment are also highly prized ( Roisman, Masten, Coatsworth, & Tellegen, 2004 ). Research on subjective well-being has shown that occupational attainment and its important correlate, income, are not as critical for happiness as many assume them to be ( Myers, 2000 ). Nonetheless, educational and occupational attainment are associated with greater access to many resources that can improve the quality of life (e.g., medical care, education) and with greater “social capital” (i.e., greater access to various resources through connections with others; Bradley & Corwyn, 2002 ; Conger & Donnellan, 2007 ). The greater income resulting from high educational and occupational attainment may also enable individuals to maintain strong life satisfaction when faced with difficult life circumstances ( Johnson & Krueger, 2006 ).

To better interpret the significance of the relations between personality traits and these outcomes, we have provided comparative information concerning the effect of SES and cognitive ability on each of these outcomes. We chose to use SES as a comparison because it is widely accepted to be one of the most important contributors to a more successful life, including better health and higher occupational attainment (e.g., Adler et al., 1994 ; Gallo & Mathews, 2003 ; Galobardes, Lynch, & Smith, 2004 ; Sapolsky, 2005 ). In addition, we chose cognitive ability as a comparison variable because, like SES, it is a widely accepted predictor of longevity and occupational success ( Deary, Batty, & Gottfredson, 2005 ; Schmidt & Hunter, 1998 ). In this article, we compare the effect sizes of personality traits with these two predictors in order to understand the relative contribution of personality to a long, stable, and successful life. We also required that the studies in this review make some attempt to control for background variables. For example, in the case of mortality, we looked for prospective longitudinal studies that controlled for previous medical conditions, gender, age, and other relevant variables.

We are not assuming that personality traits are direct causes of the outcomes under study. Rather, we were exclusively interested in whether personality traits predict mortality, divorce, and occupational attainment and in their modal effect sizes. If found to be robust, these patterns of statistical association then invite the question of why and how personality traits might cause these outcomes, and we have provided several examples in each section of potential mechanisms and causal steps involved in the process.

The Measurement of Effect Sizes in Prospective Longitudinal Studies

Before turning to the specific findings for personality, SES, and cognitive ability, we must first address the measurement of effect sizes in the studies reviewed here. Most of the studies that we reviewed used some form of regression analysis for either continuous or categorical outcomes. In studies with continuous outcomes, findings were typically reported as standardized regression weights (beta coefficients). In studies of categorical outcomes, the most common effect size indicators are odds ratios, relative risk ratios, or hazard ratios. Because many psychologists may be less familiar with these ratio statistics, a brief discussion of them is in order. In the context of individual differences, ratio statistics quantify the likelihood of an event (e.g., divorce, mortality) for a higher scoring group versus the likelihood of the same event for a lower scoring group (e.g., persons high in negative affect versus those low in negative affect). An odds ratio is the ratio of the odds of the event for one group over the odds of the same event for the second group. The risk ratio compares the probabilities of the event occurring for the two groups. The hazard ratio assesses the probability of an event occurring for a group over a specific window of time. For these statistics, a value of 1.0 equals no difference in odds or probabilities. Values above 1.0 indicate increased likelihood (odds or probabilities) for the experimental (or numerator) group, with the reverse being true for values below 1.0 (down to a lower limit of zero). Because of this asymmetry, the log of these statistics is often taken.

The primary advantage of ratio statistics in general, and the risk ratio in particular, is their ease of interpretation in applied settings. It is easier to understand that death is three times as likely to occur for one group than for another than it is to make sense out of a point-biserial correlation. However, there are also some disadvantages that should be understood. First, ratio statistics can make effects that are actually very small in absolute magnitude appear to be large when in fact they are very rare events. For example, although it is technically correct that one is three times as likely (risk ratio = 3.0) to win the lottery when buying three tickets instead of one ticket, the improved chances of winning are trivial in an absolute sense.

Second, there is no accepted practice for how to divide continuous predictor variables when computing odds, risk, and hazard ratios. Some predictors are naturally dichotomous (e.g., gender), but many are continuous (e.g., cognitive ability, SES). Researchers often divide continuous variables into some arbitrary set of categories in order to use the odds, rate, or hazard metrics. For example, instead of reporting an association between SES and mortality using a point-biserial correlation, a researcher may use proportional hazards models using some arbitrary categorization of SES, such as quartile estimates (e.g., lowest versus highest quartiles). This permits the researcher to draw conclusions such as “individuals from the highest category of SES are four times as likely to live longer than are groups lowest in SES.” Although more intuitively appealing, the odds statements derived from categorizing continuous variables makes it difficult to deduce the true effect size of a relation, especially across studies. Researchers with very large samples may have the luxury of carving a continuous variable into very fine-grained categories (e.g., 10 categories of SES), which may lead to seemingly huge hazard ratios. In contrast, researchers with smaller samples may only dichotomize or trichotomize the same variables, thus resulting in smaller hazard ratios and what appear to be smaller effects for identical predictors. Finally, many researchers may not categorize their continuous variables at all, which can result in hazard ratios very close to 1.0 that are nonetheless still statistically significant. These procedures for analyzing odds, rate, and hazard ratios produce a haphazard array of results from which it is almost impossible to discern a meaningful average effect size. 1

One of the primary tasks of this review is to transform the results from different studies into a common metric so that a fair comparison could be made across the predictors and outcomes. For this purpose, we chose the Pearson product-moment correlation coefficient. We used a variety of techniques to arrive at an accurate estimate of the effect size from each study. When transforming relative risk ratios into the correlation metric, we used several methods to arrive at the most appropriate estimate of the effect size. For example, the correlation coefficient can be estimated from reported significance levels ( p values) and from test statistics such as the t test or chi-square, as well as from other effect size indicators such as d scores ( Rosenthal, 1991 ). Also, the correlation coefficient can be estimated directly from relative risk ratios and hazard ratios using the generic inverse variance approach ( The Cochrane Collaboration, 2005 ). In this procedure, the relative risk ratio and confidence intervals (CIs) are first transformed into z scores, and the z scores are then transformed into the correlation metric.

For most studies, the effect size correlation was estimated from information on relative risk ratios and p values. For the latter, we used the r equivalent effect size indicator ( Rosenthal & Rubin, 2003 ), which is computed from the sample size and p value associated with specific effects. All of these techniques transform the effect size information to a common correlational metric, making the results of the studies comparable across different analytical methods. After compiling effect sizes, meta-analytic techniques were used to estimate population effect sizes in both the risk ratio and correlation metric ( Hedges & Olkin, 1985 ). Specifically, a random-effects model with no moderators was used to estimate population effect sizes for both the rate ratio and correlation metrics. 2 When appropriate, we first averaged multiple nonindependent effects from studies that reported more than one relevant effect size.

The Predictive Validity of Personality Traits for Mortality

Before considering the role of personality traits in health and longevity, we reviewed a selection of studies linking SES and cognitive ability to these same outcomes. This information provides a point of reference to understand the relative contribution of personality. Table 1 presents the findings from 33 studies examining the prospective relations of low SES and low cognitive ability with mortality. 3 SES was measured using measures or composites of typical SES variables including income, education, and occupational status. Total IQ scores were commonly used in analyses of cognitive ability. Most studies demonstrated that being born into a low-SES household or achieving low SES in adulthood resulted in a higher risk of mortality (e.g., Deary & Der, 2005 ; Hart et al., 2003 ; Osler et al., 2002 ; Steenland, Henley, & Thun, 2002 ). The relative risk ratios and hazard ratios ranged from a low of 0.57 to a high of 1.30 and averaged 1.24 (CIs = 1.19 and 1.29). When translated into the correlation metric, the effect sizes for low SES ranged from −.02 to .08 and averaged .02 (CIs = .017 and .026).

SES and IQ Effects on Mortality/Longevity

Note. Confidence intervals are given in parentheses. SES = socioeconomic status; HR = hazard ratio; RR = relative risk ratio; OR = odds ratio; r rr = Correlation estimated from the rate ratio; r hr = correlation estimated from the hazard ratio; r or = correlation estimated from the odds ratio; r F = correlation estimated from F test; r e = r equivalent —correlation estimated from the reported p value and sample size; BMI = body mass index; FEV = forced expiratory volume; ADLs = activities of daily living; MMSE = Mini Mental State Examination; CPS = Cancer Prevention Study; RIFLE = risk factors and life expectancy.

Through the use of the relative risk metric, we determined that the effect of low IQ on mortality was similar to that of SES, ranging from a modest 0.74 to 2.42 and averaging 1.19 (CIs = 1.10 and 1.30). When translated into the correlation metric, however, the effect of low IQ on mortality was equivalent to a correlation of .06 (CIs = .03 and .09), which was three times larger than the effect of SES on mortality. The discrepancy between the relative risk and correlation metrics most likely resulted because some studies reported the relative risks in terms of continuous measures of IQ, which resulted in smaller relative risk ratios (e.g., St. John, Montgomery, Kristjansson, & McDowell, 2002 ). Merging relative risk ratios from these studies with those that carve the continuous variables into subgroups appears to underestimate the effect of IQ on mortality, at least in terms of the relative risk metric. The most telling comparison of IQ and SES comes from the five studies that include both variables in the prediction of mortality. Consistent with the aggregate results, IQ was a stronger predictor of mortality in each case (i.e., Deary & Der, 2005 ; Ganguli, Dodge, & Mulsant, 2002 ; Hart et al., 2003 ; Osler et al., 2002 ; Wilson, Bienia, Mendes de Leon, Evans, & Bennet, 2003 ).

Table 2 lists 34 studies that link personality traits to mortality/longevity. 4 In most of these studies, multiple factors such as SES, cognitive ability, gender, and disease severity were controlled for. We organized our review roughly around the Big Five taxonomy of personality traits (e.g., Conscientiousness, Extraversion, Neuroticism, Agreeableness, and Openness to Experience; Goldberg, 1993b ). For example, research drawn from the Terman Longitudinal Study showed that children who were more conscientious tended to live longer ( Friedman et al., 1993 ). This effect held even after controlling for gender and parental divorce, two known contributors to shorter lifespans. Moreover, a number of other factors, such as SES and childhood health difficulties, were unrelated to longevity in this study. The protective effect of Conscientiousness has now been replicated across several studies and more heterogeneous samples. Conscientiousness was found to be a rather strong protective factor in an elderly sample participating in a Medicare training program ( Weiss & Costa, 2005 ), even when controlling for education level, cardiovascular disease, and smoking, among other factors. Similarly, Conscientiousness predicted decreased rates of mortality in a sample of individuals suffering from chronic renal insufficiency, even after controlling for age, diabetic status, and hemoglobin count ( Christensen et al., 2002 ).

Personality Traits and Mortality

Note. Confidence intervals are given in parentheses. HR = hazard ratio; RR = relative risk ratio; OR = odds ratio; r rr = correlation estimated from the rate ratio; r hr = correlation estimated from the hazard ratio; r or = correlation estimated from the odds ratio; r B = correlation estimated from a beta weight and standard error; r e = r equivalent (correlation estimated from the reported p value and sample size); FEV = forced expiratory volume; CHD = coronary heart disease; SES =socioeconomic status; BMI =body-ass index; ADLs =activities of daily living; MMSE =Mini Mental State Examination.

Similarly, several studies have shown that dispositions reflecting Positive Emotionality or Extraversion were associated with longevity. For example, nuns who scored higher on an index of Positive Emotionality in young adulthood tended to live longer, even when controlling for age, education, and linguistic ability (an aspect of cognitive ability; Danner, Snowden, & Friesen, 2001 ). Similarly, Optimism was related to higher rates of survival following head and neck cancer ( Allison, Guichard, Fung, & Gilain, 2003 ). In contrast, several studies reported that Neuroticism and Pessimism were associated with increases in one’s risk for premature mortality ( Abas, Hotopf, & Prince, 2002 ; Denollet et al., 1996 ; Schulz, Bookwala, Knapp, Scheier, & Williamson, 1996 ; Wilson, Mendes de Leon, Bienias, Evans, & Bennett, 2004 ). It should be noted, however, that two studies reported a protective effect of high Neuroticism ( Korten et al., 1999 ; Weiss & Costa, 2005 ).

The domain of Agreeableness showed a less clear association to mortality, with some studies showing a protective effect of high Agreeableness ( Wilson et al., 2004 ) and others showing that high Agreeableness contributed to mortality ( Friedman et al., 1993 ). With respect to the domain of Openness to Experience, two studies showed that Openness or facets of Openness, such as creativity, had little or no relation to mortality ( Osler et al., 2002 ; Wilson et al., 2004 ).

Because aggregating all personality traits into one overall effect size washes out important distinctions among different trait domains, we examined the effect of specific trait domains by aggregating studies within four categories: Conscientiousness, Positive Emotion/Extraversion, Neuroticism/Negative Emotion, and Hostility/Disagreeableness. 5 Our Conscientiousness domain included four studies that linked Conscientiousness to mortality. Because only two of these studies reported the information necessary to compute an average relative risk ratio, we only examined the correlation metric. When translated into a correlation metric, the average effect size for Conscientiousness was −.09 (CIs = −.12 and −.05), indicating a protective effect. Our Extraversion/Positive Emotion domain included six studies that examined the effect of extraversion, positive emotion, and optimism. The average relative risk ratio for the low Extraversion/Positive Emotion was 1.04 (CIs = 1.00 and 1.10) with a corresponding correlation effect size for high Extraversion/Positive Emotion being −.07 (−.11, −.03), with the latter showing a statistically significant protective effect of Extraversion/Positive Emotion. Our Negative Emotionality domain included twelve studies that examined the effect of neuroticism, pessimism, mental instability, and sense of coherence. The average relative risk ratio for the Negative Emotionality domain was 1.15 (CIs = 1.04 and 1.26), and the corresponding correlation effect size was .05 (CIs = .02 and .08). Thus, Neuroticism was associated with a diminished life span. Nineteen studies reported relations between Hostility/Disagreeableness and all-cause mortality, with notable heterogeneity in the effects across studies. The risk ratio population estimate showed an effect equivalent to, if not larger than, the remaining personality domains (risk ratio = 1.14; CIs = 1.06 and 1.23). With the correlation metric, this effect translated into a small but statistically significant effect of .04 (CIs = .02 and .06), indicating that hostility was positively associated with mortality. Thus, the specific personality traits of Conscientiousness, Positive Emotionality/Extraversion, Neuroticism, and Hostility/Disagreeableness were stronger predictors of mortality than was SES when effects were translated into a correlation metric. The effect of personality traits on mortality appears to be equivalent to IQ, although the additive effect of multiple trait domains on mortality may well exceed that of IQ.

Why would personality traits predict mortality? Personality traits may affect health and ultimately longevity through at least three distinct processes ( Contrada, Cather, & O’Leary, 1999 ; Pressman & Cohen, 2005 ; Rozanski, Blumenthal, & Kaplan, 1999 ; T.W. Smith, 2006 ). First, personality differences may be related to pathogenesis or mechanisms that promote disease. This has been evaluated most directly in studies relating various facets of Hostility/Disagreeableness to greater reactivity in response to stressful experiences (T.W. Smith & Gallo, 2001 ) and in studies relating low Extraversion to neuroendocrine and immune functioning ( Miller, Cohen, Rabin, Skoner, & Doyle, 1999 ) and greater susceptibility to colds ( Cohen, Doyle, Turner, Alper, & Skoner, 2003a , 2003b ). Second, personality traits may be related to physical-health outcomes because they are associated with health-promoting or health-damaging behaviors. For example, individuals high in Extraversion may foster social relationships, social support, and social integration, all of which are positively associated with health outcomes ( Berkman, Glass, Brissette, & Seeman, 2000 ). In contrast, individuals low in Conscientiousness may engage in a variety of health-risk behaviors such as smoking, unhealthy eating habits, lack of exercise, unprotected sexual intercourse, and dangerous driving habits ( Bogg & Roberts, 2004 ). Third, personality differences may be related to reactions to illness. This includes a wide class of behaviors, such as the ways individuals cope with illness (e.g., Scheier & Carver, 1993 ), reduce stress, and adhere to prescribed treatments ( Kenford et al., 2002 ).

These processes linking personality traits to physical health are not mutually exclusive. Moreover, different personality traits may affect physical health via different processes. For example, facets of Disagreeableness may be most directly linked to disease processes, facets of low Conscientiousness may be implicated in health-damaging behaviors, and facets of Neuroticism may contribute to ill-health by shaping reactions to illness. In addition, it is likely that the impact of personality differences on health varies across the life course. For example, Neuroticism may have a protective effect on mortality in young adulthood, as individuals who are more neurotic tend to avoid accidents in adolescence and young adulthood ( Lee, Wadsworth, & Hotopf, 2006 ). It is apparent from the extant research that personality traits influence outcomes at all stages of the health process, but much more work remains to be done to specify the processes that account for these effects.

The Predictive Validity of Personality Traits for Divorce

Next, we considered the role that SES, cognitive ability, and personality traits play in divorce. Because there were fewer studies examining these issues, we included prospective studies of SES, IQ, and personality that did not control for many background variables.

In terms of SES and IQ, we found 11 studies that showed a wide range of associations with divorce and marriage (see Table 3 ). 6 For example, the SES of the couple in one study was unsystematically related to divorce ( Tzeng & Mare, 1995 ). In contrast, Kurdek (1993) reported relatively large, protective effects for education and income for both men and women. Because not all these studies reported relative risk ratios, we computed an aggregate using the correlation metric and found the relation between SES and divorce was −.05 (CIs = −.08 and − .02), which indicates a significant protective effect of SES on divorce across these studies. Contradictory patterns were found for the two studies that predicted divorce and marital patterns from measures of cognitive ability. Taylor et al. (2005) reported that IQ was positively related to the possibility of male participants ever marrying but was negatively related to the possibility of female participants ever marrying. Data drawn from the Mills Longitudinal study ( Helson, 2006 ) showed conflicting patterns of associations between verbal and mathematical aptitude and divorce. Because there were only two studies, we did not examine the average effects of IQ on divorce.

SES and IQ Effects on Divorce

Note. Confidence intervals are given in parentheses. SES = socioeconomic status; HR = hazard ratio; RR = relative risk ratio; OR = odds ratio; r z = correlation estimated from the z score and sample size; r or = correlation estimated from the odds ratio; r F = correlation estimated from F test; r B = correlation estimated from the reported unstandardized beta weight and standard error; r e = r equivalent (correlation estimated from the reported p value and sample size); WAIS = Wechsler Adult Intelligence Scale; NLSY = National Longitudinal Study of Youth; NLSYM = National Longitudinal Study of Young Men; NLSYW = National Longitudinal Study of Young Women.

Table 4 shows the data from thirteen prospective studies testing whether personality traits predicted divorce. Traits associated with the domain of Neuroticism, such as being anxious and overly sensitive, increased the probability of experiencing divorce ( Kelly & Conley, 1987 ; Tucker, Kressin, Spiro, & Ruscio, 1998 ). In contrast, those individuals who were more conscientious and agreeable tended to remain longer in their marriages and avoided divorce ( Kelly & Conley, 1987 ; Kinnunen & Pulkkenin, 2003 ; Roberts & Bogg, 2004 ). Although these studies did not control for as many factors as the health studies, the time spans over which the studies were carried out were impressive (e.g., 45 years). We aggregated effects across these studies for the trait domains of Neuroticism, Agreeableness, and Conscientiousness with the correlation metric, as too few studies reported relative risk outcomes to warrant aggregating. When so aggregated, the effect of Neuroticism on divorce was .17 (CIs = .12 and .22), the effect of Agreeableness was − .18 (CIs = −.27 and −.09), and the effect of Conscientiousness on divorce was −.13 (CIs = −.17 and −.09). Thus, the predictive effects of these three personality traits on divorce were greater than those found for SES.

Personality Traits and Marital Outcomes

Note. Confidence intervals are given in parentheses. HR = hazard ratio; RR = relative risk ratio; OR = odds ratio; r d = Correlation estimated from the d score; r or = correlation estimated from the odds ratio; r F = correlation estimated from F test; r e = r equivalent (correlation estimated from the reported p value and sample size); MMPI = Minnesota Multiphasic Personality Inventory; IHS = Institute of Human Development.

Why would personality traits lead to divorce or conversely marital stability? The most likely reason is because personality traits help shape the quality of long-term relationships. For example, Neuroticism is one of the strongest and most consistent personality predictors of relationship dissatisfaction, conflict, abuse, and ultimately dissolution ( Karney & Bradbury, 1995 ). Sophisticated studies that include dyads (not just individuals) and multiple methods (not just self reports) increasingly demonstrate that the links between personality traits and relationship processes are more than simply an artifact of shared method variance in the assessment of these two domains ( Donnellan, Conger, & Bryant, 2004 ; Robins, Caspi, & Moffitt, 2000 ; Watson, Hubbard, & Wiese, 2000 ). One study that followed a sample of young adults across their multiple relationships in early adulthood discovered that the influence of Negative Emotionality on relationship quality showed cross-relationship generalization; that is, it predicted the same kinds of experiences across relationships with different partners ( Robins, Caspi, & Moffitt, 2002 ).

An important goal for future research will be to uncover the proximal relationship-specific processes that mediate personality effects on relationship outcomes ( Reiss, Capobianco, & Tsai, 2002 ). Three processes merit attention. First, personality traits influence people’s exposure to relationship events. For example, people high in Neuroticism may be more likely to be exposed to daily conflicts in their relationships ( Bolger & Zuckerman, 1995 ; Suls & Martin, 2005 ). Second, personality traits shape people’s reactions to the behavior of their partners. For example, disagreeable individuals may escalate negative affect during conflict (e.g., Gottman, Coan, Carrere, & Swanson, 1998 ). Similarly, agreeable people may be better able to regulate emotions during interpersonal conflicts ( Jensen-Campbell & Graziano, 2001 ). Cognitive processes also factor in creating trait-correlated experiences ( Snyder & Stukas, 1999 ). For example, highly neurotic individuals may overreact to minor criticism from their partner, believe they are no longer loved when their partner does not call, or assume infidelity on the basis of mere flirtation. Third, personality traits evoke behaviors from partners that contribute to relationship quality. For example, people high in Neuroticism and low in Agreeableness may be more likely to express behaviors identified as detrimental to relationships such as criticism, contempt, defensiveness, and stonewalling ( Gottman, 1994 ).

The Predictive Validity of Personality Traits for Educational and Occupational Attainment

The role of personality traits in occupational attainment has been studied sporadically in longitudinal studies over the last few decades. In contrast, the roles of SES and IQ have been studied exhaustively by sociologists in their programmatic research on the antecedents to status attainment. In their seminal work, Blau and Duncan (1967) conceptualized a model of status attainment as a function of the SES of an individual’s father. Researchers at the University of Wisconsin added what they considered social-psychological factors ( Sewell, Haller, & Portes, 1969 ). In this Wisconsin model, attainment is a function of parental SES, cognitive abilities, academic performance, occupational and educational aspirations, and the role of significant others ( Haller & Portes, 1973 ). Each factor in the model has been found to be positively related to occupational attainment ( Hauser, Tsai, & Sewell, 1983 ). The key question here is to what extent SES and IQ predict educational and occupational attainment holding constant the remaining factors.

A great deal of research has validated the structure and content of the Wisconsin model ( Sewell & Hauser, 1980 ; Sewell & Hauser, 1992 ), and rather than compiling these studies, which are highly similar in structure and findings, we provide representative findings from a study that includes three replications of the model ( Jencks, Crouse, & Mueser, 1983 ). As can be seen in Table 5 , childhood socioeconomic indicators, such as father’s occupational status and mother’s education, are related to outcomes, such as grades, educational attainment, and eventual occupational attainment, even after controlling for the remaining variables in the Wisconsin model. The average beta weight of SES and education was .09. 7 Parental income had a stronger effect, with an average beta weight of .14 across these three studies. Cognitive abilities were even more powerful predictors of occupational attainment, with an average beta weight of .27.

SES, IQ, and Status Attainment

Note. SES = socioeconomic status.

Do personality traits contribute to the prediction of occupational attainment even when intelligence and socioeconomic background are taken into account? As there are far fewer studies linking personality traits directly to indices of occupational attainment, such as prestige and income, we also included prospective studies examining the impact of personality traits on related outcomes such as long-term unemployment and occupational stability. The studies listed in Table 6 attest to the fact that personality traits predict all of these work-related outcomes. For example, adolescent ratings of Neuroticism, Extraversion, Agreeableness, and Conscientiousness predicted occupational status 46 years later, even after controlling for childhood IQ ( Judge, Higgins, Thoresen, & Barrick, 1999 ). The weighted-average beta weight across the studies in Table 6 was .23 (CIs = .14 and .32), indicating that the modal effect size of personality traits was comparable with the effect of childhood SES and IQ on similar outcomes. 8

Personality Traits and Occupational Attainment

Note. SES = socioeconomic status; IHD = Institute of Human Development.

Why are personality traits related to achievement in educational and occupational domains? The personality processes involved may vary across different stages of development, and at least five candidate processes deserve research scrutiny ( Roberts, 2006 ). First, the personality-to-achievement associations may reflect “attraction” effects or “active niche-picking,” whereby people choose educational and work experiences whose qualities are concordant with their own personalities. For example, people who are more conscientious may prefer conventional jobs, such as accounting and farming ( Gottfredson, Jones, & Holland, 1993 ). People who are more extraverted may prefer jobs that are described as social or enterprising, such as teaching or business management ( Ackerman & Heggestad, 1997 ). Moreover, extraverted individuals are more likely to assume leadership roles in multiple settings ( Judge, Bono, Ilies, & Gerhardt, 2002 ). In fact, all of the Big Five personality traits have substantial relations with better performance when the personality predictor is appropriately aligned with work criteria ( Hogan & Holland, 2003 ). This indicates that if people find jobs that fit with their dispositions they will experience greater levels of job performance, which should lead to greater success, tenure, and satisfaction across the life course ( Judge et al., 1999 ).

Second, personality-to-achievement associations may reflect “recruitment effects,” whereby people are selected into achievement situations and are given preferential treatment on the basis of their personality characteristics. These recruitment effects begin to appear early in development. For example, children’s personality traits begin to influence their emerging relationships with teachers at a young age ( Birch & Ladd, 1998 ). In adulthood, job applicants who are more extraverted, conscientious, and less neurotic are liked better by interviewers and are more often recommended for the job ( Cook, Vance, & Spector, 2000 ).

Third, personality traits may affect work outcomes because people take an active role in shaping their work environment ( Roberts, 2006 ). For example, leaders have tremendous power to shape the nature of the organization by hiring, firing, and promoting individuals. Cross-sectional studies of groups have shown that leaders’ conscientiousness and cognitive ability affect decision making and treatment of subordinates ( LePine, Hollenbeck, Ilgen, & Hedlund, 1997 ). Individuals who are not leaders or supervisors may shape their work to better fit themselves through job crafting ( Wrzesniewski & Dutton, 2001 ) or job sculpting ( Bell & Staw, 1989 ). They can change their day-to-day work environments through changing the tasks they do, organizing their work differently, or changing the nature of the relationships they maintain with others ( Wrzesniewski & Dutton, 2001 ). Presumably these changes in their work environments lead to an increase in the fit between personality and work. In turn, increased fit with one’s environment is associated with elevated performance ( Harms, Roberts, & Winter, 2006 ).

Fourth, some personality-to-achievement associations emerge as consequences of “attrition” or “deselection pressures,” whereby people leave achievement settings (e.g., schools or jobs) that do not fit with their personality or are released from these settings because of their trait-correlated behaviors ( Cairns & Cairns, 1994 ). For example, longitudinal evidence from different countries shows that children who exhibit a combination of poor self-control and high irritability or antagonism are at heightened risk of unemployment ( Caspi, Wright, Moffitt, & Silva, 1998 ; Kokko, Bergman, & Pulkkinen, 2003 ; Kokko & Pulkkinen, 2000 ).

Fifth, personality-to-achievement associations may emerge as a result of direct effects of personality on performance. Personality traits may promote certain kinds of task effectiveness; there is some evidence that this occurs in part via the processing of information. For example, higher positive emotions facilitate the efficient processing of complex information and are associated with creative problem solving ( Ashby, Isen, & Turken, 1999 ). In addition to these effects on task effectiveness, personality may directly affect other aspects of work performance, such as interpersonal interactions ( Hurtz & Donovan, 2000 ). Personality traits may also directly influence performance motivation; for example, Conscientiousness consistently predicts stronger goal setting and self-efficacy, whereas Neuroticism predicts these motivations negatively ( Erez & Judge, 2001 ; Judge & Ilies, 2002 ).

GENERAL DISCUSSION

It is abundantly clear from this review that specific personality traits predict important life outcomes, such as mortality, divorce, and success in work. Depending on the sample, trait, and outcome, people with specific personality characteristics are more likely to experience important life outcomes even after controlling for other factors. Moreover, when compared with the effects reported for SES and cognitive abilities, the predictive validities of personality traits do not appear to be markedly different in magnitude. In fact, as can be seen in Figures 1 – 3 , in many cases, the evidence supports the conclusion that personality traits predict these outcomes better than SES does. Despite these impressive findings, a few limitations and qualifications must be kept in mind when interpreting these data.

Average effects (in the correlation metric) of low socioeconomic status (SES), low IQ, low Conscientiousness (C), low Extraversion/Positive Emotion(E/PE), Neuroticism (N), and low Agreeableness (A) on mortality. Error bars represent standard error.

Average effects (in the standardized beta weight metric) of high socioeconomic status (SES), high parental income, high IQ, and high personality trait scores on occupational outcomes.

The requirement that we only examine the incremental validity of personality measures after controlling for SES and cognitive abilities, though clearly the most stringent test of the relevance of personality traits, is also arbitrarily tough. In fact, controlling for variables that are assumed to be nuisance factors can obscure important relations ( Meehl, 1971 ). For example, SES, cognitive abilities, and personality traits may determine life outcomes through indirect rather than direct pathways. Consider cognitive abilities. These are only modest predictors of occupational attainment when “all other factors are controlled,” but they play a much more important, indirect role through their effect on educational attainment. Students with higher cognitive abilities tend to obtain better grades and go on to achieve more in the educational sphere across a range of disciplines ( Kuncel, Crede, & Thomas, 2007 ; Kuncel, Hezlett, & Ones, 2001 , 2004 ); in turn, educational attainment is the best predictor of occupational attainment. This observation about cumulative indirect effects applies equally well to SES and personality traits.

Furthermore, the effect sizes associated with SES, cognitive abilities, and personality traits were all uniformly small-to-medium in size. This finding is entirely consistent with those from other reviews showing that most psychological constructs have effect sizes in the range between .10 and .40 on a correlational scale ( Meyer et al., 2001 ). Our hope is that reviews like this one can help adjust the norms researchers hold for what the modal effect size is in psychology and related fields. Studies are often disparaged for having small effects as if it is not the norm. Moreover, small effect sizes are often criticized without any understanding of their practical significance. Practical significance can only be determined if we ground our research by both predicting consequential outcomes, such as mortality, and by translating the results into a metric that is clearly understandable, such as years lost or number of deaths. Correlations and ratio statistics do not provide this type of information. On the other hand, some researchers have translated their results into metrics that most individuals can grasp. As we noted in the introduction, Rosenthal (1990) showed that taking aspirin prevented approximately 85 heart attacks in the patients of 10,845 physicians despite the meager −.03 correlation between this practice and the outcome of having a heart attack. Several other studies in our review provided similar benchmarks. Hardarson et al., (2001) showed that 148 fewer people died in their high education group (out of 869) than in their low education group, despite the effect size being equal to a correlation of −.05. Danner et al. (2001) showed that the association between positive emotion and longevity was associated with a gain of almost 7 years of additional life, despite having an average effect size of around .20. Of course, our ability to draw these types of conclusions necessitates grounding our research in more practical outcomes and their respective metrics.

There is one salient difference between many of the studies of SES and cognitive abilities and the studies focusing on personality traits. The typical sample in studies of the long-term effect of personality traits was a sample of convenience or was distinctly unrepresentative. In contrast, many of the studies of SES and cognitive ability included nationally representative and/or remarkably large samples (e.g., 500,000 participants). Therefore, the results for SES and cognitive abilities are generalizable, whereas it is more difficult to generalize findings from personality research. Perhaps the situation will improve if future demographers include personality measures in large surveys of the general population.

Recommendations

One of the challenges of incorporating personality measures in large studies is the cost–benefit trade off involved with including a thorough assessment of personality traits in a reasonably short period of time. Because most personality inventories include many items, researchers may be pressed either to eliminate them from their studies or to use highly abbreviated measures of personality traits. The latter practice has become even more common now that most personality researchers have concluded that personality traits can be represented within five to seven broad domains ( Goldberg, 1993b ; Saucier, 2003 ). The temptation is to include a brief five-factor instrument under the assumption that this will provide good coverage of the entire range of personality traits. However, the use of short, broad bandwidth measures can lead to substantial decreases in predictive validity ( Goldberg, 1993a ), because short measures of the Big Five lack the breadth and depth of longer personality inventories. In contrast, research has shown that the predictive validity of personality measures increases when one uses a well-elaborated measure with many lower order facets ( Ashton, 1998 ; Mershon & Gorsuch, 1988 ; Paunonen, 1998 ; Paunonen & Ashton, 2001 ).

However, research participants do not have unlimited time, and researchers may need advice on the selection of optimal measures of personality traits. One solution is to pay attention to previous research and focus on those traits that have been found to be related to the specific outcomes under study instead of using an omnibus personality inventory. For example, given the clear and consistent finding that the personality trait of Conscientiousness is related to health behaviors and mortality (e.g., Bogg & Roberts, 2004 ; Friedman, 2000 ), it would seem prudent to measure this trait well if one wanted to control for this factor or include it in any study of health and mortality. Moreover, it appears that specific facets of this domain, such as self-control and conventionality, are more relevant to health than are other facets such as orderliness ( Bogg & Roberts, 2004 ). If researchers are truly interested in assessing personality traits well, then they should invest the time necessary for the task. This entails moving away from expedient surveys to more in-depth assessments. Finally, if one truly wants to assess personality traits well, then researchers should use multiple methods for this purpose and should not rely solely on self-reports ( Eid & Diener, 2006 ).

We also recommend that researchers not equate all individual differences with personality traits. Personality psychologists also study constructs such as motivation, interests, emotions, values, identities, life stories, and self-regulation (see Mayer, 2005 , and Roberts & Wood, 2006 , for reviews). Moreover, these different domains of personality are only modestly correlated (e.g., Ackerman & Heggested, 1997 ; Roberts & Robins, 2000 ). Thus, there are a wide range of additional constructs that may have independent effects on important life outcomes that are waiting to be studied.

Conclusions

In light of increasingly robust evidence that personality matters for a wide range of life outcomes, researchers need to turn their attention to several issues. First, we need to know more about the processes through which personality traits shape individuals’ functioning over time. Simply documenting that links exist between personality traits and life outcomes does not clarify the mechanisms through which personality exerts its effects. In this article, we have suggested a number of potential processes that may be at work in the domains of health, relationships, and educational and occupational success. Undoubtedly, other personality processes will turn out to influence these outcomes as well.

Second, we need a greater understanding of the relationship between personality and the social environmental factors already known to affect health and development. Looking over the studies reviewed above, one can see that specific personality traits such as Conscientiousness predict occupational and marital outcomes that, in turn, predict longevity. Thus, it may be that Conscientiousness has both direct and indirect effects on mortality, as it contributes to following life paths that afford better health, and may also directly affect the ways in which people handle health-related issues, such as whether they exercise or eat a healthy diet ( Bogg & Roberts, 2004 ). One idea that has not been entertained is the potential synergistic relation between personality traits and social environmental factors. It may be the case that the combination of certain personality traits and certain social conditions creates a potent cocktail of factors that either promotes or undermines specific outcomes. Finally, certain social contexts may wash out the effect of individual difference factors, and, in turn, people possessing certain personality characteristics may be resilient to seemingly toxic environmental influences. A systematic understanding of the relations between personality traits and social environmental factors associated with important life outcomes would be very helpful.

Third, the present results drive home the point that we need to know much more about the development of personality traits at all stages in the life course. How does a person arrive in adulthood as an optimistic or conscientious person? If personality traits affect the ways that individuals negotiate the tasks they face across the course of their lives, then the processes contributing to the development of those traits are worthy of study ( Caspi & Shiner, 2006 ; Caspi & Shiner, in press ; Rothbart & Bates, 2006 ). However, there has been a tendency in personality and developmental research to focus on personality traits as the causes of various outcomes without fully considering personality differences as an outcome worthy of study ( Roberts, 2005 ). In contrast, research shows that personality traits continue to change in adulthood (e.g., Roberts, Walton, & Viechtbauer, 2006 ) and that these changes may be important for health and mortality. For example, changes in personality traits such as Neuroticism have been linked to poor health outcomes and even mortality ( Mroczek & Spiro, 2007 ).

Fourth, our results raise fundamental questions about how personality should be addressed in prevention and intervention efforts. Skeptical readers may doubt the relevance of the present results for prevention and intervention in light of the common assumption that personality is highly stable and immutable. However, personality traits do change in adulthood ( Roberts, Walton, & Viechtbauer, 2006 ) and can be changed through therapeutic intervention ( De Fruyt, Van Leeuwen, Bagby, Rolland, & Rouillon, 2006 ). Therefore, one possibility would be to focus on socializing factors that may affect changes in personality traits, as the resulting changes would then be leveraged across multiple domains of life. Further, the findings for personality traits should be of considerable interest to professionals dedicated to promoting healthy, happy marriages and socioeconomic success. Some individuals will clearly be at a heightened risk of problems in these life domains, and it may be possible to target prevention and intervention efforts to the subsets of individuals at the greatest risk. Such research can likewise inform the processes that need to be targeted in prevention and intervention. As we gain greater understanding of how personality exerts its effects on adaptation, we will achieve new insights into the most relevant processes to change. Moreover, it is essential to recognize that it may be possible to improve individuals’ lives by targeting those processes without directly changing the personality traits driving those processes (e.g., see Rapee, Kennedy, Ingram, Edwards, & Sweeney, 2005 , for an interesting example of how this may occur). In all prevention and intervention work, it will be important to attend to the possibility that most personality traits can have positive or negative effects, depending on the outcomes in question, the presence of other psychological attributes, and the environmental context ( Caspi & Shiner, 2006 ; Shiner, 2005 ).

Personality research has had a contentious history, and there are still vestiges of doubt about the importance of personality traits. We thus reviewed the comparative predictive validity of personality traits, SES, and IQ across three objective criteria: mortality, divorce, and occupational attainment. We found that personality traits are just as important as SES and IQ in predicting these important life outcomes. We believe these metaanalytic findings should quell lingering doubts. The closing of a chapter in the history of personality psychology is also an opportunity to open a new chapter. We thus invite new research to test and document how personality traits “work” to shape life outcomes. A useful lead may be taken from cognate research on social disparities in health ( Adler & Snibbe, 2003 ). Just as researchers are seeking to understand how SES “gets under the skin” to influence health, personality researchers need to partner with other branches of psychology to understand how personality traits “get outside the skin” to influence important life outcomes.

Average effects (in the correlation metric) of low socioeconomic status (SES), low Conscientiousness (C), Neuroticism (N), and low Agreeableness (A) on divorce. Error bars represent standard error.

Acknowledgments

Preparation of this paper was supported by National Institute of Aging Grants AG19414 and AG20048; National Institute of Mental Health Grants MH49414, MH45070, MH49227; United Kingdom Medical Research Council Grant G0100527; and by grants from the Colgate Research Council. We would like to thank Howard Friedman, David Funder, George Davie Smth, Ian Deary, Chris Fraley, Linda Gottfredson, Josh Jackson, and Ben Karney for their comments on earlier drafts of this article.

1 This situation is in no way particular to epidemiological or medical studies using odds, rate, and hazard ratios as outcomes. The field of psychology reports results in a Babylonian array of test statistics and effect sizes also.

2 The population effects for the rate ratio and correlation metric were not based on identical data because in some cases the authors did not report rate ratio information or did not report enough information to compute a rate ratio and a CI.

3 Most of the studies of SES and mortality were compiled from an exhaustive review of the literature on the effect of childhood SES and mortality ( Galobardes et al., 2004 ). We added several of the largest studies examining the effect of adult SES on mortality (e.g., Steenland et al., 2002 ), and to these we added the results from the studies on cognitive ability and personality that reported SES effects. We also did standard electronic literature searches using the terms socioeconomic status, cognitive ability , and all-cause mortality . We also examined the reference sections from the list of studies and searched for papers that cited these studies. Experts in the field of epidemiology were also contacted and asked to identify missing studies. The resulting SES data base is representative of the field, and as the effects are based on over 3 million data points, the effect sizes and CIs are very stable. The studies of cognitive ability and mortality represent all of the studies found that reported usable data.

4 We identified studies through electronic searches that included the terms personality traits, extroversion, agreeableness, hostility, conscientiousness, emotional stability, neuroticism, openness to experience , and all-cause mortality . We also identified studies through reference sections of the list of studies and through studies that cited each study. A number of studies were not included in this review because we focused on studies that were prospective and controlled for background factors.

5 We did not examine the domain of Openness to Experience because there were only two studies that tested the association with mortality.

6 We identified studies using electronic searches including the terms divorce, socioeconomic status , and cognitive ability . We also identified studies through examining the reference sections of the studies and through studies that cited each study.

7 We did not transform the standardized beta weights into the correlation metric because almost all authors failed to provide the necessary information for the transformation (CIs or standard errors). Therefore, we averaged the results in the beta weight metric instead. As the sampling distribution of beta weights is unknown, we used the formula for the standard error of the partial correlation (√ N −k−2) to estimate CIs.

8 In making comparisons between correlations and regression weights, it should be kept in mind that although the two are identical for orthogonal predictors, most regression weights tend to be smaller than the corresponding zero-order validity correlations because of predictor redundancy (R.A. Peterson & Brown, 2005 ).

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