research paper on personality tests

METHODS article

Enhancing personality assessment in the selection context: a study protocol on alternative measures and an extended bandwidth of criteria.

• Department of Psychology, University of Zurich, Zurich, Switzerland

Personality traits describe dispositions influencing individuals' behavior and performance at work. However, in the context of personnel selection, the use of personality measures has continuously been questioned. To date, research in selection settings has focused uniquely on predicting task performance, missing the opportunity to exploit the potential of personality traits to predict non-task performance. Further, personality is often measured with self-report inventories, which are susceptible to self-distortion. Addressing these gaps, the planned study seeks to design new personality measures to be used in the selection context to predict a wide range of performance criteria. Specifically, we will develop a situational judgment test and a behavior description interview, both assessing Big Five personality traits and Honesty-Humility to systematically compare these new measures with traditional self-report inventories regarding their criterion-related validity to predict four performance criteria: task performance, adaptive performance, organizational citizenship behavior, and counterproductive work behavior. Data will be collected in a simulated selection procedure. Based on power analyses, we aim for 200 employed study participants, who will allow us to contact their supervisors to gather criterion data. The results of this study will shed light on the suitability of different personality measures (i.e., situational judgment tests and behavior description interviews) to predict an expanded range of performance criteria.

Introduction

In today's fast-moving world, the demands placed on employees are constantly changing, as is the definition of job performance ( Organ, 1988 ; Borman and Motowidlo, 1993 ; Spector and Fox, 2005 ; Griffin et al., 2007 ; Koopmans et al., 2011 ). For selection procedures in organizations, the constant change of demands placed on employees may pose a challenge, especially when it comes to choosing appropriate predictor constructs to predict a wide range of job performance criteria. In this regard, assessing broad personality traits in selection seems promising given that personality traits are relatively stable in the working-age population ( Cobb-Clark and Schurer, 2012 ; Elkins et al., 2017 ) and—outside of the scope of selection research—personality traits (such as the Big Five; Goldberg, 1992 ) have been found to relate to diverse performance criteria (e.g., Barrick and Mount, 1991 ; Hurtz and Donovan, 2000 ; Judge et al., 2013 ).

However, personality traits have often been questioned as valid predictors of performance in the selection context, as past research found “that the validity of personality measures as predictors of job performance is often disappointingly low” ( Morgeson et al., 2007 , p. 693). Looking at current practice, selection research on personality traits has neglected two important points that might explain these findings. First, selection research usually focuses on the prediction of task performance, but personality traits have been shown to be better at predicting non-task performance ( Gonzalez-Mulé et al., 2014 ). Second, current practice in personnel selection often relies on self-report inventories as personality measures, which come with several limitations, especially in selection settings ( Morgeson et al., 2007 ). Specifically, personality inventories are often not job-specific and they rely on self-reports, which can be distorted ( Connelly and Ones, 2010 ; Shaffer and Postlethwaite, 2012 ; Lievens and Sackett, 2017 ).

There exist alternative measurement methods in personnel selection that do not have the same limitations as (personality) self-report inventories, but their suitability to measure personality has not yet been sufficiently studied ( Christian et al., 2010 ). Two established measurement methods in personnel selection are situational judgment tests (SJTs; Christian et al., 2010 ) and behavior description interviews (BDIs; Janz, 1982 ; Huffcutt et al., 2001 ). In contrast to personality self-report inventories, SJTs and BDIs have the advantage that they are job-related, because they ask for applicants' behavior in specific situations on the job. Moreover, BDIs incorporate interviewers' evaluations of applicants. To date, few studies have developed personality SJTs or BDIs and even fewer have measured established personality traits such as the Big Five ( Goldberg, 1992 ). The few studies that exist, however, suggest that SJTs and BDIs might be useful for measuring personality ( Van Iddekinge et al., 2005 ; Oostrom et al., 2019 ; Heimann et al., 2020 ). Accordingly, more research on complementary measurements of personality is needed to foster this initial evidence and to systematically compare these new measures with each other.

The aim of this study is twofold: (1) expand the range of criteria predicted in selection contexts, shifting the focus to non-task performance, and (2) help to identify suitable approaches to assess personality in selection by systematically comparing different measurement methods that assess identical personality traits. To this end, we will develop SJTs and BDIs to measure the same personality traits (i.e., the Big Five personality traits, including Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness/Intellect and in addition Honesty-Humility; Goldberg, 1990 ; Ashton and Lee, 2009 ) and compare them with self-report inventories assessing the same traits regarding their prediction of task performance, adaptive performance, organizational citizenship behavior (OCB), and counterproductive work behavior (CWB; Koopmans et al., 2011 ). Simultaneously investigating several performance criteria will allow us to examine which outcomes are best predicted by personality constructs. Assessing the same traits with each measurement method will allow us to directly compare these methods and their suitability to measure each trait.

Personality and Performance

Conceptually, personality is thought to drive individual job performance by influencing (a) what individuals consider to be effective behavior in work-related situations (knowledge), (b) to what extent they have learned to effectively engage in this behavior (skills), and (c) to what extent they routinely demonstrate this behavior at work (habits; Motowidlo et al., 1997 ). For example, individuals high in Agreeableness might strive to cooperate with others in everyday life. Thus, they are more likely to know which behaviors are effective at enabling cooperation (e.g., actively listening to others and asking questions to better understand them) and how to effectively display these behaviors ( Motowidlo et al., 1997 ; Hung, 2020 ). When it comes to working in a team, agreeable individuals are thus more likely to cooperate successfully with others, based on their knowledge, skills and habits ( Tasa et al., 2011 ).

Although personality predicts job performance, it does not seem to be the best predictor of the aspect personnel selection usually focuses on. The most common aspect of job performance is task performance, which is defined as the competency to fulfill central job tasks ( Campbell, 1990 ). Personality traits can predict task performance, with Conscientiousness and Emotional Stability being the strongest predictors among the Big Five traits ( Barrick et al., 2001 ; He et al., 2019 ). Yet, the fulfillment of job tasks seems to depend largely on mental processes, as recent meta-analytic evidence found that cognitive ability predicts task performance better than personality ( Gonzalez-Mulé et al., 2014 ).

Personnel selection could particularly benefit from personality traits as predictors when expanding the range of criteria to include non-task performance. Non-task performance consists of behaviors that do not directly contribute to the main goal of the organization ( Rotundo and Sackett, 2002 ) and can be specified into three aspects: adaptive performance, OCB, and CWB ( Koopmans et al., 2011 ). In contrast to task performance, non-task performance might depend largely on motivation or personality and less on general mental ability. In line with this, numerous personality traits have been linked to the three forms of non-task performance ( Barrick and Mount, 1991 ; Dalal, 2005 ; Judge et al., 2013 ; Huang et al., 2014 ; He et al., 2019 ; Lee et al., 2019 ; Pletzer et al., 2019 ). Yet, only a few of the studies linking personality to non-task performance have been conducted in personnel selection research [i.e., empirical studies that either simulate a selection procedure or use actual applicants as a sample; see for example Dilchert et al. (2007) , Lievens et al. (2003) , Swider et al. (2016) , and Van Iddekinge et al. (2005) ]. Yet, the studies conducted so far suggest that different personality traits predict different types of non-task performance.

Adaptive performance can be described as “behaviors individuals enact in the response to or anticipation of changes relevant to job-related tasks” ( Jundt et al., 2015 , p. 55). In contrast to task-based performance, adaptive performance implies that employees adapt to changes beyond the regular fulfillment of work tasks ( Lang and Bliese, 2009 ; Jundt et al., 2015 ). In accordance with this, adaptive performance can describe reactive behaviors such as coping with changes in core tasks ( Griffin et al., 2007 ) and relearning how to perform changed tasks ( Lang and Bliese, 2009 ). Going beyond reactive behavior, some researchers also highlight the relevance of proactive behaviors for adaptive performance such as producing new ideas or taking initiative ( Griffin et al., 2007 ). 1 Research outside of personnel selection has shown that reactive adaptive performance is related to Emotional Stability (e.g., being unenvious, relaxed, unexcitable; Huang et al., 2014 ), whereas proactive adaptive performance is thought to relate to Openness/Intellect (e.g., being creative, imaginative, innovative) as well as Extraversion (e.g., being talkative, assertive, bold; Marinova et al., 2015 ). Empirical findings for Conscientiousness (e.g., being organized, efficient, goal-oriented) are mixed ( Jundt et al., 2015 ). Yet, conceptually, conscientious individuals strive for success and are thus likely to show proactive behavior ( Roberts et al., 2005 ). Even though the rapid changes in the work environment today require individuals to show adaptive performance ( Griffin and Hesketh, 2003 ) personnel selection research has rarely considered this form of non-task performance as a criterion ( Lievens et al., 2003 ).

OCB describes individual behavior outside the formally prescribed work goals ( Borman and Motowidlo, 1993 ) and has been shown to contribute to an organization's performance ( Podsakoff et al., 2009 ). Research distinguishes between OCB directed at other individuals (e.g., helping newcomers; OCB-I) and OCB directed at the organization (e.g., taking extra tasks or working overtime; OCB-O). Research outside of personnel selection has shown that personality is particularly suited to predict this type of non-task performance. Whereas, some studies have found that OCB-I and OCB-O are predicted equally well by Conscientiousness, and Agreeableness (being kind, sympathetic, warm; Chiaburu et al., 2011 ; Judge et al., 2013 ), other results suggest that OCB-I is best predicted by Agreeableness and OCB-O is best predicted by Conscientiousness ( Ilies et al., 2009 ). Despite the relevance of OCB for organizations, there exist only a few studies on its relationship with personality in selection research ( Anglim et al., 2018 ; Heimann et al., 2020 ).

CWB is defined as actions that harm the legitimate interests of an organization ( Bennett and Robinson, 2000 ) and either damage other members of the organization (CWB directed at other individuals such as bullying co-workers; CWB-I) or the organization itself (CWB directed at the organization such as theft or absenteeism; CWB-O). Research outside of personnel selection has found some evidence that, overall, CWB is best predicted by Conscientiousness, Agreeableness ( He et al., 2019 ), Honesty-Humility (e.g., being sincere, fair, and modest; de Vries and van Gelder, 2015 ; Lee et al., 2019 ), and Emotional Stability ( Berry et al., 2007 ). Going beyond the traditional Big Five personality traits, Honesty-Humility has been shown to explain a significant proportion of variance in CWB over and above the other personality traits ( Pletzer et al., 2019 ). Despite its harm to organizational success ( Berry et al., 2007 ), CWB has rarely been considered as a criterion in selection research ( Dilchert et al., 2007 ; Anglim et al., 2018 ).

Assessing Personality in the Selection Context

Personality is typically assessed via self-report inventories, which face three major limitations in the selection context: (1) a lack of contextualization, (2) relying on applicants as the only source of information, and (3) a close-ended response format ( Connelly and Ones, 2010 ; Oh et al., 2011 ; Shaffer and Postlethwaite, 2012 ; Lievens and Sackett, 2017 ; Lievens et al., 2019 ). Contextualization describes the degree to which a measurement method refers to a specific situation or context, such as the work context. The problem of generic (i.e., non-contextualized) personality inventories is that people do not necessarily behave consistently across different contexts ( Mischel and Shoda, 1995 ). The same person might show different behavior at work compared to in their free time. In generic personality inventories, the same applicant might apply a different frame-of-reference when replying to different items, causing within-person inconsistency. Within-person inconsistency has been shown to affect the reliability and validity of personality inventories ( Lievens et al., 2008 ). Further, different applicants might think of very different situations when replying to the same generic item, thereby increasing the between-person variability. Between-person variability has been shown to affect the validity of personality inventories ( Lievens et al., 2008 ). In addition, when applicants complete a personality measure without referring to the context of work, there will be a mismatch with the criteria that we want to predict in selection contexts (i.e., performance and behavior at work ). A simple way to address this problem is to contextualize inventories by adding the term “at work” to every generic item. Although the change is minor, adding this frame-of-reference increases the validity of personality inventories ( Lievens et al., 2008 ; Shaffer and Postlethwaite, 2012 ).

The source of information refers to the person who responds to the personality inventory ( Lievens and Sackett, 2017 ). Personality inventories rely only on one information source, namely the self-report of applicants. The use of one-sided information can lead to inaccurate assessments because the target group of applicants has a specific interest to present themselves most favorably and to potentially distort their answers ( Ellingson and McFarland, 2011 ). Research has shown that assessing personality in applicant samples leads to different factor structures compared to non-applicant samples ( Schmit and Ryan, 1993 ). Furthermore, one's own self-perception can differ from the perception of others ( McAbee and Connelly, 2016 ). Thus, answers can be distorted not only through intentional self-distortion but also through self-evaluations, which might not completely represent a person. It is therefore not surprising that personality traits are better predictors when they are assessed via other-reports compared to self-reports ( Oh et al., 2011 ).

The response format describes whether a measurement method provides predefined response options ( Lievens and Sackett, 2017 ). Personality inventories use a close-ended response format. Close-ended response formats do not allow applicants to generate their answer freely. Thus, they provide a smaller information base to assess the applicant's personality compared to open-ended response formats, in which applicants can generate detailed answers and get the opportunity to share additional information about themselves. Furthermore, close-ended response formats may facilitate response distortion, because a limited number of presented response options makes them more transparent than open-ended formats. In a closed-ended response format, applicants might identify or guess the “right” or most desired response option and can thus more easily direct their response in the intended direction.

SJTs and BDIs could be used as alternative or complementary measurement methods to help overcome the limitations of personality measurement in personnel selection. SJTs and BDIs are established instruments in personnel selection and have been shown to predict job performance ( Christian et al., 2010 ; Culbertson et al., 2017 ). Both measurement methods provide a precise frame-of-reference and thus have a high contextualization.

In SJTs, short work-related situations are presented to applicants along with several response options, describing possible behaviors in this situation. Applicants are asked to choose the response option that most likely describes their own behavior in this situation ( Mussel et al., 2018 ). In comparison to contextualized self-report personality inventories, SJTs are more contextualized because they present a clear frame-of-reference for behavior by describing a specific work-related situation. Yet, like personality inventories, they rely on only self-reports and have a close-ended response format.

In BDIs, applicants receive descriptions of situations that employees have typically experienced within the context of work ( Janz, 1982 ). Interviewers present the description and ask applicants to describe a corresponding or similar situation in their past working experience, and to report their personal feelings and behavior in this situation. Responses are rated on behaviorally anchored rating scales ( Klehe and Latham, 2006 ). BDIs are a popular method in personnel selection and can predict performance across different domains ( Culbertson et al., 2017 ). BDIs have three advantages over SJTs. First, interviewers serve as an additional information source, because they can specify, interpret, and evaluate the information provided by the applicant. Second, BDIs use an open-ended response format, which allows applicants to share more information of themselves and thereby provide a richer information base ( Van Iddekinge et al., 2005 ; Raymark and Van Iddekinge, 2013 ). As interviewees' answers are rated directly after the interview on behaviorally anchored rating scales, this results in a quantitative data format. Third, the cognitive demand of BDIs should make them the least prone to self-distortion. Both BDIs and SJTs place higher cognitive demands on applicants than personality inventories and should thus reduce response distortion ( Sweller, 1988 ; Sackett et al., 2017 ) because they require the applicant to process more information. In BDIs, applicants simultaneously describe situations and interact with interviewers, causing high cognitive demand. To distort their answers, applicants would need to fabricate past situations in a short time-frame while monitoring their own behavior to appear truthful and also preparing to answer follow-up questions ( Van Iddekinge et al., 2005 ). Table 1 presents an overview of different features of self-report inventories, SJTs, and BDIs regarding contextualization, information source (self- vs. other-rating), and response format.

Table 1 . Characteristics of personality measures adapted from Heimann and Ingold (2017) and Lievens and Sackett (2017) .

Aims and Hypotheses

The overall objective of this study is twofold: (1) to widen and shift the focus of selection research from solely predicting task performance to predicting other relevant performance criteria; and (2) to identify suitable measurement methods assessing personality to predict these criteria. Therefore, we will develop an SJT and BDI to measure the Big Five personality traits and Honesty-Humility. As depicted in Figure 1 , we will use the Big Five traits and Honesty-Humility measured by a contextualized personality inventory, an SJT, and a BDI to predict different performance criteria. We assume that personality traits will predict both task- and non-task performance criteria (task performance, adaptive performance, OCB, CWB) within a personnel selection setting. Specifically, we expect the same pattern of relationships between specific sets of personality traits with specific performance criteria as they have been found outside of personnel selection research ( Barrick and Mount, 1991 ; Dalal, 2005 ; Judge et al., 2013 ; Huang et al., 2014 ; He et al., 2019 ; Lee et al., 2019 ; Pletzer et al., 2019 ). Regarding the comparison of personality measures, we predict that the criterion-related validity of personality measures will depend on (1) the contextualization of methods, such that more contextualization should lead to higher validity, (2) the source of information, such that other ratings (i.e., interviewer ratings) should be superior to self-reports, and (3) the response format, such that open-ended formats should be superior to close-ended formats. As a result, both the SJT and BDI should explain incremental validity in performance criteria over the contextualized personality inventory. BDIs should be superior to both the personality inventory and SJT.

Figure 1 . Overview of constructs and measures.

Methods and Analyses

Participants.

Participants will be employed individuals who are willing to participate in a simulated selection procedure to prepare and practice for their future job applications. We will recruit individuals who plan to apply for a new job and will contact them through universities and career services. Participants must be employed to participate, and they must name their supervisor so that we can collect supervisor performance ratings. Within the simulated selection procedure, participants can gain experience with established selection instruments and they will receive extensive feedback on their performance. A power analysis was conducted in G * Power ( Faul et al., 2007 ) for hierarchical regression analyses with the conventional alpha level of α = 0.05 and power of 80. Based on previous results ( Chiaburu et al., 2011 ; Heimann et al., 2020 ) we assume a mean correlation of 0.13 between personality predictors (measured with self-report inventories) and performance criteria and predict that measures of personality by alternative methods can explain between 4 and 5% of additional variance compared to traditional personality inventories. Further, we expect a participant dropout of 10%, based on experiences in previous studies. Accounting for dropout, the power analysis resulted in a total sample size of N = 200.

Data will be collected in a simulated selection procedure, allowing us to administer personality measures under controlled conditions and collect various performance data. Similar study designs have been successfully used in previous selection research ( Van Iddekinge et al., 2005 ; Klehe et al., 2008 ; Kleinmann and Klehe, 2011 ; Ingold et al., 2016 ; Swider et al., 2016 ). The simulated one-day selection procedure will consist of different personality measures to assess personality predictors (i.e., a contextualized personality inventory, an SJT, a BDI), behavioral observations rated in work simulations and standardized situations during the day to assess performance criteria, and other measures. All participants will complete all measures. Measures will be presented in randomized order to control for order effects.

A panel of interviewers will evaluate participants' personality (i.e., Big Five traits and Honesty-Humility) in the BDI and an independent panel of assessors will evaluate performance dimensions (i.e., task performance, OCB, adaptive performance, and CWB) in proxy criteria (work simulations; e.g., group discussion, presentation exercise). Interviewers will only rate predictors (i.e., personality) and assessors will only rate criteria (i.e., job performance) to avoid rater-based common method variance between predictor and criteria. Interviewers and assessors will be industrial-organizational psychology graduate students who will receive rater training prior to participating in this study.

The simulated selection procedure will be designed as realistically as possible so that participants' behavior is as close as possible to their behavior in a real selection process. For example, participants will be asked to dress as they would for a job interview. To further motivate participants to perform well, the best participant of the day will receive a cash prize (CHF 100). Participants will fill out a manipulation check at the end of the simulated selection procedure. Similar to previous studies using this type of design, the manipulation check will contain questions asking how realistic participants perceived the selection procedure to be and whether they felt and acted like they would in a real selection procedure ( Klehe et al., 2008 ; Jansen et al., 2013 ; Heimann et al., 2020 ). Participants will give their informed consent prior to participating in the simulated selection procedure. Their participation will be voluntary, and they will be allowed to quit at any time during the procedure.

Personality

We will measure the broad Big Five personality traits (including Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness/Intellect) and Honesty-Humility as predictors in this study. The broad personality predictors will be assessed with three different measures: a contextualized self-report inventory, an SJT, and a BDI. In addition, given that former research suggests that narrow facets are useful for predicting specific behavior ( Paunonen and Ashton, 2001 ), we will measure selected facets relevant for our criteria in the personality inventory (e.g., achievement striving, ingenuity).

For the contextualized personality inventory, we will use the 50-item IPIP representation of the Goldberg (1992) makers for the Big Five factor structure and the subscale “Honesty-Humility” from the HEXACO scale ( Ashton and Lee, 2009 ) with all items adapted to the context of work [similar to Lievens et al. (2008) and Heimann et al. (2020) ]. Items will be contextualized by adding the term “at work” at the beginning of each item (e.g., “At work, I am always prepared”). Internal consistencies for the original scales ranged between α = 0.76 (for Openness/Intellect) and α = 0.89 (for Emotional Stability) for the Big Five scale ( Lievens et al., 2008 ) and between α = 0.74 and α = 0.79 for the Honesty-Humility Subscale ( Ashton and Lee, 2009 ).

The SJT and BDI will be newly developed for this study. To allow for valid comparisons of personality measures, the SJT and BDI will be designed in parallel and they will be based and closely aligned with established personality self-report items. Thus, the SJT and BDI will contain similar, but not identical situations. Given that theory assumes that personality is only expressed if a situation contains certain situational cues that activate a trait (trait activation; Tett and Guterman, 2000 ), we will design situations to be equivalent in terms of the trait-activating cues.

The development of the SJT and BDI will proceed in four steps in line with previous studies that developed situation-based personality measures ( Van Iddekinge et al., 2005 ; Mussel et al., 2018 ; Heimann et al., 2020 ). First, we will select items from the 100 item IPIP Big Five scale ( Goldberg et al., 2006 ) and the Honesty-Humility subscale of the HEXACO model ( Ashton and Lee, 2009 ) from different facets of each personality dimension to serve as the basis for SJT items and BDI questions. In case of the Big Five traits, we will ensure that the selected items cover both aspects of the model by DeYoung et al. (2007) . The model indicates that each Big Five trait encompasses two distinct aspects, based on factor analytical results. For example, the personality dimension Conscientiousness encompasses the aspects Industriousness and Orderliness. By covering both aspects, we will ensure that the corresponding personality dimensions will be comprehensively measured. We will select items that (a) could be related to the criterion on the basis of conceptual and/or empirical arguments, (b) could be adapted to the working context, and (c) express an observable behavior.

Second, for each selected item, the first author of this study will generate situations that typically occur in working life and in which the respective traits would influence behavior; that is, situations in which a person who scores high on the item would behave differently compared to someone who scores low. Given that research shows that situations can be clustered into different types of situations based on the perceptions they elicit (e.g., Sherman et al., 2013 ; Rauthmann et al., 2014 ; Funder, 2016 ), and that these clusters are tied to certain traits ( Rauthmann et al., 2014 ), we will systematically design different situations in order to ensure fit between the situation described and the trait we aim to activate (trait activation; Tett and Guterman, 2000 ). To reduce transparency and socially desirable responding, every situation will be designed to contain a dilemma, meaning that more than one response to the given situation would be socially desirable. For example, participants will have to think of a situation in which they are under time pressure at work and a co-worker asks for help with a different task. Thus, both concentrating on their own tasks in order to meet the deadline and helping the co-worker would be socially acceptable behaviors in this situation. To make the situation more specific, we included different examples in each SJT item and BDI question. Each situation is constructed to measure a single trait. For each item, the first author will generate one hypothetical situation (for the SJT) and one past-behavior/typical situation (for the BDI).

Third, for each SJT item the first author of this study will further generate five response options. Response options will represent behavioral alternatives in this situation. Behavioral alternatives will express five different gradations of the item. The dilemma presented in the situation description will be mentioned in each response option. For example, in case of the aforementioned situation, a response option corresponding to a high expression of Agreeableness could be “I will help my co-worker, even if it means that I cannot meet the deadline for my own tasks.” For each BDI item, the first author will develop behaviorally anchored rating scales expressing high, medium, and low expressions of the respective trait.

Fourth, the co-authors of this study will thoroughly review SJT items and BDI questions and the response options several times, with regard to (a) the fit between the described situation and the trait ( Rauthmann et al., 2014 ); (b) their trait activation, that is, the strength of the cues that are assumed to activate the relevant behavior in the situation ( Tett and Guterman, 2000 ); (c) the strength of the dilemma described in the situation, that is, whether the behavioral alternatives are equally socially desirable [see also Latham et al. (1980) ]; (d) similar phrasing of items across measures. The co-authors are researchers in the field of I/O psychology with a focus on personnel selection or interview research. Based on these reviews, the first author will carefully revise the items several times. If necessary, situations will be newly developed and again reviewed and revised. We aim to design SJT items and BDI questions to be as parallel as possible by ensuring that all situations meet the aforementioned criteria (i.e., items and questions should describe a dilemma situation, provide specific examples, and not be too transparent). At the same time, we aim to keep SJT items and BDI questions as short as possible. As a pretest, a sample of at least four students will complete all SJT items and BDI questions to check the extent that they are comprehensible and how much time will be required to complete them. The first author of this study will then check whether the provided answers show variability in the respective traits and whether answers for BDI items correspond with the intended rating scales. The first author will then revise the items again based on the evaluation and the feedback provided by the test sample.

Samples for the SJT items and BDI questions are shown in Table 2 . Past studies on personality-based SJTs have reported internal consistencies between α = 0.55 and α = 0.75 ( Mussel et al., 2018 ), and between α = 0.22 and α = 0.66 ( Oostrom et al., 2019 ). Past studies on personality-based BDIs reported ICCs (interrater reliability) of 0.78 ( Heimann et al., 2020 ) and 0.74 ( Van Iddekinge et al., 2005 ).

Table 2 . Sample items of situational judgment test and behavior description interview based on the conscientiousness item “I am always prepared.”

Performance

All performance criteria (i.e., task performance, adaptive performance, OCB, and CWB) will be assessed with three different measurement approaches: self-reports, supervisor ratings, and proxy criteria. Self-reports and supervisor ratings will be assessed with established scales for all performance criteria. For task performance, we will use items by Bott et al. (2003) and Williams and Anderson (1991) . This composite scale has been used in previous studies and showed a reliability of α = 0.92 ( Jansen et al., 2013 ). For adaptive performance, we will use the individual task adaptivity and individual task proactivity scales from Griffin et al. (2007) . Reliability of the scales range from α = 0.88 to α = 0.89 for adaptivity and from α = 0.90 to α = 0.92 for proactivity ( Griffin et al., 2007 ). For OCB, we will use the OCB-I and OCB-O scales from Lee and Allen (2002) . Reliabilities of the scales were between α = 0.83 and α = 0.88. For CWB, we will use the workplace deviance scale from Bennett and Robinson (2000) with reliabilities ranging from α = 0.78 to α = 0.81. Example items for all measures can be found in Table 3 . We will use the same scales with small adaptations in items for both self-reports and supervisor ratings of performance criteria.

Table 3 . Main measures.

Proxy criteria will be behavioral observations rated in standardized situations during the selection procedure. More precisely, we will use (a) assessment center exercises, (b) standardized staged situations and, (c) compliance in the simulated selection procedure to assess participants' performance. For example, we will assess the performance of participants in a presentation exercise (i.e., whether the presentation is well-structured, whether it includes all relevant information) as a proxy criterion for task performance. As an example of a staged situation, interviewers will pick up each participant in a room for their interview, while carrying several items of material (e.g., folders). On the way to the interview room, interviewers will have difficulty opening the doors to the stairway due to the material they carry. Interviewers will observe whether participants help them to open the door as a proxy criterion for OCB. Behavior will be rated using behaviorally anchored rating scales. A more detailed description of proxy criteria for each performance dimension and an overview of all measures is presented in Table 3 . We will use proxy criteria in addition to self-reports and supervisor ratings of all performance criteria to add a behavioral observation and to ensure that one source of performance ratings is assessed in a standardized setting. Such proxy criteria have already been successfully employed in previous studies in selection research (e.g., Kleinmann and Klehe, 2011 ; Klehe et al., 2014 ).

Planned Analyses

Statistical analyses will be carried out using R. Data will be screened separately for each participant in order to identify spurious data. We will report all data exclusions (if any). We will first check whether applicants perceived the simulation setting as realistic. We will check plausibility of data with descriptive analysis using the psych -package for the R environment ( Revelle and Revelle, 2015 ). We will also check if variables are normally distributed (especially for data on proxy criteria) and transform non-normally distributed data. All measures will be designed as interval scales, and we will additionally check whether they can be analyzed accordingly, depending on the actual distribution of the data on the scales. Otherwise, we will adjust the analysis accordingly (i.e., evaluate them with methods for ordinal data).

To investigate the extent to which the SJT items and the BDI questions accurately measure personality traits, we will first examine the internal data structure (i.e., construct-related validity) of the newly developed SJT and BDI using multitrait-multimethod analyses within and across methods (similar to Van Iddekinge et al., 2005 ). First, to conduct correlative analyses of the data structure, we will use the psy -package ( Fallissard, 1996 ) and multicon -package ( Sherman, 2015 ). Regarding analyses within methods (i.e., examining the internal data structure of the SJT and BDI separately), we will investigate whether SJT items or BDI questions measuring the same traits show stronger intercorrelations than SJT items or interview questions measuring different traits. Regarding analyses across methods (i.e., examining the data structure across the personality inventory, SJT, and BDI), we will investigate whether the same traits measured with different methods correlate to test for convergent validity (average monotrait-heteromethod correlation). Further, we will calculate the correlation of different traits assessed with the same method (average heterotrait-monomethod correlation) to test for divergent validity. Thereby, we will verify whether the different traits can be distinguished when measured with the same method (personality inventory, SJT or BDI).

Second, to further examine the latent data structure within and across methods with confirmatory factor analyses (CFAs), we will use the lavaan package ( Rosseel, 2012 ). Regarding analyses within methods, we will conduct separate CFAs for each method (personality inventory, SJT and BDI). Regarding analyses across methods, we will conduct multitrait-multimethod CFAs on data from all three methods. The personality traits (Big Five traits plus Honesty-Humility) will be specified as latent trait factors and the three methods (personality inventory, SJT, and BDI) will be specified as latent method factors. Thereby, we will examine to what extent the different methods (personality inventory, SJT, and BDI) measure the same constructs.

Second, to further examine the latent data structure within and across methods with confirmatory factor analyses (CFAs), we will use the lavaan package ( Rosseel, 2012 ). Regarding analyses within methods, we will conduct separate CFAs for each method (personality inventory, SJT and BDI). Regarding analyses across methods, we will conduct multitrait-multimethod CFAs on data from all three methods. The personality traits (Big Five traits plus Honesty-Humility) will be specified as latent trait factors and the three methods (personality inventory, SJT, and BDI) will be specified as latent method factors. Thereby, we will examine to which extent the different methods (personality inventory, SJT, and BDI) measure the same constructs.

In order to test the assumption that BDIs and SJTs both explain incremental variance in performance criteria over and above personality inventories, we will conduct construct-driven comparisons [see for example Lievens and Sackett (2006) ] of personality measures predicting each criterion. To this end, we will conduct hierarchical regression analyses and relative weights analyses ( Johnson, 2000 ) using the relaimpo package for R ( Grömping, 2006 ). More precisely, we will conduct separate analyses for each performance criteria with the predictor constructs relevant for the specific performance criteria. As predictors, the respective personality constructs measured with different methods (i.e., personality inventory, SJT, and BDI) will be added to the model. Relative weights analyses will be used to test the hypothesis that personality traits assessed with the BDI are the strongest predictors of performance criteria (as compared to personality traits assessed with SJTs and personality inventories). Finally, we will test all hypotheses simultaneously in a path model using the lavaan package for R ( Rosseel, 2012 ). This allows us to test hypotheses while accounting for the interdependencies among criterion constructs. The first author has already programmed the R script, which will be used to analyze data.

The aim of this study is to identify suitable approaches to personality assessment in the context of personnel selection for predicting a wide range of performance criteria. Personality has faced an up and down history in personnel selection, resulting in the conclusion that “personality constructs may have value for employee selection, but future research should focus on finding alternatives to self-report personality measures” ( Morgeson et al., 2007 , p. 683). Critics of the use of personality assessment for selection purposes further point to their low validities when predicting job performance ( Morgeson et al., 2007 ). The proposed study is among the first to address this issue by systematically comparing different approaches to measure personality (personality inventory, SJT, BDI) to predict both task- and non-task performance dimensions. Specifically, we aim to enhance the criterion-related validity of personality constructs with two approaches. First, we develop measures with favorable features compared to personality inventories. We will vary different method characteristics, namely contextualization, source of information, and response-format. This modular approach was suggested in an earlier study because it allows for the systematic examination of the influence of measurement methods on criterion-related validity ( Lievens and Sackett, 2017 ). Second, we shift the focus to non-task performance, thereby aiming to enhance the conceptual fit between personality predictors and performance criteria. Thus, this study aims to provide important insights on how to optimize the use of personality measures in the context of selection research and practice.

Anticipated Results

We have three expectations regarding the results of this study. First, we expect different sets of personality constructs to predict task performance and especially different non-task performance criteria (i.e., adaptive performance, OCB, and CWB). Second, we expect that complementary measures of personality (i.e., SJTs and BDIs) will explain a significant proportion of performance criteria beyond personality inventories. Third, we expect BDIs to be superior to all other measurement methods in predicting all performance criteria. Specifically, we expect that personality constructs assessed with methods with a higher contextualization, which rely on self- and other-ratings and use an open-ended response format will be the strongest predictors of corresponding performance criteria. This implies that measuring personality with a BDI should lead to the strongest prediction, followed by SJTs and contextualized personality inventories.

Nevertheless, findings that are not in line with our assumptions could also generate valuable knowledge for research and practice. A different possible outcome of this study could be that SJTs and BDIs do not explain variance beyond personality inventories, or that the magnitude of difference in explained variance might be very small. If so, this could indicate that the respective method characteristics of SJTs and BDIs are not decisive for validity and selection research and practice would be advised to continue the use of personality self-report inventories (if assessing personality at all). Another different outcome could be that the variance explained by a measurement method depends on the traits that are measured (e.g., Extraversion might be better assessed with BDIs than with SJTs or personality inventories). This would imply that practitioners should base their choice of method based on the traits they aim to measure.

In each case, we hope that the findings of this study will encourage future research to examine alternative methods to measure personality in the context of personnel selection. If we find support for the assumption that specific method characteristics (e.g., open-ended vs. closed-ended response formats) affect the criterion-related validity of personality measures, future studies should further examine the mechanisms explaining why these method characteristics are particularly relevant. For example, the examined method characteristics could lead to differences in faking or applicant motivation, influencing the measurement of personality. Further, if SJTs and BDIs are suited to measure personality, an important next step will be to examine the fairness of different, but parallel designed measurement methods, for example by studying subgroup differences. This will help researchers investigate whether these measurement methods might have further favorable effects in personnel selection processes beyond their suitability to predict performance.

Anticipated Limitations

A relevant limitation of this study is that participants will not be actual applicants. Thus, it might be that effects are not generalizable to a real selection setting ( Culbertson et al., 2017 ; Powell et al., 2018 ). For example, participants in this study might feel less nervous compared to a real selection setting, because they are not applying for a real job. Further, they might behave less competitively in group-exercises, because they do not perceive other participants as their rivals. Yet, we chose this setting because it will allow us to compare a parallel personality inventory, SJT, and BDI all processed by each participant, with conditions close to a real selection setting. The setting further permits us to keep circumstances constant (e.g., interview rooms, schedule over the day of selection training, training of assessors and interviewers), thereby reducing error variance inherent to real selection settings. By creating an atmosphere close to reality (e.g., by asking both participants and assessors to wear professional clothes, by awarding a prize for the best participant) we will minimize the difference to a real selection process as much as possible. Yet, this limitation leads to a cautious estimation of criterion validity.

Even though we compare a number of important method characteristics, the comparisons in this study are not exhaustive. For example, we will compare open-ended and close-ended response formats (consent scales and single choice scales), but not other formats, such as forced-choice response formats, which are also used in personality testing ( Zuckerman, 1971 ; SHL, 1993 ) and can positively affect validity ( Bartram, 2007 ). Future studies using systematic comparisons of personality methods should consider further method characteristics, such as forced-choice formats.

Practical Implications

Depending on the results, this study will inform practitioners about which set of personality traits they can use for the prediction of specific performance outcomes (e.g., adaptive performance). This would help them to design selection procedures purposefully in order to collect the information that is most helpful to predict the outcome of interest.

Further, this study will provide insights on which measurement method is most useful for assessing personality and predicting related outcomes in the context of personnel selection. These insights could help to better exploit the potential of personality in applied contexts. Specifically, the systematic comparison of three different personality measures (with varying method characteristics) that are designed in parallel to assess the same traits will provide detailed guidance on how to develop more valid personality measures in the future.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethics Committee of the Faculty of Arts and Social Sciences, University of Zurich. The participants provided their written informed consent to participate in this study.

Author Contributions

All authors have shaped the research idea and study protocol. MK and PI developed the initial ideas. VS, AH, and MK planned the study in detail. VS wrote the study protocol. AH, PI, and MK provided substantial feedback in writing the study protocol.

The study described in this paper was supported by a grant from the Swiss National Science Foundation (Grant No. 179198).

Conflict of Interest

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.

1. ^ We acknowledge that a relevant stream in the body of literature on adaptive performance examines employee performance before and directly after a task change and distinguishes transition adaptation and reacquisition adaptation ( Lang and Bliese, 2009 ; Jundt et al., 2015 ; Niessen and Lang, 2020 ). Given that we aim to predict more generic adaptive behavior across different jobs with limited control over the nature of their task changes, the current study focuses on reactive and proactive forms of adaptive behavior.

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Keywords: personality, criterion-related validity, behavior description interview, situational judgment test, organizational citizenship behavior, counterproductive work behavior, adaptive performance, performance

Citation: Schröder VS, Heimann AL, Ingold PV and Kleinmann M (2021) Enhancing Personality Assessment in the Selection Context: A Study Protocol on Alternative Measures and an Extended Bandwidth of Criteria. Front. Psychol. 12:643690. doi: 10.3389/fpsyg.2021.643690

Received: 18 December 2020; Accepted: 15 February 2021; Published: 10 March 2021.

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Copyright © 2021 Schröder, Heimann, Ingold and Kleinmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Valerie S. Schröder, v.schroeder@psychologie.uzh.ch

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• Research article
• Open access
• 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.

Peer Review reports

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

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

Sahar Obeid

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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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El Othman, R., El Othman, R., Hallit, R. et al. Personality traits, emotional intelligence and decision-making styles in Lebanese universities medical students. BMC Psychol 8 , 46 (2020). https://doi.org/10.1186/s40359-020-00406-4

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There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using ‘selfies’. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality.

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Introduction.

A growing number of studies have linked facial images to personality. It has been established that humans are able to perceive certain personality traits from each other’s faces with some degree of accuracy 1 , 2 , 3 , 4 . In addition to emotional expressions and other nonverbal behaviours conveying information about one’s psychological processes through the face, research has found that valid inferences about personality characteristics can even be made based on static images of the face with a neutral expression 5 , 6 , 7 . These findings suggest that people may use signals from each other’s faces to adjust the ways they communicate, depending on the emotional reactions and perceived personality of the interlocutor. Such signals must be fairly informative and sufficiently repetitive for recipients to take advantage of the information being conveyed 8 .

Studies focusing on the objective characteristics of human faces have found some associations between facial morphology and personality features. For instance, facial symmetry predicts extraversion 9 . Another widely studied indicator is the facial width to height ratio (fWHR), which has been linked to various traits, such as achievement striving 10 , deception 11 , dominance 12 , aggressiveness 13 , 14 , 15 , 16 , and risk-taking 17 . The fWHR can be detected with high reliability irrespective of facial hair. The accuracy of fWHR-based judgements suggests that the human perceptual system may have evolved to be sensitive to static facial features, such as the relative face width 18 .

There are several theoretical reasons to expect associations between facial images and personality. First, genetic background contributes to both face and personality. Genetic correlates of craniofacial characteristics have been discovered both in clinical contexts 19 , 20 and in non-clinical populations 21 . In addition to shaping the face, genes also play a role in the development of various personality traits, such as risky behaviour 22 , 23 , 24 , and the contribution of genes to some traits exceeds the contribution of environmental factors 25 . For the Big Five traits, heritability coefficients reflecting the proportion of variance that can be attributed to genetic factors typically lie in the 0.30–0.60 range 26 , 27 . From an evolutionary perspective, these associations can be expected to have emerged by means of sexual selection. Recent studies have argued that some static facial features, such as the supraorbital region, may have evolved as a means of social communication 28 and that facial attractiveness signalling valuable personality characteristics is associated with mating success 29 .

Second, there is some evidence showing that pre- and postnatal hormones affect both facial shape and personality. For instance, the face is a visible indicator of the levels of sex hormones, such as testosterone and oestrogen, which affect the formation of skull bones and the fWHR 30 , 31 , 32 . Given that prenatal and postnatal sex hormone levels do influence behaviour, facial features may correlate with hormonally driven personality characteristics, such as aggressiveness 33 , competitiveness, and dominance, at least for men 34 , 35 . Thus, in addition to genes, the associations of facial features with behavioural tendencies may also be explained by androgens and potentially other hormones affecting both face and behaviour.

Third, the perception of one’s facial features by oneself and by others influences one’s subsequent behaviour and personality 36 . Just as the perceived ‘cleverness’ of an individual may lead to higher educational attainment 37 , prejudice associated with the shape of one’s face may lead to the development of maladaptive personality characteristics (i.e., the ‘Quasimodo complex’ 38 ). The associations between appearance and personality over the lifespan have been explored in longitudinal observational studies, providing evidence of ‘self-fulfilling prophecy’-type and ‘self-defeating prophecy’-type effects 39 .

Fourth and finally, some personality traits are associated with habitual patterns of emotionally expressive behaviour. Habitual emotional expressions may shape the static features of the face, leading to the formation of wrinkles and/or the development of facial muscles.

Existing studies have revealed the links between objective facial picture cues and general personality traits based on the Five-Factor Model or the Big Five (BF) model of personality 40 . However, a quick glance at the sizes of the effects found in these studies (summarized in Table  1 ) reveals much controversy. The results appear to be inconsistent across studies and hardly replicable 41 . These inconsistencies may result from the use of small samples of stimulus faces, as well as from the vast differences in methodologies. Stronger effect sizes are typically found in studies using composite facial images derived from groups of individuals with high and low scores on each of the Big Five dimensions 6 , 7 , 8 . Naturally, the task of identifying traits using artificial images comprised of contrasting pairs with all other individual features eliminated or held constant appears to be relatively easy. This is in contrast to realistic situations, where faces of individuals reflect a full range of continuous personality characteristics embedded in a variety of individual facial features.

Studies relying on photographic images of individual faces, either artificially manipulated 2 , 42 or realistic, tend to yield more modest effects. It appears that studies using realistic photographs made in controlled conditions (neutral expression, looking straight at the camera, consistent posture, lighting, and distance to the camera, no glasses, no jewellery, no make-up, etc.) produce stronger effects than studies using ‘selfies’ 25 . Unfortunately, differences in the methodologies make it hard to hypothesize whether the diversity of these findings is explained by variance in image quality, image background, or the prediction models used.

Research into the links between facial picture cues and personality traits faces several challenges. First, the number of specific facial features is very large, and some of them are hard to quantify. Second, the effects of isolated facial features are generally weak and only become statistically noticeable in large samples. Third, the associations between objective facial features and personality traits might be interactive and nonlinear. Finally, studies using real-life photographs confront an additional challenge in that the very characteristics of the images (e.g., the angle of the head, facial expression, makeup, hairstyle, facial hair style, etc.) are based on the subjects’ choices, which are potentially influenced by personality; after all, one of the principal reasons why people make and share their photographs is to signal to others what kind of person they are. The task of isolating the contribution of each variable out of the multitude of these individual variables appears to be hardly feasible. Instead, recent studies in the field have tended to rely on a holistic approach, investigating the subjective perception of personality based on integral facial images.

The holistic approach aims to mimic the mechanisms of human perception of the face and the ways in which people make judgements about each other’s personality. This approach is supported by studies of human face perception, showing that faces are perceived and encoded in a holistic manner by the human brain 43 , 44 , 45 , 46 . Put differently, when people identify others, they consider individual facial features (such as a person’s eyes, nose, and mouth) in concert as a single entity rather than as independent pieces of information 47 , 48 , 49 , 50 . Similar to facial identification, personality judgements involve the extraction of invariant facial markers associated with relatively stable characteristics of an individual’s behaviour. Existing evidence suggests that various social judgements might be based on a common visual representational system involving the holistic processing of visual information 51 , 52 . Thus, even though the associations between isolated facial features and personality characteristics sought by ancient physiognomists have emerged to be weak, contradictory or even non-existent, the holistic approach to understanding the face-personality links appears to be more promising.

An additional challenge faced by studies seeking to reveal the face-personality links is constituted by the inconsistency of the evaluations of personality traits by human raters. As a result, a fairly large number of human raters is required to obtain reliable estimates of personality traits for each photograph. In contrast, recent attempts at using machine learning algorithms have suggested that artificial intelligence may outperform individual human raters. For instance, S. Hu and colleagues 40 used the composite partial least squares component approach to analyse dense 3D facial images obtained in controlled conditions and found significant associations with personality traits (stronger for men than for women).

A similar approach can be implemented using advanced machine learning algorithms, such as artificial neural networks (ANNs), which can extract and process significant features in a holistic manner. The recent applications of ANNs to the analysis of human faces, body postures, and behaviours with the purpose of inferring apparent personality traits 53 , 54 indicate that this approach leads to a higher accuracy of prediction compared to individual human raters. The main difficulty of the ANN approach is the need for large labelled training datasets that are difficult to obtain in laboratory settings. However, ANNs do not require high-quality photographs taken in controlled conditions and can potentially be trained using real-life photographs provided that the dataset is large enough. The interpretation of findings in such studies needs to acknowledge that a real-life photograph, especially one chosen by a study participant, can be viewed as a holistic behavioural act, which may potentially contain other cues to the subjects’ personality in addition to static facial features (e.g., lighting, hairstyle, head angle, picture quality, etc.).

The purpose of the current study was to investigate the associations of facial picture cues with self-reported Big Five personality traits by training a cascade of ANNs to predict personality traits from static facial images. The general hypothesis is that a real-life photograph contains cues about personality that can be extracted using machine learning. Due to the vast diversity of findings concerning the prediction accuracy of different traits across previous studies, we did not set a priori hypotheses about differences in prediction accuracy across traits.

Prediction accuracy

We used data from the test dataset containing predicted scores for 3,137 images associated with 1,245 individuals. To determine whether the variance in the predicted scores was associated with differences across images or across individuals, we calculated the intraclass correlation coefficients (ICCs) presented in Table  2 . The between-individual proportion of variance in the predicted scores ranged from 79 to 88% for different traits, indicating a general consistency of predicted scores for different photographs of the same individual. We derived the individual scores used in all subsequent analyses as the simple averages of the predicted scores for all images provided by each participant.

The correlation coefficients between the self-report test scores and the scores predicted by the ANN ranged from 0.14 to 0.36. The associations were strongest for conscientiousness and weakest for openness. Extraversion and neuroticism were significantly better predicted for women than for men (based on the z test). We also compared the prediction accuracy within each gender using Steiger’s test for dependent sample correlation coefficients. For men, conscientiousness was predicted more accurately than the other four traits (the differences among the latter were not statistically significant). For women, conscientiousness was predicted more accurately, and openness was predicted less accurately compared to the three other traits.

The mean absolute error (MAE) of prediction ranged between 0.89 and 1.04 standard deviations. We did not find any associations between the number of photographs and prediction error.

Trait intercorrelations

The structure of the correlations between the scales was generally similar for the observed test scores and the predicted values, but some coefficients differed significantly (based on the z test) (see Table  3 ). Most notably, predicted openness was more strongly associated with conscientiousness (negatively) and extraversion (positively), whereas its association with agreeableness was negative rather than positive. The associations of predicted agreeableness with conscientiousness and neuroticism were stronger than those between the respective observed scores. In women, predicted neuroticism demonstrated a stronger inverse association with conscientiousness and a stronger positive association with openness. In men, predicted neuroticism was less strongly associated with extraversion than its observed counterpart.

To illustrate the findings, we created composite images using Abrosoft FantaMorph 5 by averaging the uploaded images across contrast groups of 100 individuals with the highest and the lowest test scores on each trait. The resulting morphed images in which individual features are eliminated are presented in Fig.  1 .

Composite facial images morphed across contrast groups of 100 individuals for each Big Five trait.

This study presents new evidence confirming that human personality is related to individual facial appearance. We expected that machine learning (in our case, artificial neural networks) could reveal multidimensional personality profiles based on static morphological facial features. We circumvented the reliability limitations of human raters by developing a neural network and training it on a large dataset labelled with self-reported Big Five traits.

We expected that personality traits would be reflected in the whole facial image rather than in its isolated features. Based on this expectation, we developed a novel two-tier machine learning algorithm to encode the invariant facial features as a vector in a 128-dimensional space that was used to predict the BF traits by means of a multilayer perceptron. Although studies using real-life photographs do not require strict experimental conditions, we had to undertake a series of additional organizational and technological steps to ensure consistent facial image characteristics and quality.

Our results demonstrate that real-life photographs taken in uncontrolled conditions can be used to predict personality traits using complex computer vision algorithms. This finding is in contrast to previous studies that mostly relied on high-quality facial images taken in controlled settings. The accuracy of prediction that we obtained exceeds that in the findings of prior studies that used realistic individual photographs taken in uncontrolled conditions (e.g., selfies 55 ). The advantage of our methodology is that it is relatively simple (e.g., it does not rely on 3D scanners or 3D facial landmark maps) and can be easily implemented using a desktop computer with a stock graphics accelerator.

In the present study, conscientiousness emerged to be more easily recognizable than the other four traits, which is consistent with some of the existing findings 7 , 40 . The weaker effects for extraversion and neuroticism found in our sample may be because these traits are associated with positive and negative emotional experiences, whereas we only aimed to use images with neutral or close to neutral emotional expressions. Finally, this appears to be the first study to achieve a significant prediction of openness to experience. Predictions of personality based on female faces appeared to be more reliable than those for male faces in our sample, in contrast to some previous studies 40 .

The BF factors are known to be non-orthogonal, and we paid attention to their intercorrelations in our study 56 , 57 . Various models have attempted to explain the BF using higher-order dimensions, such as stability and plasticity 58 or a single general factor of personality (GFP) 59 . We discovered that the intercorrelations of predicted factors tend to be stronger than the intercorrelations of self-report questionnaire scales used to train the model. This finding suggests a potential biological basis of GFP. However, the stronger intercorrelations of the predicted scores can be explained by consistent differences in picture quality (just as the correlations between the self-report scales can be explained by social desirability effects and other varieties of response bias 60 ). Clearly, additional research is needed to understand the context of this finding.

We believe that the present study, which did not involve any subjective human raters, constitutes solid evidence that all the Big Five traits are associated with facial cues that can be extracted using machine learning algorithms. However, despite having taken reasonable organizational and technical steps to exclude the potential confounds and focus on static facial features, we are still unable to claim that morphological features of the face explain all the personality-related image variance captured by the ANNs. Rather, we propose to see facial photographs taken by subjects themselves as complex behavioural acts that can be evaluated holistically and that may contain various other subtle personality cues in addition to static facial features.

The correlations reported above with a mean r = 0.243 can be viewed as modest; indeed, facial image-based personality assessment can hardly replace traditional personality measures. However, this effect size indicates that an ANN can make a correct guess about the relative standing of two randomly chosen individuals on a personality dimension in 58% of cases (as opposed to the 50% expected by chance) 61 . The effect sizes we observed are comparable with the meta-analytic estimates of correlations between self-reported and observer ratings of personality traits: the associations range from 0.30 to 0.49 when one’s personality is rated by close relatives or colleagues, but only from −0.01 to 0.29 when rated by strangers 62 . Thus, an artificial neural network relying on static facial images outperforms an average human rater who meets the target in person without any prior acquaintance. Given that partner personality and match between two personalities predict friendship formation 63 , long-term relationship satisfaction 64 , and the outcomes of dyadic interaction in unstructured settings 65 , the aid of artificial intelligence in making partner choices could help individuals to achieve more satisfying interaction outcomes.

There are a vast number of potential applications to be explored. The recognition of personality from real-life photos can be applied in a wide range of scenarios, complementing the traditional approaches to personality assessment in settings where speed is more important than accuracy. Applications may include suggesting best-fitting products or services to customers, proposing to individuals a best match in dyadic interaction settings (such as business negotiations, online teaching, etc.) or personalizing the human-computer interaction. Given that the practical value of any selection method is proportional to the number of decisions made and the size and variability of the pool of potential choices 66 , we believe that the applied potential of this technology can be easily revealed at a large scale, given its speed and low cost. Because the reliability and validity of self-report personality measures is not perfect, prediction could be further improved by supplementing these measures with peer ratings and objective behavioural indicators of personality traits.

The fact that conscientiousness was predicted better than the other traits for both men and women emerges as an interesting finding. From an evolutionary perspective, one would expect the traits most relevant for cooperation (conscientiousness and agreeableness) and social interaction (certain facets of extraversion and neuroticism, such as sociability, dominance, or hostility) to be reflected more readily in the human face. The results are generally in line with this idea, but they need to be replicated and extended by incorporating trait facets in future studies to provide support for this hypothesis.

Finally, although we tried to control the potential sources of confounds and errors by instructing the participants and by screening the photographs (based on angles, facial expressions, makeup, etc.), the present study is not without limitations. First, the real-life photographs we used could still carry a variety of subtle cues, such as makeup, angle, light facial expressions, and information related to all the other choices people make when they take and share their own photographs. These additional cues could say something about their personality, and the effects of all these variables are inseparable from those of static facial features, making it hard to draw any fundamental conclusions from the findings. However, studies using real-life photographs may have higher ecological validity compared to laboratory studies; our results are more likely to generalize to real-life situations where users of various services are asked to share self-pictures of their choice.

Another limitation pertains to a geographically bounded sample of individuals; our participants were mostly Caucasian and represented one cultural and age group (Russian-speaking adults). Future studies could replicate the effects using populations representing a more diverse variety of ethnic, cultural, and age groups. Studies relying on other sources of personality data (e.g., peer ratings or expert ratings), as well as wider sets of personality traits, could complement and extend the present findings.

Sample and procedure

The study was carried out in the Russian language. The participants were anonymous volunteers recruited through social network advertisements. They did not receive any financial remuneration but were provided with a free report on their Big Five personality traits. The data were collected online using a dedicated research website and a mobile application. The participants provided their informed consent, completed the questionnaires, reported their age and gender and were asked to upload their photographs. They were instructed to take or upload several photographs of their face looking directly at the camera with enough lighting, a neutral facial expression and no other people in the picture and without makeup.

Our goal was to obtain an out-of-sample validation dataset of 616 respondents of each gender to achieve 80% power for a minimum effect we considered to be of practical significance ( r  = 0.10 at p < 0.05), requiring a total of 6,160 participants of each gender in the combined dataset comprising the training and validation datasets. However, we aimed to gather more data because we expected that some online respondents might provide low-quality or non-genuine photographs and/or invalid questionnaire responses.

The initial sample included 25,202 participants who completed the questionnaire and uploaded a total of 77,346 photographs. The final combined dataset comprised 12,447 valid questionnaires and 31,367 associated photographs after the data screening procedures (below). The participants ranged in age from 18 to 60 (59.4% women, M = 27.61, SD = 12.73, and 40.6% men, M = 32.60, SD = 11.85). The dataset was split randomly into a training dataset (90%) and a test dataset (10%) used to validate the prediction model. The validation dataset included the responses of 505 men who provided 1224 facial images and 740 women who provided 1913 images. Due to the sexually dimorphic nature of facial features and certain personality traits (particularly extraversion 1 , 67 , 68 ), all the predictive models were trained and validated separately for male and female faces.

Ethical approval

The research was carried out in accordance with the Declaration of Helsinki. The study protocol was approved by the Research Ethics Committee of the Open University for the Humanities and Economics. We obtained the participants’ informed consent to use their data and photographs for research purposes and to publish generalized findings. The morphed group average images presented in the paper do not allow the identification of individuals. No information or images that could lead to the identification of study participants have been published.

Data screening

We excluded incomplete questionnaires (N = 3,035) and used indices of response consistency to screen out random responders 69 . To detect systematic careless responses, we used the modal response category count, maximum longstring (maximum number of identical responses given in sequence by participant), and inter-item standard deviation for each questionnaire. At this stage, we screened out the answers of individuals with zero standard deviations (N = 329) and a maximum longstring above 10 (N = 1,416). To detect random responses, we calculated the following person-fit indices: the person-total response profile correlation, the consistency of response profiles for the first and the second half of the questionnaire, the consistency of response profiles obtained based on equivalent groups of items, the number of polytomous Guttman errors, and the intraclass correlation of item responses within facets.

Next, we conducted a simulation by generating random sets of integers in the 1–5 range based on a normal distribution (µ = 3, σ = 1) and on the uniform distribution and calculating the same person-fit indices. For each distribution, we generated a training dataset and a test dataset, each comprised of 1,000 simulated responses and 1,000 real responses drawn randomly from the sample. Next, we ran a logistic regression model using simulated vs real responses as the outcome variable and chose an optimal cutoff point to minimize the misclassification error (using the R package optcutoff). The sensitivity value was 0.991 for the uniform distribution and 0.960 for the normal distribution, and the specificity values were 0.923 and 0.980, respectively. Finally, we applied the trained model to the full dataset and identified observations predicted as likely to be simulated based on either distribution (N = 1,618). The remaining sample of responses (N = 18,804) was used in the subsequent analyses.

Big Five measure

We used a modified Russian version of the 5PFQ questionnaire 70 , which is a 75-item measure of the Big Five model, with 15 items per trait grouped into five three-item facets. To confirm the structural validity of the questionnaire, we tested an exploratory structural equation (ESEM) model with target rotation in Mplus 8.2. The items were treated as ordered categorical variables using the WLSMV estimator, and facet variance was modelled by introducing correlated uniqueness values for the items comprising each facet.

The theoretical model showed a good fit to the data (χ 2  = 147854.68, df = 2335, p < 0.001; CFI = 0.931; RMSEA = 0.040 [90% CI: 0.040, 0.041]; SRMR = 0.024). All the items showed statistically significant loadings on their theoretically expected scales (λ ranged from 0.14 to 0.87, M = 0.51, SD = 0.17), and the absolute cross-loadings were reasonably low (M = 0.11, SD = 0.11). The distributions of the resulting scales were approximately normal (with skewness and kurtosis values within the [−1; 1] range). To assess the reliability of the scales, we calculated two internal consistency indices, namely, robust omega (using the R package coefficientalpha) and algebraic greatest lower bound (GLB) reliability (using the R package psych) 71 (see Table  4 ).

Image screening and pre-processing

The images (photographs and video frames) were subjected to a three-step screening procedure aimed at removing fake and low-quality images. First, images with no human faces or with more than one human face were detected by our computer vision (CV) algorithms and automatically removed. Second, celebrity images were identified and removed by means of a dedicated neural network trained on a celebrity photo dataset (CelebFaces Attributes Dataset (CelebA), N > 200,000) 72 that was additionally enriched with pictures of Russian celebrities. The model showed a 98.4% detection accuracy. Third, we performed a manual moderation of the remaining images to remove images with partially covered faces, those that were evidently photoshopped or any other fake images not detected by CV.

The images retained for subsequent processing were converted to single-channel 8-bit greyscale format using the OpenCV framework (opencv.org). Head position (pitch, yaw, roll) was measured using our own dedicated neural network (multilayer perceptron) trained on a sample of 8 000 images labelled by our team. The mean absolute error achieved on the test sample of 800 images was 2.78° for roll, 1.67° for pitch, and 2.34° for yaw. We used the head position data to retain the images with yaw and roll within the −30° to 30° range and pitch within the −15° to 15° range.

Next, we assessed emotional neutrality using the Microsoft Cognitive Services API on the Azure platform (score range: 0 to 1) and used 0.50 as a threshold criterion to remove emotionally expressive images. Finally, we applied the face and eye detection, alignment, resize, and crop functions available within the Dlib (dlib.net) open-source toolkit to arrive at a set of standardized 224 × 224 pixel images with eye pupils aligned to a standard position with an accuracy of 1 px. Images with low resolution that contained less than 60 pixels between the eyes, were excluded in the process.

The final photoset comprised 41,835 images. After the screened questionnaire responses and images were joined, we obtained a set of 12,447 valid Big Five questionnaires associated with 31,367 validated images (an average of 2.59 images per person for women and 2.42 for men).

Neural network architecture

First, we developed a computer vision neural network (NNCV) aiming to determine the invariant features of static facial images that distinguish one face from another but remain constant across different images of the same person. We aimed to choose a neural network architecture with a good feature space and resource-efficient learning, considering the limited hardware available to our research team. We chose a residual network architecture based on ResNet 73 (see Fig.  2 ).

Layer architecture of the computer vision neural network (NNCV) and the personality diagnostics neural network (NNPD).

This type of neural network was originally developed for image classification. We dropped the final layer from the original architecture and obtained a NNCV that takes a static monochrome image (224 × 224 pixels in size) and generates a vector of 128 32-bit dimensions describing unique facial features in the source image. As a measure of success, we calculated the Euclidean distance between the vectors generated from different images.

Using Internet search engines, we collected a training dataset of approximately 2 million openly available unlabelled real-life photos taken in uncontrolled conditions stratified by race, age and gender (using search engine queries such as ‘face photo’, ‘face pictures’, etc.). The training was conducted on a server equipped with four NVidia Titan accelerators. The trained neural network was validated on a dataset of 40,000 images belonging to 800 people, which was an out-of-sample part of the original dataset. The Euclidean distance threshold for the vectors belonging to the same person was 0.40 after the training was complete.

Finally, we trained a personality diagnostics neural network (NNPD), which was implemented as a multilayer perceptron (see Fig.  2 ). For that purpose, we used a training dataset (90% of the final sample) containing the questionnaire scores of 11,202 respondents and a total of 28,230 associated photographs. The NNPD takes the vector of the invariants obtained from NNCV as an input and predicts the Big Five personality traits as the output. The network was trained using the same hardware, and the training process took 9 days. The whole process was performed for male and female faces separately.

Data availability

The set of photographs is not made available because we did not solicit the consent of the study participants to publish the individual photographs. The test dataset with the observed and predicted Big Five scores is available from the openICPSR repository: https://doi.org/10.3886/E109082V1 .

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Acknowledgements

We appreciate the assistance of Oleg Poznyakov, who organized the data collection, and we are grateful to the anonymous peer reviewers for their detailed and insightful feedback.

Contributions

A.K., E.O., D.D. and A.N. designed the study. K.S. and A.K. designed the ML algorithms and trained the ANN. A.N. contributed to the data collection. A.K., K.S. and D.D. contributed to data pre-processing. E.O., D.D. and A.K. analysed the data, contributed to the main body of the manuscript, and revised the text. A.K. prepared Figs. 1 and 2. All the authors contributed to the final version of the manuscript.

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Correspondence to Alexander Kachur or Evgeny Osin .

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A.K., K.S. and A.N. were employed by the company that provided the datasets for the research. E.O. and D.D. declare no competing interests.

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Kachur, A., Osin, E., Davydov, D. et al. Assessing the Big Five personality traits using real-life static facial images. Sci Rep 10 , 8487 (2020). https://doi.org/10.1038/s41598-020-65358-6

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AI-based personality prediction for human well-being from text data: a systematic review

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In recent years, people have preferred interacting on social media instead of physical meetings. Researchers have explored social media text data to predict user personality using AI techniques automatically. To date, no comprehensive analysis offers a unified view of the literature in the area. To help researchers better understand the state-of-the-art, we summarise datasets, feature selection, text mapping, and AI techniques for personality prediction from text data. The standard systematic literature review protocol was followed, and the articles published between 2016 and 2022 were selected for the review. Measuring all the personality traits with a single AI model is quite difficult. The contribution of this systematic literature review shows that the increased efforts in personality prediction will surely help measure the Subjective Well-Being of an individual or a group. We conclude our work by providing an extensive discussion pointing requirement of labelled datasets, multiple personality dimensions, and advanced AI-based technologies to make an optimal system to predict personality.

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Text-Based Automatic Personality Recognition: Recent Developments

Personality Prediction from Text of Social Networking Sites by Combining Myers–Briggs and Big Five Models

A deep learning approach to text-based personality prediction using multiple data sources mapping

Data availability.

The data supporting the findings of this study are available from the corresponding author on request.

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Singh, S., Singh, W. AI-based personality prediction for human well-being from text data: a systematic review. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17282-w

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Recent assessments of the Myers-Briggs Type Indicator

• PMID: 3900330
• DOI: 10.1207/s15327752jpa4904_3

The present paper focuses on approximately two dozen recent published studies that examined reliability and validity of the Myers-Briggs Type Indicator (MBTI) in clinical, counseling, and research settings. Several assessments of split-half and test-retest reliability of the standard Form F and shorter Form G of the Inventory have yielded generally satisfactory correlations for all four scales. A larger number of studies of construct validity of the MBTI have yielded support for research hypotheses in situations ranging from correlations of the MBTI with a personality inventory, to couples problems in a counseling setting, to line judgment in groups, and others. Therefore, the applications of the MBTI have been broad, although somewhat unsystematic, and with generally favorable validity assessment. Continued attempts to validate the instrument in a variety of settings are needed.

• Bipolar Disorder / diagnosis
• Depressive Disorder / diagnosis
• Extraversion, Psychological
• Introversion, Psychological
• Mental Disorders / diagnosis*
• Mental Disorders / psychology
• Mental Disorders / therapy
• Neurotic Disorders / diagnosis
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October 10, 2018

How Accurate Are Personality Tests?

Precious few personality assessments are known to be reliable, and researchers say their use outside academia is debatable

By Angus Chen

Tim Robberts Getty Images

If you’re looking for insight into the true you, there’s a buffet of personality questionnaires available. Some are silly—like the internet quiz that tells everyone who takes it that they are procrastinators at the core. Other questionnaires, developed and sold as tools to help people hire the right candidate or find love, take themselves more seriously.

The trouble is, if you ask the experts, most of these might not be worth the money. “You should be skeptical,” says Simine Vazire, a personality researcher at the University of California, Davis. “Until we test them scientifically we can’t tell the difference between that and pseudoscience like astrology.”

One famous example of a popular but dubious commercial personality test is the Myers–Briggs Type Indicator. This questionnaire divides people into 16 different “types” and, often, the assessment will suggest certain career or romantic pairings. It costs $15 to $40 for an individual, but psychologists say the questionnaire is one of the worst personality tests in existence for a wide range of reasons. It is unreliable because a person’s type may change from day to day. It gives false information (“bogus stuff,” one researcher puts it). The questions are confusing and poorly worded. Vazire sums it up as “shockingly bad.”

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Personality questionnaires began evolving about a century ago, says Jim Butcher, an emeritus psychologist at the University of Minnesota. “They started asking questions about an individual’s thinking and behavior during World War I,” he says. “These were to study personality problems and mental health problems.” And importantly, he adds, the U.S. military wanted the questionnaires to help weed out soldiers who weren’t fit to fly military aircraft.

According to Butcher, during the first half of the 20th century many academics started creating different personality scales. “Not just on mental health diagnoses, but what personality is like,” he says. The problem with practically all of the assessments at the time was they were a built on the creators’ subjective feelings about personality, he notes. “Then people started to raise questions about do they really measure what they think they’re measuring? How reliable are those conclusions, and are they valid?”

Butcher describes what followed as a mass culling of personality systems and questionnaires by the scientific method. There is one personality model that did survive the 20th century, though. It is popular among academics today, and is what Vazire uses in her research. It’s called the Big 5 Personality Traits (aka 5-Factor Model), and it was developed over three decades beginning in 1961 at Brooks Air Force Base. From then to the 1990s, several psychologists including Lewis Goldberg, Warren Norman, Paul Costa and Robert McCrae helped develop the model into its modern form.

Vazire says in developing the Big 5 Personality model, psychologists tried to avoid pitfalls that plagued early personality researchers—like selecting criteria based primarily on intuition. Instead, the Big 5 model took a holistic tack by compiling every word that could be considered a personality trait and creating simple, straightforward questions about them. For example, on a scale of 1 to 5, are you outgoing, sociable? Have a forgiving nature? Based on how people answered initial surveys, researchers used statistical methods to group traits that seemed to cluster together (like  “talkative” and “sociable”) into five basic categories: extraversion, conscientiousness, agreeableness, neuroticism and openness to experience. The other model, the HEXACO model of personality structure created in 2000 by psychologists Kibeom Lee at the University of Calgary and Michael Ashton at Brock University in Ontario , is similar but adds an extra category: honesty–humility.

The key to the Big 5 model is its simplicity. It doesn’t sort anybody into a “type,” just informs them where they fall on a continuum of personality traits. There are no tricks and no surprises to be revealed, Vazire says. “In a way, it’s disappointing. It just means that personality tests can only tell you what you tell it.” You won’t learn anything that you didn’t already know about yourself, she adds, and its accuracy comes entirely from how honest and self-reflective you were with your answers.

At best, Vazire says you could use it as a comparative tool that can tell you how you rank on extroversion compared with others who have taken the same test. There have been studies that show certain Big 5 factor scores correlate with certain outcomes—conscientiousness correlates with longer life, for instance, and extroversion correlates with higher sales for sales reps. “But that doesn’t mean someone with high extroversion will be a better salesperson,” Vazire says. Correlations are just that; they could be incidental. But commercial personality assessments seem to depend heavily on such correlations. For example, one assessment from The Predictive Index, a company that measures behavioral characteristics and matches personality profiles to jobs, views such correlations in their own studies as a measure of success. “[We showed] in one client, a retail jeweler, that increases in dominance or aggression was responsible for $125,000 in revenue,” says Thad Peterson, one of the company’s executives. The idea behind The Index, Peterson says, is to use those measures to help “marry people to [job] positions.”

Such personality assessments—particularly those targeted toward hiring recruiters and managers—aim to uncover a kind of “hidden truth about the person,” says Randy Stein, a psychologist at California Polytechnic State University, Pomona. “They assume that there is an essence of you and an essence of the job, and you should be matching up those two things in hiring,” he says. “But I don’t think there is a hidden truth—and even if there is, a personality test doesn’t do it.”

Like the Big 5 model, any personality or behavior assessment can’t know things you haven’t explicitly answered in the questionnaire, Stein says. Sometimes commercial personality tests ask odd questions—like, Do you identify with snakes? or How do you react to a certain color?—and try to draw inferences from your answers. Those kinds of conclusions venture into the pseudoscientific, Stein says.

There are other reasons why Stein thinks some personality assessments may be pseudoscientific. “What those tests will tell people is true or false is determined by what people are willing to pay for,” he says. “Their process as a company is to tell people whatever will sell the product.” By contrast, the Big 5 and HEXACO models were shaped by an empirical process and independent peer review that showed people’s scores tended to be consistent, and predictions made using the models are reproducible. Without that, Stein says personality tests should be treated with extreme suspicion.

Some companies like The Predictive Index say their product meets such standards. The company invested in an audit, paying over $20,000 dollars to Norwegian classification firm DNV GL to review their product and certify that it complies with a standard set by the European Federation of Psychologists’ Associations. Two Index representatives, Greg Barnett and Austin Fossey, also say predictions based on their methods are accurate.

Perhaps. U.C. Davis’s Vazire says it is fairly easy to reach some level of validity. “If I just asked you to make a questionnaire on extroversion, you would probably do a pretty good job,” she says. It is because we are all judges of character, and we often do well at intuiting whom to date or hire and who we are, Vazire says. If the process seems confusing or if questions veer off into the abstract, that’s a red flag. Personality, she says, is just not that mysterious.

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Title: phi-3 technical report: a highly capable language model locally on your phone.

Abstract: We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench).

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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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