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Stability and Change in the Big Five Personality Traits: Findings from a Longitudinal Study of Mexican-Origin Adults

Olivia e. atherton.

1 University of California, Davis;

Angelina R. Sutin

2 Florida State University

Antonio Terracciano

Richard w. robins, associated data.

A large body of research has documented how personality develops across adulthood, yet very little longitudinal work has examined whether these findings generalize beyond predominantly middle-class, highly-educated White American or Western European individuals. This pre-registered study uses longitudinal data from 1,110 Mexican-origin adults who completed a well-validated personality measure, the Big Five Inventory, up to 6 times across 12 years. Individuals generally maintained their rank ordering on the Big Five over time ( r s=.66–.80), and the relative ordering of the Big Five within persons was also highly stable ( r s=.58–.66). All of the Big Five traits showed small, linear mean-level decreases across adulthood. These trajectories showed few associations with sociodemographic factors (sex, education level, and IQ) and cultural factors (generational status, age at immigration, Spanish/English language preference, Mexican cultural values, American cultural values, and ethnic discrimination). The statistically significant findings we did observe mostly concerned associations between cultural values and Extraversion, Agreeableness, and Openness. Acquiescence bias was also positively associated with Big Five personality trait scores at every wave. There was no evidence of mean-level change in the Big Five when including time-varying acquiescence scores as covariates in the models. Divergences between the present findings and previous research highlight the need to study personality development with more diverse aging samples.

Decades of research have been dedicated to understanding how personality changes across the lifespan, and there seems to be a consensus that personality traits: (1) are both stable and changing, and (2) develop in socially-desirable ways over time (i.e., individuals increase on “positive” traits with age; McCrae et al., 1999 ; Roberts et al., 2006 ). Although personality psychologists have dedicated a significant amount of effort towards understanding cross-sectional age differences in personality traits across cultures ( Terracciano, 2015 ), almost all longitudinal research on personality development has been conducted with predominantly middle-class, highly educated White American or Western European individuals. People who fall in this particular demographic sub-group only comprise 12% of the world population ( Arnett, 2008 ), yet are dramatically overrepresented in psychological research more generally ( Rad et al., 2018 ) and most longitudinal studies of personality stability and change are no exception. Thus, the question remains as to whether the personality development findings documented in past longitudinal research generalize to individuals from different socioeconomic backgrounds and ethnicities. The present study is the first to examine Big Five personality development using longitudinal data from a sample comprised exclusively of Mexican-origin adults, the vast majority of whom are 1 st generation immigrants who have endured considerable economic hardship and other forms of adversity.

Personality development can be quantified in three main ways: rank-order stability, profile stability, and mean-level change. Rank-order stability characterizes the degree to which individuals maintain their relative ordering on traits over time (e.g., if an individual is more extraverted than most people in young adulthood, are they also more extraverted than most people in middle adulthood and old age?). Previous work has shown that the Big Five are moderately-to-highly stable across adulthood, with test-retest correlations ranging from .54 to .70 across shorter time intervals and from .31 to .45 across long spans of time, such as 20–50 years ( Damian et al., 2018 ; Roberts & DelVecchio, 2000 ).

Profile (or ipsative) stability characterizes the extent to which each individual’s configuration of personality traits (i.e., the relative ordering of traits within a person) remains consistent over time (e.g., if an individual has high levels of Extraversion and Openness and low levels of Conscientiousness, Agreeableness, Neuroticism as a young adulthood, do they have this same configuration of trait levels in middle adulthood and old age?). Previous work has shown that most individuals maintain a similar trait configuration over time, with the correlation averaging .61 over a 4-year period ( Robins et al., 2001 ) and .63 across a 6-year period ( Terracciano et al., 2010 ). These results suggest that individuals largely maintain their personality trait configuration over time, although there has been little work on this topic.

Finally, mean-level change characterizes the degree to which individuals, on average, show absolute increases, decreases, or no changes in a personality trait over time (e.g., does an individual’s level of Extraversion increase, decrease, or remain the same across the lifespan?). Previous work has suggested that individuals, on average, show marked increases in Agreeableness, Conscientiousness, and Emotional Stability from young adulthood to midlife (a pattern often referred to as the maturity principle ), and then declines in these traits in old age ( Costa et al., 2019 ; Roberts et al., 2006 ; Wortman et al., 2012 ; but see Graham et al., 2020 for some discrepant findings). Further, individuals generally exhibit small (or no) mean-level increases in Extraversion and Openness to Experience from early to middle adulthood, and then subsequently decline in these traits in late life ( Costa et al., 2019 ; Roberts et al., 2006 ; Wortman et al., 2012 ).

Generalizability of Big Five Personality Development Trends

Personality scientists are quick to tout the replicability of the developmental trends described above, but their enthusiasm should be tempered by a critical unanswered question: to what extent do these findings generalize to more diverse samples? This question remains unanswered because there are remarkably few longitudinal studies of personality development in ethnic minority and/or low socioeconomic status (SES) samples, and the vast majority of prior longitudinal work has been conducted using predominantly middle-class, highly educated White American or Western European samples (but see Chopik & Kitayama, 2017; Löckenhoff et al., 2008 ). Although cross-sectional studies have explored the generalizability of age differences in personality ( De Bolle et al., 2015 ; McCrae et al., 1999 ; McCrae et al., 2004; Walton et al., 2013 ) and age stereotypes of personality ( Chan et al., 2012 ) across cultures, these studies provide little insight into developmental changes in personality. Prospective, longitudinal data are needed to understand how patterns of personality stability and change vary across sociocultural groups. We can begin to redress this imbalance using data from our large, longitudinal study of Mexican adults. We extend prior work not just in terms of sample characteristics, but also with regard to whether the personality trends observed in prior research vary as a function of cultural factors such as generational status (1 st generation vs. 2 nd + generation), age of immigration to the U.S., language of interview (Spanish vs. English), Mexican cultural values, American cultural values, and personal experiences with ethnic discrimination. Moreover, as in past research, we also examine whether Big Five personality development varies as a function of other factors including sex, education level, IQ, and acquiescence response bias (the tendency to agree with all interview questions).

Apart from gaining a better understanding of the generalizability of prior work to our sample of Mexican-origin adults, the present study also allows us to examine the predictors of within-group variability in personality development. Examining within-group variability in developmental and aging processes among ethnic minority adults is rare, but important, as it allows researchers to move away from the false need for a “comparison” or “control” group ( Whitfield & Baker-Thomas, 1999 ). Further, examining individual differences in psychological processes within groups promotes a better understanding of the human experience and allows researchers to draw more comprehensive and inclusive conclusions about how all individuals develop.

There are two competing possibilities as to how personality develops for individuals from different sociocultural groups. On the one hand, an argument could be made that personality development is a normative (i.e., universal) developmental process, due to intrinsic maturational processes that occur with age (e.g., based on age-related biologically programmed changes) and common developmental milestones, tasks, and environments in which individuals find themselves as they get older such as adult social roles. Research has shown that genetic factors contribute to personality maturation in adulthood ( Briley & Tucker-Drob, 2014 ), and these factors can account for normative personality development because they likely exert the same age-graded influences on traits across all individuals (regardless of the socio-cultural group to which they belong). Likewise, environmental perspectives, such as Social Investment Theory, suggest that universal social roles are responsible for normative personality maturation across the life course ( Roberts & Wood, 2006 ). That is, individuals who enter into social roles (e.g., becoming an employee, spouse, and/or parent) experience socially desirable changes in Agreeableness, Conscientiousness, and Neuroticism as a result of the norms, constraints, and expectations that new social roles place on their personalities ( Roberts & Wood, 2006 ). Because these social roles are “universal” (i.e., almost everyone experiences them), the direction in which personality traits change with age are also universal ( Bleidorn et al., 2013 ). However, the timing of personality trait change differs depending on when the transition into “universal” social roles occurs ( Bleidorn et al., 2013 ; but see McCrae et al., 2021 ; Terracciano, 2014 ). To the extent that the intrinsic maturational (e.g., age-graded biological changes) and social investment perspectives are true, we might expect that the generalizability of personality development trends is quite high across individuals from different nationalities, races/ethnicities, and socioeconomic circumstances because the same developmental genetic and environmental pressures operate regardless of the sociocultural groups to which one belongs.

On the other hand, because individuals develop in ever-changing and complex cultural environments, an argument could be made that individuals from different socio-cultural backgrounds should show important differences in the way their personality develops. This is likely due to complex interactions between one’s intrinsic propensities and sociocultural environments, given that the heritability of biologically-based behavioral phenotypes can shift as a result of generational and other socio-contextual factors shared among members of the same sociocultural group ( Tropf et al., 2017 ). Specific to the present study, personality change may be affected by socio-contextual factors that uniquely affect ethnic minority groups in the United States. For example, although Garcia Coll and colleagues’ (1996) Integrative Model was initially proposed to explain the developmental competencies of ethnic minority children, it is also useful for thinking about the development and aging of ethnic minority adults. The Integrative Model proposes that developmental processes for ethnic minority individuals have unique components that are not shared by the majority culture such that ethnic minority individuals experience social stratification mechanisms – racism, segregation, and prejudice – that permeate all levels of one’s environment and thus, influence all subsequent developmental processes ( Causadias & Umaña-Taylor, 2018 ; Garc’ıa Coll et al., 1996 ). Specific to the present study, not only are the participants of Mexican-origin, but the vast majority are also first-generation, Spanish-speaking individuals who immigrated to the United States as adults and have faced many socioeconomic challenges. It would be remiss to suggest that the acculturative stress of immigration and the social stratification mechanisms (racism, segregation, prejudice) these individuals have experienced would not affect their psychological development. The present study will be the first to examine how within-group variability in generational status, age at immigration, language preference, cultural values, and personal experiences with ethnic discrimination among Mexican-origin adults impacts the way their Big Five personality traits develop across adulthood.

Previous meta-analytic work suggests that there are little or no systematic differences between men and women in Big Five mean-level change patterns across adulthood ( Roberts, Walton, & Viechtbauer, 2006 ). More recently, a coordinated analysis of 16 longitudinal studies of Big Five development across adulthood also showed few differences between men and women in Big Five mean-level change, with one exception: women tend to have higher levels of Neuroticism and steeper declines over time compared to men ( Graham et al., 2020 ).

Prior cross-sectional work on the Big Five correlates of education level and IQ has found that Openness to Experience shows the most robust associations with education level and IQ (e.g., Bartels et al., 2012 ; Osmon et al., 2018 ; Rammstedt et al., 2016 ; Rammstedt et al., 2018 ; Sutin et al., 2011 ; von Stumm et al., 2009 ). However, there is very little longitudinal research on whether education level or IQ are associated with personality development ( Löckenhoff et al., 2008 ; Sutin et al., 2017 ). Based on the few extant studies, we expect that education level and IQ will have the strongest impact on the development of Openness to Experience and Conscientiousness, such that more educated individuals, and those with higher IQ, will show greater increases in Openness to Experience and Conscientiousness over time.

Last, we expect acquiescence bias to be relatively strong in the present sample, given that the participants in our study have less formal education than the participants used to validate the Big Five Inventories (John et al., 2008; Laajaj et al., 2019 ; Soto & John, 2016 ). Previous research has found that acquiescent responding for the Big Five is remarkably stable across eight years ( Wetzel et al., 2016 ), but to our knowledge, there has been no work on the impact of acquiescence bias on stability and change in the Big Five. We have no predictions about the impact of acquiescence bias on mean-level change in the Big Five; the present analyses are exploratory.

The Present Study

The present study uses a cohort-sequential (accelerated) longitudinal design spanning 12 years to examine stability and change in the Big Five, with participants ranging in age from 26 to 65 at the first assessment. Thus, our study design and sample covers personality development across most of adulthood. Moreover, we use one of the most frequently used and well-validated personality tests, the Big Five Inventory ( John & Soto, in press ; Soto & John, 2016 ). We also examine how the observed developmental trajectories vary as a function of several important cultural and sociodemographic factors (generational status, age at immigration, language preference, Mexican cultural values, American cultural values, personal experiences with ethnic discrimination, sex, education level, IQ) and acquiescence bias. Most important, the present study will be one of the first to use longitudinal data to examine the generalizability of personality development trends across adulthood in a sample comprised entirely of Mexican-origin individuals, the majority of whom immigrated to the United States as adults and have experienced a high degree of socioeconomic disadvantage.

The present study addresses the following research questions: (1) What are the rank-order stabilities of the Big Five domains across adulthood? (2) What is the profile stability of the Big Five across adulthood? (3) What are the mean-level trajectories of the Big Five domains across adulthood? (4) Does the degree of mean-level change in the Big Five vary by cultural factors (i.e., generational status, language of Big Five measure, age at immigration, Mexican cultural values, American cultural values, personal experiences with ethnic discrimination) and socio-demographic factors (i.e., sex, education level, IQ)? and (5) Do the personality trajectories differ when controlling for acquiescence bias?

Participants and Procedures

We used data from the California Families Project, a longitudinal study of 674 Mexican-origin families. In the present study, we focused on data from the mothers and fathers who participated in the study (total N = 1,110 at the first assessment). 1 , 2 To recruit families, children were drawn at random from rosters of students from the Sacramento and Woodland, CA school districts. The focal child had to be in the 5 th grade, of Mexican origin, and living with his or her biological mother, in order to participate in the study. Approximately 72.6% of the eligible families agreed to participate in the study, which was granted approval by the University of California Davis Institutional Review Board (Protocol # 217484-21; Mexican Family Culture and Substance Use Risk and Resilience). The first assessment occurred in 2006–07 (Wave 1), and subsequent follow-up assessments were conducted annually through Wave 10 and then biennially to Wave 11 (2017–18). Participants were interviewed in their homes in Spanish or English, depending on their preference. Interviewers were all bilingual and almost all of Mexican heritage.

The analysis plan for the present study was pre-registered on the Open Science Framework (OSF). The pre-registration, a list of deviations from the pre-registration, study materials, Mplus syntax, and output can be found on the project OSF page here: https://osf.io/2gpm9/ . 3 The present study used data from the mothers and fathers at Waves 1, 3, 5, 7, 10 (mothers only), and 11 (total timespan = 12 years). 4 Of the original 1,110 mothers and fathers, 87%, 90%, 86%, 81%, and 80% were interviewed at Waves 3, 5, 7, 10, and 11, respectively (note that in some cases, participants were interviewed but did not complete the Big Five Inventory (BFI); see Table 1 for exact Ns by wave). At the first assessment, the median participant age was 37.7 years old ( Mean = 38.3 years, SD = 6.09, range = 26 to 65; 61% female). The median ages at Waves 3 through 11 were 39.7 years, 41.4 years, 43.4 years, 45.7 years, and 48.3 years, respectively. The median household income at Wave 1 was $32,500, and approximately 38% of families were living below the U.S. federal poverty line.

Descriptive Statistics

Note . Only mothers (no fathers) were assessed at Wave 10. The distribution of the number of data points per age group was 109 data points for < 30 years old, 1,889 data points for 30–40 years old, 2,589 data points for 40–50 years old, 808 data points for 50–60 years old, and 97 data points for 60+ years old.

To investigate the potential role of attrition, we compared individuals who did and did not participate in the Wave 11 assessment on study variables (i.e., Big Five, generational status, language use, age at immigration, Mexican and American cultural values, ethnic discrimination, sex, education level and IQ) assessed at Wave 1. Compared to individuals who did not participate at Wave 11, individuals who participated at Wave 11 had higher education levels ( M = 9.26 vs. 8.81, p = .04, d = .13), higher neuroticism ( M = 2.29 vs. 2.22, p = .02, d = .18), lower conscientiousness ( M = 3.04 vs. 3.10, p = .01, d = −.18), and endorsed fewer American cultural values ( M = 2.71 vs. 2.79, p = .02, d = .18) at Wave 1. The Wave 11 participation rates for mothers and fathers were significantly different from one another, with 84.3% of mothers and 62.7% of fathers participating at the Wave 11 assessment ( p < .001). There were no significant attrition differences for Extraversion, Agreeableness, Openness to Experience, generational status, age at immigration, language, Mexican cultural values, ethnic discrimination, verbal IQ, and fluid IQ, all p s > .05.

Big Five personality traits.

Participants were administered (in an interview format) the 44-item Big Five Inventory (BFI; John et al., 2008) at Waves 1, 3, 5, 7, and the 60-item Big Five Inventory-2 (BFI-2; Soto & John, 2016 ) at Waves 10 and 11. For both the BFI and the BFI-2, participants rated each item on a 4-point Likert scale, ranging from 1 ( strongly disagree ) to 4 ( strongly agree ). Benet-Martinez and John (1998) translated the BFI into Spanish and tested it with college students in Spain and the United States, a college-educated sample of bilingual Hispanics, and a working-class bilingual Hispanic sample. All items that were new to the BFI-2 (i.e., items that were not on the original BFI) were translated into Spanish using standard translation-back translation procedures by California Families Project staff interviewers, all of whom were native Spanish-speakers and of Mexican heritage.

Given that one of our research questions concerns mean-level change over time, it is critical to ensure that the switch from using the BFI at Waves 1, 3, 5, and 7 to using the BFI-2 at Waves 10 and 11 did not create a measurement artifact that would lead to erroneous conclusions about mean-level changes in the Big Five. Exploratory analyses revealed that two items that were only on the BFI (“prefers work that is routine”; “is reserved”) and two items that were only on the BFI-2 (“feels little sympathy for others”; “rarely feels anxious or afraid”) did not function well in the present sample and were not used in scoring the Big Five scales (see the Supplemental Material and Table S1 for more details). After removing these items, there were 7 BFI and 12 BFI-2 items for Extraversion, 9 BFI and 11 BFI-2 items for Agreeableness, 9 BFI and 12 BFI-2 items for Conscientiousness, 8 BFI and 11 BFI-2 items for Neuroticism, and 9 BFI and 12 BFI-2 items for Openness to Experience.

Using these items, we examined longitudinal measurement invariance and found that all of the Big Five domains were partially strong or strong invariant (see Table S2 ), suggesting that we measured the same constructs over time and are able to draw conclusions about meaningful mean-level changes in the Big Five that are not attributable to differences in assessment wave or instrument. The latent variable factor scores correlated between .94 and .99 with the observed scores for the Big Five domains, suggesting that there were few differences between scoring the Big Five as latent versus observed variables. Thus, in the service of model parsimony and ease of interpretation, we used observed scale scores for all subsequent analyses. Observed scale scores were computed by averaging across items. Across waves, alpha reliabilities ranged from .69 to .76 for Extraversion, .60 to .79 for Agreeableness, .67 to .83 for Conscientiousness, .65 to .78 for Neuroticism (scored in the direction of Neuroticism), and .63 to .80 for Openness to Experience (see Table 1 for alphas at each wave).

Generational status.

At Wave 1, participants reported where they were born. 86% of participants were born in Mexico and classified as 1 st generation. The remaining 14% were born in the U.S. and classified as 2 nd + generation.

Age of immigration.

At Wave 1, participants reported the age when they first came to live in the U.S. The median age of immigration was 20 years old, and ranged from 0 years (born in the U.S.) to 50 years old ( Mean = 21.6, SD = 7.49).

Language preference.

Because language preferences were highly stable over time, we used the language (English vs. Spanish) of the BFI at Wave 1 as a time-invariant predictor of the Big Five trajectories. Language was coded as “0” if the participant completed the BFI in English (17%), and as “1” if the participant completed the BFI in Spanish (83%).

Mexican and American cultural values.

At Wave 1, participants completed the 50-item Mexican American Cultural Values Scale (MACVS; Knight et al., 2010 ), which assesses the extent to which they endorse Mexican cultural values (traditional gender roles, religiosity, respect, and familism; 36 items) and American cultural values (self-reliance, material satisfaction, competition, and independence; 14 items). Participants reported on their Mexican and American cultural values using a 4-point scale ranging from 1 ( not at all ) to 4 ( very much ). We computed the average of 36 items for Mexican cultural values ( M = 3.41, SD = .31) and 14 items for American cultural values ( M = 2.73, SD = .45) to use as time-invariant predictors of the Big Five trajectories. Alpha reliabilities were .88 for Mexican cultural values and .78 for American cultural values.

Ethnic discrimination.

At Wave 1, participants reported on 10 items that assessed the degree to which they had personal experiences with discrimination and prejudice in their community because of their Mexican/Mexican-American identity. These items were adapted for the present study from two measures of workplace discrimination ( Hughes & Dodge, 1997 ; James, Lovato, & Cropanzano, 1994 ) and from the University of Michigan’s National Study of American Lives, and includes items such as, “ People assume that you are not as smart or capable as others because you are [Mexican/Mexican-American] ” and “ You are called names or insulted because you are [Mexican/Mexican-American] .” Participants responded to the items on a 4-point Likert scale ranging from 1 ( almost never or never ) to 4 ( almost always or always ). We computed the average of the 10 items at Wave 1 ( M = 1.35, SD = .39) to use as a time-invariant predictor of the Big Five trajectories. Alpha reliability for perceived ethnic discrimination was .87.

Education level.

At Wave 1, participants reported their education level using an open-ended question, “ What is the highest grade you have completed? ” Responses were recorded as numeric values equivalent to the highest grade completed (e.g., 9 = 9 th grade). The median education level was 9 th grade in the present sample ( Mean = 9.1, SD = 3.5). Two percent of the sample have a college degree.

At Wave 1, participants completed several subscales from the Woodcock-Johnson III Test ( Woodcock, Mather, & McGrew, 2001 ), a widely-used measure of cognitive ability, including four subscales that assessed verbal ability (vocabulary, synonyms, antonyms, verbal analogies) and one subscale that assessed fluid intelligence (visual matching). In the present sample, the median verbal IQ score was 88 ( Mean = 88.1, SD = 7.61) and the median fluid IQ score was 86 ( Mean = 85.6, SD = 11.42).

Statistical Analyses

All analyses were conducted using M plus Version 8 ( Muthén & Muthén, 1998–2011 ). Mplus syntax and output can be found here: https://osf.io/2gpm9/ . We used a robust maximum likelihood estimator (MLR) to account for non-normal distributions of observed variables and full information maximum likelihood procedure (FIML) to account for missing data ( Allison, 2003 ; Schafer & Graham, 2002 ). We used observed variables for the Big Five and all predictors (i.e., sex, generational status, age at immigration, language, cultural values, ethnic discrimination, education level, and IQ).

To test rank-order stability, we computed test-retest correlations of the Big Five at adjacent assessments and across the entire study period (Wave 1 to Wave 11). We also calculated disattenuated test-retest correlations to account for measurement error ( Heise, 1969 ). To test profile stability of the Big Five, we computed double-entry intraclass correlation coefficients (ICCs) at adjacent assessments and across the entire study period ( McCrae, 2008 ; Terracciano, McCrae, & Costa, 2010 ).

Because of our longitudinal cohort sequential (accelerated) design, we conducted univariate latent growth curve (LGC) models scaled by continuous age (rather than by assessment wave) to examine growth (i.e., mean-level change) in the Big Five domains over time. These models aggregate the developmental trajectories from each person across the study period into one overall developmental trajectory, which spanned from the youngest (26 years) to oldest (84 years) time-points available in the data. Generally, LGC models describe the average initial level (intercept) and growth over time (slope) of a construct, as well as how much variability there is in the intercept and slope. In all univariate LGC models we centered the time scores at age 26 (the age of the youngest participant at the first assessment) to facilitate interpretation.

To find the best-fitting growth trajectory for each trait, we conducted a series of model comparisons and evaluated changes in Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Specifically, we compared three models: (1) no growth model , where only an intercept parameter is specified; (2) linear growth model , where a fixed linear change parameter is specified and the slope factor loadings are set to be equal to the participants’ centered age at each measurement occasion; and (3) a quadratic model , where a fixed quadratic change parameter is specified and the slope factor loadings are set to be equal to the participants’ squared centered age at each measurement occasion. Last, we conducted additional models to examine whether there are significant individual differences in participants’ initial level and trajectories by modeling the intercept and growth parameters as random (vs. fixed) effects.

To examine whether the personality trajectories differed by sex (women vs. men), generational status (1 st vs. 2 nd + generation), and language (Spanish vs. English), we estimated multiple-group LGC models. Specifically, we compared a multiple group model that constrained the means of the intercepts and slopes to be equal across groups to a multiple group model that allowed the means of the intercepts and slopes to be freely estimated across groups. If the constrained model did not fit significantly worse than the freely estimated model, then we concluded that the developmental trajectory was the same across groups. To examine the association between age at immigration, Mexican and American cultural values, personal experiences with ethnic discrimination, education level, IQ and the Big Five trajectories, we entered each as a time-invariant covariate into the univariate LGC models by regressing the level and slope of each trait trajectory onto each predictor (in separate models). We adjusted the p-value to correct for multiple testing and consider p ≤ .001 as statistically significant.

Last, to examine the role of acquiescence bias, we computed an acquiescence index for each participant using the procedure recommended by Soto, John, Gosling, and Potter (2008) for the BFI and by Soto and John (2016) for the BFI-2. Specifically, for the BFI, we computed individual differences in acquiescence by averaging together responses on 32 items (a set of 16 pairs of BFI items with opposite implications for personality) at Waves 1, 3, 5, and Wave 7. Because the original BFI contained imbalanced content, using this subset of items (instead of all 44) to compute acquiescence eliminates the possibility of conflating the direction of item keying with personality content, and instead, equates the number of true-keyed and false-keyed items for each Big Five domain. For the BFI-2, we computed individual differences in acquiescence by averaging together all 60 items (without reversing the false-keyed items) at Waves 10 and 11 because the BFI-2 was revised to have a fully content-balanced item set. Then, we included the acquiescence indices as time-varying covariates of the occasion-specific personality scores in the LGC model.

Table 1 shows descriptive statistics (Ns, means, standard deviations, alphas) of the Big Five at each wave. Because the men and women in the present study are in a long-term relationship with each other, we also conducted preliminary analyses to examine whether there is dependency in the Big Five scores. When we computed the intraclass coefficients (ICC) of the Big Five domains within couples at the first assessment, we found that Agreeableness (ICC = .19, p < .001), Neuroticism (ICC = .11, p = .02), and Openness (ICC = .09, p = .04) scores were significantly associated, suggesting some evidence for assortative mating. These associations indicated that we should use the CLUSTER option in Mplus to account for the dependency in the data and estimate less biased standard errors. For all subsequent latent growth curve analyses, we report the results when we use the CLUSTER option in Mplus. To maintain consistency across the Big Five, we used the CLUSTER estimation for the Extraversion and Conscientiousness models as well, even though men and women’s scores were not significantly associated at the first assessment (ICC = −.07, p = .92 and ICC = .07, p = .08, respectively).

Rank-Order Stability of the Big Five across Adulthood

Table 2 shows the test-retest correlations of the Big Five domains at adjacent assessments, as well as from the first to last assessment (see Table S3 for the full correlation matrix). There are several patterns worth noting from these associations. First, the Big Five were moderately-to-highly stable across 2- and 3-year intervals in adulthood; on average, these test-retest correlations ranged from .49 to .62 (.66 to .80 after disattenuating for measurement error). Second, the Big Five domains showed a moderate degree of rank-order stability even across the entire period from Wave 1 to 11 (average test-retest correlation was .33, and .48 after disattenuating for measurement error), suggesting that individuals maintain their relative ordering on the Big Five across more than a decade of life. Third, Extraversion generally showed the highest rank-order stability, whereas Agreeableness and Openness showed the lowest rank-order stabilities; this same pattern held after disattenuating for measurement error.

Test-Retest Correlations of the Big Five Personality Traits

Note . All coefficients are statistically significant at p < .001. Values in parentheses are the disattenuated test-retest correlations, which correct for measurement error in the scales.

We also conducted analyses to examine whether there were any age effects on rank-order stability (i.e., do individuals become more or less stable with age?). The average test-retest correlations by age group (across the Big Five domains) were .53 for age 30–40, .55 for age 40–50, and .59 for 50–60 year olds. 5 These results suggest that there are small age effects on rank-order stability, in that people become more stable on the Big Five domains as they get older.

Profile Stability of the Big Five across Adulthood

The average profile stability of the Big Five (as assessed by double-entry ICCs) was: .58 from Wave 1 to Wave 3, .63 from Wave 3 to Wave 5, .66 from Wave 5 to Wave 7, .59 from Wave 7 to Wave 10, .66 from Wave 10 to Wave 11, and .48 across the entire study period, from Wave 1 to Wave 11. These findings show that the within-person configuration of Big Five traits was moderately stable over time, indicating that ordering of the Big Five within each person is largely maintained across adulthood. The average double-entry ICCs by age group were .63 for age 30–40, .64 for age 40–50, and .60 for 50–60 year olds.

Mean-Level Change of the Big Five across Adulthood

All Big Five scores at each wave were z-scored (according to the Wave 1 means and standard deviations) to facilitate interpretation. 6 Table S4 shows the model comparisons from the univariate latent growth curve models. For all Big Five domains, the linear change models showed the lowest AIC and BIC values compared to the no change and quadratic change models, suggesting that we have the most certainty that linear trends are evident in this dataset. 7

In Figures 1 – 5 , the Big Five mean-level trajectories of the sample are represented by the bold, black lines, and all of the individual participants’ Big Five trajectories are represented by the thin colored lines. We found that all of the Big Five domain scores linearly decreased across adulthood. We conducted follow-up analyses to further examine whether there were significant individual differences in change across adulthood. We compared the models where the variances of the intercept and linear slopes were fixed at zero versus when the variances of the intercept and linear slope are freely estimated, and examined changes in AIC and BIC. Table S5 ( supplemental material ) shows the results from these model comparisons. In all cases, when we modeled individual differences in change (freely estimated model), the AIC and BIC values were lower. Thus, we have some certainty that allowing for individual differences in Big Five change across adulthood are a better representation of the data.

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Note . Black solid line represents the mean-level trajectory of the sample. Colored lines (or gray lines in print version) represent each participant’s trajectory.

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As noted above, we found strong/partial strong measurement invariance for all Big Five domains, which suggests that we measured the same constructs over time. Nonetheless, to alleviate concerns that the switch from the BFI to the BFI-2 might account for the observed mean-level trends, we replicated our continuous-age latent growth curve models using only the 32 items that are on both the BFI and the BFI-2. These follow-up analyses led us to draw the same conclusions about the linear trends for all of the Big Five domains based on AIC and BIC. Moreover, the means of the slope factors were the same in terms of magnitude and statistical significance (see Table S6 ) compared to the LGC models with the full set of BFI/BFI-2 items, with the exception of Openness to Experience which showed no statistically significant mean-level change or variability in change with the reduced subset of items. Thus, the observed mean-level trajectories largely seem to reflect actual age changes, rather than the switch from the BFI to the BFI-2.

Big Five Trajectories as a Function of Cultural and Socio-Demographic Factors

For the dichotomous predictors (sex, generational status, and language preference), we conducted multiple group models of the univariate latent growth trajectories for the Big Five domains. Table S7 shows model comparisons from when we constrain versus free the means and variances of the intercepts and slopes across groups. For all model comparisons, there were few differences in AIC and BIC for the constrained versus freely estimated multiple group models. There were multiple cases where either the AIC or BIC was lower in the freely estimated model when compared to the constrained model, but there were only three models (i.e., sex differences in Neuroticism and Openness, and language differences in Openness) where both AIC and BIC were lower in the freely estimated model compared to the constrained model. Taken together, these findings suggest that, for all of the Big Five domains, there were little-to-no differences in the mean-level trajectories for females and males, 1 st vs. 2 nd + generation immigrants, or those who were interviewed in English versus Spanish. The exceptions were that women started out at a higher level of Neuroticism in young adulthood and showed greater declines in Neuroticism across adulthood compared to men (see Figure S5 ); men and women started out at the same level of Openness in young adulthood, but then women showed more rapid declines across adulthood compared to men (see Figure S6 ); and predominantly Spanish-speakers declined in Openness across adulthood whereas predominantly English-speakers increased in Openness across adulthood (see Figure S7 ).

For age at immigration, Mexican cultural values, American cultural values, and ethnic discrimination, we entered each as a time-invariant predictor of the Big Five trajectories. Table 3 shows the unstandardized beta coefficients for the association between these cultural predictors and the levels and slopes of each Big Five trajectory. Out of 56 effects, eight effects were statistically significant at p ≤ .001. Individuals who had more personal experiences with ethnic discrimination tended to be higher in Neuroticism, and individuals who endorsed more American cultural values tended to be higher in Openness. Mexican cultural values were also associated with initial levels and changes in Extraversion, Agreeableness, and Openness. Individuals who endorsed more Mexican cultural values tended to be higher in Extraversion, Agreeableness, and Openness. Further, compared to individuals who endorsed fewer Mexican cultural values, individuals who endorsed more Mexican cultural values tended to show higher initial levels of, and greater declines in, Extraversion, Agreeableness, and Openness over time (see Figures S8 – S10 ).

Conditional Latent Growth Curve Models with Age at Immigration, Cultural Values, and Ethnic Discrimination as Predictors

Note . B = unstandardized beta coefficients. SE = Standard error. Values are unstandardized beta coefficients based on z-scored Big Five scores (using Wave 1 means and standard deviations).

For education level, verbal IQ, and fluid IQ, we entered each as a time-invariant predictor of the Big Five trajectories (in separate models). Table 4 shows the unstandardized beta coefficients for the association between education level, verbal IQ, and fluid IQ, and the level and slope(s) of each univariate trajectory. Out of 42 effects, only two were statistically significant at p ≤ .001. Education level was a significant predictor of the initial levels of Conscientiousness and Neuroticism, such that individuals who had higher education levels had higher Conscientiousness scores and lower Neuroticism scores in young adulthood.

Conditional Latent Growth Curve Models with Education Level and IQ as Predictors

The Role of Acquiescence on Big Five Trajectories

Table 1 also shows the descriptive statistics (Ns, means, standard deviations) for individual differences in acquiescence at each wave. Acquiescence scores showed small declines across waves and were correlated between .27 and .48 at adjacent waves, suggesting that individual differences in acquiescent responding were moderately consistent over time and on average, acquiescent responding showed small declines over time (see Figure S11 ).

Table 5 shows the unstandardized beta coefficients of the role of acquiescence on occasion-specific personality scores in the LGC models. These findings show that all of the Big Five domains were consistently associated with acquiescent responding across the study period, such that individuals who had higher acquiescence scores tended to score significantly higher on Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness at all Waves (with the exception of Conscientiousness at Waves 1, 10, and 11). The role of acquiescence on occasion-specific personality scores was largest for Openness to Experience and smallest for Conscientiousness.

Associations between Acquiescent Responding and LGC Occasion-Specific Personality Scores

Note . Values in the table are unstandardized beta coefficients based on z-scored Big Five scores (using Wave 1 means and standard deviations). ACQ = acquiescence score. B5 = Big Five score.

Additionally, with these models, we were able to examine the extent to which the means of the slope factors remained statistically significant while including the time-varying acquiescence variables in the model. When these acquiescence covariates were included in the model, the means of the slope factors became non-significant for all of the Big Five domains (see Figures S12 – S16 for how the mean-level trajectories change when acquiescence is in included in the models).

Personality development is a vibrant area of research, yet conclusions about patterns of stability and change in personality across the life course rest primarily on research involving highly-educated White Americans or Western Europeans. The generalizability of personality development trends to other populations is not well established due to the dearth of prospective longitudinal data on ethnic minority populations. In the present pre-registered study, we aimed to fill this gap by using data from a longitudinal study of 1,110 Mexican-origin adults who completed a well-validated personality measure up to 6 times over a 12-year period. Through this work, we were able to address fundamental questions about stability and change in the Big Five across adulthood, as well as investigate how these trends vary as a function of cultural and sociodemographic factors. Several noteworthy results emerged.

Stability and Change in the Big Five across Adulthood

As in previous research on rank-order stability, we found that individuals generally maintained their rank ordering on the Big Five across adulthood (2- and 3-year test-retest r s = .49 to .62; r s =.66–.80, corrected for measurement error), suggesting that if an individual tends to be higher on a trait (relative to others) in young adulthood, they also tend to be higher on that trait (relative to others) in middle adulthood. These rank-order stability coefficients are higher than what was found in previous work with a Black/African-American sample (Lockenhoff et al., 2008) and comparable to previous meta-analytic work showing that the average one-year test-retest correlation was estimated to be .55 (not corrected for measurement error; Roberts & DelVecchio, 2000 ).

With regard to profile stability, we found that individuals generally maintained their within-person configurations of Big Five trait scores across adulthood ( r s=.58–.66), with .58 and .57 for 4- and 6-year stabilities, respectively. These profile stability coefficients are similar to, but slightly lower than, previous research that found an average profile stability of .61 across 4 years ( Robins et al., 2001 ) and .63 across 6 years ( Terracciano et al., 2010 ).

Last, we partially replicated previous work on mean-level changes in personality. Contrary to some prior work that has shown mean-level increases in Agreeableness, Conscientiousness, Emotional Stability and little to no mean-level changes or declines in Extraversion or Openness ( Costa et al., 2019 ; Roberts et al., 2006 ; Wortman et al., 2012 ), we found that there were mean-level declines in all Big Five domains across adulthood in the present sample. These decreasing patterns more closely replicate recent work on the development of the Big Five in 16 longitudinal samples from five countries, which shows declines in the majority of the Big Five from middle adulthood into old age (e.g., Graham et al., 2020 ).

In terms of generalizability, it seems as though there are at least some common developmental processes that universally affect individuals regardless of the sociocultural group to which they belong, given the commonalities in the directions of Big Five change in the present sample and some prior work (e.g., Graham et al., 2020 ). These commonalities are likely due to both intrinsic maturational factors and environmental mechanisms that affect all individuals, regardless of their socio-cultural background. For example, it is possible that accelerated aging is at play, where Mexican-origin individuals perceive themselves to be older than they are, maybe due to adopting social roles sooner or for objectively faster biological aging due to the burden of fewer economic resources, poor work conditions, and frequent experiences with discrimination. Subjective perceptions of age have been shown to contribute to earlier maturity in the Big Five (Stephan et al., 2013), suggesting that there may be psychological and/or biological aging processes that underlie personality development trends across adulthood. Specifically for Mexican-origin individuals, it is possible that we find less evidence for personality maturity in the present study because our assessments generally begin later in adulthood at approximately age 30, which may be missing gains in personality maturity due to accelerated aging or intrinsic factors that have already occurred prior to age 30. It will be critical for future research to collect data earlier in adulthood (and even in late adolescence) to examine directly whether accelerated aging is at play.

Likewise, universally-experienced environmental contexts may lead to common personality changes with age. Although more work is needed, our results are not necessarily inconsistent with Social Investment Theory. The personality assessments in the present study began at age 26 and continued to approximately age 75, with most observations occurring between 30 and 60 years old. Given that Mexican-origin individuals tend to transition into the work force and family life earlier in adulthood than do adults from other cultural and socioeconomic groups (e.g., Bleidorn et al., 2013 ; but see McCrae et al., 2021 ; Terracciano, 2014 ), it is possible that the participants in the present study experienced personality maturity prior to the age at which our assessments of the Big Five started (i.e., before age 30). Thus, these “universal” social roles could be operating on personality changes earlier in development, but future work will need to collect longitudinal personality data on younger Mexican-origin individuals to confirm this explanation. Further, these results highlight the growing need to consider the “universal” environmental experiences that might lead to population-level declines in personality maturity from middle adulthood to late life, as well as the need to better understand how intrinsic and environmental forces interact to shape personality development (e.g., Tropf et al., 2017 ).

Last, it would be beneficial for future researchers to consider patterns of stability and change in Big Five facets and nuances (i.e., individual questionnaire items). It is possible that there are divergent patterns of stability and change among smaller units of personality that are masked at the broad domain level. These follow-up analyses would likely have important implications for our general understanding of the explanatory mechanisms underlying stability and change in personality across the lifespan (e.g., Mõttus et al., 2019 ; Mõttus et al., 2020 ).

Associations among Cultural Factors and Big Five Development

In addition to comparing the generalizability of the present results to prior research, we were also able to examine cultural factors as predictors of within-group variability in Big Five development. Generally, there were no differences in the mean-level trajectories for individuals who were interviewed in English versus Spanish and 1st vs. 2nd+ generation immigrants, with the exception of the language differences in Openness change. Individuals who were predominantly Spanish-speakers showed linear declines in Openness across adulthood, whereas individuals who were predominantly English-speakers showed linear increases in Openness across adulthood. Of the remaining cultural factors (i.e., age at immigration, Mexican cultural values, American cultural values, personal experiences with discrimination), we found several statistically significant results, most of which were concurrent associations with initial trait levels. Not surprisingly, individuals who had more personal experiences of ethnic discrimination tended to be more anxious and depressed. Individuals who endorsed more American cultural values tended to be higher in Openness. Last, individuals who endorsed more Mexican cultural values showed higher initials levels of, and greater declines in, Extraversion, Agreeableness, and Openness.

There are two main takeaways from these findings. First, given the large number of tests, there were relatively few associations between cultural factors and personality development across adulthood in this sample, which may or may not be surprising. On the one hand, this is surprising because we might expect that, for example, being an immigrant and experiencing ethnic discrimination would shape personality development in important ways. On the other hand, the lack of cultural effects may not be surprising because, similar to the weathering hypothesis ( Geronimus, 1992 ), the difficulties of being an immigrant and experiencing ethnic discrimination impact virtually all of the individuals in the present sample, leaving less room for these factors to account for within-group variability in personality maturation. More generally, it is worth noting that these are the kinds of compelling observations we can make by studying personality development in ethnic minority populations, which would not be possible if we did not study these populations or focused on between-group comparisons. Future work should explore the weathering hypothesis as it applies to personality development trends among ethnic minority samples in order to empirically explore these issues.

Second, of the statistically significant results, the associations between cultural values and Extraversion, Agreeableness, and Openness are consistent with recent work showing that motivational units, such as values and life goals, are systematically related to personality traits (e.g., Atherton et al., 2020 ). Many Mexican cultural values revolve around the importance of social connections, fostering harmonious relationships especially in the family, and respect toward others, all of which are related to consistent patterns of thinking, feeling, and behaving in an extraverted and agreeable way. Further, consistent with what has previously been shown, Openness often has the largest number of associations with life goals (e.g., Atherton et al., 2020 ) and values (e.g., Parks-Leduc et al., 2015 ), which was partially evident with the positive associations between Openness and the endorsement of both Mexican and American cultural values. Finally, similar to prior work on the association between motivational units and personality change (e.g., Atherton et al., 2020 ), higher levels of Mexican cultural values were related to greater declines in Extraversion, Agreeableness, and Openness, suggesting that: (a) endorsing Mexican cultural values may become more normative and stable with age, thus rendering traits like Extraversion, Agreeableness, and Openness as less important for maintaining those broader values or (b) these individuals simply had more room to decline because they had higher scores initially.

Associations among Socio-Demographic Factors and Big Five Development

In terms of socio-demographic factors, we found that mean-level changes in the Big Five generally did not vary as a function of sex, with the exception of Neuroticism and Openness. Women start out at a higher level of Neuroticism in young adulthood and show greater declines in Neuroticism across adulthood compared to men, which is consistent with some previous work (e.g., Graham et al., 2020 ). Men and women start out at comparable levels of Openness in young adulthood, but women show more rapid declines across adulthood compared to men. Further, Big Five development was largely unaffected by education level, verbal IQ, and fluid IQ. Out of 42 effects, only two were statistically significant and both were concurrent associations. Individuals who had higher education levels were less neurotic and more conscientious. Thus, mean-level changes in the Big Five across adulthood seem to generalize across several socio-demographic indicators, though more work is needed in this area.

Regarding acquiescence bias, we found that acquiescent responding was consistently and positively associated with occasion-specific Big Five personality scores, with the largest associations between acquiescence and Openness and the smallest associations between acquiescence and Conscientiousness. Additionally, we found that the mean-level change trajectories became non-significant when including acquiescence scores into the model. There are three main issues to consider when interpreting these findings. First, the prevalence of acquiescence is unlikely to be unique to our sample, although acquiescence bias tends to be more pronounced among individuals with less formal education and lower literacy ( Costello & Roodenburg, 2015 ; Sutin et al., 2013 ). The vast majority of participants in the present sample are first-generation immigrants to the United States who have less formal education than the typical samples studied in personality development research. Thus, it will be pertinent for future research to examine whether the present findings generalize to samples with higher literacy levels. Second, the acquiescence measures include true score variance, methodological artifact variance, and common variance among the Big Five traits, which leaves very little variance leftover for growth in the Big Five trait after accounting for these components. Moreover, this is the first attempt to integrate acquiescence into longitudinal models of personality change and thus, it is important to remain cautious when interpreting these findings. Last, to the extent that we can interpret these findings, the most straightforward interpretation is that acquiescence is positively associated with the Big Five so when it declines over time, it “pulls down” all of the Big Five scores with it. It also seems to be the case that the effects of acquiescence on Big Five scores get weaker over the study period, and therefore, it is possible that as participants gain more familiarity with interview protocols, they acquiesce less on these items and get closer to their “true score” over time.


The present research has several limitations. First, we used self-report measures of the Big Five, and thus, the present results may not hold for other methods of personality assessment. Second, the participants in the present study were married to each other. Although we corrected for this dependency in the data by using the CLUSTER function in Mplus, it is possible that there is more homogeneity in personality trajectories in the present study because partnered individuals are presumably experiencing many of the same environments and events within their households that could lead to personality change. Third, we did not observe an ideal amount of variability in some of our cultural and socio-demographic predictors; the vast majority of participants were Spanish-speaking (83%), first-generation immigrants (86%), and had less than a high school diploma or equivalent (66%). This lack of variability truncates the predictive validity that these factors might otherwise have on Big Five development in a more heterogeneous sample. Fourth, we conducted a large number of statistical tests, and specifically with respect to the associations among the sociodemographic and cultural predictors of the Big Five trajectories, approximately 10% (10 out of 98) were statistically significant at p ≤ .001. Although we adjusted the p-level to correct for multiple testing, it is still important to interpret the present results with caution until replications are conducted. Last, although we had 109 data points for individuals younger than 30, and 97 data points for individuals older than 60, these are relatively few compared to the data points available in middle adulthood (e.g., from age 30 to 60 we have 5,286 data points). Consequently, we have more statistical power and smaller confidence intervals for our latent growth curve models in mid-adulthood compared to young adulthood and old age. This goes hand-in-hand with our observation that, despite the quadratic personality change models fitting worse than the linear personality change models, the means of the linear slopes switch directions (from negative to positive) and the means of the quadratic slopes were statistically significant in the quadratic personality change models, suggesting we may lack the data points at younger and older ages to be able to detect non-linear changes in the Big Five in the present study. Future work should aim to improve upon the limitations of the present study when studying how personality develops in ethnic minority adults.


Taken together, the present study makes an important contribution to our understanding of Big Five personality development across adulthood by highlighting the extent to which, and in which ways, personality development findings generalize beyond the thin slice of the world’s population comprised of middle-class, highly educated White American or Western European individuals. We also see the present study as an opportune time to initiate a call for action. If psychologists truly want to understand how personality develops in humans, then it is critical to gather prospective, longitudinal data from participants who are diverse in terms of race/ethnicity and socioeconomic status, as well as gender identity, sexual orientation, nationality, religious affiliation, and so on. Identifying both generalizable and unique patterns of personality development across sociocultural groups will not only improve our understanding of the human experience, but also inform prevention and intervention efforts aimed at improving the health and well-being of individuals across the globe.

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Supplementary Material


This research was supported by a grant from the National Institute on Aging to Angelina R. Sutin and Richard W. Robins (U01AG060164), and by grants from the National Institute on Drug Abuse and the National Institute on Alcohol Abuse and Alcoholism (DA017902) to Richard W. Robins. The first author (OEA) conducted the work on this study while at the University of California Davis, but her present affiliation is Northwestern University. The pre-registration, list of deviations from the pre-registration, study materials, analytic syntax, and output can be found here: https://osf.io/2gpm9/ .

1 Two papers have used the parent BFI data from the California Families Project ( Clark et al., 2018 ; Weidmann et al., 2018 ), but neither examined stability and change in, or predictors of, the Big Five across adulthood. For a full list of California Families Project publications, see: https://www.californiafamiliesproject.org/publications.html .

2 The sample size at Wave 1 is 1,110. However, in subsequent waves, new parental figures (e.g., step-parents) were sometimes included in the study when the biological parent was not available due to divorce, separation, death, and other factors; in addition, some biological fathers did not participate at Wave 1, but agreed to participate at later waves. Consequently, the Wave 1 sample size does not represent the total number of parents who participated across all waves

3 The California Families Project participants have not given informed consent to have their personal data publicly shared, and we do not have IRB approval to post data publicly. Therefore, we are legally and ethically not allowed to publicly post participants’ data. Data are only available from the authors by request. Interested readers can contact the corresponding author to request access to a limited dataset to reproduce analyses.

4 In a small number of cases ( n = 34), the mother was unable to participate at Wave 10 so we allowed the father to participate instead.

5 Group sizes for people younger than 30 and older than 60 were too small to get reliable test-retest correlations.

6 If interested in computing how much change occurs as a function of different age units (e.g., scaling age by decade rather than year), it is possible to plug in the age unit of interest into the following equation: Y[t]n = y0n + Age[t]*ysn, where y0n is equal to the mean of intercept plus the error of the intercept and ysn is equal to the mean of the slope plus the error of the slope.

7 It is worth noting that in the quadratic models the linear and quadratic effects were significant for all of the Big Five domains except Neuroticism, suggesting that there was some evidence for non-linear patterns of change. However, given that the AIC and BIC values dramatically increased for the quadratic change models, we have less certainty that the quadratic function is the best representation of the data. See Figures S1 – S4 to view how the quadratic functions fit to these data for all Big Five domains (except Neuroticism).

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  • Published: 22 May 2020

Assessing the Big Five personality traits using real-life static facial images

  • Alexander Kachur   ORCID: orcid.org/0000-0003-1165-2672 1 ,
  • Evgeny Osin   ORCID: orcid.org/0000-0003-3330-5647 2 ,
  • Denis Davydov   ORCID: orcid.org/0000-0003-3747-7403 3 ,
  • Konstantin Shutilov 4 &
  • Alexey Novokshonov 4  

Scientific Reports volume  10 , Article number:  8487 ( 2020 ) Cite this article

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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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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 .

figure 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 ).

figure 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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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.


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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big 5 personality research paper


The relationship between big five personality and social well-being of chinese residents: the mediating effect of social support.

\r\nYanghang Yu&#x;

  • 1 School of Public Finance and Management, Yunnan University of Finance and Economics, Kunming, China
  • 2 Tourism and Cultural Industry Research Institute, Yunnan University of Finance and Economics, Kunming, China
  • 3 International College, National Institute of Development Administration, Bangkok, Thailand
  • 4 National Centre for Borderlands Ethnic Studies in Southwest China at Yunnan University (NaCBES), Yunnan University, Kunming, China

Previous studies have noted that personality traits are important predictors of well-being, but how big five personality influences social well-being is still unknown. This study aims to examine the link between big five personality and five dimensions of social well-being in the Chinese cultural context and whether social support can play the mediating effect in the process. This study included 1,658 participants from different communities in China, and regression analyses were conducted. Results revealed that five personality traits were significantly related to overall social well-being; extraversion was significantly related to social integration; agreeableness was positively related to all five dimensions of social well-being; conscientiousness was positively related to social actualization, social coherence, and social contribution; neuroticism was negatively related to social integration, social acceptance, social actualization, and social coherence; openness was positively related to social integration, social acceptance, social coherence, and social contribution. Social support plays mediating roles in the relationships between extraversion/agreeableness/conscientiousness/neuroticism/openness and social well-being, respectively.


Personality variables are strong predictors of well-being, a large body of research has explored the associations between big five personality and subjective well-being ( DeNeve and Cooper, 1998 ; Gutiérrez et al., 2005 ). Unfortunately, the psychological construct of well-being portrays adult well-being as a primarily private phenomenon largely neglecting individuals’ social lives ( Keyes, 2002 ; Hill et al., 2012 ). Individuals are embedded in social structures and communities; as such, it is necessary to evaluate one’s circumstance and functioning in a society; more attention needs to be devoted on the topic of social well-being ( Keyes, 1998 ). Previous studies focused on the social well-being from the perspective of interpersonal factors, such as sense of community ( Sohi et al., 2017 ), and civic engagement ( Albanesi et al., 2010 ). However, less work has examined social well-being from the level of the individual ( Keyes and Shapiro, 2004 ).

Although there are few studies focusing on the relationship between five personality traits and social well-being ( Hill et al., 2012 ; Joshanloo et al., 2012 ), their data come from United States or Iran; Chinese cultural background has been conducted to a lesser extent. Different countries have different cultural traditions. Personality is created through the process of enculturation ( Hofstede and McCrae, 2004 ). The interplay of personality and cultural factors was found to predict residents’ well-being significantly ( Diener and Diener, 1995 ). Confucius culture has embedded itself in the daily life of the Chinese, however, studies about the relationship between personality and social well-being under the context of Chinese culture are largely overlooked.

In addition, present studies ( Hill et al., 2012 ; Joshanloo et al., 2012 ) examine only the direct effect of personality on social well-being. The mechanism between big five personality and five dimensions of social well-being has been neglected. Additionally, social support can help individuals protect against the health consequences of life stress and increase their well-being ( Cobb, 1976 ; Siedlecki et al., 2014 ). Thus, following a social support perspective, the present study examined not only the relationship between five personality traits and domains of social well-being, but also whether social support can play a mediating effect in the relationship between big five personality and social well-being.

Literature Review and Hypothesis

Big five personality and social well-being.

The big five personality consists of five general traits: extraversion, neuroticism, openness, agreeableness, and conscientiousness ( John and Srivastava, 1999 ). Extraversion refers to the degree to which one is energetic, social, talkative, and gregarious. Agreeableness reflects the extent to which one is warm, caring, supportive, and cooperative and gets along well with others. Conscientiousness involves the extent to which one is well-organized, responsible, punctual, achievement-oriented, and dependable. Neuroticism means the degree to which one is worry, anxious, impulsive, and insecure. Openness reflects the degree to which one is imaginative, creative, curious, and broad-minded ( Barrick et al., 2001 ; Funder and Fast, 2010 ). Many scholars assessed personality under different culture context by a combined emic–etic approach ( John and Srivastava, 1999 ; Cheung et al., 2001 ). Even if there were researches that demonstrated several unique dimensions of personality under the Chinese culture ( Cheung et al., 2001 ; Cheung, 2004 ), the generalizability of the big five trait taxonomy in China is still confirmed ( Li and Chen, 2015 ; Minkov et al., 2019 ). Previous studies have consistently demonstrated that the big five are associated with subjective well-being ( DeNeve and Cooper, 1998 ; Gutiérrez et al., 2005 ), however, the findings are mixed under different cultural context. For instance, Ha and Kim (2013) found openness has a positive effect on subjective well-being in South Korea residents, whereas another study by Hayes and Joseph (2003) in England found that openness was not associated with each of the three measures of subjective well-being.

Culture variables can explain differences in mean levels of well-being ( Diener et al., 2003 ). With the uniqueness of Confucian cultural tradition and social setting, it is noteworthy to discuss the relationship between personality and well-being in Chinese cultural background, especially social-well-being.

Individuals are embedded in social structures. They need to face social challenges and evaluate their life quality and personal functioning by comparison to social criteria ( Keyes and Shapiro, 2004 ). However, the research about social well-being has been almost completely neglected in the hedonic and psychological well-being models ( Keyes, 2002 ; Joshanloo et al., 2012 ). Keyes (1998) proposed social well-being, which indicates to what degree individuals are functioning well in the social world they are embedded in. Social well-being can be described on multiple dimensions, including social integration, social contribution, social acceptance, social coherence, and social actualization. Social integration is the extent to which people feel commonality and connectedness to their neighborhood, community, and society. Social contribution refers to a value evaluation that one can provide to the society. Social acceptance entails a positive view of human nature and believes that people are kind. Social coherence refers to the perception of the quality and operation of the social world and reflects a belief that society is meaningful. Social actualization is the evolution of the potential and of society and includes a sense that social potentials can be realized through its institutions and citizens. In summary, social well-being emphasizes individuals’ perceptions of and attitudes toward the whole society. Prior studies have found the effect of sense of community ( Sohi et al., 2017 ), and social participation ( Albanesi et al., 2010 ) on social well-being, Also, some studies have shown the outcomes of social well-being, such as anxiety problems ( Keyes, 2005 ), general mental and physical health ( Zhang et al., 2011 ), and prosocial behaviors ( Keyes and Ryff, 1998 ). Personality traits and cultural factors are important predictors of well-being ( Diener et al., 2003 ). However, the only studies about personality and social well-being were conducted in Iran or United States. It is still not known whether the association would be similar in a different cultural context ( Hill et al., 2012 ; Joshanloo et al., 2012 ). For example, with the data from the MIDUS sample, Hill et al. (2012) found social well-being is positively related to extraversion, agreeableness, conscientiousness, emotional stability, and openness. In addition, previous studies did not test the correlation between five personality traits and five domains of social well-being entirely ( Joshanloo et al., 2012 ). Personality shapes many of the attitudes and behaviors that form Keyes’ different dimensions of social well-being. Thus, certain personalities would predict social well-being; for example, extraverted persons should be more socially integrated, whereas agreeable individuals should possess higher levels of social acceptance. Based on the above, we hypothesize the following:

Hypothesis 1 a : Extraversion is positively related to social well-being.

Hypothesis 1 b : Agreeableness is positively related to social well-being.

Hypothesis 1 c : Conscientiousness is positively related to social well-being.

Hypothesis 1 d : Neuroticism is negatively related to social well-being.

Hypothesis 1 e : Openness is positively related to social well-being.

The Mediating Effect of Social Support

Social support refers to individuals’ psychological or material resources from their own social networks that can assist them to cope with stressful challenges in daily lives ( Cohen, 2004 ). It comes from a variety of sources, such as friends, family, and significant others ( Taylor, 2011 ). Social support comprised both received and perceived social support ( Oh et al., 2014 ; Hartley and Coffee, 2019 ). However, many studies showed that perceived social support is more effective at predicting residents’ mental health than the received social support ( Cohen and Syme, 1985 ). Perceived social support indicates recipients’ perceptions concerning the general availability of support ( Sarason et al., 1990 ), which fosters a sense of social connectedness in a network and provides resources with which to overcome obstacles in their lives ( Lee et al., 2001 ; Chen, 2013 ). Social support theory emphasizes that social support is an important resource that can help individuals protect against life stress and increase their quality of lives ( Cobb, 1976 ; Cohen and Wills, 1985 ). Numerous studies have explored the associations between social support and well-being, including subjective well-being ( Brannan et al., 2013 ; Siedlecki et al., 2014 ) and psychological well-being ( Jasinskaja-Lahti et al., 2006 ; Wong et al., 2007 ). Although Inoue et al. (2015) found social support mediated the effect of team identification on community coherence, little research has addressed the effect of social support on social well-being. The benefits of social support come into play when individuals have to deal with social challenges and problems. Individuals with high level of social supports will better face social tasks ( Cox, 2000 ). Harmonious social relationships can help residents to satisfy their social needs, better understand, and be confident of the social world. Therefore, their social well-being will increase.

Personality traits are stable predictors of social support ( Swickert et al., 2010 ; Udayar et al., 2018 ; Barańczuk, 2019 ). Big five personality traits are found to be related to social support. Individuals with high levels of neuroticism report greater vulnerability to stress and negative affectivity, which could decrease the availability of social support ( Ayub, 2015 ). Individuals who score high on extraversion always seek social interactions and tend to be cheerful and friendly. The positive emotions could increase their social support ( Swickert et al., 2010 ). Individuals with high openness to experience are characterized by greater openness to emotions, appreciation of art and beauty, intellect, and liberalism. These characteristics would be significantly related to social support ( Barańczuk, 2019 ). Agreeableness characteristics, such as modesty, compliance, and trust, may facilitate individuals building a more extensive social support network ( Barańczuk, 2019 ). Conscientiousness are characterized by achievement-striving, self-discipline, orderliness, and dutifulness. These tendencies can help individuals better cope with life stress, so it is positively related to social support ( Ayub, 2015 ). Culture is an important moderator between big five personality traits and social support association, but it has been largely overlooked in previous studies ( Barańczuk, 2019 ). Therefore, studies about the relationship between five personality traits and social support under Chinese background are needed.

Previous studies discuss only the direct effect of personality on social well-being, but it remains unknown what mechanism(s) may explain this relation. Social support plays an important stress-buffering role when individuals are under high levels of life stress ( Cohen, 2004 ). Individuals with different levels of personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness) will form different types of social support network. Further, social support will help individuals cope with social challenges and increase their social well-being. Based on the above, we hypothesize the following:

Hypothesis 2 a : Social support mediates the relationship between extraversion and social well-being.

Hypothesis 2 b : Social support mediates the relationship between agreeableness and social well-being.

Hypothesis 2 c : Social support mediates the relationship between conscientiousness and social well-being.

Hypothesis 2 d : Social support mediates the relationship between neuroticism and social well-being.

Hypothesis 2 e : Social support mediates the relationship between openness and social well-being.

Materials and Methods

Participants and procedure.

Community residents from five different districts in Kunming, Yunnan Province, were selected as participants by stratified random sampling technique. Four hundred questionnaires were distributed to each district. Participants would complete the questionnaires in a face-to-face interaction with an enumerator who helped them to answer the questionnaire that was in paper format. When we administered the survey, we emphasized that the data were collected for research purposes. Participants were encouraged to answer all the questions honestly and were reminded that their responses would be anonymous. Upon completion of answering the questionnaire, participants received a small gift (e.g., tissue) as compensation for their participation. A total of 2,000 questionnaires were distributed, and 1,721 responded. After dropping incomplete and invalid data, 1,658 respondents remained. The final sample consisted of 932 females (56.2%) and 726 males (43.8%), aged 18–81 years (mean = 30.73 years, SD = 11.98 years).

Big Five Personality

The 44-item Big Five Inventory (BFI; John et al., 1991 ) was used to measure the five broad personality traits. All items were evaluated on a 5-point Likert scale, ranging from “strongly disagree” to “strongly agree.” Coefficient α reliabilities for the five trait scales in the present study were 0.707 for extraversion, 0.712 for agreeableness, 0.729 for conscientiousness, 0.706 for neuroticism, and 0.733 for openness. The Chinese version of BFI we used had been translated from English using common back-translation procedures ( Brislin, 1970 ; Li and Chen, 2015 ), and the validity had been conformed in previous studies ( Zhou, 2010 ; Li and Chen, 2015 ).

Social Support

Participants rated their social support from Chen and Yu (2019) using scales that ranged from 1 (strongly disagree) to 5 (strongly agree). The measure comprised three items, such as “It is easy for me to find someone to help when I meet with difficulties.” The entire survey demonstrated good reliability (α = 0.733).

Social Well-Being

Social well-being was measured through Keyes’s (1998) 15-item scale composed of five dimensions: social actualization, social integration, social acceptance, social contribution, and social coherence. Responses to this measure were assessed on a 5-point scale, from “strongly disagree” to “strongly agree.” An example of measure items was “I believe that people are kind.” The reliabilities of five dimensions were good (ranging from 0.702 to 0.725), and overall α reliability for the present sample was 0.791. Previous studies had confirmed the validity of social well-being measurement of Chinese version we used ( Miao and Wang, 2009 ; Chen and Yu, 2019 ; Chen et al., 2020 ).

The Common Method Bias Examination

As one of the main sources of measurement error, common method variance is a potential problem, which may be a threat to the validity of the conclusions. We tested for common method bias with a single-factor measurement model by combining all items into a single factor ( Podsakoff et al., 2003 ; Rhee et al., 2017 ). Results showed a poor model fit [Comparative Fit Index (CFI) = 0.763, Tucker-Lewis Index (TLI) = 0.695, Goodness-of-Fit Index (GFI) = 0.719, Root Mean square Residual (RMR) = 0.025, Root Mean Square Error of Approximation (RMSEA) = 0.109]. The above results suggested that there was no common method bias effect.

Descriptive Statistics and Correlations Between the Study Variables

There is no significant difference between the five different districts in Kunming. The correlation coefficients, means, and standard deviations are shown in Table 1 . All the big five personality traits were correlated significantly with social support and five domains of social well-being (expect agreeableness and social coherence). Extraversion, agreeableness, conscientiousness, and openness were correlated positively with domains of social well-being (expect agreeableness and social coherence) and social support, whereas neuroticism correlated negatively with domains of social well-being and social support.


Table 1. Correlations, means, and standard deviations of all study variables.

Regression Analyses

Statistical analyses were conducted with the Statistical Package for Social Sciences (SPSS, version 22.0). Based on preliminary analyses, multiple regression analyses were conducted to assess the relationship between the big five personality domains and dimensions of social well-being. Both gender and age were statistically controlled during the regression analysis, because there is evidence to show that social well-being likely increases with one’s age ( Chen and Li, 2014 ) and that men generally score higher on well-being than women do ( Miao and Wang, 2009 ). OLS regression was used to test the hypothesis. In each regression analysis, one social well-being dimension was entered as the dependent variable; gender, age, and all five personality domains were entered as potential predictors. Results of the regression analyses are presented in Table 2 . Five personality traits were significant predictors of overall social well-being. Extraversion (β = 0.052, p ≤ 0.05), agreeableness (β = 0.197, p ≤ 0.001), conscientiousness (β = 0.138, p ≤ 0.001), and openness (β = 0.156, p ≤ 0.001) are positively related to social well-being, whereas neuroticism (β = −0.171, p ≤ 0.001) is negatively related to social well-being. H1 a , H1 b , H1 c , H1 d , and H1 e are supported. Extraversion (β = 0.118, p ≤ 0.001), agreeableness (β = 0.162, p ≤ 0.001), neuroticism (β = −0.065, p ≤ 0.05), and openness (β = 0.086, p ≤ 0.001) were significant predictors of social integration. Agreeableness (β = 0.268, p ≤ 0.001), neuroticism (β = −0.102, p ≤ 0.001), and openness (β = 0.089, p ≤ 0.001) were significantly associated with social acceptance. Agreeableness (β = 0.168, p ≤ 0.001), conscientiousness (β = 0.111, p ≤ 0.001), and neuroticism (β = −0.110, p ≤ 0.001) predicted social actualization significantly. Agreeableness (β = −0.088, p ≤ 0.001), conscientiousness (β = 0.060, p ≤ 0.05), neuroticism (β = −0.241, p ≤ 0.001), and openness (β = 0.125, p ≤ 0.001) were found to be predicting social coherence. Agreeableness (β = 0.120, p ≤ 0.001), conscientiousness (β = 0.191, p ≤ 0.001), and openness (β = 0.164, p ≤ 0.001) were found to be predictors of social contribution.


Table 2. Results of regression analyses for five personality traits predicting dimensions of social well-being.

Mediation Analyses

Further, mediation analysis was performed to determine whether the effect of big five personality on social well-being was mediated by social support. Mediation analyses were conducted following the recommendations of Preacher and Hayes (2004) , using the PROCESS macro (version 3.0), developed by Hayes (2013) . The current study used 5,000 bootstrapped samples with a 95% confidence interval. The results of this analysis are shown in Table 3 . The results suggested five personality traits are related to social support significantly, and social support is positively related to social well-being. In addition, social support mediated the relationship between five personality traits and social well-being. H2 a , H2 b , H2 c , H2 d , and H2 e are supported.


Table 3. Summary of mediation analyses on five personality traits and social well-being (5,000 bootstraps).

Discussion and Conclusion

The results obtained from the survey of 1,658 Chinese residents demonstrated the effects of five personality traits on five dimensions of social well-being and the mediating role of social support in the associations between big five personality and social well-being.

Theoretical Contributions

Research on linkages between big five personality domains and five dimensions of social well-being conducted in China will likely contribute to the extant personality and well-being literature. First, this study provides empirical evidence about the relationship between big five personality and social well-being. The association between the big five personality and social well-being was evidenced in our study. However, our research also showed some inconsistencies with previous researches ( Joshanloo et al., 2012 ). From our results, extraversion was significantly related to social integration; agreeableness was positively related to all five dimensions of social well-being; conscientiousness was positively related to social actualization, social coherence, and social contribution; neuroticism was negatively related to social integration, social acceptance, social actualization, and social coherence; openness was positively related to social integration, social acceptance, social coherence, and social contribution. This inconsistency may be explained by the fact that the differences between Iran and China. For instance, Iran is a non-Arab Muslim country; the interactions in Iran are regulated partly by religious norms ( Joshanloo et al., 2012 ). In China, with the Reform and Opening, the way of thinking and behavior of Chinese are becoming more and more open and innovative ( Ma, 2013 ). The goal of community construction in China is to establish the autonomous system of community residents ( Fei, 2002 ). Community residents’ committee is an important organization of residents’ self-governing and self-service ( Sun, 2016 ). Thus, most community residents can participate in community management and satisfy their own service needs via residents’ committee, which will benefit residents’ life quality.

Second, the study highlights the effect of social support on social well-being. The existing literature has shown the relationship between social support and subjective well-being or psychological well-being ( Jasinskaja-Lahti et al., 2006 ; Brannan et al., 2013 ). Further, our study demonstrated social support is positively related to social well-being. Well-being is increasingly being associated with social and cultural relationships ( Helliwell and Putnam, 2004 ). Community in China is increasingly becoming a place for residents to integrate into urban society ( Chen et al., 2020 ). One of the most important responsibilities of the community is to achieve the society reconstruction ( Fei, 2002 ). Thus, during the development of community, the Chinese government was committed to improving the quality of community services, which may provide more opportunities for residents to get more social support. Individuals having high social support means they had selected and built large and effective social networks, which can help to overcome difficulties in lives. With the help from their social relations, they will give a high appraisal to their circumstances and functioning in society; their social well-being also increases.

Third, the mediating effects were found for social support for relation between extraversion/agreeableness/conscientiousness/neuroticism/openness and social well-being. This may contribute to the literature on the relationship between big five personality and social well-being ( Hill et al., 2012 ; Joshanloo et al., 2012 ). Previous studies neglected to examine the relationship and the mechanism between big five personality and social well-being from the perspective of the community. Community is an important place for residents’ daily activities. Individuals with different personality traits may build their social relations in different ways. Friends or family or neighbors around them may behave with different reactions. The different levels of social support will influence their evaluation of the social world, which may cause different levels of social well-being.

Practical Implications

Our study provides valuable insight into how individuals of different traits to improve their social well-being. Social support serves as a mediator in the relationship between big five personality and social well-being. The results also affirm the importance of social support that can enhance social well-being. When one’s psychological, social, and/or resource needs are met, one is likely to experience greater social support, which is important for their well-being. Therefore, it is possible for residents to promote social support. Individuals should spend more time participating in community public affairs or other social activities that could offer opportunities for them to establish meaningful relationship with neighbors or friends.

Limitations and Future Research

Despite these findings, our research is not without limitations. First, culture is an important factor that can influence both personality traits and well-being ( Diener et al., 2003 ; Hofstede and McCrae, 2004 ). Our study just discussed the mediating effect of social support between personality and social well-being. Future research should explore the effects of different cultural variables (such as power distance, collectivism/individualism etc.,). In addition, comparative studies among different countries or regions are needed. Second, the cross-sectional design means that no causal conclusions for the found relationship can be made. Consequently, future researches should adopt longitudinal or experimental design to ascertain the relationship. Third, social support has usually been classified into several specific forms, such as informational support, emotional support, perceived social support ( Taylor, 2011 ). In current study, we just regarded perceived social support as the mediating variable. So, future research should examine the effects of different forms of social support.

The research used a sample drawn from 1,658 Chinese residents to investigate the relationship between big five personality and social well-being and the mediating effect of social support in the relationship between big five personality and social well-being. Results of this study support previous studies that highlighted the relationship between big five personality and social support ( Swickert et al., 2010 ; Barańczuk, 2019 ). In addition, this study demonstrated the effects of five personality traits on five dimensions of social well-being. Lastly, the results demonstrated the mediating role of social support in the associations between extraversion/agreeableness/conscientiousness/neuroticism/open ness and social well-being, respectively.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Yunnan University of Finance and Economics. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

YY, YZ, and JL designed the research and wrote the manuscript. YY and YZ are co-first authors of the article. All authors planned and conducted the data collection. YZ, JZ, and DL analyzed the data and revised the manuscript. All authors listed have made direct and intellectual contribution to the article and approved the final version for publication.

This study was supported by the Chinese National Natural Science Fund (72064042), the Post-project of Chinese Ministry of Education (18JHQ080), the Philosophy and Social Science Research Project in Yunnan Province (QN202026), and the Science Research Fund of Yunnan Provincial Department of Education (2020J0384).

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.

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Keywords : big five personality, social support, social well-being, China, mediating effect

Citation: Yu Y, Zhao Y, Li D, Zhang J and Li J (2021) The Relationship Between Big Five Personality and Social Well-Being of Chinese Residents: The Mediating Effect of Social Support. Front. Psychol. 11:613659. doi: 10.3389/fpsyg.2020.613659

Received: 03 October 2020; Accepted: 31 December 2020; Published: 05 March 2021.

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The big five personality traits and psychological biases: an exploratory study

  • Published: 21 June 2021
  • Volume 42 , pages 6587–6597, ( 2023 )

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  • Vibhash Kumar   ORCID: orcid.org/0000-0002-6729-6366 1 ,
  • Ria Dudani   ORCID: orcid.org/0000-0001-6769-1203 1 &
  • Latha K. 1  

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This study examines the relationship between behavioral biases (herd behavior bias, overconfidence bias, and loss aversion bias) and the Big Five Personality traits. An exploratory study is devised to explore the links between biases and personality. The study develops a structured test battery to measure the biases. The items measuring overconfidence, loss aversion, and herd behavior bias was conceptualized and validated in the study. The test battery was sent out to 294 investors, and with a response percentage of 68%, 200 respondents returned the survey. The study employed confirmatory factor analysis (CFA) to validate the psychological biases questionnaire and employed structural equation modeling for path analysis. Two of the big five personality traits, i.e., Extraversion and Openness to experience, reported a significant causal relationship with all three biases.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Kumar, V., Dudani, R. & K., L. The big five personality traits and psychological biases: an exploratory study. Curr Psychol 42 , 6587–6597 (2023). https://doi.org/10.1007/s12144-021-01999-8

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Research Article

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

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

* E-mail: [email protected]

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

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

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

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

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


  • Published: January 7, 2021
  • https://doi.org/10.1371/journal.pone.0244849
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Fig 1

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

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

Editor: Stephan Doering, Medical University of Vienna, AUSTRIA

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

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

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

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

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


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

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

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

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

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

The empirical ground of personality types

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

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

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

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

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

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

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

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


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


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

Research gap and novelty of this study

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

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

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

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

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

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

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

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

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

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

Materials and methods

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

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



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

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

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

Risk taking.

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


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

Affective and cognitive wellbeing.

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

Locus of control.

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


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

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

Derivation of the prototypes

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

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

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

Determining the number of clusters

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

External validity criteria.

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

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

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

Internal validity criteria.

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

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

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


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


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

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

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

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

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

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

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

Internal and external cluster fit indices

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



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

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

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



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

Description of the prototypes

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

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

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

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

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

Stability and consistency

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

Predictive validity

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



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

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

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

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

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

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



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



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

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

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

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

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

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

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

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

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

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

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


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

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


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

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  • Published: 19 February 2023

Big five model personality traits and job burnout: a systematic literature review

  • Giacomo Angelini 1  

BMC Psychology volume  11 , Article number:  49 ( 2023 ) Cite this article

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Job burnout negatively contributes to individual well-being, enhancing public health costs due to turnover, absenteeism, and reduced job performance. Personality traits mainly explain why workers differ in experiencing burnout under the same stressful work conditions. The current systematic review was conducted with the PRISMA method and focused on the five-factor model to explain workers' burnout risk.

The databases used were Scopus, PubMed, ScienceDirect, and PsycINFO. Keywords used were: “Burnout,” “Job burnout,” “Work burnout,” “Personality,” and “Personality traits”.

The initial search identified 3320 papers, from which double and non-focused studies were excluded. From the 207 full texts reviewed, the studies included in this review were 83 papers. The findings show that higher levels of neuroticism (r from 0.10** to 0.642***; β from 0.16** to 0.587***) and lower agreeableness (r from − 0.12* to − 0.353***; β from − 0.08*** to − 0.523*), conscientiousness (r from -0.12* to -0.355***; β from − 0.09*** to − 0.300*), extraversion (r from − 0.034** to − 0.33***; β from − 0.06*** to − 0.31***), and openness (r from − 0.18*** to − 0.237**; β from − 0.092* to − 0.45*) are associated with higher levels of burnout.


The present review highlighted the relationship between personality traits and job burnout. Results showed that personality traits were closely related to workers’ burnout risk. There is still much to explore and how future research on job burnout should account for the personality factors.

Peer Review reports


Burnout: origin, evolution, and definition.

Since the 1970s, when most research in occupational health psychology was focused on industrial workers, studies on burnout have seen a substantial increase. Initially considered a syndrome exclusively linked to helping professions [ 1 , 2 , 3 , 4 ], burnout has been adopted by a broader range of human services professionals [ 5 , 6 ]. Job burnout’s construct has undergone considerable conceptual and methodological attention in the last fifty years. Nowadays, job burnout is considered a multidimensional construct closely referred to as repeated exposure to work-related stress (e.g., [ 7 ]). According to the original theoretical framework, job burnout is defined chiefly as referring to feelings of exhaustion and emotional fatigue, cynicism, negative attitudes toward work, and reduced professional efficacy [ 6 ].

While the relationship between socio-demographic, organizational, and occupational factors and burnout syndrome have received significant attention, the relationship between burnout and individual factors, such as personality, is less explored (for a meta-analysis, see [ 8 ]).

Therefore, it is interesting to investigate whether there is sufficiently convincing evidence to indicate that personality factors play a role in predictors of job burnout. Investigating to what extent personality factors predict job burnout could include a measure of these factors in the selection processes of workers. At the same time, it could also allow preventive actions to support all those at risk of job burnout. This literature review involved a search for cohort studies published since 1993, which used self-report measures of personality traits and job burnout and investigated the relationships between these variables.

Personality and job burnout

In the past, research on this issue has been chiefly haphazard and scattered ([ 9 , 10 ] for a meta-analysis; [ 11 ]). Indeed, personality has often been evaluated in terms of positive or negative affectivity (respectively, e.g., [ 12 , 13 ]), adopting the type A personality model (e.g., [ 14 ]), or the concept of psychological hardiness [ 15 ]. More recently, burnout research focused on the relationship between workers’ personalities measured by the Big Five personality model and their burnout syndrome [ 16 , 17 ]. More specifically, neuroticism (e.g., [ 18 , 19 ]) and extraversion personalities (e.g., [ 20 ]) were abundantly investigated in the scientific panorama (for review; [ 21 ]).

Personality traits according to the five-factor model (FFM)

Since the twentieth century, scholars and researchers have increasingly dedicated themselves to studying this topic, given the importance assumed by personality in the psychological panorama. One of the most famous and relevant approaches to the study of character is the five-factor model (FFM) of personality traits (often referred to as the “Big Five”) proposed by McCrae & Costa [ 22 , 23 ]. As a multidimensional set, personality traits include individuals’ emotions, cognition, and behavior patterns [ 23 – 26 ]. Furthermore, the FFM is the most robust and parsimonious model adopted to understand personality traits and behavior reciprocal relationships [ 27 ] due to two main reasons: its reliability across ages and cultures [ 28 , 29 ] and its stability over the years [ 30 ]. According to several scholars, the FFM consists of five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness [ 23 , 25 , 26 , 31 ]. Agreeableness refers to being cooperative, sympathetic, tolerant, and forgiving towards others, avoiding competition, conflict, pressuring, and using force [ 32 ]. Conscientiousness is reflected in being precise, organized, disciplined, abiding by principles and rules, and working hard to achieve success [ 33 ]. Extraversion is related to the quantity and intensity of individual social interaction characteristics. It is displayed through higher degrees of sociability, assertiveness, talkativeness, and self-confidence [ 32 ]. Neuroticism reflects people’s loss of emotional balance and impulse control. It is characterized by a prevalence of negative feelings and anxiety that are attempted to cope with through maladaptive coping strategies, such as delay or denial [ 29 , 34 ]. Openness is reflected in intellectual curiosity, open-mindedness, untraditionality and creativity, the preference for independence, novelty, and differences [ 33 , 35 ]. In the last thirty years, the Big Five model has been recognized as a primary representation of salient and non-pathological aspects of personality, the alteration of which contributes to the development of personality disorders [ 36 – 40 ], such as antisocial, borderline, and narcissistic personality disorders [ 41 ].

Although the role of the work environment as a predictor of burnout has been broadly documented (e.g., [ 5 , 6 , 11 ]), it cannot be neglected the effect that personality has on the development of this syndrome. Even reducing or eliminating stressors related to the work environment, some people may still experience high levels of burnout (e.g., [ 42 ]). For this reason, it is necessary to know the associations between personality traits and job burnout to identify the workers most prone to burnout and implement more risk-protection activities. Consequently, based on the literature presented above, this PRISMA review aimed to shed some light on the role that personality traits according to the Five Factors Model—Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness—play in the development of job burnout.

Protocol and registration

The systematic analysis of the relevant literature for this review followed procedures based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) process [ 43 – 45 ], a checklist of 27 items which together with a flow-chart (see Fig.  1 ) constitute the most rigorous guide to systematic reviews with or without meta-analysis. The systematic analysis of the relevant literature for this review followed procedures based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) process [ 43 – 45 ].

figure 1

Diagram flow of information through the different phases of a systematic review

The PRISMA method intends to provide a checklist tool for creating systematic reviews of quality literature.

Eligibility criteria

The study was conducted by extensively searching articles published before June 30th, 2021 (time of research), limited to papers in journals published in English. Review articles, meta-analyses, book chapters, and conference proceedings were excluded. Articles investigating the relationship between personality traits and job burnout in any field of employment, except athletic and ecclesiastical, were included.

Information sources

The databases PsycINFO, PubMed, Scopus, and ScienceDirect, were used for the systematic search of relevant studies applying the following keywords:

* Burnout * AND * Personality *

* Burnout * AND * Personality traits *

* Job burnout * AND * Personality *

* Work burnout * AND * Personality *

* Job burnout * AND * Personality traits *

* Work burnout * AND * Personality traits *

The initial search identified 3320 papers. The details (title; author/s; year of publication; journal) of the documents identified for inclusion across all inquiries were placed in a separate excel document. After removing duplicates, reviewing titles, and reading abstracts (see Fig.  1 ), the papers were reduced to 207, of which full-text records were read. Studies selected in total for inclusion in this review were limited to the five dimensions of the Big Five Factor model [ 46 ] and were 83 papers.

Study selection

As shown by the Prisma Diagram flow (Fig.  1 ), a total of 83 studies were identified for inclusion in the review. Via the initial search process have been identified total of 3320 studies (Scopus, n = 1339; PubMed, n = 515; ScienceDirect, n = 181; PsycInfo, n = 1285). After excluding duplicates, the remaining studies were 1455 of these 1421 records analyzed, and 1195 were discarded. After reviewing the abstracts, these papers did not meet the criteria. Of the remaining 226 full texts, the 207 papers available were examined in more detail, and it emerged that 112 studies did not meet the inclusion criteria as described. Furthermore, to ensure that only studies that had received peer review and met certain quality indicators were included, the SCImago Journal Rank (SJR) was inspected. SCImago considers the reputation and quality of a journal on citations, based on four quartiles used to classify journals from the highest (Q1) to the lowest (Q4). As suggested by Peters and colleagues [ 47 ], SCImago represents a widely accepted measure of the quality of journals and reduces the possibility of including in systematic reviews papers that do not meet certain quality indices. Based on this, 12 papers were excluded. Finally, 83 studies were included in the systematic review that met the inclusion criteria. Of the articles included in the review, more than half (60%) are published in journals indexed as Q1. The others were in Q2 (28%), Q3 (5%), and finally Q4 (7%).

Study characteristics


The included studies have involved 36,627 participants. Based on the inclusion criteria, all reviewed studies included (1) adult samples (18 years or older), (2) workers from the general population rather than clinical samples, (3) regardless of the type of work, and for most studies (4) more female participants than male (female, 57.79%; male, 42.21%). Six studies did not include participants’ demographic information [ 48 – 53 ]. The above percentages refer to the available data (n = 33,299).

The sample consisted of about 26% Teachers or Professors, 22% Nurses, 11% Physicians with various specializations, 10% Policemen, 10% Health professionals, 8% Clerks, of which about 5% worked with IT. Furthermore, the sample was made up of almost 3% Drivers, and less than 2% ICT Manager and Firefighters. Finally, about 9% of the sample carried out different types of jobs.

Countries of collecting data

The 83 articles included in this review have been published between 1993 and 2021 (see Fig.  2 ). In terms of geographic dispersion, more than half of the studies (n = 45; 54.21%) were conducted in Europe (France, Belgium, Bulgaria, Croatia, Germany, Greece, Italy, Netherland, Norway, Poland, Romania, Serbia, Spain, Sweden, Switzerland, and the UK). In contrast, the others were conducted either in America (n = 18; Canada, Jamaica, and the USA), Asia (n = 13; China, India, Iran, Israel, Jordan, and Singapore), Africa (n = 6; Nigeria, South Africa, and Turkey) and Oceania (n = 1; Australia).

figure 2

Research records achieving the inclusion criteria from 1993 to June 30th, 2021

A summary of information about the general characteristics and main methodological properties of all included 83 studies is reported in Table 1 .

Concerning the key methodological features of studies, all studies reviewed involved empirical and quantitative research design. Most of the papers included (n = 73; 88%) in this review were cross-sectional and descriptive studies, except nine (11%) papers presenting longitudinal studies [ 50 , 54 – 61 ]. Furthermore, one paper (1%; [ 62 ]) presented two different studies within it, one cross-sectional and the other longitudinal.

Most of the studies, 84% (n = 70), assessed job burnout via the Maslach Burnout Inventory, both in the original version (MBI; [ 3 , 63 ]), and in the subsequent versions [ 64 , 65 ], or its adaptation [ 66 ]. The other studies, 16% (n = 13), used tools other than MBI, but which share with it the theoretical approach to job burnout and the dimensions of (emotional) exhaustion, depersonalization or cynicism, and reduced personal or professional accomplishment (see Table 1 ). Five papers used the Shirom-Melamed Burnout Measure (SMBM; [ 67 ]), four the Oldenburg burnout inventory (OLBI; [ 68 , 69 ]), one the Bergen Burnout Indicator (BBI; [ 70 ]), one the Brief Burnout Questionnaire (CBB; [ 71 ]), one the Burnout Measure [ 72 ] and one the Short Burnout Measure (SBM; [ 73 ]).

According to the Big Five model, the outcome of the analyzed studies was the correlational and regressive between work burnout and personality traits. The data of the models in which the personality traits mediated or moderated the relationships with other variables, which were not the study’s object, were not considered in this review. Concerning personality, all included studies were compatible with the "Big Five" model [ 74 , 75 ] and investigated traits of Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness.

In detail, about 28% (n = 23) of the studies used the NEO Five-Factor Inventory (NEO-FFI; [ 33 , 76 – 79 ]), 17% (n = 14) have used the Big Five Inventory (BFI; [ 31 , 75 , 80 – 83 ]), one of which is the 10-item version [ 84 ]. Yet, 10% (n = 8) used the Eysenck Personality Questionnaire (EPQ; [ 85 , 86 ]), with one study with the revised version [ 87 ], and four studies with the revised and short version [ 88 ]. Furthermore, 7% (n = 6) involved the International Personality Item Pool (IPIP; [ 89 , 90 ]), with two studies adopting the mini version [ 91 ], while another 7% (n = 6) involved the NEO-Personality Inventory (NEO-PI; [ 81 ]), with five studies adopting the revised version. About 5% (n = 4) has used the Ten-Item Personality Inventory (TIPI; [ 92 ]), 4% (n = 3) has used the Big Five mini markers scale [ 93 ], and 4% (n = 3) involved the Big Five Questionnaire (BFQ; [ 94 ]) Finally, about 2% (n = 2) has submitted the Five Factor Personality Inventory (FFPI; [ 95 ]), and 2% (n = 2) used the Mini Markers Inventory [ 93 ].

The remaining studies, about 14% (n = 12), used the following tools: the Basic Character Inventory (BCI; [ 96 ]), the Big Five factor markers [ 90 ], the Big Five measure-Short version [ 32 , 97 ], the Big Five Plus Two questionnaire-Short version [ 98 ], the Brief Big five Personality Scale [ 92 ], the Basic Traits Inventory (BTI; [ 99 ]), the Comprehensive Personality and Affect Scales (COPAS; [ 100 ]), the Eysenck Personality Inventory (EPI; [ 101 ]), the Freiburg Personality Inventory (FPI; [ 102 ]), the M5-120 Questionnaire [ 103 ], the Minimal Redundant Scales (MRS-30; [ 104 ][ 104 ]), and the Personality Characteristics Inventory (PCI; [ 105 , 106 ]).

All instruments included in the studies were in line with the “Big Five” domains [ 26 ], such as e.g., the NEO-FFI and the NEO-PI, widely used measures of the Big Five [ 81 ], the dimensions of the TIPI and the IPIP [ 89 , 92 ], or the factors of the EPQ and the EPI, compatible with the Big Five model [ 107 , 108 ].

Risk of bias in individual studies

Study design, sampling, and measurement bias were assessed regarding the evaluation risk of bias in each study. Table 2 summarizes the limits reported in each study. Where not registered, no limitations related to the study were referred by the authors of the original studies.

Study design bias

Although most of the studies (89%) have a cross-sectional design, this review reported in the table (see Table 2 ) this bias only on the studies that highlighted this as a weakness (50%). Cross-sectional methods are cheap to conduct, agile for both the researcher and the participant, and can give answers to many research questions [ 109 ]. At the same time, however, since it is a one-time measurement, it does not allow us to test dynamic and progressive effects to conclude the causal relationships among variables.

Three longitudinal studies reported a shortness [ 56 , 58 ] or longness [ 55 ] time-lag between the first and successive administrations. The time length between the study’s waves is an essential issue in longitudinal research methodology. The time interval between the first and following measurements should correspond with the underlying causal lag (e.g., [ 110 ]). If the time lag is too short, probably the antecedent variable does not affect the outcome variable. If, on the contrary, the time lag is too long, the effect of the antecedent variable may already have disappeared. In both cases, the possibility of detecting the impact of the antecedent variable on the outcome variable may decrease.

Furthermore, it is possible that in the period between the first and subsequent measurements, several events may occur affecting the outcome. Finally, the same participant in the sample could change the condition under study (to know more, [ 177 ]). Especially in work-related studies, employees may be subject to changes in context, needs, and working hours [ 178 ]. Despite this, longitudinal designs offer substantial advantages over cross-sectional methods in examining the causal links between the variables [ 177 ].

Sampling bias

About 29% of the studies (n = 24) reported the small samples as limitation. Among these, one study that had two different samples reported a small sample only in second one [ 62 ], while another study, in investigating differences, highlighted that certain groups have a relatively small sample size and reported this as a limitation [ 140 ]. Additionally, about 10% of the studies reported having received an inadequate response rate. About 18% of the studies reported a non-probabilistic sampling as a limitation, and 6% of studies examined reported having a gender-biased sample (male/female). Other studies (13%) reported collecting data in a single organization, country, or an imbalance among workers’ categories. Finally, three studies [ 154 , 168 , 170 ] reported a cultural or geographical bias. To sum up, studies’ limitations regarding the sample characteristics may significantly impact scores’ reliability [ 179 , 180 ]. Specifically, this research’s limits prevent to generalize the findings.

Measurement and response bias

Since inclusion evaluated burnout and personality traits through self-reports that respected the previously illustrated models, all the studies examined used self-report measures. Again, only 40% report this as a limitation. Using perceptual measures, one could be subject to the Common Method Bias (CMB; [ 181 ]). The CMB occurs when the estimated relationships among variables are biased due to a unique-measure method [ 182 ]. This bias may be due to several factors, including response trends due to social desirability, similar responses of respondents due to proximity and wording of items, and similarity in the conditions of time, medium, and place of measurements [ 183 – 185 ]. These variations in responses are artificially attributed to the instrument rather than to the basic predispositions of the participants [ 181 , 186 , 187 ]. Suppose the systematic method variance is not contained. In that case, it can result in an incorrect evaluation of the scale's reliability and convergent validity, inflating the reliability estimates of correlations [ 188 ] and distorting the estimates of the effects of the predictors in the regressions [ 184 ].

Furthermore, about 5% of studies reported using single-item measures. Personality characteristics were often measured through self-reports with single items and assessed through a Likert scale [ 189 ]. This type of assessment is susceptible to social desirability (SDR; [ 184 , 185 ]), i.e., the tendency to respond coherently with what others perceive as desirable [ 190 ]. Furthermore, this type of assessment is also susceptible to acquiescent responding (ACQ; [ 191 ]), i.e., the tendency to prefer positive scores on the Likert scale, regardless of the meaning of the item [ 192 ]. Response-style-induced errors can influence reliability estimates (e.g., [ 193 , 194 ]) and overestimate or underestimate the relationships between the variables examined [ 195 ]. Despite these response biases, widely documented in the literature [ 184 – 186 , 196 – 198 ], it appears that this bias is overstated in psychological research [ 185 ]. Indeed, self-reports would seem to be the most valid measurement method for evaluating personality factors because the same participant is the most suitable person to report their personality and level of burnout [ 42 ]. Other studies (10%) reported using a poor reliability scale: employing imprecise psychometric procedures in a study is likely to distort the outcome, therefore not allowing to make inferences about an individual and creating a response bias [ 199 ]. Finally, about 16% of the studies examined reported that the study did not review all the variables relating to the constructs investigated. Table 2 also identifies some specific limitations of the studies examined, such as, e.g., the comparison between non-numerically equivalent samples [ 174 ], the long compilation time required [ 165 ], and the lack of a control group [ 57 , 138 ]. Furthermore, some studies have used tools that evaluate only a total score of burnout [ 17 ] or personality [ 54 ] Finally, other studies have focused only on individual factors, leaving out job-related and organizational factors [ 147 ].

This systematic review was conducted to identify, categorize, and evaluate the studies investigating the relationship between job burnout and personality traits addressed to date. Specifically, the interest of this review was to explore the role of personality traits as individual factors related to job burnout. To do this, only studies that analyzed the direct relationship between personality traits and job burnout were included, leaving out all those studies that investigated additional variables that could in any way mediate or moderate this relationship.

Results of the studies included

Table 3 summarizes the results, the correlation and regression indices, and the power of significance of the studies included in this review.

The results of the included studies based on the five personality traits and the association with a dimension of job burnout are discussed below. The correlations between the personality trait and the size of the job burnout report first, while subsequently those of the regressions, presenting the cross-sectional studies first, which are most of them, and then also the longitudinal ones.

As seen previously, job burnout is a multidimensional construct that consists of the individual response to stressors at work [ 3 , 9 ]. The literature has long investigated the association between organizational and occupational factors and burnout. However, a recent meta-analysis shows that there is a bidirectional relationship between occupational stressors and burnout [ 200 ]. Because the research on individual factors has been less systematic, partial, and contradictory [ 113 ], this review aimed to synthesize research evidence about the role that FFM personality traits play in the development of job burnout. To do this, 83 independent studies that used different tools to assess both job burnout and personality traits while maintaining the same reference theory were identified. The most investigated personality traits were, in order, neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience.

The present review extracted data from the reviewed studies, including (1) main characteristics of participants (including job type), (2) data collected country, (3) personality traits related to job burnout, (4) risk of bias in individual studies, and (5) methodological features of studies. As for the participants, all reviewed studies included (1) adult samples, (2) workers from the general population rather than clinical samples, (3) regardless of the type of work, and for most studies (4) more female participants than male. Based on these observations, future studies examining personality traits and work burnout should employ other samples (e.g., clinical samples) to enhance external validity.

This systematic review focused exclusively on personality traits and the relationship between them and job burnout. Results of the included studies confirmed a relationship between job burnout and the five distinct personality traits of the Big Five model [ 46 ] and that some of these were risk factors for job burnout (although not always in the same direction). A descriptive picture of the relationship between the five personality traits and job burnout will be discussed.


A negative association between Agreeableness and job burnout was reported (range, r from − 0.12* to − 0.353***; β from − 0.08*** to − 0.523*). Longitudinal studies also suggest a role of Agreeableness as a protective factor of dimensions of Emotional Exhaustion, Depersonalization, and reduced Professional Accomplishment (EE; β, − 0.83*; β, − 0.48*; D; β, − 0.31*; PA; β, − 0.22*; rPA; β, − 0.28**). As seen previously, the Agreeableness trait has been described as a sense of cooperation, tolerance, and avoidance of conflict on problematic issues [ 32 ]. Agreeable individuals are warm, supportive, and good-natured [ 201 , 202 ], protecting them from feelings of frustration and emotional exhaustion [ 113 ]. Indeed, their tendency towards a positive understanding of others, coupled with interpersonal relationships based on feelings of affection and warmth [ 201 ], could protect them from developing job burnout and greater depersonalization [ 8 , 203 ]. Although most of the studies found a negative relationship between Agreeableness and job burnout, in some studies Agreeableness was positively correlated with Emotional exhaustion [ 159 ], and reduced Professional Accomplishment [ 50 , 62 ].


A negative association between Conscientiousness and job burnout was reported (range, r from − 0.12* to − 0.355***; β from − 0.09*** to − 0.300*). Longitudinal studies also suggest the role of Conscientiousness as a protective factor against Burnout (B; β, -0.21*). As seen previously, the Conscientiousness trait is reflected in precise, organized, and disciplined individuals who respect the rules and work hard to achieve success [ 33 ]. Their perseverance in work and success orientation would protect these people from developing emotional exhaustion [ 76 , 204 ] and poor personal accomplishment, as they are unlikely to perceive themselves as unproductive. Although most studies found a negative relationship between Conscientiousness and job burnout dimensions, some studies pointed out an unexpected inverse correlation between Conscientiousness and reduced Professional Accomplishment [ 60 , 62 , 143 , 159 , 166 ]. Furthermore, Conscientiousness was positively associated with Emotional exhaustion and Depersonalization [ 131 ]. This result would be due to the greater commitment and effort employed in their work, which would have greater levels of exhaustion and depersonalization [ 131 ]. Finally, another longitudinal study [ 56 ] attributes Conscientiousness as a negative predictor role for the dimensions of Personal/Professional Accomplishment. However, the authors do not provide reasons for this discordant result from the literature.


A negative association between Extraversion and job burnout was reported (range, r from − 0.034** to − 0.33***; β from − 0.06*** to − 0.31***). Longitudinal studies also suggest the role of Extraversion as a protective factor against burnout and its dimension of Exhaustion (B; β, − 0.16*; EE; β, − 0.26*). As seen previously, the Extraversion trait has been identified as the intensity of social interaction and the level of self-esteem of individuals [ 32 ]. People with higher levels of extraversion appear positive, cheerful, optimistic, and have more likely to experience positive emotions [ 206 ]. This positive view of their level of job-related self-efficacy [ 207 ], often associated with the interpersonal bonds they tend to create [ 208 ] can protect outgoing individuals from experiencing high levels of emotional exhaustion. On the contrary, introverted individuals tend to experience greater feelings of helplessness and lower levels of ambition [ 204 ], which instead results in a risk factor for job burnout. Although the negative association is the most frequent, some studies have found a directly proportional association between Burnout and Extraversion [ 54 ], Cynicism [ 127 , 173 ], and reduced Professional Accomplishment [ 50 , 60 , 62 , 143 , 146 , 159 ]. Again, the authors do not provide reasons for this discordant result from the literature.


A positive association between Neuroticism and job burnout was reported (range, r from 0.10** to 0.642***; β from 0.16** to 0.587***). Longitudinal studies also suggest a role of Neuroticism as a predictor of Burnout and its extent of Exhaustion, while predicting a decrease in Professional Accomplishment (B; β, 0.21*; EE; β, 0.31***; β, 0.15**; β, 0.19**; PA; β, − 0.23**). As seen previously, it is possible to define Neuroticism as the inability of people to control their impulses and manage their emotional balance. Neurotic people experience a series of feelings of insecurity, anxiety, anger, and depression [ 25 , 76 , 204 ] that they try to manage through maladaptive coping strategies, such as delay or denial [ 29 , 34 ]. These characteristics of the personality trait of Neuroticism would interfere with job functioning and satisfaction, operating a negative "filter" that magnifies the impact of adverse events (see [ 209 ]) and constitutes a significant risk factor for job burnout [ 8 , 174 ]. Feelings of anxiety and nervousness could lead them more easily to experience higher levels of emotional exhaustion, and by focusing on more aspects of their work, they are more likely to manifest depersonalization. Although most studies report a positive association between Neuroticism and Burnout [ 164 ], Burnout [ 159 , 169 ], Depersonalization [ 133 , 159 ], and reduced Professional Accomplishment [ 60 , 62 , 126 ]. Ye and colleagues [ 164 ] tie this result to the Chinese cultural situation, whereby the observed greater sense of responsibility and discipline could reduce the effects of extroversion on job burnout. Farfán and colleagues [ 169 ], on the contrary, link this result to the tendency of the neurotic personality trait to use rationalization as a defense against job burnout. Unlike most of the studies included in this review, some results show a negative association between Neuroticism and Burnout [ 159 , 164 ], Emotional exhaustion, and Depersonalization [ 155 ]. Furthermore, a study indicates that Neuroticism is positively associated with reduced Personal/Professional Accomplishment [ 131 ]. Finally, in the longitudinal study by Armon and colleagues [ 54 ], Neuroticism even seems to protect against Emotional exhaustion. The authors explain the association over time of Neuroticism with job burnout as due to an underrepresentation in the measurement scales used or the moderating effect of gender on these associations [ 159 ].

A negative association between Openness and job burnout was reported (range, r from − 0.18*** to − 0.237**; β from − 0.092* to − 0.45*). Longitudinal studies have suggested the role of Openness as a protective factor of reduced Professional Accomplishment (rPA; β, 0.10*). As seen previously, individuals with high levels of Openness tend to be more intellectually curious about novelty and open-minded and have a predisposition to independence [ 35 , 76 , 202 ]. These characteristics protect individuals from experiencing discomfort, experiencing novelty and failures as opportunities [ 203 ], and protecting them from job burnout from emotional exhaustion. Conversely, when faced with stressors at work, less open individuals can adopt quick but suboptimal strategies, such as depersonalization [ 8 ]. Although most of the studies found a negative relationship between Openness and job burnout, five studies found a positive correlation between Openness and Emotional exhaustion [ 54 , 122 ] and Depersonalization [ 159 ], while negative with Personal/Professional Accomplishment [ 62 , 131 , 159 ]. The authors do not provide reasons for this discordant result from the literature. Other studies instead have found a positive association between Openness and all dimensions of Burnout [ 116 ]: Exhaustion [ 131 , 173 ], Depersonalization [ 131 ], and reduced Personal/Professional Accomplishment [ 142 ]. Finally, the longitudinal study by Ghorpade and colleagues [ 120 ] attributes Openness to the role of the positive predictor of Emotional exhaustion. According to the authors, this result could be attributed to the work of the professors (Professors) which, requiring a greater openness to listening to students' different problems and encouraging different positions in them, could increase emotional exhaustion.

The findings of most of the studies reviewed indicate that individuals who have higher levels of neuroticism and lower agreeableness, conscientiousness, extraversion, and openness to experience are more prone to experiencing job burnout. However, the few studies that show other results than this theoretical line cannot explain the conflicting results. Some authors adduce these results to a measurement bias (e.g., [ 159 ]) or sample characteristics (e.g., [ 120 ]) but fail to explain the reason for this relationship and believe that it is due to further variables to be explored.


Although the literature review was conducted as rigorously as possible, the search strategy was limited to four scientific search engines. Furthermore, it was impossible to find all the relevant studies if the search terms were not mentioned in the articles' titles, abstracts, or keywords. Therefore, some related papers might be missed due to the selected terms. Furthermore, the search included only studies published in English, thus excluding relevant studies in other languages. Additionally, gray literature was not included in the study, and therefore, it may not have been considered essential data contained in non-peer-reviewed studies, unpublished theses, and dissertation studies. Furthermore, one of the exclusion criteria was the journal ranking of SCImago. Although this is a widely accepted and recognized measure to reduce the possibility of including in systematic reviews papers that do not meet certain quality indices [ 47 ], they may not have been considered relevant data. In addition, the Big Five model [ 46 ] was used as a conceptual model of reference to compare the results of the studies on job burnout. Studies that did not include the Big Five models or that explored the relationship between Burnout and personality disorders (e.g., Antisocial Personality Disorder, Narcissistic Personality Disorder, Borderline Personality Disorder, etc.) were therefore not examined in this study. Restricting studies to a single conceptual model of personality was necessary to focus the review, but at the same time, it limited our investigation. Furthermore, the heterogeneity of the study samples' work type, burnout measurement tools, and personality traits prevented comparing results across studies. Finally, despite precautions to reduce selection bias, confounding, and measurement bias, no studies have addressed reverse causality problems in the relationship between personality traits and burnout. Although the cross-sectional research design does not allow us to investigate the causal links between personality and burnout, an answer to the existence of this link is offered by the longitudinal studies included in the review. This type of study demonstrates that personality traits play a role in the development of burnout, but future research must investigate this relationship, especially with the help of longitudinal studies that can reduce the problems related to reverse causality.

The findings obtained in the present review highlight the importance of examining the role of personality traits in the development of job burnout syndrome. At the same time, it is possible to observe how scientific evidence places us in front of a picture that is not fully defined. In line with Guthier's meta-analysis [ 200 ], the findings of this review highlight the need for expanding job stress theories focusing more on the role that personality plays in burnout.

I am convinced of the value of this review in directing future empirical research on job burnout, especially in the light of new approaches to burnout as a multi-component factor (see [ 210 , 211 ]). Even more future research will have the task of encouraging the use of methodologies that evaluate personality traits in work contexts. An assessment of personality traits and continuous monitoring of occupational stress levels (e.g., [ 212 ]) could help identify the people who are most likely to develop burnout syndrome to prevent or limit its damage. Future research should improve understanding and intervention on burnout, too often limited by universal approaches that have neglected the uniqueness of the antecedents of burnout [ 213 ]. Some traits related to burnout predict work outcomes such as job performance, job satisfaction, and turnover [ 203 , 214 – 218 ]. It is, therefore, necessary to investigate the antecedents of Burnout to provide implications practices for jobs and organizations.

Availability of data and materials

As this is a systematic review of the literature, this study indicates the information to obtain all data analyzed in the databases used. However, the datasets used during the current study remain available from the corresponding author upon reasonable request.


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Angelini, G. Big five model personality traits and job burnout: a systematic literature review. BMC Psychol 11 , 49 (2023). https://doi.org/10.1186/s40359-023-01056-y

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big 5 personality research paper

Big Five Personality Traits

The Big Five model of personality, also known as the Five Factor Model (FFM), is a framework that outlines five core dimensions of personality. Based on decades of personality research and validity tests across the world, the Five Factor Model is the most commonly accepted theory of personality today. The five dimensions represent broad categories designed to capture much of the individual variation in personality and were determined by analyzing and grouping common adjectives used to describe peopleÕs personality and behavior. The Five Factor Model is also commonly referred to using the acronyms OCEAN and CANOE.

View All Term Definitions

Breakdown by Domain

Key features, context & culture.

  • Originally developed through a lexical analysis of English terms, research has also been conducted in Chinese, Czech, Dutch, German, Greek, Hebrew, Hungarian, Italian, Polish, Russian, Spanish, Tagalog, Turkish, and more
  • Research suggests the Big Five traits capture much of the variability in personality across cultures; however, languages other than English often produce additional important traits and there is some evidence to suggest that ÒopennessÓ in particular may be understood differently across cultures (e.g., intellect vs. rebelliousness)

Developmental Perspective

  • Research on the validity of the Big Five traits has been conducted with all ages, but primarily with adults
  • Research has shown that while relatively stable, traits develop and change with age
  • No learning progression provided

Associated Outcomes

  • Evidence suggests personality traits are correlated with life outcomes such as educational attainment, health, and labor market outcomes

Available Resources

Support materials.

  • No materials provided

Programs & Strategies

  • No programs or strategies provided

Measurement Tools

Personality traits are often measured through questionnaire scales such as:

  • NEO Personality Inventory (NEO-PI-R)
  • Big Five Inventory (BFI)
  • Trait-Descriptive Adjectives (TDA)

Key Publications

  • John, O.P., Naumann, L.P., & Soto, C.J. (2008), Paradigm Shift to the Integrative Big Five Trait Taxonomy in Handbook of Personality: Theory and Research, 114-156.
  • McCrae, R. R. and John, O. P. (1992), An Introduction to the Five?Factor Model and Its Applications. Journal of Personality, 60: 175-215.

Multiple researchers

Developer Type

To create a model of personality that encompasses as much variation in personality as possible using a manageable number of dimensions

Common Uses

The Five Factor Model serves as a unifying taxonomy in the field of personality research; it is widely used in many countries throughout the world

Key Parameters

Level of detail, compare domains, compare frameworks, compare terms, explore other frameworks.

  • Visual Tools
  • Our Methods


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    Two of the big five personality traits, i.e., Extraversion and Openness to experience, reported a significant causal relationship with all three biases. ... Journal of Research in Personality, 37(6), 504-528. ... Mathematical essays on rational human behavior in a social setting, 241-260. Tversky, A., & Kahneman, D. (1975). Judgment under ...

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