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Trajectories of Big Five Personality Traits: A Coordinated Analysis of 16 Longitudinal Samples

Eileen k. graham.

1 Northwestern University, Chicago, IL USA

SARA J. WESTON

2 University of Oregon, Eugene, OR USA

DENIS GERSTORF

3 Humboldt University, Berlin, Germany

4 German Institute for Economic Research, Berlin, Germany

TOMIKO B. YONEDA

5 University of Victoria, Victoria, BC Canada

6 University of Edinburgh, Edinburgh, UK

CHRISTOPHER R. BEAM

7 University of Southern California, Los Angeles, CA USA

ANDREW J. PETKUS

Johanna drewelies, andrew n. hall, emily d. bastarache, ryne estabrook, mindy j. katz.

8 Albert Einstein College of Medicine, New York, NY USA

NICHOLAS A. TURIANO

9 University of West Virginia, Morgantown, WV USA

ULMAN LINDENBERGER

10 Max Planck Institute for Human Development, Berlin, Germany

JACQUI SMITH

11 University of Michigan, Ann Arbor, MI USA

GERT G. WAGNER

12 Berlin University of Technology, Berlin, Germany

NANCY L. PEDERSEN

13 Karolinska Institute, Solna, Sweden

MATHIAS ALLEMAND

14 University of Zurich, Zurich, Switzerland

AVRON SPIRO, III

15 VA Boston Healthcare System, Boston, MA USA

16 Boston University School of Public Health, Boston, MA USA

17 Boston University School of Medicine, Boston, MA USA

DORLY J.H. DEEG

18 VU University Medical Center, Amsterdam, The Netherlands

BOO JOHANSSON

19 University of Gothenburg, Gothenburg, Sweden

ANDREA M. PICCININ

Richard b. lipton, k. warner schaie.

20 Pennsylvania State University, State College, PA USA

SHERRY WILLIS

21 University of Washington, Seattle, WA USA

CHANDRA A. REYNOLDS

22 University of California, Riverside, Riverside, IL USA

IAN J. DEARY

Scott m. hofer, daniel k. mroczek, associated data.

This study assessed change in self-reported Big Five personality traits. We conducted a coordinated integrative data analysis using data from 16 longitudinal samples, comprising a total sample of over 60 000 participants. We coordinated models across multiple datasets and fit identical multi-level growth models to assess and compare the extent of trait change over time. Quadratic change was assessed in a subset of samples with four or more measurement occasions. Across studies, the linear trajectory models revealed declines in conscientiousness, extraversion, and openness. Non-linear models suggested late-life increases in neuroticism. Meta-analytic summaries indicated that the fixed effects of personality change are somewhat heterogeneous and that the variability in trait change is partially explained by sample age, country of origin, and personality measurement method. We also found mixed evidence for predictors of change, specifically for sex and baseline age. This study demonstrates the importance of coordinated conceptual replications for accelerating the accumulation of robust and reliable findings in the lifespan developmental psychological sciences.

INTRODUCTION

Questions remain regarding personality change in adulthood (especially in midlife and older adulthood), despite many studies over many decades. Recent work has embraced a more nuanced perspective than earlier approaches, which whipsawed from one extreme perspective (personality is unstable for nearly everyone) in the 1970s ( Mischel, 1969 , 1977 ), to its opposite (personality is stable for nearly everyone) in the 1980s and 1990s ( Costa & McCrae, 1980 , 1986 ). Over the last 10–15 years, a more integrative view has emerged, recognizing that personality does change, but likely in systematic ways, and that in any population or sample there will almost certainly be individual differences in change; that is, while some individuals may change, others may remain stable ( Roberts & Mroczek, 2008 ).

Recent work has also focused on antecedents to ( Allemand, Job, & Mroczek, 2019 ; Bleidorn et al., 2013 ; Mroczek & Spiro, 2003 ; Mühlig-Versen, Bowen, & Staudinger, 2012 ) or consequences of ( Graham & Lachman, 2012 ; Human et al., 2013 ; Mroczek & Spiro, 2007 ; Steiger, Allemand, Robins, & Fend, 2014 ; Turiano et al., 2011 ) personality change during middle and older adulthood. However, it is useful to step back from efforts to predict and explain personality change and carry out confirmatory work that circles back to fundamental questions of how personality changes. The current study aimed to address four basic research questions. First, what are the basic patterns of overall change for each of the Big Five traits, particularly in older adulthood? Second, are there individual differences in variations around those overall trajectories ( Nesselroade & Baltes, 1979 )? Third, if there are variations, what factors are associated with individual differences in personality change? Last, we posed a fourth question addressing issues of reproducibility, replicability, and generalizability ( Condon, Graham, & Mroczek, 2017 ). With the use of the language of multi-level (or mixed effects) modelling, the first question addressed the issue of fixed effects (average change), whereas the second two addressed random effects (individual variation around that average). Specifically, the second question concerned individual differences in rate of change, and the third question addressed predictors of these differences. We focused on sex and baseline age as variables that may be associated with individual differences in personality change, using 16 large longitudinal samples that were primarily comprised of midlife and older adults.

The fourth question is more meta-scientific in nature. Given that there is great heterogeneity of related findings in research on personality change, how can we enhance the reproducibility, replicability, and generalizability of our own findings? Using a methodological approach designed to enhance and inform replicability and drawing upon the data sharing network of the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA) project ( Hofer & Piccinin, 2009 ), we identified 16 longitudinal samples that included at least three occasions of personality assessment. Within each study, we independently estimated personality trait trajectories using the multi-level model (MLM) for change ( Singer & Willett, 2003 ), and then we summarized and synthesized the average trajectories applying meta-analytic techniques. This form of separate, but coordinated, modelling within each study is known as coordinated analysis and is a type of integrative data analysis ( Hofer & Piccinin, 2009 ). This technique is similar to individual participant data meta-analysis, which is being used increasingly in medicine and epidemiology to synthesize evidence across studies ( Riley, Lambert, & Abo-Zaid, 2010 ). This approach produces a group of replications (more accurately, a group of identical models fit to different samples) that otherwise would be very difficult to obtain owing to the extended data collection period of long-term longitudinal data. Additionally, a coordinated analysis preserves the separate findings across samples, permitting assessment of heterogeneity in effect sizes that may be due to sample differences (e.g. country of origin, age of sample, and measurement differences) or even to differences in true effect sizes across studies. This also permits an evaluation of the generalizability of the findings across varying samples, an issue we have discussed elsewhere in more detail ( Mroczek, Graham, Turiano, & Oro-Lambo, 2019 ; Mroczek, Weston, & Willroth, 2019 ). Thus, in addition to our substantive questions regarding the functional forms of the overall trajectories of Big Five traits, individual differences in trajectories, and predictors of change, our fourth question was more methodological, addressing whether we could identify between-sample variation in either overall patterns or individual differences in change.

Overall trajectories and current theoretical models of personality development

Overall or general patterns of personality change are nomothetic, or variable centred. These patterns capture how a given trait changes over time in a given sample, in contrast to how different persons change with time. They may be termed ‘meta-trajectories’ when they refer to population-level estimates of change ( Mroczek, Graham, et al., 2019 ). One of the more prominent theories of nomothetic or population-level trajectories is the neo-socioanalytic theory with its corollaries, the social investment principle, and the maturity principle ( Roberts & Mroczek, 2008 ; Roberts, Walton, & Viechtbauer, 2006 ; Roberts, Wood, & Caspi, 2008 ; Roberts, Wood, & Smith, 2005 ). These ideas posit that trait changes in adulthood tend to reflect an increase in socially acceptable characteristics (i.e. maturity) and a successful transition into adult roles that require additional responsibility (i.e. social investment: parenthood and work promotions) ( Caspi, Roberts, & Shiner, 2005 ; Roberts et al., 2006 , 2005 ; Roberts & Mroczek, 2008 ). Personality changes in young to mid-adulthood that are predicted by this theory include increasing conscientiousness, extraversion, openness, and agreeableness in young adulthood, along with declining neuroticism. Similarly, Denissen et al. (2013) proposed that norm-based reference values that operate via self-regulation strategies can bring about normative change such as that described by the maturity principle. This theory suggests that self-regulatory strategies are guided by social norms, as well as by personal values thus providing an explanation for both overall population-level trajectories (e.g. increasing conscientiousness) and variation around such overall trajectories (e.g. individual differences in rate of conscientiousness change). In essence, both the neo-socioanalytic ( Bogg, Voss, Wood, & Roberts, 2008 ) and self-regulation ( Denissen, van Aken, Penke, & Wood, 2013 ) theories argue that personality change is driven by the societal or cultural norms that govern adulthood roles, although Denissen et al. (2013) added personal values and expectations as a second set of influences.

However, the trait trends described above (decreasing neuroticism and increasing conscientiousness) are not consistent throughout older adulthood, suggesting that the above theories may be under-specified. One of the most theoretically compelling explanations for these personality trends in later life involves the concept of Baltes (1997) and Baltes and Baltes (1990) of selection, optimization, and compensation (SOC; ( Kandler, Kornadt, Hagemeyer, & Neyer, 2015 ; Mueller et al., 2019 ). The Baltes theory posits that development includes both gains and losses across most of the lifespan, whereby older adulthood is typically characterized by more losses. In order to optimize development in older adulthood, individuals must automatically or intentionally compensate for increasing losses and select by restricting domains and goals ( Baltes, 1987 ). The SOC theory of developmental regulation may help to explain trait trends that appear to decelerate or even reverse in later life ( Kandler et al., 2015 ; Mueller et al., 2016 ). For example, developing and maintaining a career and relevant skills may become less of a priority (or not a priority at all) in older adulthood and retirement. Thus, behaviours associated with accomplishing those goals are no longer needed and are ‘selected out’, thereby exerting downward pressure on traits such as conscientiousness that are associated with these behaviours. Similarly, the need to be vigilant against health risks in late life, alongside financial uncertainty when living on fixed or limited income, may promote selection and optimization processes that increase neuroticism. Perhaps the most obvious example involves extraversion, in which older adults select and pare down their social circles ( Carstensen, Isaacowitz, & Charles, 1999 ), allowing optimization of affect and behaviour. In turn, a smaller, but higher-quality, social network compensates for the loss of large (but lower quality) social networks typically seen in young adulthood and midlife.

Consistent with the broad array of theories described above, several studies that examined traits over different ages, or estimated overall trajectories (using either MLM or latent trajectory models), found that during the emerging adult and midlife years, agreeableness, conscientiousness, openness, and extraversion tend to increase and neuroticism tends to decrease ( Lucas & Donnellan, 2011 ; Marsh, Nagengast, & Morin, 2013 ; Roberts et al., 2006 , 2008 ). After midlife, these overall trajectories shift somewhat, with various studies showing decreases in most of the Big Five traits ( Berg & Johansson, 2013 ; Kandler et al., 2015 ; Lucas & Donnellan, 2011 ; Mõttus, Johnson, Starr, & Deary, 2012 ; Roberts & Mroczek, 2008 ). Some of these studies show that decreases occur mainly in older adulthood, particularly after age 65 or 70 ( Mõttus, Johnson, & Deary, 2012 ; Mroczek & Spiro, 2003 ). These findings also align with meta-analyses of mean-level change in traits ( Roberts et al., 2006 ), which indicate that certain traits (e.g. conscientiousness and emotional stability) increase in younger adulthood, whereas others (e.g. agreeableness) only change in older adulthood.

Interestingly, when the neo-socioanalytic (especially its maturity principle) and the SOC theories are combined, we would expect age-related average curvilinear personality change. Some traits may increase in the earlier part of adulthood, before decreasing in older adulthood. Several of the studies we analysed included four or more occasions, which allowed estimation of quadratic trajectories, and several studies had a wide age range. Thus, we were able to utilize several of these theoretical perspectives to guide our analysis plan to comprehensively test the different ways in which personality traits change in mid to late adulthood.

Individual differences in trajectories and lifespan developmental theory

Although overall patterns of change along with theories that explain them are important, individual differences that surround these overall trajectories may enable a more accurate and comprehensive description of personality change over time. Both SOC and Denissen’s self-regulation theory imply such individual variation. But can we empirically identify individual variation in trajectories around those population trajectory estimates? Even when overall patterns are well-replicated (e.g. decreasing neuroticism in adulthood), to what extent do individuals vary around that population-level trajectory, especially with respect to individual differences in rate of change, or slope? Large estimates of individual variation in rate of change tell us that the overall sample trajectory describes only a fraction of people, such that many individuals deviate from the average. Previously, many researchers had argued that personality is fixed and unlikely to change once adulthood is reached, commonly citing the popular William James quote that personality is set like plaster after age 30 ( Costa & McCrae, 1980 , 1986 ; McCrae & Costa, 1994 ). In general, a given collection of personality trajectories has historically been assumed to show very little variation around the population-level trajectory (meta-trajectory), thereby indicating stability. However, consideration and estimation of individual variation around such trajectories form a key tenet of lifespan developmental theory (LDT) ( Baltes, Reese, & Nesselroade, 1977 ), which posits that development (including personality development) is a lifelong process and that both general patterns and individual differences around these patterns are critical for understanding development of any particular variable over the lifespan ( Baltes, 1987 ; Baltes, Lindenberger, & Staudinger, 2006 ). As such, theories that focus on meta-trajectories, such as the maturity principle, are not mutually exclusive with LDT. The neo-socioanalytic theory, self-regulation theory, and SOC can explain the shape of the population-level trajectories, with self-regulation theory and LDT explaining individual differences around those overall patterns at the within-person level.

Existing literature examining adult personality change has reported estimates of individual variation that, in almost all cases, are consistent with the LDT principle of individual differences in person-level change ( Berg & Johansson, 2013 ; Bleidorn, Kandler, Riemann, Angleitner, & Spinath, 2009 ; Helson, Jones, & Kwan, 2002 ; Mroczek, Almeida, Spiro, & Pafford, 2006 ; Mroczek & Spiro, 2003 , 2007; Pedersen & Reynolds, 1998 ; Roberts et al., 2006 ; Terracciano, McCrae, Brant, & Costa, 2005 ; Vaidya, Gray, Haig, Mroczek, & Watson, 2008 ; Vecchione, Alessandri, Barbaranelli, & Caprara, 2012 ). However, systematic inquiry of consistency in individual differences in change is warranted, given the varying interpretations of the literature, and a coordinated analysis approach is aptly suited to investigate these cross-sample differences and similarities.

One goal of this project was to apply techniques designed to enhance replicability of results to longitudinal research on personality development across adulthood using existing data. Researchers across many fields are currently focused on enhancing replicability and reproducibility, and encouraging open science practices ( Condon et al., 2017 ; Nelson, Simmons, & Simonsohn, 2018 ; Vazire, 2018 ; Weston, Graham, & Piccinin, 2019 ). However, many of the shifts in research practices have focused on experimental sub-fields of psychology, leaving longitudinal researchers with more limited options regarding how to enhance the replicability of their findings. Given that longitudinal research often relies on large extant datasets, p-hacking and publication bias are concerns, and many published longitudinal studies may have capitalized on chance as a result.

With these issues in mind, we developed an analytic plan to replicate results within a set of analyses, chiefly through achieving model harmonization for optimal comparability across multiple distinct samples. This enabled us to approximate wide-scale replicability of results by applying the same analytic techniques to multiple samples in a controlled manner. The first step in this procedure was to identify the type of models we wanted to test. To examine stability and change of traits across time, we estimated MLMs for change in personality traits, using both linear slopes and (when appropriate, given the available data) quadratic slopes. To test predictors of personality change, we utilized a slopes-as-outcomes modelling technique to predict slopes from covariates of interest. Before fitting any models, we wrote analysis scripts in R, and we subsequently identified longitudinal studies that had the requisite data to fit those models (the minimum data required were three measurement occasions of at least one of the Big Five personality traits). Prior to analysis, we also identified sex and baseline age as predictors of change upon which to focus. See Table S1 for descriptive statistics, including reliability estimates (Cronbach’s alpha) for all traits.

We used the framework of the IALSA to identify participating longitudinal studies of aging that met study eligibility. IALSA is a platform for facilitating replication of analyses in multiple studies using longitudinal data ( Graham et al., 2017 ; Hofer & Piccinin, 2009 ). Longitudinal research requires years of data collection and substantial financial resources, thereby creating significant pragmatic barriers to replication. The IALSA network facilitates data access for researchers to test longitudinal models in a multi-study framework, allowing for conceptual replication. While direct replication is not feasible in this context, conceptual replication demonstrates the robustness and generalizability of effects across diverse methodologies and measures.

The current study used a coordinated analysis approach, which preserves measurement and sample heterogeneity, while allowing examination of the strengths and similarities in associations across studies ( Hofer & Piccinin, 2009 ). A coordinated analysis is different from a pooled analysis, which has strict harmonization requirements (usually identical measures) and merges each study’s data into a single dataset, thereby producing one set of models ( Curran et al., 2008 ). Pooled analysis assumes homogeneity across datasets and a single true effect size underlying all studies. This is often an untenable assumption ( Borenstein, Hedges, Higgins, & Rothstein, 2010 ); and coordinated analysis, by analysing each dataset separately and then synthesizing afterward, does not make this assumption. We identified 16 samples that achieved our data requirements, allowing analysis using multi-level growth models; see Table 1 for basic study-level descriptions.

Study descriptions

The Berlin Aging Study

The Berlin Aging Study (BASE) began in 1990 in Berlin, Germany. Neuroticism, extraversion, and openness were assessed using six items each, selected from the NEO-FFI ( Costa & McCrae, 1985a , 1985b) at baseline, with reassessment at four additional occasions (five total measurements) over the following 13 years ( Wagner, Ram, Smith, & Gerstorf, 2016 ). A total of 516 ( M age = 84.92, SD age = 8.66, 50% female) participants completed at least one measurement of personality. There were 132 individuals who completed three or more measurements and 83 who completed four or more measurements.

The Berlin Aging Study-II

The Berlin Aging Study-II (BASE-II) data used in the current report came from older adults (age range 60–84 at personality baseline) residing in the greater Berlin (East and West), Germany metropolitan area ( Bertram et al., 2013 ; Gerstorf et al., 2016 ; Mueller et al., 2016 ). Starting in 2009, a total of 1276 ( M age = 67.68, SD age = 3.73, 50% female) participants completed at least one measurement of personality. There were 957 individuals who completed three or more measurements and 671 participants who completed four or more measurements of the full Big Five personality traits using a short version of the Big Five Inventory (BFI-S; John & Srivastava, 1999 ; Lang, John, Lüdtke, Schupp, & Wagner, 2011 ).

The Einstein Aging Study

The Einstein Aging Study (EAS) is a sample of older, ethnically diverse, community-residing individuals from the Bronx. Data collection began in 1993, with rolling enrolment. At study entry, participant age ranged from 70 to 99 years, with follow-up occasions every 18 months ( Katz et al., 2012 ). Although some participants have as many as 16 measurement occasions of personality, most have 3 to 5. A total of 342 ( M age = 77.54, SD age = 4.99, 60% female) participants completed at least one measurement of personality, using adjectives from the International Personality Item Pool (IPIP) ( Goldberg, 1992 ). There were 214 individuals who completed three or more measurements and 179 participants who completed four or more measurements.

The Health and Retirement Study

The Health and Retirement Study (HRS) is a nationally representative longitudinal panel study of over 20 000 adults who were surveyed every 2 years starting in 1992 ( Juster & Suzman, 1995 ; Sonnega et al., 2014 ). Half of the sample completed a personality assessment in 2006, and the second half completed personality in 2008. The full Big Five was assessed using adjectives from the Midlife Development Inventory ( Lachman & Weaver, 1997 ). A total of 14 513 ( M age = 68.50, SD age = 10.50, 59% female) completed at least one measurement of personality. There were 8869 individuals who completed three or more measurements.

Interdisciplinary Longitudinal Study of Adult Development

The Interdisciplinary Longitudinal Study of Adult Development (ILSE and ILSE.Y) is a representative sample of 1390 individuals from two cohorts (1930–1932: older adults in their early 60s when ILSE was launched; 1950–1952: middle-aged adults in their early 40s at baseline) in Germany ( Sattler et al., 2015 ). For ILSE, three measurements of the full Big Five (NEO-FFI; Costa & McCrae, 1988 ) were collected in 1994, 1998, and 2006. A total of 485 ( M age = 62.50, SD age = 0.96, 48% female) completed at least one measurement of personality. There were 306 individuals who completed three or more measurements ( Allemand, Schaffhuser, & Martin, 2015 ; Allemand, Zimprich, & Martin, 2008 ). The younger cohort (ILSE.Y) was also included in this project, containing 496 ( M age = 43.78, SD age = 0.91, 48% female) who completed at least one measurement of personality and 328 individuals who completed three or more measurements.

Longitudinal Aging Study of Amsterdam

The Longitudinal Aging Study of Amsterdam (LASA) is a nationally representative cohort sample of 5132 older adults in the Netherlands. Of the Big Five traits, neuroticism was the sole trait collected in this sample, assessed using the neuroticism scale from the DPQ ( Luteijn, Starren, & van Dijk, 2000 ). Four measurements of neuroticism were taken in 1992, 1995, 1998, and 2001 ( Huisman et al., 2011 ). A total of 2111 ( M age = 69.75, SD age = 8.57, 52% female) participants completed at least one measurement of personality. There were 1662 individuals who completed three measurements and 1267 who completed four measurements.

Lothian Birth Cohort 1936

The Lothian Birth Cohort 1936 (LBC) consists of surviving participants of the 1947 Scottish Mental Health Survey. The 1936 cohort was recruited between 2004 and 2007 by identifying individuals from the original (1947) cohort who were residing in Edinburgh and the surrounding areas. In total, 1091 participants entered the study. Personality traits were measured using 50 items from the IPIP when participants were 67–71 years old in 2006 and then three times more in 2008, 2012, and 2016 for a total of four measurement occasions ( Deary, Gow, Pattie, & Starr, 2012 ). A total of 950 ( M age = 69.51, SD age = 0.84, 50% female) completed at least one measurement of personality. There were 680 individuals who completed three measurements and 528 individuals who completed four measurements.

Midlife in the United States

The Midlife in the United States (MIDUS) study is a national sample of 7108 adults, with a baseline age range of 28 to 74 years. The initial wave of measurement began in 1994–1995, and two additional waves of data collection took place in 2004–2005 ( N = 4963) and in 2013 ( N = 3294) ( Brim, Ryff, & Kessler, 2004 ). The Midlife Developmental Inventory (adjectives) was used to assess the full Big Five traits ( Lachman & Weaver, 1997 ). A total of 6265 ( M age = 46.80, SD age = 12.90, 53% female) completed at least one measurement of personality. There were 2717 individuals who completed three measurements.

VA Normative Aging Study

The VA Normative Aging Study (NAS) is a US Department of Veterans Affairs study, focused on the medical and psychosocial aspects of aging among men. The original sample consists of 2280 men living in Boston. At the first measurement occasion of personality, they had an age range of 30–78 years, with follow-up visits every 3–5 years. A total of 1645 ( M age = 51.40, SD age = 9.20, 0% female) completed at least one measurement of the Eysenck (EPI-Q, Floderus, 1974 ; based on Eysenck & Eysenck, 1968 , EPI) personality scale beginning in 1975. A total of 1423 individuals completed three measurements and 1255 who completed four measurements ( Bosse, Ekerdt, & Silbert, 1984 ).

The Origin of Variance in the Oldest-Old: Octogenerian Twins

The Origin of Variance in the Oldest-Old: Octogenerian Twins (Octo-Twin) study includes 351 Swedish twin pairs (702 individuals) aged 80 years and older (80–97). Baseline interviews occurred between 1991 and 1993 ( McClearn et al., 1997 ). Four additional waves of data were collected at 2-year intervals. Extraversion and neuroticism were assessed at the first four occasions using a shortened, 19-item, version of The Eysenck Personality Inventory (EPI-Q; Eysenck & Eysenck, 1968 ). A total of 469 ( M age = 83.17, SD age = 2.89, 64% female) completed at least one measurement of personality. There were 213 individuals who completed three measurements and 122 who completed four measurements.

Swedish Adoption/Twin Study of Aging

The Swedish Adoption/Twin Study of Aging (SATSA) began in 1984 on adults aged 26–93 years, with the objective of studying the genetic and environmental factors associated with aging ( Pedersen et al., 1991 ). Personality traits (neuroticism, extraversion, and openness) were assessed seven times between 1984 and 2010, using the NEO-PI ( Costa & McCrae, 1985a , 1985b ) inventory for openness and EPQ ( Eysenck, 1975 ) for neuroticism and extraversion. A total of 1925 ( M age = 59.80, SD age = 13.96, 58% female) participants completed at least one measurement of personality. There were 1438 individuals who completed three measurements and 1407 who completed four measurements.

Seattle Longitudinal Study

The Seattle Longitudinal Study (SLS) began in 1956 to study psychological development in adulthood ( Schaie, Willis, & Caskie, 2004 ). The full Big Five personality traits were assessed at four measurement occasions, beginning in 2001, using the NEO-PI-R Personality Inventory ( Costa & McCrae, 1992 ). A total of 1541 ( M age = 63.21, SD age = 15.61, 56% female) completed at least one measurement of personality. There were 785 individuals who completed three measurements and 639 who completed four measurements.

German Socio-Economic Panel

The German Socio-Economic Panel (SOEP) is an ongoing annual longitudinal study that began in 1984 with approximately 15 000 private households in Germany ( Goebel et al., 2018 ; Headey, Muffels, & Wagner, 2011 ; Wagner, Joachim, & Schupp, 2007 ). A total of 21 030 ( M age = 47.40, SD age = 17.58, 52% female) completed at least one measurement of personality, using a shortened version of the BFI personality inventory ( John & Srivastava, 1999 ; Lang et al., 2011 ). There were 19 076 individuals who completed three measurements and 13 229 who completed four measurements.

Wisconsin Longitudinal Study

The Wisconsin Longitudinal Study (WLSG/WLSS) contains two samples used in the current project. The first (WLSG) is a sample of Wisconsin residents who graduated from high school in 1957. Data collection was started in 1957, and participants were reassessed periodically over the following decades. Personality assessment was added to the study protocol in the early 1990s, providing personality data using the BFI personality inventory ( John, Donahue, & Kentle, 1991 ) in 1992, 2003, and 2011. Age at personality assessment baseline ranged from 51 to 56. A total of 6720 ( M age = 53.23, SD age = 0.64, 53% female) participants completed at least one measurement of personality and 5154 individuals completed three measurements. The second sample (WLSS) is composed of siblings of these graduates. Data collection for the sibling sample began in 1975, and personality data for these participants ( N = 4804) were collected in 1993, 2004, and 2011 ( Herd, Carr, & Roan, 2014 ; Sewell, Hauser, Springer, & Hauser, 2003 ). A total of 3987 ( M age = 53.27, SD age = 7.32, 53% female) completed at least one measurement of personality and 2853 individuals completed three measurements.

Personality

All studies contained at least a subset of the Big Five, assessed via different but reliable and validated measures of the Big Five. The IPIP ( Goldberg, 1992 ) was used to develop the measures for all five traits in the LBC1936 and EAS. Goldberg’s adjectives were also used to create the Midlife Development personality inventory, which was used in the MIDUS and HRS ( Lachman & Weaver, 1997 ). The Eysenck measures ( Eysenck, 1975 ; EPI-Q, EPQ; Eysenck & Eysenck, 1968 ) were used to measure neuroticism and extraversion in Octo-Twin, NAS, and SATSA. The NEO ( Costa Jr & McCrae, 1992 ) was used to assess the Big Five in ILSE, BASE (NEO-FFI), SLS (NEO-PI-R), and SATSA (NEO-PI; Openness only), while LASA measured neuroticism using the neuroticism scale from the DPQ ( Huisman et al., 2011 ). A BFI-S ( John et al., 1991 ; John & Srivastava, 1999 ; Lang et al., 2011 ) was used to assess the Big Five in the BASE-II, SOEP, and WLS samples. Each of the scales is measured in different units, with most on a 1–5 scale, but some used a 1–30 scale (sum scores). We transformed personality for comparability by converting each trait to a 1–10 scale. We then calculated the baseline mean and standard deviation of the transformed scale, standardized the transformed scores on the basis of these new baseline statistics, and finally, added back the baseline mean. For example, hypothetically, for a score of 3.5 on a Likert-type 1–5 scale, this would mean that this score would be converted to 6.25 [10 * (value – max/max – min)]. If this hypothetical sample had a transformed mean of 5 ( SD = 2), then this score would be standardized {[(value – mean)/ SD ] + mean} for a new score of 5.625. The units are interpreted in standard deviation units, and the mean is on a 1–10 scale. For the duration of this paper, ‘baseline’ refers to the initial assessment of personality for each study.

The time metric in our trajectory models was chronological age, centred at 60 and divided by 10 (to convert to change per decade). Centred age was squared for use in models estimating quadratic change. See Figure S1 for the distribution of age across all samples.

Sex and baseline age were used as predictors of personality change across studies. Each study used predictors from the same measurement occasion as the first personality assessment, to address the question of whether baseline predictor ‘status’ was prospectively associated with subsequent personality change. All predictors were added into separate models. Studies with less than 5% prevalence of a given predictor were dropped from those particular models. Models including health events, marital status, and retirement status as predictors of personality change are provided in the supplemental material ( https://osf.io/xqmfw/?view_only=126dbaafa46043038688e04bdcc23899 ). 1

All studies coded baseline sex in a binary manner such that 1 = female and 0 = male.

Baseline age

To explore whether personality change varied as a function of baseline age group, each study created a binary variable indicating whether a participant was over or under age 60 at study baseline, coded as 1 = 60 or over at baseline and 0 = under 60 at baseline. Individual studies had varying start years (1975–2009) with the majority being in the early 1990s, and a range of mean ages at baseline (average baseline age range = 44–85). Adding baseline age group as a level 2 predictor gave us a rough estimate of whether birth cohort in a given study start year (or more accurately, age group) could account for differences in personality trajectories within each study. The visualization of these trajectories across studies will give an illustration of age group differences in these trajectories, regardless of the year in which baseline age was assessed.

Data analysis

Individual study analyses.

We used MLMs for change, also known as individual growth or individual trajectory modelling ( Raudenbush & Bryk, 2002 ; Singer & Willett, 2003 ), to estimate trajectories of each personality trait in each sample. To provide a baseline for comparing subsequent models, we first tested intercept-only (unconditional means) models, expressed as Y ti = π 0 i + ε ti , where Y is personality trait at a given measurement occasion t for person i . This is a function of a person-level intercept (level of trait), plus the within-person residual. The variance, τ , is used in conjunction with the residual to calculate the intra-class correlation coefficient (ICC), or ρ . This is the ratio of between-person variance to total variation in each trait. We then fit a linear growth model (level 2 model) with the fixed-slope only, and next, we fit the linear growth model with both the fixed and random slopes. Likelihood ratio tests compared these two models so as to assess whether adding the random slopes improved model fit and thus whether there were individual differences in linear change. Next (for studies with 4+ measurement occasions) quadratic growth models were fit to assess non-linear change. We did this by squaring the time-metric (age) and entering this into the model. Lastly, using linear growth models, a series of slopes-as-outcomes models were fit, each with one of the above described predictors. Each predictor was added individually into separate models. The interaction of the binary predictor by the linear time metric (age) was calculated and added to the linear growth model.

All studies tested all models are based on data availability. Participants were included in the models if they had, at minimum, the initial assessment of personality. For a given trait, there were a total of six possible models (Intercept-Only, Fixed-Slope, Fixed+Random Slope, Quadratic Change, and two Slopes-As-Outcomes models). Because there were so many models estimated across the five traits and 16 studies, we included the most relevant model results in the main manuscript, and we put all other models in the supplemental material , available on OSF. All scripts and output files from this and prior drafts of this paper are available here ( http://osf.io/xqmfw/?view_only=126dbaafa46043038688e04bdcc23899 ). Analyses were completed using R ( R Core Team, 2014 ), including the packages lme4 ( Bates, Mächler, Bolker, & Walker, 2015 ), metafor ( Viechtbauer, 2019 ), and ggplot ( Wickham et al., 2019 ). Our analyses were not preregistered, as the planning stages of this project took place before pre-registration was common practice. In the results below, we report the plots for the linear and quadratic trajectories of each trait, as well as a selection of moderators. These plots include an average effect that is weighted by sample size. For interested readers, plots with the unweighted averages are available in the supplemental material ( https://osf.io/xqmfw/?view_only=126dbaafa46043038688e04bdcc23899 ).

Meta-analyses

We estimated a meta-analytic summary for linear, quadratic, and predictor models, to summarize the average effect across individual studies (e.g. linear slope estimate for neuroticism across samples; sex by extraversion slope estimate across samples). Each meta-analysis included an overall effect (weighted by sample size), with corresponding standard errors/confidence intervals, as well as estimates of heterogeneity ( I 2 , Q ) ( Borenstein, Higgins, Hedges, & Rothstein, 2017 ). We used the significance test of the Q statistic to guide our decision to report the study-level moderators of heterogeneity. For this project, three study-level moderators were considered: average baseline age (over or under 60), country that the study was based in, and personality scale used. We used random-effects models to meta-analyse each set of estimates.

Summaries for all individual study models, including tables, figures, and full meta-analytic summaries are included in the Supporting Information . For brevity, the main manuscript includes a table of the meta-analytic slope estimates for each model type (e.g. linear, quadratic, and predictors; Table 2 ) and key figures.

Meta analytic summary of slope estimates

The results reported below are organized by model. First, we describe the level 1 (intercept-only) models by reporting the between-study range of the ICC, which provides a proportion of between-person and within-person variability in repeated personality trait measurement, and related heterogeneity estimates for each trait. We then describe the trajectory models (linear and quadratic) by trait. When heterogeneity estimates for the growth trajectory models were substantial, predictor and subgroup analyses are warranted; we therefore added study-level moderators (age, country, and personality measurement) to the models to test if any of these predictors accounted for between-study variability. For each trait, we present a figure illustrating either the linear or non-linear trajectories, depending on which model was a better overall fit for a given trait. The trajectory figures not presented in the main manuscript can be found in the Supporting Information . Lastly, we discuss the slopes-as-outcomes models, and we describe the extent to which each predictor accounts for individual differences in change for each trait. Given the large number of analyses, we used a more conservative alpha ( α = .01) to evaluate statistical significance. For each of these models, there is a table, a figure, and a meta-analysis. All of these results can be found in the Supporting Information , organized by trait.

Intercept-only models

Neuroticism.

The ICC from the intercept-only model for neuroticism ranged across the 16 samples from 0.57 to 0.84 ( Table S2 ). The meta-analytic summary indicates that the average ICC across studies is B = 0.68 ( se = 0.02), with substantial heterogeneity ( I 2 = 99.09, Q = 2855.39, df = 15, p ≤ .001).

Extraversion

Of the 15 studies with extraversion data, the ICC ranged from 0.62 to 0.86 ( Table S18 ). The meta-analytic summary indicates that the average ICC across studies is B = 0.72 ( se = 0.02), with heterogeneity estimates of ( I 2 = 99.40, Q = 3782.33, df = 14, p ≤ .001).

Of the 13 samples with openness data, the ICC ranged from 0.59 to 0.88 ( Table S34 ). The meta-analytic summary indicates that the average ICC across studies is B = 0.70 ( se = 0.02), with heterogeneity estimates of ( I 2 = 99.54, Q = 4775.56, df = 12, p ≤ .001).

Conscientiousness

Of the 11 studies with conscientiousness data, the ICC ranged from 0.53 to 0.84 ( Table S50 ). The meta-analytic summary indicates that the average ICC across studies is B = 0.67 ( se = 0.03), with heterogeneity estimates of ( I 2 = 99.53, Q = 4013.80, df = 10, p < .001).

Agreeableness

Of the 11 studies with agreeableness data, the ICC ranged across these studies from 0.51 to 0.80 ( Table S66 ). The meta-analytic summary indicates that the average ICC across studies is B = 0.64 ( se = 0.02), with heterogeneity estimates of ( I 2 = 99.16, Q = 2121.47, df = 10, p < .001).

The ICCs indicate that across all studies and traits, half to over three-quarters of the total variation across persons and measurement occasions were between-person variation (individual differences in levels of trait). The remainder of variation is true trait change, plus variance due to measurement error. Variation in personality traits across individuals and occasions is not simply due to differences in level among persons but also includes variation over measurement occasions (across time) within persons, suggesting within-person change.

Average trajectories

Results from the linear growth models for neuroticism indicate that neuroticism changes over time. The likelihood ratio of the fixed ( Table S3 ) versus random ( Table S4 ) slope models indicate that, for most studies, there are individual differences in change. Across studies, there was a linear effect of age for all but five samples (ILSE.Y, LBC1936, Octo-Twin, SOEP, and LASA; Table S4 ). Most studies showed a decrease in neuroticism, with few showing an increase. The meta-analytic estimate for these models was not statistically significant ( p > .01) but suggests a pattern of decline over time ( Figures S2 and S3 ) ( B = – 0.05, 95% CI = [−0.09, – 0.01], p = .011). Because the meta-analysis also showed substantial heterogeneity ( I 2 = 98.67, Q = 1013.31, df = 15, p ≤ .001), moderators were added to account for this variability. Age and country did not account for the heterogeneity in neuroticism trajectories, but personality scale did, indicating that estimates of change in neuroticism were likely varying owing to measurement differences.

Ten studies had sufficient data to estimate quadratic growth models for neuroticism ( Table S5 ). Of these, three showed evidence for non-linear change. See Figure 1 for visualization of the quadratic trajectory. The thick black line indicates the overall average pattern, which suggests that neuroticism declines in early adulthood and increases again in older adulthood. See also Figures S4 and S5 for these figures in colour and with the unweighted average slopes. The meta-analysis suggests an overall quadratic effect ( B = – 0.10, 95% CI = [−0.15, – 0.04], p = .001), although there is also significant heterogeneity ( I 2 = 95.50, Q = 239.32, df =9, p ≤ .001). Neither country nor scale accounted for any of the heterogeneity, but age did. This result suggests that as average sample age increases, neuroticism slopes become less steep, indicating that the trajectory for neuroticism may be better described as a U-shaped trend.

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Quadratic trajectories of neuroticism. The thick black line indicates the average trajectory weighted by N . At the individual study level, several studies showed evidence of a U-shaped curve, and the meta-analytic average was significant ( p < .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

Results from the linear growth models for extraversion indicate that extraversion changes over time. The likelihood ratio of the fixed ( Table S19 ) versus random ( Table S20 ) slope models indicates that, for many studies, there are individual differences in change for all studies except with the exception of BASE, BASE-II, EAS, and Octo-Twin. Across studies, the linear effect of age was statistically significant ( p < .001) for over half of the studies, and these effects showed a consistent pattern of decline ( Table S20 ). This was confirmed in the meta-analytic summary, showing an overall decrease in extraversion ( B = – 0.09, 95% CI = [−0.13, – 0.04], p ≤ .001). See Figure 2 for a visualization of linear effects. The thick black line depicts the overall average trajectory, weighted by N , which shows an overall pattern of decline. The meta-analysis also showed significant heterogeneity ( I 2 = 99.07, Q = 431.15, df = 14, p < .001). Neither age, nor country, nor scale accounted for the variability in extraversion, suggesting that the detected heterogeneity is likely due to an untested moderator. The figure suggests that the older samples have somewhat steeper slopes than younger samples. See also Figures S30 and S31 for these figures in colour and with the unweighted average slopes.

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Linear trajectories of extraversion. The thick black line indicates average trajectory weighted by N . At the individual study level, many showed evidence of decline, and the meta-analytic average was significant ( p < .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

Nine of the studies had sufficient data to estimate quadratic growth models for extraversion. Three of the nine studies showed evidence for a non-linear trajectory of extraversion ( Table S21 ). The meta-analytic summary does not confirm this, showing a non-significant average effect ( B = 0.04, 95% CI = [−0.14,0.22], p = .645), which may be due to the smaller effects in the larger studies (e.g. SOEP). See Figures S32 and S33 . There was also high heterogeneity ( I 2 = 99.71, Q = 293.48, df =8, p ≤ .001) in the quadratic estimate. Neither country nor age accounted for any of the heterogeneity, but scale did. This result suggests that studies using the IPIP more frequently showed significant quadratic estimates (LBC, EAS). Overall, this indicates that the trajectory for extraversion over time may be better described as a linear trend.

Results from the linear growth models for openness indicate that openness changes over time. The likelihood ratio of the fixed ( Table S35 ) versus random ( Table S36 ) slope models indicate that, for most studies, there are individual differences in change, with the exception of BASE-II, EAS, ILSE, and SLS. There was a linear effect of age for most studies, and all trends showed a pattern of decline ( Table S36 ). This was supported in the meta-analytic summary, showing a decrease in openness ( B = – 0.09, 95% CI = [−0.13, – 0.05], p < .001). See Figure 3 for a visualization of linear effects. The thick black line depicts the overall average trajectory, weighted by N, which shows a pattern of decline. The meta-analysis also showed significant heterogeneity ( I 2 = 98.84, Q = 607.99, df = 12, p ≤ .001). The figure suggests that the older samples are less consistent in the pattern of decline than the younger samples. However, the tests of between-study moderators did not account for variation in these effects, suggesting the possibility that the effects estimated may be are due to some other untested moderator. See also Figures S58 and S59 for this figure in colour and with the unweighted average slopes.

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Linear trajectories of openness. The thick black line indicates average trajectory weighted by N . At the individual study level, most showed evidence of decline, although the meta-analytic average was significant ( p = .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

Seven of the studies had sufficient data to estimate quadratic growth models for openness ( Table S37 ). Of these, three showed evidence for non-linear change. The pattern depicted in the individual studies suggests that openness may be somewhat stable through middle adulthood and then decrease more sharply through older adulthood, although the older samples appear to have less consistency. The meta-analytic summary of the quadratic slopes was not significant ( B = – 0.07, 95% CI = [−0.14, – 0.01], p = .032). Based on the figure ( Figures S60 and S61 ), we tentatively conclude that the effects from the older samples are the estimates that deviate the most from the average trajectory.

The linear growth models for conscientiousness indicate that conscientiousness changes over time. The likelihood ratio of the fixed ( Table S51 ) versus random ( Table S52 ) slope models indicate that, for most studies, there are individual differences in change with the exception of BASE-II, EAS, and ILSE.Y. Results from the linear growth models for conscientiousness are more mixed than the other traits thus far. Several showed evidence of decline (WLSS, WLSG, HRS, EAS, and LBC1936), while others showed a weaker effect ( p > .01) but still in the negative direction (ILSE, BASE-II, and SLS). Only SOEP showed a significant increase in conscientiousness ( Table S52 ). See Figure 4 for a visualization, indicating an overall pattern of declining conscientiousness over time. The meta-analytic summary supports this interpretation, with an overall significant slope ( B = – 0.05, 95% CI = [−0.09, – 0.02], p = .004). This result was highly heterogeneous ( I 2 = 97.72, Q = 884.99, df = 10, p ≤ .001), and adding study age to the meta-analysis accounted for a significant amount of the variation in linear slope. See also Figures S86 and S87 for these figures in colour and with the unweighted average slopes.

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Linear trajectories of conscientiousness. The thick black line indicates average trajectory weighted by N . At the individual study level, many showed evidence of decline, although the meta-analytic average was significant ( p = .004). [Colour figure can be viewed at wileyonlinelibrary.com ]

Five of the studies had sufficient data to estimate quadratic growth models for conscientiousness. Of these, two showed significant curvature (SLS and SOEP). The pattern depicted in the individual studies suggests that conscientiousness increases through younger and middle age and then decreases in older adulthood ( Table S53 , Figures S88 and S89 ). The meta-analytic summary of the quadratic estimate was not significant (B = – 0.02, 95% CI = [−0.07,0.03], p = .459).

The linear growth models for agreeableness were the least consistent of the five traits across individual studies. The likelihood ratios of the fixed ( Table S67 ) versus random ( Table S68 ) slope models indicate that, for approximately half of the studies, there are individual differences in change. Seven of the 11 studies showed evidence for significant ( p < .01) change in agreeableness; however, two of these studies showed decreasing agreeableness (HRS and BASE-II), while the other five showed increasing agreeableness (ILSE.Y, SLS, SOEP, WLSG, and WLSS). Two more (EAS and ILSE) showed an increase, but with p > .01. The last two (LBC1936 and MIDUS) showed an average decrease, but with p > .01 ( Table S68 ). The linear pattern depicted in Figure 5 shows this mix of findings, and the meta-analytic summary was not significant ( B = 0.02, 95% CI = [−0.02,0.07], p = .296). From these results, we tentatively suggest that agreeableness may increase over time, although not all samples replicate this finding. See Figures S114 and S115 .

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Linear trajectories of agreeableness. The thick black line indicates the average trajectory weighted by N . At the individual study level, most showed evidence of decline, although some showed an increase. The meta-analytic average was not significant ( p = .296). [Colour figure can be viewed at wileyonlinelibrary.com ]

Five of the 11 studies had sufficient agreeableness data to estimate quadratic growth models for agreeableness ( Table S69 , Figures S116 and S117 ). Of these, two showed significant curvature (SLS and SOEP), but in opposite directions. The average pattern suggests that agreeableness may be relatively stable in younger adulthood and increase during older adulthood, although the individual studies do not show consistent patterns, and the meta-analytic summary was not significant ( B = 0.03, 95% CI = [−0.16,0.21], p = .777).

Predictors of change

Adding sex to the linear growth models for neuroticism yielded primarily null results with one exception (SOEP, Table S6 ). The overall pattern suggests that female participants may have slightly higher overall neuroticism levels and slightly steeper decreases, which was supported by the meta-analytic summary ( B = – 0.01, 95% CI = [−0.02,0.00], p = .002), and no observed heterogeneity ( I 2 = 0.00, Q = 18.35, df = 14, p = .191); see Figure 6 and Figures S6 and S7 .

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Linear trajectories of neuroticism, moderated by sex. The thick black line indicates the average trajectory weighted by N . Female participants show slightly steeper decreases in neuroticism. [Colour figure can be viewed at wileyonlinelibrary.com ]

Adding sex to the linear growth models for all other traits yielded predominantly null results with a few exceptions. The overall trajectory pattern is very similar for male and female participants, and this is confirmed by the meta-analytic summary for extraversion ( B = 0.00, 95% CI = [−0.02,0.01], p = .517; Table S22 , Figure S34 and S35 ), openness ( B = 0.00, 95% CI = [−0.02,0.02], p = .851; see Table S38 , Figure S62 and S63 ), conscientiousness ( B = – 0.02, 95% CI = [−0.04,0.01], p = .169; see Table S54 , Figures S90 and S91 ), and agreeableness ( B = 0.00, 95% CI = [−0.02,0.02], p = .890; see Table S70 , Figure S118 and S119 ).

Adding baseline age to the linear neuroticism models yielded significant ( p < .01) effects at the individual study level for all but one study ( Table S17 ), and this is confirmed by the meta-analytic summary ( B = 0.11, 95% CI = [0.04,0.18], p = .001), although there is high heterogeneity across samples ( I 2 = 93.84, Q = 173.55, df = 7, p ≤ .001). These effects indicate that individuals who were under 60 at study baseline experienced overall decline in neuroticism, while those 60 and over tended to have flatter neuroticism slopes; see Figure 7 . The meta-analytic moderator results show that sample age accounts for some of the between-study variability.

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Linear trajectories of neuroticism, moderated by baseline age. The thick black line indicates average trajectory weighted by N . At the individual study level, all showed evidence that being over 60 at baseline was associated with decreasing neuroticism, and the meta-analytic average was significant ( p < .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

Adding baseline age to the linear extraversion models yielded significant effects for all but one study as well ( Table S33 , Figure S56 and S57 ), and this is confirmed by the meta-analytic summary ( B = – 0.09, 95% CI = [−0.12, – 0.06], p ≤ .001), and moderate heterogeneity ( I 2 = 60.10, Q = 12.84, df = 5, p = .025). These results suggest that while the overall pattern for both younger and older adults is that of decline, the decline is somewhat steeper for older adults; see Figure 8 .

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Linear trajectories of extraversion, moderated by baseline age. The thick black line indicates average trajectory weighted by N . At the individual study level, all showed evidence that being over 60 at baseline was associated with decreasing extraversion, and the meta-analytic average was significant ( p < .01). [Colour figure can be viewed at wileyonlinelibrary.com ]

Adding baseline age to the linear openness models yielded significant effects for all but one study ( Table S49 , Figures S84 and S85 ), and this is confirmed by the meta-analytic summary ( B = – 0.12, 95% CI = [−0.15, – 0.08], p ≤ .001), and moderate heterogeneity ( I 2 = 67.10, Q = 11.68, df = 4, p = .020). These effects suggest that while there is an overall pattern of decline for both younger and older adults, this decline is steeper for older adults; see Figure 9 .

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Linear trajectories of openness, moderated by baseline age. The thick black line indicates average trajectory weighted by N . At the individual study level, all showed evidence that being over 60 at baseline was associated with decreasing openness, and the meta-analytic average was significant ( p < .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

Adding baseline age to the linear conscientiousness models yielded significant effects for all studies ( Table S65 , Figures S112 and S113 ), and this is confirmed by the meta-analytic summary ( B = – 0.18, 95% CI = [−0.21, – 0.16], p ≤ .001), and lack of observed heterogeneity ( I 2 = 0.00, Q = 2.02, df = 3, p = .568). The plot suggests that conscientiousness increases somewhat for individuals who were under 60 at baseline but declines for individuals who were older at baseline; see Figure 10 .

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Linear trajectories of conscientiousness, moderated by baseline age. The thick black line indicates average trajectory weighted by N . At the individual study level, all showed evidence that being over 60 at baseline was associated with decreasing conscientiousness, and the meta-analytic average was significant ( p < .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

Adding baseline age to the linear agreeableness models yielded effects for all but one study ( Table S81 , Figures S139 and S140 ), and this is confirmed by the meta-analytic summary ( B = – 0.10, 95% CI = [−0.14, – 0.05], p ≤ .001), and moderate heterogeneity ( I 2 = 68.02, Q = 8.38, df = 3, p = .039). These results indicate that agreeableness increased among individuals who were younger at baseline, and either stabilized or decreased among older individuals see Figure 11 .

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Linear trajectories of agreeableness, moderated by baseline age. The thick black line indicates average trajectory weighted by N . At the individual study level, all showed evidence that being over 60 at baseline was associated with decreasing agreeableness, and the meta-analytic average was significant ( p < .001). [Colour figure can be viewed at wileyonlinelibrary.com ]

The current study had two major goals, one substantive and one methodological. The substantive aim was to examine the extent of change in the Big Five personality traits and whether sex and age at baseline were prospectively associated with these changes. The second was to demonstrate the utility of the coordinated analysis approach for enhancing replication efforts in long-term longitudinal research, an area where replication is typically quite difficult. This approach allows for conceptual replications of effects using longitudinal existing data and can help accelerate the accumulation of knowledge garnered from expensive long-term longitudinal datasets. Using the infrastructure of the IALSA and coordinated analysis ( Graham et al., 2017 ; Hofer & Piccinin, 2009 ), we identified 16 longitudinal samples in which Big Five personality traits were measured three or more times, providing an opportunity to examine up to 16 replications of personality trait trajectories. Using MLMs for change ( Singer & Willett, 2003 ), we identified patterns of overall change and of individual variation in change for the Big Five personality traits across a wide expanse of adulthood. In sum, we found evidence for personality change in midlife and older adulthood in ways that are consistent with theory. We also detected individual differences in change, which is also consistent with theory. However, clear and unambiguous evidence for predictors of change was not found, although relatively few individual difference factors were examined.

Synthesis of linear and quadratic models

Our consideration of overall trajectories (sample level and meta-trajectories) showed personality change over time for most of the Big Five Traits. Of the five traits, the models for extraversion and conscientiousness suggested that a linear pattern of decline best describes the data. Both of these traits show a steady (to a lesser degree for conscientiousness) decrease over time. For conscientiousness, while the quadratic model term was not significant, evidence from the baseline age models suggests that the decreasing pattern of conscientiousness is most evident among individuals who were over 60 at baseline. This is also consistent with several theories, including self-regulation theory, neo-socioanalytic theory (especially the maturity principle), and the SOC model ( Baltes & Baltes, 1990 ; Denissen et al., 2013 ; Marsh et al., 2013 ; Roberts et al., 2006 ), such that individuals are stable or increasing in conscientiousness through early and middle adulthood but may be unknowingly ‘selecting’ out of trait-typical behaviours (e.g. reducing the number of hours spent at their workplace) in older adulthood as the social demands on them begin to wane.

Neuroticism showed evidence for U-shaped change, and we can infer that neuroticism decreases through most of adulthood but may begin to increase again in older age. This is confirmed by both the quadratic models and linear models including baseline age as a predictor and is consistent with literature suggesting that increasing neuroticism in older adulthood may be a reflection of an increasing anxiety regarding onset of terminal diseases and approaching mortality ( Baltes, 1987 ; Mueller, Wagner, & Gerstorf, 2017 ). Openness remains somewhat stable through middle adulthood before decreasing in older age. Agreeableness is the only trait that did not yield a significant linear or quadratic effect. This could be due to study-level differences (including possible study differences in true effect size; e.g. Borenstein et al., 2010 ), or due to a true overall null effect: perhaps agreeableness is, in fact, relatively stable on average over the course of adulthood, although our analyses do reveal individual differences in agreeableness trajectories over time.

Our findings are consistent with much of the prior literature, suggesting that most or all traits decline over time ( Berg & Johansson, 2013 ; Kandler et al., 2015 ; Lucas & Donnellan, 2011 ; Mõttus, Johnson, Starr, & Deary, 2012 ; Roberts & Mroczek, 2008 ), and we found some evidence for non-linear change as well (see both the curvilinear results and baseline age predictor models). Our findings are inconsistent with recent work suggesting positive age differences in agreeableness and conscientiousness, and negative differences in neuroticism ( Bleidorn et al., 2009 ; Soto, John, Gosling, & Potter, 2011 ). However, the Soto et al. (2011) study was cross-sectional and did not test within-person change. The Bleidorn et al. (2009) report used a single small sample and a different modelling approach and should be interpreted within the context of other work (such as the current study). Additionally, several of the datasets included in the current study have published estimates of personality change, for example, the GSOEP ( Lucas & Donnellan, 2011 ), NAS ( Mroczek & Spiro, 2003 ), BASE-II ( Mueller et al., 2016 ), LASA ( Steunenberg, Twisk, Beekman, Deeg, & Kerkhof, 2005 ), and ILSE ( Allemand et al., 2015 ). Differences could be attributed to updated data (e.g. for NAS), or different modelling approaches (e.g. MLM vs. SEM), as well as different covariates and moderators.

Individual differences in personality change

There was evidence for individual differences in change in most traits across most samples. For the vast majority, there was support for the lifespan development principle of individual differences in intra-individual change ( Baltes, 1987 ) as well as self-regulation theory ( Denissen et al., 2013 ), both of which predict individual variation in trajectories. Not everyone changes at the same rate, nor in the same direction ( Mroczek & Spiro, 2003 ). This idea forms the basis of our efforts to identify predictors of personality change, and also indicates that the concept of individual differences applies to rate and direction of change, in addition to level or amount of a trait. Further, even if the models suggest only a small amount of intra-individual variation in slopes, we can still test predictors of variation.

Synthesis of predictor models

We tested a number of predictors of change to assess whether sex or age at baseline could reliably account for the within-study individual differences in change. We observed minimal evidence that sex accounted for individual differences. For most traits, the meta-analytic summary was null, with the exception of neuroticism. For neuroticism, it appears that female participants have slightly steeper declines in neuroticism than male participants. For the baseline age models, there was consistent evidence for an effect both at the individual study-level and in the meta-analytic summaries for all traits. This suggests that personality change may occur differently among younger and older adults. The differing patterns in linear change among younger and older adults, as evidenced by the baseline age predictor models, are consistent with the theories discussed above, such as neo-socioanalytic theory, self-regulation, and the SOC model ( Baltes & Baltes, 1990 ; Denissen et al., 2013 ; Marsh et al., 2013 ; Roberts et al., 2006 ). According to these theories, there is a clear benefit to having higher levels of conscientiousness, openness, agreeableness, and extraversion in younger and middle adulthood. As social demands begin to wane in older adulthood, so might these traits. Our findings support these theories. Similarly, the findings for neuroticism indicate that older individuals increase in neuroticism more than younger adults. This is consistent with the idea that, as a person ages, becomes more likely to develop serious conditions, and becomes more aware of their mortality, neuroticism increases as part of the heightened anxiety surrounding these new realities.

Variation in trajectories over samples

We observed statistical heterogeneity, as assessed by I 2 and Cochran’s Q , across samples, and we attempted to account for this variability using four study-level factors: sample age (average, at baseline), country, and personality scale. These analyses yielded mixed results. Even with a cross-study sample size of over 60 000, we were relatively low powered at the study level (maximum N = 16) to reliably account for variation in trajectories, and this became more apparent as the studies without a given variable, or measurement of a certain personality trait, were not included in the meta-analysis. A distribution of effects (in both magnitude and direction) is to be expected in any replication project, and the current study is no exception. Calculation of the average effect for each model helps to reduce this remaining uncertainty, and using identical variable transformations and harmonized models strengthens our confidence in these average estimates. However, heterogeneity is still widespread in our results and is not fully accounted for by our study-level moderators. The three most likely reasons for between-study heterogeneity are untested moderators, random variation due to measurement error, and variation in true effect sizes across studies due to different underlying populations. The latter reason is the basis of random-effects meta-analysis ( Borenstein et al., 2010 ), which does not assume that there is one true effect size in the population, and that different studies (because of variation on so many characteristics such as country of origin, age at entry, etc.) have different true effect sizes. Further, several personality scales were used to assess traits across our studies, and the measurement differences in these scales could be partially driving variation in a way that was not detected by our tests.

The value of coordinated analysis

These findings demonstrate the value of coordinated analysis as a technique for synthesizing long-term longitudinal findings over a shorter time frame than otherwise would be the case if each study published its own single-study analysis (some have called this technique coordinated replication, such as Duggan et al., 2019 ). Technically, coordinated analysis is not a replication study in the strict sense but rather a method that provides an initial set of multi-study findings that can be thought of as a set of replicates or as a synthesized group of findings centred around a common research question. It establishes a moderate-sized group of replications, along with synthesized products such as meta-trajectories, both of which enhance the robustness of future work.

Coordinated analysis has the additional advantage of preserving the heterogeneity in trajectories, as well as permitting analyses that identify factors that explain differences across samples. While we found limited evidence for between-study factors explaining variation in effects, future research should examine this further. Pooling of data is an alternative technique and can be quite valuable ( Jokela et al., 2014 ) but presents unique challenges with respect to data harmonization and therefore tends to use smaller subsets of studies (or downplays the issue of harmonization). Perhaps a bigger concern of pooled analysis is that it assumes a common true effect size underlying all of the included studies. By coordinating models across multiple datasets, we preserve the heterogeneity of the studies. By aligning with the assumptions of the random-effects model ( Borenstein et al., 2010 ), coordinated analysis allows the possibility of not one but many true effects. This touches on the issue of generalizability and the relationship between replicability and generalizability. Similarities in results across studies are an indication not only of replicability but also of generalizability. By contrast, single-study analysis has its own challenges. Any one of the samples used here could have comprised a publication on its own, with some claiming given trait increases, others claiming a decrease, and still others claiming no change at all. Add to this the inclusion of predictors, and the single-study publication possibilities from the current study could have yielded wildly different stories. This has the potential to sow great confusion and to send future researchers down ultimately fruitless pathways. The method used in this project thus demonstrates the utility and importance of coordinated analysis in enhancing the synthesis of findings across studies and in evaluating their replicability and generalizability.

Limitations and future directions

The samples used in the current study were primarily WEIRD (White, Educated, Industrialized, Rich, and Democratic) ( Henrich, Heine, & Norenzayan, 2010 ). As such, we cannot be confident that our results would translate to other cultures. An additional limitation of the current study is that we relied solely on self-reported measures of personality. This mono-method approach is limiting ( Costa, McCrae, & Lockenhoff, 2019 ), and future studies could execute a similar coordinated analysis of personality change using informant reports. It is also possible that the number of measurement occasions (i.e. test effects) could be partially responsible for differences in the trajectories. We added a measurement occasions variable to the meta-analysis as a study-level moderator, and we found that measurement occasions did not account for any of the observed heterogeneity. These additional analyses can be found in the Supporting Information . Also, the datasets used were somewhat asymmetrical; some were panel studies including individuals from across the entire adult lifespan, and others were focused solely on older adulthood. We used the datasets available as part of the IALSA network that had the requisite data for our analyses, and while IALSA is fairly comprehensive in its coverage of age-focused studies, it does not contain all available panel studies. The implications of this are that our selection of studies was unintentionally biased. We recommend that researchers with access to other panel data with longitudinal personality data to compare the trajectories reported in the current paper to those in their own data, to further enrich our understanding of personality change across the full adult lifespan.

Additionally, future research using ‘time-to-death’ as a temporal metric could yield theoretically interesting results ( Wagner et al., 2016 ). In most trajectory models, chronological time in the form of time-in-study or age is the preferred time metric. However, in studies where most participants are midlife or older a reverse time metric, time from death, is an alternative that has led to interesting results in the area of cognitive aging ( Gerstorf, Ram, Lindenberger, & Smith, 2013 ). Such alternative time metrics could be examined with respect to personality trajectories. Measurement differences are also a limitation. Some have found that different scales (e.g. BFI vs. NEO) measure certain traits (e.g. agreeableness) in categorically different ways and capture very different individual characteristics ( Miller, Gaughan, Maples, & Price, 2011 ). This being said, we acknowledge that the comparability of a given trait is limited by the scale used to measure it. However, in the context of a coordinated analysis, these measurement differences (as well as all study-level differences) are not strictly a liability: they can be considered an asset as well. Consistency in results in spite of study-level differences adds to the generalizability of the results ( Hofer & Piccinin, 2009 ). To limit our models to only a single measurement approach would constrain the external validity of our findings.

Across 16 diverse longitudinal samples of aging adults, we observed change in the Big Five personality traits. Linear models suggest that conscientiousness, extraversion, and openness decline on average over time, while agreeableness is relatively stable. Quadratic models pointed to potential late-life increases in neuroticism. Nearly all samples showed individual differences in change for all five traits. Age of samples, country, and scale account for some of the variations in sample trajectories.

This coordinated analysis makes three primary contributions to the field. The first contribution is empirical. We brought to bear a great deal of data to address continuing controversies regarding personality development over the lifespan. The second contribution is methodological and centres on replication and current concerns regarding reproducibility and robustness. We used a multi-study framework that by its very nature was designed to evaluate and promote replicability. This is particularly important at this point in the history of our science, given the need for greater credibility in many areas of psychology and other fields. Third, our analyses support current theory in lifespan personality development, as our findings are somewhat consistent with the Baltes theory of selection, optimization, and compensation (SOC), self-regulation theory ( Denissen et al., 2013 ), and neo-socioanalytic theory ( Roberts et al., 2008 ). Specifically, traits differed in patterns of change with age. However, people change differently on different traits, personality is not stable for everyone across the lifespan (but is for some people), and accounting for or explaining these changes is difficult. Overall, in addition to improving our understanding of typical personality development across the adult lifespan, as well as highlighting the prevalence of individual differences in personality trajectories, this research highlights the strengths and feasibility of a coordinated analysis approach.

Supplementary Material

Supplemental material, acknowledgement.

We would like to thank Martin Sliwinski for his contributions to this project as PI of the Einstein Aging Study.

1 We were not able to effectively account for anticipation effects or the timing of events in a coordinated manner across so many studies in a way that would be comparable and informative. As such, models including health events, marital status, and retirement status as predictors of personality change are not reported in the current manuscript, but interested readers may view the results from these models in the Supporting Information . See Tables S7 – S16 , S23 – 32 , S39 – S38 , S55 – S64 , and S71 – S80 and Figures S10 – S27 , S34 – 55 , S62 – S83 , S90 – S110 , and S118 – S138 .

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of the article.

Data S1. Supporting Information

  • Allemand M, Job V, & Mroczek DK (2019). Self-control development in adolescence predicts love and work in adulthood . Journal of Personality and Social Psychology , 117 , 621–634. 10.1037/pspp0000229. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Allemand M, Schaffhuser K, & Martin M (2015). Long-term correlated change between personality traits and perceived social support in middle adulthood . Personality and Social Psychology Bulletin , 41 , 420–432. 10.1177/0146167215569492. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Allemand M, Zimprich D, & Martin M (2008). Long-term correlated change in personality traits in old age . Psychology and Aging , 23 , 545–557. 10.1037/a0013239. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Baltes P, Reese H, & Nesselroade J (1977). Life-span developmental psychology: Introduction to research methods . Monterey, CA: Brooks. Cole Publishing Co. [ Google Scholar ]
  • Baltes PB (1987). Theoretical propositions of life-span developmental psychology: On the dynamics between growth and decline . Developmental Psychology , 23 , 611–626. [ Google Scholar ]
  • Baltes PB (1997). On the incomplete architecture of human ontogeny: Selection, optimization, and compensation as foundation of developmental theory . American Psychologist , 52 , 366–380. 10.1037//0003-066x.52.4.366. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Baltes PB, & Baltes MM (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation . Successful Aging: Perspectives from the Behavioral Sciences , 1 , 1–34. [ Google Scholar ]
  • Baltes PB, Lindenberger U, & Staudinger U (2006). Life span theory in developmental psychology In Damon W, & Lerner R (Eds.), Handbook of child psychology theoretical models of human development (− 569 , 595). New Jersey. [ Google Scholar ]
  • Bates D, Mächler M, Bolker B, & Walker S (2015). Fitting linear mixed-effects models using lme4 . Journal of Statistical Software , 67 , 1–48. 10.18637/jss.v067.i01. [ CrossRef ] [ Google Scholar ]
  • Berg AI, & Johansson B (2013). Personality change in the oldest-old: Is it a matter of compromised health and functioning? Journal of Personality , 82 , 25–31. 10.1111/jopy.12030. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bertram L, Bockenhoff A, Demuth I, Duzel S, Eckardt R, Li S-C, ... Wagner GG (2013). Cohort profile: The Berlin Aging Study II (BASE-II) . International Journal of Epidemiology , 43 , 703–712. 10.1093/ije/dyt018. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bleidorn W, Kandler C, Riemann R, Angleitner A, & Spinath FM (2009). Patterns and sources of adult personality development: Growth curve analyses of the NEO PI-R scales in a longitudinal twin study . Journal of Personality and Social Psychology , 97 , 142–155. 10.1037/a0015434. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bleidorn W, Klimstra TA, Denissen JJA, Rentfrow PJ, Potter J, & Gosling SD (2013). Personality maturation around the world: A cross-cultural examination of social-investment theory . Psychological Science , 24 , 2530–2540. 10.1177/0956797613498396. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bogg T, Voss MW, Wood D, & Roberts BW (2008). A hierarchical investigation of personality and behavior: Examining neo-socioanalytic models of health-related outcomes . Journal of Research in Personality , 42 , 183–207. https://doi.org/10.1016Zj.jrp.2007.05.003 . [ Google Scholar ]
  • Borenstein M, Hedges LV, Higgins JP, & Rothstein HR (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis . Research Synthesis Methods , 1 , 97–111. 10.1002/jrsm.12. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Borenstein M, Higgins JPT, Hedges LV, & Rothstein HR (2017). Basics of meta-analysis: I2 is not an absolute measure of heterogeneity . Research Synthesis Methods , 8 , 5–18. 10.1002/jrsm.1230. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bosse R, Ekerdt D, & Silbert J (1984). The Veteran Administration Normative Aging Study In Mednick SA, Harway M, & Finello KM (Eds.), Handbook of longitudinal research vol. teen-age and adult cohorts (pp. 273–289). New York. [ Google Scholar ]
  • Brim OG, Ryff CD, & Kessler RC (2004). How healthy are we? A national study of well-being at midlife . Chicago: University of Chicago Press. [ Google Scholar ]
  • Carstensen LL, Isaacowitz DM, & Charles ST (1999). Taking time seriously: A theory of socioemotional selectivity . American Psychologist , 54 , 165–181. 10.1037//0003-066x.54.3.165. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Caspi A, Roberts BW, & Shiner RL (2005). Personality development: Stability and change . Annual Review of Psychology , 56 , 453–484. 10.1146/annurev.psych.55.090902.141913. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Condon DM, Graham EK, & Mroczek DK (2017). On replication research In The Wiley-Blackwell encyclopedia of personality and individual differences: Vol. II. Research methods and assessment techniques . Hoboken, NJ: John Wiley & Sons. [ Google Scholar ]
  • Costa PT Jr, & McCrae RR (1980). Still stable after all these years: Personality as a key to some issues in adulthood and old age . Life-Span Development and Behavior . [ Google Scholar ]
  • Costa PT Jr., & McCrae RR (1985a). The NEO Personality Inventory manual . Odessa, FL: Psychological Assessment Resources. [ Google Scholar ]
  • Costa PT, & McCrae RR (1985b). The neo personality inventtory . FL: Psychological Assessment Resources Odessa. [ Google Scholar ]
  • Costa PT Jr., & McCrae RR (1986). Personality stability and its implications for clinical psychology . Clinical Psychology Review , 6 , 407–423. [ Google Scholar ]
  • Costa PT, & McCrae RR (1988). Personality in adulthood: A six-year longitudinal study of self-reports and spouse ratings on the neo personality inventory . Journal of Personality and Social Psychology , 54 , 853–863. [ PubMed ] [ Google Scholar ]
  • Costa PT Jr., & McCrae RR (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory . Psychological Assessment , 4 , 5–13. [ Google Scholar ]
  • Costa PT Jr., McCrae RR, & Lockenhoff CE (2019). Personality across the life span . Annual Review of Psychology , 70 , 423–448. 10.1146/annurev-psych-010418-103244. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Curran PJ, Hussong AM, Cai L, Huang W, Chassin L, Sher KJ, & Zucker RA (2008). Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis . Developmental Psychology , 44 , 365–380. 10.1037/0012-1649.44.2365. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Deary IJ, Gow AJ, Pattie A, & Starr JM (2012). Cohort profile: The Lothian Birth Cohorts of 1921 and 1936 . International Journal of Epidemiology , 41 , 1576–1584. 10.1093/ije/dyr197. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Denissen JJ, van Aken MA, Penke L, & Wood D (2013). Self-regulation underlies temperament and personality: An integrative developmental framework . Child Development Perspectives , 7 , 255–260. 10.1111/cdep.12050. [ CrossRef ] [ Google Scholar ]
  • Duggan EC, Piccinin AM, Clouston S, Koval AV, Robitaille A, Zammit AR, … Finkel D (2019). A multi-study coordinated meta-analysis of pulmonary function and cognition in aging . The Journals of Gerontology: Series A , 74 , 1793–1804. 10.1093/gerona/glz057. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Eysenck HJ (1975). Manual of the Eysenck Personality Questionnaire (junior and adult) . London: Hodder; Stoughton. [ Google Scholar ]
  • Eysenck HJ, & Eysenck SBG (1968). Manual for the Eysenck Personality Inventory . San Diego, CA: Education; Industrial Testing Service. [ Google Scholar ]
  • Floderus B (1974). Psycho-social factors in relation to coronary heart disease and associated risk factors . Nordisk Hygienisk Tidskrift . [ Google Scholar ]
  • Gerstorf D, Bertram L, Lindenberger U, Pawelec G, Demuth I, Steinhagen-Thiessen E, & Wagner GG (2016). The Berlin Aging Study II—An overview . Gerontology , 62 , 311–315. 10.1159/000441495. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gerstorf D, Ram N, Lindenberger U, & Smith J (2013). Age and time-to-death trajectories of change in indicators of cognitive, sensory, physical, health, social, and self-related functions . Developmental Psychology , 49 , 1805–1821. 10.1037/a0031340. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Goebel J, Grabka MM, Liebig S, Kroh M, Richter D, Schroder C, & Schupp J (2018). The German Socio-Economic Panel (SOEP) . Jahrbücher Fur Nationalökonomie Und Statistik , 239 , 345–360. 10.1515/jbnst-2018-0022. [ CrossRef ] [ Google Scholar ]
  • Goldberg LR (1992). The development of markers for the Big-Five factor structure . Psychological Assessment , 4 , 26–42. [ Google Scholar ]
  • Graham EK, & Lachman ME (2012). Personality stability is associated with better cognitive performance in adulthood: Are the stable more able? Journal of Gerontology: Psychological Sciences , 67 , 545–554. 10.1093/geronb/gbr149. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Graham EK, Rutsohn JP, Turiano NA, Bendayan R, Batterham PJ, Gerstorf D, … Mroczek DK (2017). Personality predicts mortality risk: An integrative data analysis of 15 international longitudinal studies . Journal of Research in Personality , 70 , 174–186. 10.1016/j.jrp.2017.07.005. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Headey B, Muffels R, & Wagner G (2011). Choices which change life satisfaction: Replicating results for Australia, Britain and Germany . Social Indicators Research , 102 , 1–31. 10.1007/sl1205-012-0079-8. [ CrossRef ] [ Google Scholar ]
  • Helson R, Jones C, & Kwan VSY (2002). Personality change over 40 years of adulthood: Hierarchical linear modeling analyses of two longitudinal samples . Journal of Personality and Social Psychology , 83 , 752–766. 10.1037//0022-3514.83.3.752. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Henrich J, Heine SJ, & Norenzayan A (2010). The weirdest people in the world? Behavioral and Brain Sciences , 33 , 61–83. 10.1017/S0140525X0999152X. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Herd P, Carr D, & Roan C (2014). Cohort profile: Wisconsin Longitudinal Study (WLS) . International Journal of Epidemiology , 43 , 34–41. 10.1093/ije/dys194. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hofer SM, & Piccinin AM (2009). Integrative data analysis through coordination of measurement and analysis protocol across independent longitudinal studies . Psychological Methods , 14 , 150–164. 10.1037/a0015566. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Huisman M, Poppelaars J, van der Horst M, Beekman AT, Brug J, van Tilburg TG, & Deeg DJ (2011). Cohort profile: The longitudinal aging study Amsterdam . International Journal of Epidemiology , 40 , 868–876. 10.1093/ije/dyq219. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Human LJ, Biesanz JC, Miller GE, Chen E, Lachman ME, & Seeman TE (2013). Is change bad? Personality change is associated with poorer psychological health and greater metabolic syndrome in midlife . Journal of Personality , 81 , 249–260. 10.1111/jopy.12002. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • John OP, Donahue EM, & Kentle RL (1991). The Big Five Inventory—Versions 4a and 54 . Berkeley, CA: University of California, Berkeley, Institute of Personality. [ Google Scholar ]
  • John OP, & Srivastava S (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives . In Handbook of personality: Theory and research (pp. 102–138). [ Google Scholar ]
  • Jokela M, Elovainio M, Nyberg ST, Tabak AG, Hintsa T, Batty GD, & Kivimaki M (2014). Personality and risk of diabetes in adults: Pooled analysis of 5 cohort studies . Health Psychology , 33 , 1618–1621. 10.1037/hea0000003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Juster FT, & Suzman R (1995). An overview of the Health and Retirement Study . The Journal of Human Resources , 30 , S7–S56. [ Google Scholar ]
  • Kandler C, Kornadt AE, Hagemeyer B, & Neyer FJ (2015). Patterns and sources of personality development in old age . Journal of Personality and Social Psychology , 109 , 175–191. 10.1037/pspp0000028. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Katz MJ, Lipton RB, Hall CB, Zimmerman ME, Sanders AE, Verghese J, … Derby CA (2012). Age-specific and sex-specific prevalence and incidence of mild cognitive impairment, dementia, and Alzheimer dementia in blacks and whites: A report from the Einstein Aging Study . Alzheimer Disease & Associated Disorders , 26 , 335–343. 10.1097/WAD.0b013e31823dbcfc. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lachman ME, & Weaver SL (1997). The Midlife Development Inventory (MIDI) Personality Scales: Scale construction and scoring (pp. 1–9). Waltham, MA: Brandeis University. [ Google Scholar ]
  • Lang FR, John D, Lüdtke O, Schupp J, & Wagner GG (2011). Short assessment of the Big Five: Robust across survey methods except telephone interviewing . Behavior Research Methods , 43 , 548–567. 10.3758/s13428-011-0066-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lucas RE, & Donnellan MB (2011). Personality development across the life span: Longitudinal analyses with a national sample from Germany . Journal of Personality and Social Psychology , 101 , 847–861. 10.1037/a0024298. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Luteijn F, Starren JCMG, & van Dijk H (2000). Handleiding Nederlandse Persoonlijkheids Vragenlijst , NPV (herziene uitgave). [ Google Scholar ]
  • Marsh HW, Nagengast B, & Morin AJS (2013). Measurement invariance of big-five factors over the life span: ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects . Developmental Psychology , 49 , 1194–1218. 10.1037/a0026913. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McClearn GE, Johansson B, Berg S, Pedersen NL, Ahern E , Petrill SA, & Plomin R (1997). Substantial genetic influence on cognitive abilities in twins 80 or more years old . Science , 276 , 1560–1563. 10.1126/science.276.5318.1560. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McCrae RR, & Costa PT Jr. (1994). The stability of personality: Observations and evaluations . Current Directions in Psychological Science , 3 , 173–175. [ Google Scholar ]
  • Miller JD, Gaughan ET, Maples J, & Price J (2011). A comparison of agreeableness scores from the Big Five Inventory and the NEO PI-R: Consequences for the study of narcissism and psychopathy . Assessment , 18 , 335–339. 10.1177/1073191111411671. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mischel W (1969). Continuity and change in personality . American Psychologist , 24 , 1012–1018. 10.1037/h0028886. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mischel W (1977). On the future of personality measurement . American Psychologist , 32 , 246–254. 10.1037/0003-066X.32.4.246. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mõttus R, Johnson W, & Deary IJ (2012). Personality traits in old age: Measurement and rank-order stability and some mean-level change . Psychology and Aging , 27 , 243–249. 10.1037/a0023690. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mõttus R, Johnson W, Starr JM, & Deary IJ (2012). Correlates of personality trait levels and their changes in very old age: The Lothian Birth Cohort 1921 . Journal of Research in Personality , 46 , 271–278. 10.1016/jjrp.2012.02.004. [ CrossRef ] [ Google Scholar ]
  • Mroczek D, Weston SJ, & Willroth EC (2019). A lifespan perspective on the interconnections between personality, health, and optimal aging , 1–22. Doi: 10.31234/osf.io/sc74d [ CrossRef ] [ Google Scholar ]
  • Mroczek DK, Almeida DM, Spiro A, & Pafford C (2006). Modeling intraindividual stability and change in personality In Mroczek DK, & Little TD (Eds.), Handbook of personality development (pp. 163–180). Mahwah: Lawrence Erlbaum Associates. [ Google Scholar ]
  • Mroczek DK, Graham EK, Turiano NA, & Oro-Lambo MO (2019). Personality development in adulthood and later life In Handbook of personality: Theory and research (4th ed.). Guilford. [ Google Scholar ]
  • Mroczek DK, & Spiro A (2003). Modeling intraindividual change in personality traits: Findings from the Normative Aging Study . The Journals of Gerontology Series B: Psychological Sciences and Social Sciences , 58 , P153–P165. 10.1093/geronb/58.3.P153. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mroczek DK, & Spiro A (2007). Personality change influences mortality in older men . Psychological Science , 18 , 1–7. 10.1111/j.1467-9280.2007.01907.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mueller S, Ram N, Conroy DE, Pincus AL, Gerstorf D, & Wagner J (2019). Happy like a fish in water? The role of personality-situation fit for momentary happiness in social interactions across the adult lifespan . European Journal of Personality , 33 , 298–316. 10.1002/per.2198. [ CrossRef ] [ Google Scholar ]
  • Mueller S, Wagner J, Drewelies J, Duezel S, Eibich P, Specht J, … Gerstorf D (2016). Personality development in old age relates to physical health and cognitive performance: Evidence from the Berlin Aging Study II . Journal of Research in Personality , 65 , 94–108. 10.1016/j.jrp.2016.08.007. [ CrossRef ] [ Google Scholar ]
  • Mueller S, Wagner J, & Gerstorf D (2017). On the role of personality in late life In Personality development across the lifespan (pp. 69–84). Elsevier; doi: 10.1016/B978-0-12-804674-6.00006-5. [ CrossRef ] [ Google Scholar ]
  • Mühlig-Versen A, Bowen CE, & Staudinger UM (2012). Personality plasticity in later adulthood: Contextual and personal resources are needed to increase openness to new experiences . Psychology and Aging , 27 , 855–866. 10.1037/a0029357. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nelson LD, Simmons J, & Simonsohn U (2018). Psychology’s renaissance . Annual Review of Psychology , 69 , 511–534. 10.1146/annurev-psych-122216-011836. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nesselroade JR, & Baltes PB (1979). Longitudinal research in the study of behavior and development . Academic Press. [ Google Scholar ]
  • Pedersen NL, McClearn GE, Plomin R, Nesselroade JR, Berg S, & DeFaire U (1991). The Swedish Adoption Twin Study of Aging: An update . Acta Geneticae Medicae et Gemellologiae: Twin Research , 40 , 7–20. 10.1017/S0001566000006681. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Pedersen NL, & Reynolds CA (1998). Stability and change in adult personality: Genetic and environmental components . European Journal of Personality , 12 , 365–386. 10.1002/(SICI)1099-0984(1998090)12:5<365::AID-PER335>3.0.CO;2-N. [ CrossRef ] [ Google Scholar ]
  • R Core Team (2014). R: A language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Com-puting; Retrieved from http://www.R-project.org/ . [ Google Scholar ]
  • Raudenbush SW, & Bryk AS (2002). Hierarchical linear models: Applications and data analysis methods 1 . SAGE. [ Google Scholar ]
  • Riley RD, Lambert PC, & Abo-Zaid G (2010). Meta-analysis of individual participant data: Rationale, conduct, and reporting . British Medical Journal , 340 , 521–525. 10.1136/bmj.c221. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Roberts BW, & Mroczek D (2008). Personality trait change in adulthood . Current Directions in Psychological Science , 17 , 31–35. 10.1111/j.1467-8721.2008.00543.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Roberts BW, Walton KE, & Viechtbauer W (2006). Patterns of mean-level change in personality traits across the life course: A meta-analysis of longitudinal studies . Psychological Bulletin , 132 , 1–25. 10.1037/0033-2909.132.L1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Roberts BW, Wood D, & Caspi A (2008). The development of personality traits in adulthood In John O, Robins R, & Pervin L (Eds.), Handbook of personality theory and research (pp. 375–398). [ Google Scholar ]
  • Roberts BW, Wood D, & Smith JL (2005). Evaluating five factor theory and social investment perspectives on personality trait development . Journal of Research in Personality , 39 , 166–184. 10.1016/jjrp.2004.08.002. [ CrossRef ] [ Google Scholar ]
  • Sattler C, Wahl H-W, Schroder J, Kruse A, Schonknecht P, Kunzmann U, & Zenthofer A (2015). Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) In Pachana N (Ed.), Encyclopedia of geropsychology (pp. 1–10). [ Google Scholar ]
  • Schaie KW, Willis SL, & Caskie GIL (2004). The Seattle Longitudinal Study: Relationship between personality and cognition . Aging, Neuropsychology, and Cognition , 11 , 304–324. 10.1080/13825580490511134. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sewell WH, Hauser RM, Springer KW, & Hauser TS (2003). As we age: A review of the Wisconsin Longitudinal Study, 1957-2001 . Research in Social Stratification and Mobility , 20 , 3–111. 10.1016/S0276-5624(03)20001-9. [ CrossRef ] [ Google Scholar ]
  • Singer JD, & Willett JB (2003). Applied longitudinal data analysis . Oxford University Press. [ Google Scholar ]
  • Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, & Weir DR (2014). Cohort profile: The Health and Retirement Study (HRS) . International Journal of Epidemiology , 43 , 576–585. 10.1093/ije/dyu067. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Soto CJ, John OP, Gosling SD, & Potter J (2011). Age differences in personality traits from 10 to 65: Big five domains and facets in a large cross-sectional sample . Journal of Personality and Social Psychology , 100 , 330–348. 10.1037/a0021717. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Steiger AE, Allemand M, Robins RW, & Fend HA (2014). Low and decreasing self-esteem during adolescence predict adult depression two decades later . Journal of Personality and Social Psychology , 106 , 325–338. 10.1037/a0035133. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Steunenberg B, Twisk JW, Beekman AT, Deeg DJ, & Kerkhof AJ (2005). Stability and change of neuroticism in aging . The Journals of Gerontology Series B: Psychological Sciences and Social Sciences , 60 , P27–P33. 10.1093/geronb/60.1.P27. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Terracciano A, McCrae RR, Brant LJ, & Costa PT Jr. (2005). Hierarchical linear modeling analyses of the NEO-PI-R scales in the Baltimore Longitudinal Study of Aging . Psychology and Aging , 20 , 493–506. 10.1037/0882-7974.20.3.493. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Turiano NA, Pitzer L, Armour C, Karlamangla A, Ryff CD, & Mroczek DK (2011). Personality trait level and change as predictors of health outcomes: Findings from a national study of Americans (MIDUS) . The Journals of Gerontology Series B: Psychological Sciences and Social Sciences , 67B , 4–12. 10.1093/geronb/gbr072. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vaidya JG, Gray EK, Haig JR, Mroczek DK, & Watson D (2008). Differential stability and individual growth trajectories of big five and affective traits during young adulthood . Journal of Personality , 76 , 267–304. 10.1111/j.1467-6494.2007.00486.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vazire S (2018). Implications of the credibility revolution for productivity, creativity, and progress . Perspectives on Psychological Science , 13 , 411–417. 10.1177/1745691617751884. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vecchione M, Alessandri G, Barbaranelli C, & Caprara G (2012). Gender differences in the big five personality development: A longitudinal investigation from late adolescence to emerging adulthood . Personality and Individual Differences , 53 , 740–746. 10.1016/j.paid.2012.05.033. [ CrossRef ] [ Google Scholar ]
  • Viechtbauer W (2019). Metafor: Meta-analysis package for r . Retrieved from https://CRAN.R-project.org/package=metafor [ Google Scholar ]
  • Wagner GG, Joachim R, & Schupp J (2007). The German Socio-Economic Panel Study (SOEP)—Scope, evolution and enhancements . Schmollders Jahrbuch , 1 , 139–169. 10.2139/ssrn.1028709. [ CrossRef ] [ Google Scholar ]
  • Wagner J, Ram N, Smith J, & Gerstorf D (2016). Personality trait development at the end of life: Antecedents and correlates of mean-level trajectories . Journal of Personality and Social Psychology , 111 , 411–429. 10.1037/pspp0000071. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Weston SJ, Graham EK, & Piccinin A (2019). Coordinated data analysis: A new method for the study of personality and health In Hill P, & Allemand M (Eds.), Personality and healthy aging in adulthood . Springer Nature; doi: 10.31234/osf.io/k9up8. [ CrossRef ] [ Google Scholar ]
  • Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, & Woo K (2019). Ggplot2: Create elegant data visualisations using the grammar of graphics . Retrieved from https://CRAN.R-project.org/package=ggplot2 [ Google Scholar ]

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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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  • Computer science
  • Human behaviour

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.

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

Prediction accuracy

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

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

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

Trait intercorrelations

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

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

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 .

Kramer, R. S. S., King, J. E. & Ward, R. Identifying personality from the static, nonexpressive face in humans and chimpanzees: Evidence of a shared system for signaling personality. Evol. Hum. Behav . https://doi.org/10.1016/j.evolhumbehav.2010.10.005 (2011).

Walker, M. & Vetter, T. Changing the personality of a face: Perceived big two and big five personality factors modeled in real photographs. J. Pers. Soc. Psychol. 110 , 609–624 (2016).

Article   Google Scholar  

Naumann, L. P., Vazire, S., Rentfrow, P. J. & Gosling, S. D. Personality Judgments Based on Physical Appearance. Personal. Soc. Psychol. Bull. 35 , 1661–1671 (2009).

Borkenau, P., Brecke, S., Möttig, C. & Paelecke, M. Extraversion is accurately perceived after a 50-ms exposure to a face. J. Res. Pers. 43 , 703–706 (2009).

Shevlin, M., Walker, S., Davies, M. N. O., Banyard, P. & Lewis, C. A. Can you judge a book by its cover? Evidence of self-stranger agreement on personality at zero acquaintance. Pers. Individ. Dif . https://doi.org/10.1016/S0191-8869(02)00356-2 (2003).

Penton-Voak, I. S., Pound, N., Little, A. C. & Perrett, D. I. Personality Judgments from Natural and Composite Facial Images: More Evidence For A “Kernel Of Truth” In Social Perception. Soc. Cogn. 24 , 607–640 (2006).

Little, A. C. & Perrett, D. I. Using composite images to assess accuracy in personality attribution to faces. Br. J. Psychol. 98 , 111–126 (2007).

Kramer, R. S. S. & Ward, R. Internal Facial Features are Signals of Personality and Health. Q. J. Exp. Psychol. 63 , 2273–2287 (2010).

Pound, N., Penton-Voak, I. S. & Brown, W. M. Facial symmetry is positively associated with self-reported extraversion. Pers. Individ. Dif. 43 , 1572–1582 (2007).

Lewis, G. J., Lefevre, C. E. & Bates, T. Facial width-to-height ratio predicts achievement drive in US presidents. Pers. Individ. Dif. 52 , 855–857 (2012).

Haselhuhn, M. P. & Wong, E. M. Bad to the bone: facial structure predicts unethical behaviour. Proc. R. Soc. B Biol. Sci. 279 , 571 LP–576 (2012).

Valentine, K. A., Li, N. P., Penke, L. & Perrett, D. I. Judging a Man by the Width of His Face: The Role of Facial Ratios and Dominance in Mate Choice at Speed-Dating Events. Psychol. Sci . 25 , (2014).

Carre, J. M. & McCormick, C. M. In your face: facial metrics predict aggressive behaviour in the laboratory and in varsity and professional hockey players. Proc. R. Soc. B Biol. Sci. 275 , 2651–2656 (2008).

Carré, J. M., McCormick, C. M. & Mondloch, C. J. Facial structure is a reliable cue of aggressive behavior: Research report. Psychol. Sci . https://doi.org/10.1111/j.1467-9280.2009.02423.x (2009).

Haselhuhn, M. P., Ormiston, M. E. & Wong, E. M. Men’s Facial Width-to-Height Ratio Predicts Aggression: A Meta-Analysis. PLoS One 10 , e0122637 (2015).

Lefevre, C. E., Etchells, P. J., Howell, E. C., Clark, A. P. & Penton-Voak, I. S. Facial width-to-height ratio predicts self-reported dominance and aggression in males and females, but a measure of masculinity does not. Biol. Lett . 10 , (2014).

Welker, K. M., Goetz, S. M. M. & Carré, J. M. Perceived and experimentally manipulated status moderates the relationship between facial structure and risk-taking. Evol. Hum. Behav . https://doi.org/10.1016/j.evolhumbehav.2015.03.006 (2015).

Geniole, S. N. & McCormick, C. M. Facing our ancestors: judgements of aggression are consistent and related to the facial width-to-height ratio in men irrespective of beards. Evol. Hum. Behav. 36 , 279–285 (2015).

Valentine, M. et al . Computer-Aided Recognition of Facial Attributes for Fetal Alcohol Spectrum Disorders. Pediatrics 140 , (2017).

Ferry, Q. et al . Diagnostically relevant facial gestalt information from ordinary photos. Elife 1–22 https://doi.org/10.7554/eLife.02020.001 (2014).

Claes, P. et al . Modeling 3D Facial Shape from DNA. PLoS Genet. 10 , e1004224 (2014).

Carpenter, J. P., Garcia, J. R. & Lum, J. K. Dopamine receptor genes predict risk preferences, time preferences, and related economic choices. J. Risk Uncertain. 42 , 233–261 (2011).

Dreber, A. et al . The 7R polymorphism in the dopamine receptor D4 gene (<em>DRD4</em>) is associated with financial risk taking in men. Evol. Hum. Behav. 30 , 85–92 (2009).

Bouchard, T. J. et al . Sources of human psychological differences: the Minnesota Study of Twins Reared Apart. Science (80-.). 250 , 223 LP–228 (1990).

Article   ADS   Google Scholar  

Livesley, W. J., Jang, K. L. & Vernon, P. A. Phenotypic and genetic structure of traits delineating personality disorder. Arch. Gen. Psychiatry https://doi.org/10.1001/archpsyc.55.10.941 (1998).

Bouchard, T. J. & Loehlin, J. C. Genes, evolution, and personality. Behavior Genetics https://doi.org/10.1023/A:1012294324713 (2001).

Vukasović, T. & Bratko, D. Heritability of personality: A meta-analysis of behavior genetic studies. Psychol. Bull. 141 , 769–785 (2015).

Godinho, R. M., Spikins, P. & O’Higgins, P. Supraorbital morphology and social dynamics in human evolution. Nat. Ecol. Evol . https://doi.org/10.1038/s41559-018-0528-0 (2018).

Rhodes, G., Simmons, L. W. & Peters, M. Attractiveness and sexual behavior: Does attractiveness enhance mating success? Evol. Hum. Behav . https://doi.org/10.1016/j.evolhumbehav.2004.08.014 (2005).

Lefevre, C. E., Lewis, G. J., Perrett, D. I. & Penke, L. Telling facial metrics: Facial width is associated with testosterone levels in men. Evol. Hum. Behav. 34 , 273–279 (2013).

Whitehouse, A. J. O. et al . Prenatal testosterone exposure is related to sexually dimorphic facial morphology in adulthood. Proceedings. Biol. Sci. 282 , 20151351 (2015).

Penton-Voak, I. S. & Chen, J. Y. High salivary testosterone is linked to masculine male facial appearance in humans. Evol. Hum. Behav . https://doi.org/10.1016/j.evolhumbehav.2004.04.003 (2004).

Carré, J. M. & Archer, J. Testosterone and human behavior: the role of individual and contextual variables. Curr. Opin. Psychol. 19 , 149–153 (2018).

Swaddle, J. P. & Reierson, G. W. Testosterone increases perceived dominance but not attractiveness in human males. Proc. R. Soc. B Biol. Sci . https://doi.org/10.1098/rspb.2002.2165 (2002).

Eisenegger, C., Kumsta, R., Naef, M., Gromoll, J. & Heinrichs, M. Testosterone and androgen receptor gene polymorphism are associated with confidence and competitiveness in men. Horm. Behav. 92 , 93–102 (2017).

Article   CAS   Google Scholar  

Kaplan, H. B. Social Psychology of Self-Referent Behavior . https://doi.org/10.1007/978-1-4899-2233-5 . (Springer US, 1986).

Rosenthal, R. & Jacobson, L. Pygmalion in the classroom. Urban Rev . https://doi.org/10.1007/BF02322211 (1968).

Masters, F. W. & Greaves, D. C. The Quasimodo complex. Br. J. Plast. Surg . 204–210 (1967).

Zebrowitz, L. A., Collins, M. A. & Dutta, R. The Relationship between Appearance and Personality Across the Life Span. Personal. Soc. Psychol. Bull. 24 , 736–749 (1998).

Hu, S. et al . Signatures of personality on dense 3D facial images. Sci. Rep. 7 , 73 (2017).

Kosinski, M. Facial Width-to-Height Ratio Does Not Predict Self-Reported Behavioral Tendencies. Psychol. Sci. 28 , 1675–1682 (2017).

Walker, M., Schönborn, S., Greifeneder, R. & Vetter, T. The basel face database: A validated set of photographs reflecting systematic differences in big two and big five personality dimensions. PLoS One 13 , (2018).

Goffaux, V. & Rossion, B. Faces are ‘spatial’ - Holistic face perception is supported by low spatial frequencies. J. Exp. Psychol. Hum. Percept. Perform . https://doi.org/10.1037/0096-1523.32.4.1023 (2006).

Schiltz, C. & Rossion, B. Faces are represented holistically in the human occipito-temporal cortex. Neuroimage https://doi.org/10.1016/j.neuroimage.2006.05.037 (2006).

Van Belle, G., De Graef, P., Verfaillie, K., Busigny, T. & Rossion, B. Whole not hole: Expert face recognition requires holistic perception. Neuropsychologia https://doi.org/10.1016/j.neuropsychologia.2010.04.034 (2010).

Quadflieg, S., Todorov, A., Laguesse, R. & Rossion, B. Normal face-based judgements of social characteristics despite severely impaired holistic face processing. Vis. cogn. 20 , 865–882 (2012).

McKone, E. Isolating the Special Component of Face Recognition: Peripheral Identification and a Mooney Face. J. Exp. Psychol. Learn. Mem. Cogn . https://doi.org/10.1037/0278-7393.30.1.181 (2004).

Sergent, J. An investigation into component and configural processes underlying face perception. Br. J. Psychol . https://doi.org/10.1111/j.2044-8295.1984.tb01895.x (1984).

Tanaka, J. W. & Farah, M. J. Parts and Wholes in Face Recognition. Q. J. Exp. Psychol. Sect. A https://doi.org/10.1080/14640749308401045 (1993).

Young, A. W., Hellawell, D. & Hay, D. C. Configurational information in face perception. Perception https://doi.org/10.1068/p160747n (2013).

Calder, A. J. & Young, A. W. Understanding the recognition of facial identity and facial expression. Nature Reviews Neuroscience https://doi.org/10.1038/nrn1724 (2005).

Todorov, A., Loehr, V. & Oosterhof, N. N. The obligatory nature of holistic processing of faces in social judgments. Perception https://doi.org/10.1068/p6501 (2010).

Junior, J. C. S. J. et al . First Impressions: A Survey on Computer Vision-Based Apparent Personality Trait Analysis. (2018).

Wang, Y. & Kosinski, M. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. J. Pers. Soc. Psychol. 114 , 246–257 (2018).

Qiu, L., Lu, J., Yang, S., Qu, W. & Zhu, T. What does your selfie say about you? Comput. Human Behav. 52 , 443–449 (2015).

Digman, J. M. Higher order factors of the Big Five. J.Pers.Soc.Psychol . https://doi.org/10.1037/0022-3514.73.6.1246 (1997).

Musek, J. A general factor of personality: Evidence for the Big One in the five-factor model. J. Res. Pers . https://doi.org/10.1016/j.jrp.2007.02.003 (2007).

DeYoung, C. G. Higher-order factors of the Big Five in a multi-informant sample. J. Pers. Soc. Psychol. 91 , 1138–1151 (2006).

Rushton, J. P. & Irwing, P. A General Factor of Personality (GFP) from two meta-analyses of the Big Five: Digman (1997) and Mount, Barrick, Scullen, and Rounds (2005). Pers. Individ. Dif. 45 , 679–683 (2008).

Wood, D., Gardner, M. H. & Harms, P. D. How functionalist and process approaches to behavior can explain trait covariation. Psychol. Rev. 122 , 84–111 (2015).

Dunlap, W. P. Generalizing the Common Language Effect Size indicator to bivariate normal correlations. Psych. Bull. 116 , 509–511 (1994).

Connolly, J. J., Kavanagh, E. J. & Viswesvaran, C. The convergent validity between self and observer ratings of personality: A meta-analytic review. Int. J. of Selection and Assessment. 15 , 110–117 (2007).

Harris, K. & Vazire, S. On friendship development and the Big Five personality traits. Soc. and Pers. Psychol. Compass. 10 , 647–667 (2016).

Weidmann, R., Schönbrodt, F. D., Ledermann, T. & Grob, A. Concurrent and longitudinal dyadic polynomial regression analyses of Big Five traits and relationship satisfaction: Does similarity matter? J. Res. in Personality. 70 , 6–15 (2017).

Cuperman, R. & Ickes, W. Big Five predictors of behavior and perceptions in initial dyadic interactions: Personality similarity helps extraverts and introverts, but hurts “disagreeables”. J. of Pers. and Soc. Psychol. 97 , 667–684 (2009).

Schmidt, F. L. & Hunter, J. E. The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychol. Bull. 124 , 262–274 (1998).

Brown, M. & Sacco, D. F. Unrestricted sociosexuality predicts preferences for extraverted male faces. Pers. Individ. Dif. 108 , 123–127 (2017).

Lukaszewski, A. W. & Roney, J. R. The origins of extraversion: joint effects of facultative calibration and genetic polymorphism. Pers. Soc. Psychol. Bull. 37 , 409–21 (2011).

Curran, P. G. Methods for the detection of carelessly invalid responses in survey data. J. Exp. Soc. Psychol. 66 , 4–19 (2016).

Khromov, A. B. The five-factor questionnaire of personality [Pjatifaktornyj oprosnik lichnosti]. In Rus. (Kurgan State University, 2000).

Trizano-Hermosilla, I. & Alvarado, J. M. Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Front. Psychol . https://doi.org/10.3389/fpsyg.2016.00769 (2016).

Liu, Z., Luo, P., Wang, X. & Tang, X. Deep Learning Face Attributes in the Wild. in 2015 IEEE International Conference on Computer Vision (ICCV) 3730–3738 https://doi.org/10.1109/ICCV.2015.425 (IEEE, 2015).

He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 https://doi.org/10.1109/CVPR.2016.90 (IEEE, 2016).

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Acknowledgements

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

Contributions

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

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Correspondence to Alexander Kachur or Evgeny Osin .

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

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

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What Are the Big 5 Personality Traits?

Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

research on big 5 personality traits

Verywell / Catherine Song

  • Universality
  • Influential Factors

Frequently Asked Questions

Many contemporary personality psychologists believe that there are five basic dimensions of personality, often referred to as the "Big 5" personality traits. The Big 5 personality traits are extraversion (also often spelled extroversion), agreeableness , openness , conscientiousness , and neuroticism .

Extraversion is sociability, agreeableness is kindness, openness is creativity and intrigue, conscientiousness is thoughtfulness, and neuroticism often involves sadness or emotional instability.

Understanding what each personality trait is and what it means to score high or low in that trait can give you insight into your own personality —without taking a personality traits test . It can also help you better understand others, based on where they fall on the continuum for each of the personality traits listed.

An Easy Way to Remember the Big 5

Some use the acronym OCEAN (openness, conscientiousness, extraversion, agreeableness, and neuroticism) to remember the Big 5 personality traits. CANOE (for conscientiousness, agreeableness, neuroticism, openness, and extraversion) is another option.

History of the Big 5 Personality Theory

Trait theories of personality have long attempted to pin down exactly how many traits exist. Earlier theories have suggested various numbers. For instance, Gordon Allport's list contained 4,000 personality traits, Raymond Cattell had 16 personality factors, and Hans Eysenck offered a three-factor theory.

Many researchers felt that Cattell's theory was too complicated and Eysenck's was too limited in scope. As a result, the Big 5 personality traits emerged and are used to describe the broad traits that serve as building blocks of personality .

Several researchers support the belief that there are five core personality traits. Evidence of this theory has been growing for many years in psychology, beginning with the research of D. W. Fiske (1949), and later expanded upon by others, including Norman (1967), Smith (1967), Goldberg (1981), and McCrae & Costa (1987).

The Big 5 Personality Traits

It is important to note that each of the five primary personality traits represents a range between two extremes. For example, extraversion represents a continuum between extreme extraversion and extreme introversion. In the real world, most people lie somewhere in between.

While there is a significant body of literature supporting these primary personality traits, researchers don't always agree on the exact labels for each dimension. That said, these five traits are usually described as follows.

Openness (also referred to as openness to experience) emphasizes imagination and insight the most out of all five personality traits. People who are high in openness tend to have a broad range of interests. They are curious about the world and other people and are eager to learn new things and enjoy new experiences.

People who are high in this personality trait also tend to be more adventurous and  creative . Conversely, people low in this personality trait are often much more traditional and may struggle with abstract thinking.

Very creative

Open to trying new things

Focused on tackling new challenges

Happy to think about abstract concepts

Dislikes change

Does not enjoy new things

Resists new ideas

Not very imaginative

Dislikes abstract or theoretical concepts

Conscientiousness

Among each of the personality traits, conscientiousness is one defined by high levels of thoughtfulness, good impulse control, and goal-directed behaviors. Highly conscientious people tend to be organized and mindful of details. They plan ahead, think about how their behavior affects others, and are mindful of deadlines.

Someone scoring lower in this primary personality trait is less structured and less organized. They may procrastinate to get things done, sometimes missing deadlines completely.

Spends time preparing

Finishes important tasks right away

Pays attention to detail

Enjoys having a set schedule

Dislikes structure and schedules

Makes messes and doesn't take care of things

Fails to return things or put them back where they belong

Procrastinates  important tasks

Fails to complete necessary or assigned tasks

Extraversion

Extraversion (or extroversion) is a personality trait characterized by excitability, sociability, talkativeness, assertiveness, and high amounts of emotional expressiveness. People high in extraversion are outgoing and tend to gain energy in social situations. Being around others helps them feel energized and excited.

People who are low in this personality trait or introverted tend to be more reserved. They have less energy to expend in social settings and social events can feel draining. Introverts often require a period of solitude and quiet in order to "recharge."

Enjoys being the center of attention

Likes to start conversations

Enjoys meeting new people

Has a wide social circle of friends and acquaintances

Finds it easy to make new friends

Feels energized when around other people

Say things before thinking about them

Prefers solitude

Feels exhausted when having to socialize a lot

Finds it difficult to start conversations

Dislikes making small talk

Carefully thinks things through before speaking

Dislikes being the center of attention

Agreeableness

This personality trait includes attributes such as trust,  altruism , kindness, affection, and other  prosocial behaviors . People who are high in agreeableness tend to be more cooperative while those low in this personality trait tend to be more competitive and sometimes even manipulative.

Has a great deal of interest in other people

Cares about others

Feels empathy and concern for other people

Enjoys helping and contributing to the happiness of other people

Assists others who are in need of help

Takes little interest in others

Doesn't care about how other people feel

Has little interest in other people's problems

Insults and belittles others

Manipulates others to get what they want

Neuroticism

Neuroticism is a personality trait characterized by sadness, moodiness, and emotional instability. Individuals who are high in neuroticism tend to experience mood swings , anxiety, irritability, and sadness. Those low in this personality trait tend to be more stable and emotionally resilient .

Experiences a lot of stress

Worries about many different things

Gets upset easily

Experiences dramatic shifts in mood

Feels anxious

Struggles to bounce back after stressful events

Emotionally stable

Deals well with stress

Rarely feels sad or depressed

Doesn't worry much

Is very relaxed

How to Use the Big 5 Personality Traits

Where you fall on the continuum for each of these five primary traits can be used to help identify whether you are more or less likely to have other more secondary personality traits. These other traits are often split into two categories: positive personality traits and negative personality traits.

Positive Personality Traits

Positive personality traits are traits that can be beneficial to have. These traits may help you be a better person or make it easier to cope with challenges you may face in life. Personality traits that are considered positive include:

  • Considerate
  • Cooperative
  • Well-rounded

Negative Personality Traits

Negative personality traits are those that may be more harmful than helpful. These are traits that may hold you back in your life or hurt your relationships with others. (They're also good traits to focus on for personal growth.) Personality traits that fall in the negative category include:

  • Egotistical

For example, if you score high in openness, you are more likely to have the positive personality trait of creativity. If you score low in openness, you may be more likely to have the negative personality trait of being unimaginative.

Universality of Primary Personality Traits

McCrae and his colleagues found that the Big 5 personality traits are remarkably universal. One study that looked at people from more than 50 different cultures found that the five dimensions could be accurately used to describe personality.

Based on this research, many psychologists now believe that the five personality dimensions are not only universal but that they also have biological origins. Psychologist David Buss has proposed an evolutionary explanation for these five core personality traits, suggesting that they represent the most important qualities that shape our social landscape.

Factors Influencing Personality Traits

Research suggests that both biological and environmental influences play a role in shaping our personalities. Twin studies suggest that both nature and nurture play a role in the development of each of the five personality traits.

One study of the genetic and environmental underpinnings of the five traits looked at 123 pairs of identical twins and 127 pairs of fraternal twins. The findings suggested that the heritability of each personality trait was 53% for extraversion, 41% for agreeableness, 44% for conscientiousness, 41% for neuroticism, and 61% for openness. 

Longitudinal studies also suggest that these big five personality traits tend to be relatively stable over the course of adulthood. One four-year study of working-age adults found that personality changed little as a result of adverse life events .

Studies show that maturation may have an impact on the five personality traits. As people age, they tend to become less extraverted, less neurotic, and less open to an experience. Agreeableness and conscientiousness, on the other hand, tend to increase as people grow older.

A Word From Verywell

Always remember that behavior involves an interaction between a person's underlying personality and situational variables. The situation that someone finds themselves in plays a role in how they might react . However, in most cases, people offer responses that are consistent with their underlying personality traits.

These dimensions represent broad areas of personality. But personality is also complex and varied. So, a person may display behaviors across several of these personality traits.

The big 5 personality theory is widely accepted today because this model presents a blueprint for understanding the main dimensions of personality. Experts have found that these traits are universal and provide an accurate portrait of human personality.

The big 5 personality model is not a typology system, so there are no specific "types" identified. Instead, these dimensions represent qualities that all people possess in varying amounts. One study found that most people do tend to fall into one of four main types based on the Big 5 traits:  

  • Average (the most common type, characterized by high levels of extroversion and neuroticism and low levels of openness)
  • Self-centered (high in extroversion and low in conscientiousness, openness, and agreeableness)
  • Reserved (low on extroversion, neuroticism, and openness, and high on conscientiousness and agreeableness)
  • Role models (high on every big 5 trait other than neuroticism)

Power RA, Pluess M. Heritability estimates of the Big Five personality traits based on common genetic variants . Translation Psychiatry . 2015;5:e604. doi:10.1038/tp.2015.96

Jang KL, Livesley WJ, Vernon PA. Heritability of the big five personality dimensions and their facets: a twin study . J Pers . 1996;64(3):577-91. doi:10.1111/j.1467-6494.1996.tb00522.x

Gerlach M, Farb B, Revelle W, Nunes Amaral LA. A robust data-driven approach identifies four personality types across four large data sets . Nat Hum Behav . 2018;2(10):735-742.

 doi:10.1038/s41562-018-0419-z

Cobb-Clark DA, Schurer S. The stability of big-five personality traits . Econ Letters . 2012;115(2):11–15. doi:10.1016/j.econlet.2011.11.015

Lang KL, Livesley WJ, Vemon PA. Heritability of the big five personality dimensions and their facets: A twin study . J Personal . 1996;64(3):577–591. doi:10.1111/j.1467-6494.1996.tb00522.x

Marsh HW, Nagengast B, Morin AJS. Measurement invariance of big-five factors over the lifespan: ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects . Develop Psychol . 2013;49(6):1194-1218. doi:10.1037/a0026913

McCrae RR, Terracciano A, Personality Profiles of Cultures Project. Universal features of personality traits from the observer's perspective: Data from 50 different cultures . J Personal Soc Psychol. 2005;88:547-561. doi:10.1037/0022-3514.88.3.547

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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What are the big 5 personality traits inside psychology's core personality system.

Nafeesah Allen, Ph.D.

An individual's "personality" refers to their patterns of behaviors, thoughts, and feelings. To help capture the seemingly infinite number of personalities that appear across humankind, researchers have developed models for measuring their most common manifestations.

Many psychologists consider the so-called Big Five personality traits the most reputable. This model states that personality comes down to five core factors: openness, conscientiousness, extroversion, agreeableness, and neuroticism.

We asked psychology experts to help us unpack the Big 5 personality traits and the ways in which mental health professionals use them.

What are the Big Five personality traits?

The Big Five personality traits are openness, conscientiousness, extroversion, agreeableness, and neuroticism. These five fundamental traits attempt to summarize the human personality on a comparative scale. 

"Personality is defined as someone's usual patterns of behaviors, feelings, and thoughts. While these usual patterns are complex, there are some personality traits that organize our understanding of someone's personality," explains licensed clinical psychologist Ernesto Lira de la Rosa, Ph.D. , of the Hope for Depression Research Foundation . That's where personality frameworks like the Big Five, also known as the Five Factor model , come in.

According to the Big Five theory of personality, all human personalities are composed of these five core personality dimensions, and any individual's personality boils down to where they fall on each of these five scales. Although not without its criticisms, decades of research have validated this theory.

An infographic depicting the Big Five personality traits.

The Big Five personality framework was first developed in 1949 by personality psychologist D.W. Fiske. Later, other scientists, including Warren T. Norman, Robert McCrae & Paul Costa, Gene M. Smith, and Lewis R. Goldberg, further developed Fiske's theories and research.

As with any personality test, there is controversy over the model itself and how it is best applied, says psychotherapist Lee Phillips, Ed.D., LCSW, CST . That said, today the Big Five personality traits are widely accepted as an accurate way of understanding human personality among most psychologists in the United States and in the broader Western world, supported by ample research.

And as one 2020 paper in The Wiley Encyclopedia of Personality and Individual Differences notes, "The five factors have provided a framework for understanding psychopathology. Neuroscience has identified neural correlates of the five factors, and cross-cultural research has underscored how people across the globe are both similar and different."

Below is a breakdown of each of the Big Five personality traits: 

Openness 

Openness to experience represents intellectual curiosity, creative imagination, and valuable insights. This trait includes thinking outside the box and being willing to learn new things. 

According to Lira de la Rosa, "People who score high on openness tend to enjoy trying new things, playing with complex ideas, and considering alternative perspectives. Those who score lower on openness may dislike change, trying new things, and dislike abstract concepts."

Conscientiousness 

Conscientiousness indicates organization, productivity, responsibility, and impulse control. Highly conscientious people have goal-oriented behaviors. Phillips says, "Conscientiousness measures the organizational skills of the individual. For example, it looks at how careful, deliberate, and self-disciplined they are. Conscientiousness looks at the foretelling of employee productivity."

According to Lira de la Rosa, those who score high on conscientiousness may spend more time preparing for things. They pay close attention to detail and enjoy a set schedule. "However, those who score low on conscientiousness may dislike structure and schedules and may procrastinate on important tasks," he says.

Extroversion 

Extroversion looks at how sociable and outgoing a person is, and where they feel most energized. High scores indicate a person energized by the company of others and excited by being the center of attention. Low scores indicate a more reserved person who enjoys solitude.

Introverts don't necessarily dislike social gatherings; however, they may get fatigued by them and require time alone to regain their energy.

Agreeableness

Agreeableness is aligned with attributes like kindness, affection, and trust. People with high scores are interested in others. They are emphatic and enjoy contributing to others' happiness.

"Those who score high may feel empathy and concern for others, enjoy helping others and contributing to their happiness. They love to assist those who are in need. In contrast, those who score low on agreeableness may take little interest in others, insult or belittle others, and have little interest in other people's problems," says Lira de la Rosa.

Neuroticism 

Neuroticism indicates emotional instability. It often refers to sadness and moodiness . 

Phillips explains that "high scores indicate the person is anxious, irritable, they are capable of anger outbursts, and they can have dramatic shifts in their mood. Low scores indicate the person does not worry as much, they are calm and emotionally stable, and they rarely feel sad or depressed."

Why are the Big Five personality traits so important?

The Big Five personality traits model helps people identify on a spectrum, recognizing that all people exhibit some of these traits at some point in their lives.

"These traits are important because they are useful in understanding our social interactions with others. They are also helpful in increasing our self-awareness and how our personality traits may impact how others perceive or experience us," Lira de la Rosa tells mbg.

The Big Five model has evolved with time, research, and technology. These days, it's regularly applied in social, academic, and professional contexts. 

The Big Five personality traits are foundational to personality tests that have become popular in dating, family, and work. Drawing from the same scientific research that generated the Big Five, the Myers-Briggs (MBTI) , Likability Test , and the Difficult Person Test are related personality assessments meant to understand how an individual's traits manifest in relationships with others. Tools and tests like these are often used to build relationships, romantic or professional.

In the field of organizational behavior, tests based on the Big Five personality traits are often used in employee assessment tests, offering rubrics to understand employee character and to guide teams composed of diverse individuals.

Psychology and research.

The Big Five model of personality has been studied by psychologists over the course of nearly a century, starting with D.W. Fiske's research in 1949.

Gordon Allport, an American psychologist sometimes described as a founder of the field of personality psychology, published in the 1920s about what he termed "cardinal traits," core characteristics thought to define a person's personality. His research developed a lexicon of over 4,500 vocabulary words to describe personality traits. Then in 1949, through a study of clinical trainees, Fiske attempted to find consistent structural factors of personalities 1 . He identified a core group of four similar factors, with three distinct levels of behavioral ratings.

As the field of psychology developed, personality research became more refined and competing, but related frameworks developed—some with as many as 16 factors and others with as few as four. But, somehow the number five kept coming up. Robert Costa and Paul McCrae developed the so-called Five Factor Model in 1987, and Lewis Goldberg developed the " Big Five Model " in 1993, both using the same core personality factors: openness, conscientiousness, extroversion, agreeableness, and neuroticism. Since then, these Big Five personality traits have been studied and validated time and time again by many researchers over decades.

Some of the most interesting recent research suggests that biological and environmental factors play a role in personality development. For example, a 2015 study of the personalities of twins 2 suggests that both nature and nurture affect the development of each of the Big Five personality traits. In that study, 127 pairs of fraternal twins and 123 pairs of identical twins were put to the Big Five test. The findings showed the heritability of openness and neuroticism, and subsequent research has been done to further explore the genetic basis for some of the other traits. 

There is also some valid criticism of the Big Five personality traits. "In particular, most of the research on personality is done with people from western, educated, industrialized, rich, and democratic countries," explains Lira de la Rosa. "As such, the Big Five personality traits may not capture personality traits across cultures." He says that research shows that some of the Big Five personality traits are not observed as often in some other cultures.

Phillips also adds that critics ask, "How can one test determine a person's personality?" After all, personalities may shift over time. And it's the mix of traits—not each one individually—that defines our personalities. So, tests like these—when not taken under the supervision of a trained professional—can sometimes be used to justify ill-conceived or overly simplified conclusions about people's characters.

How to use the Big Five personality system:

Get to know yourself better..

"Having awareness of ourselves can be critical to our sense of self and relationship with others," Lira de la Rosa says. The average person can use this framework of personality traits to better understand themselves and to recognize how some of these traits impact their day-to-day lives. 

Leverage your strengths.

Using newfound knowledge of your personality, you can craft relationships and opportunities around your strengths. People with a low openness score, for example, might target jobs in an office where they can become subject matter experts rather than roles that entail rotating into various areas of the company. In this way, their strengths and personality disposition are aligned with success in that context. Use what you know about your general tendencies to set yourself up for success at work and in your personal relationships.

Date thoughtfully.

Speaking of personal relationships, your Big Five personality traits could be a good conversation starter on a date—and even a good way to assess compatibility. Phillips says a person serious about dating "can take the test, and post the results on a dating app," adding, "By scoring high and low on these personality traits, a person can see if they match with another person's personality type." 

Help others understand you better.

Once you know yourself better, it becomes easier to explain your boundaries and reactions to co-workers, roommates, and romantic partners. Take the test together or simply share your own results. Sharing vulnerabilities and tendencies will help the people you spend the most time with better understand you and get ahead of any misunderstandings.

Why is the Big Five personality test important?

The Big Five model of personality determines where a person's personality traits stand on a spectrum in comparison to others, as well as how other people may perceive them. Self-awareness tools based on the model can help you adjust behaviors to better suit group contexts and wider society.

Are the Big Five personality traits genetic?

There are some indications that these traits could be genetically linked. According to one 2015 study, there is evidence of the heritability of at least two of the Big Five traits: openness and neuroticism.  

Can you change your Big Five personality traits?

Multiple studies and psychologists say these traits are not fixed and can be intentionally changed with effort, intentionality, and support from mental health care professionals. 

The takeaway.

The Big Five personality model is widely reputed; however, self-assessment tests always have an element of bias. Also, it is important not to take the results of any personality test as any kind of definitive diagnosis. These tests are simply meant to help you learn about yourself and identify possible areas for personal growth.

"The average person can use personality traits to better understand themselves and how some of these traits impact their day-to-day functioning," explains Lira de la Rosa. "It is important to note that these traits will not mean the same for each person, and it is the combination of these traits that informs our unique personalities."

  • https://psycnet.apa.org/doiLanding?doi=10.1037%2Fh0057198
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5068715/

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Big Five Personality Traits: The 5-Factor Model of Personality

Annabelle G.Y. Lim

Psychology Graduate

BA (Hons), Psychology, Harvard University

Annabelle G.Y. Lim is a graduate in psychology from Harvard University. She has served as a research assistant at the Harvard Adolescent Stress & Development Lab.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

The Big Five Model, also known as the Five-Factor Model, is the most widely accepted personality theory held by psychologists today. The theory states that personality can be boiled down to five core factors, known by the acronym CANOE or OCEAN.
  • Conscientiousness – impulsive, disorganized vs. disciplined, careful
  • Agreeableness – suspicious, uncooperative vs. trusting, helpful
  • Neuroticism – calm, confident vs. anxious, pessimistic
  • Openness to Experience – prefers routine, practical vs. imaginative, spontaneous
  • Extraversion – reserved, thoughtful vs. sociable, fun-loving

The Big Five remain relatively stable throughout most of one’s lifetime. They are influenced significantly by genes and the environment, with an estimated heritability of 50%. They also predict certain important life outcomes such as education and health.

Each trait represents a continuum. Individuals can fall anywhere on the continuum for each trait.

Unlike other trait theories that sort individuals into binary categories (i.e. introvert or extrovert ), the Big Five Model asserts that each personality trait is a spectrum.

Therefore, individuals are ranked on a scale between the two extreme ends of five broad dimensions:

big five personality scale

For instance, when measuring Extraversion, one would not be classified as purely extroverted or introverted, but placed on a scale determining their level of extraversion.

By ranking individuals on each of these traits, it is possible to effectively measure individual differences in personality.

Conscientiousness

Conscientiousness describes a person’s ability to regulate impulse control in order to engage in goal-directed behaviors (Grohol, 2019). It measures elements such as control, inhibition, and persistence of behavior.

Facets of conscientiousness include the following (John & Srivastava, 1999):
  • Dutifulness
  • Achievement striving
  • Self-disciplined
  • Deliberation
  • Incompetent
  • Disorganized
  • Procrastinates
  • Indiscipline

Conscientiousness vs. Lack of Direction

Those who score high on conscientiousness can be described as organized, disciplined, detail-oriented, thoughtful, and careful. They also have good impulse control, which allows them to complete tasks and achieve goals.

Those who score low on conscientiousness may struggle with impulse control, leading to difficulty in completing tasks and fulfilling goals.

They tend to be more disorganized and may dislike too much structure. They may also engage in more impulsive and careless behavior.

Agreeableness

Agreeableness refers to how people tend to treat relationships with others. Unlike extraversion which consists of the pursuit of relationships, agreeableness focuses on people’s orientation and interactions with others (Ackerman, 2017).

Facets of agreeableness include the following (John & Srivastava, 1999):
  • Trust (forgiving)
  • Straightforwardness
  • Altruism (enjoys helping)
  • Sympathetic
  • Insults and belittles others
  • Unsympathetic
  • Doesn’t care about how other people feel

Agreeableness vs. Antagonism

Those high in agreeableness can be described as soft-hearted, trusting, and well-liked. They are sensitive to the needs of others and are helpful and cooperative. People regard them as trustworthy and altruistic.

Those low in agreeableness may be perceived as suspicious, manipulative, and uncooperative. They may be antagonistic when interacting with others, making them less likely to be well-liked and trusted.

Extraversion

Extraversion reflects the tendency and intensity to which someone seeks interaction with their environment, particularly socially. It encompasses the comfort and assertiveness levels of people in social situations.

Additionally, it also reflects the sources from which someone draws energy.

Facets of extraversion include the following (John & Srivastava, 1999):
  • Energized by social interaction
  • Excitement-seeking
  • Enjoys being the center of attention
  • Prefers solitude
  • Fatigued by too much social interaction
  • Dislikes being the center of attention

Extraversion vs. Introversion

Those high on extraversion are generally assertive, sociable, fun-loving, and outgoing. They thrive in social situations and feel comfortable voicing their opinions. They tend to gain energy and become excited from being around others.

Those who score low in extraversion are often referred to as introverts . These people tend to be more reserved and quieter. They prefer listening to others rather than needing to be heard.

Introverts often need periods of solitude in order to regain energy as attending social events can be very tiring for them. Of importance to note is that introverts do not necessarily dislike social events, but instead find them tiring.

Openness to Experience

Openness to experience refers to one’s willingness to try new things as well as engage in imaginative and intellectual activities. It includes the ability to “think outside of the box.”

Facets of openness include the following (John & Srivastava, 1999):
  • Imaginative
  • Open to trying new things
  • Unconventional
  • Predictable
  • Not very imaginative
  • Dislikes change
  • Prefer routine
  • Traditional

Openness vs. Closedness to Experience

Those who score high on openness to experience are perceived as creative and artistic. They prefer variety and value independence. They are curious about their surroundings and enjoy traveling and learning new things.

People who score low on openness to experience prefer routine. They are uncomfortable with change and trying new things, so they prefer the familiar over the unknown. As they are practical people, they often find it difficult to think creatively or abstractly.

Neuroticism

Neuroticism describes the overall emotional stability of an individual through how they perceive the world. It takes into account how likely a person is to interpret events as threatening or difficult.

It also includes one’s propensity to experience negative emotions.

Facets of neuroticism include the following (John & Srivastava, 1999):
  • Angry hostility (irritable)
  • Experiences a lot of stress
  • Self-consciousness (shy)
  • Vulnerability
  • Experiences dramatic shifts in mood
  • Doesn”t worry much
  • Emotionally stable
  • Rarely feels sad or depressed

Neuroticism vs. Emotional Stability

Those who score high on neuroticism often feel anxious, insecure and self-pitying. They are often perceived as moody and irritable. They are prone to excessive sadness and low self-esteem.

Those who score low on neuroticism are more likely to calm, secure and self-satisfied. They are less likely to be perceived as anxious or moody. They are more likely to have high self-esteem and remain resilient.

Behavioral Outcomes

Relationships.

In marriages where one partner scores lower than the other on agreeableness, stability, and openness, there is likely to be marital dissatisfaction (Myers, 2011).

Neuroticism seems to be a risk factor for many health problems, including depression, schizophrenia, diabetes, asthma, irritable bowel syndrome, and heart disease (Lahey, 2009).

People high in neuroticism are particularly vulnerable to mood disorders such as depression . Low agreeableness has also been linked to higher chances of health problems (John & Srivastava, 1999).

There is evidence to suggest that conscientiousness is a protective factor against health diseases. People who score high in conscientiousness have been observed to have better health outcomes and longevity (John & Srivastava, 1999).

Researchers believe that such is due to conscientious people having regular and well-structured lives, as well as the impulse control to follow diets, treatment plans, etc.

A high score on conscientiousness predicts better high school and university grades (Myers, 2011). Contrarily, low agreeableness and low conscientiousness predict juvenile delinquency (John & Srivastava, 1999).

Conscientiousness is the strongest predictor of all five traits for job performance (John & Srivastava, 1999). A high score of conscientiousness has been shown to relate to high work performance across all dimensions.

The other traits have been shown to predict more specific aspects of job performance. For instance, agreeableness and neuroticism predict better performance in jobs where teamwork is involved.

However, agreeableness is negatively related to individual proactivity. Openness to experience is positively related to individual proactivity but negatively related to team efficiency (Neal et al., 2012).

Extraversion is a predictor of leadership, as well as success in sales and management positions (John & Srivastava, 1999).

Media Preference

Manolika (2023) examined how the Big Five personality traits relate to preferences for different genres of movies and books. The study surveyed 386 university students on their Big Five traits and preferences for 21 movie and 27 book types.

Results showed openness to experience predicted liking complex movies like documentaries and unconventional books like philosophy. This aligns with past research showing open people like cognitively challenging art (Swami & Furnham, 2019).

Conscientiousness predicted preferring informational books, while agreeableness predicted conventional genres like family movies and romance books.

Neuroticism only predicted preferring light books, not movies. Extraversion did not predict preferences, contrary to hypotheses.

Overall, the Big Five traits differentially predicted media preferences, suggesting people select entertainment that satisfies psychological needs and reflects aspects of their personalities (Rentfrow et al., 2011).

Open people prefer complex stimulation, conscientious people prefer practical content, agreeable people prefer conventional genres, and neurotic people use light books for mood regulation. Extraversion may relate more to social motivations for media use.

Critical Evaluation

Descriptor rather than a theory.

The Big Five was developed to organize personality traits rather than as a comprehensive theory of personality. Therefore, it is more descriptive than explanatory and does not fully account for differences between individuals (John & Srivastava, 1999). It also does not sufficiently provide a causal reason for human behavior.

Cross-Cultural Validity

Although the Big Five has been tested in many countries and its existence is generally supported by findings (McCrae, 2002), there have been some studies that do not support its model. Most previous studies have tested the presence of the Big Five in urbanized, literate populations.

A study by Gurven et al. (2013) was the first to test the validity of the Big Five model in a largely illiterate, indigenous population in Bolivia. They administered a 44-item Big Five Inventory but found that the participants did not sort the items in consistency with the Big Five traits.

More research on illiterate and non-industrialized populations is needed to clarify such discrepancies.

Gender Differences

Differences in the Big Five personality traits between genders have been observed, but these differences are small compared to differences between individuals within the same gender.

Costa et al. (2001) gathered data from over 23,000 men and women in 26 countries. They found that “gender differences are modest in magnitude, consistent with gender stereotypes, and replicable across cultures” (p. 328). Women reported themselves to be higher in Neuroticism, Agreeableness, Warmth (a facet of Extraversion), and Openness to Feelings compared to men. Men reported themselves to be higher in Assertiveness (a facet of Extraversion) and Openness to Ideas.

Another interesting finding was that bigger gender differences were reported in Western, industrialized countries. Researchers proposed that the most plausible reason for this finding was attribution processes.

They surmised that actions of women in individualistic countries would be more likely to be attributed to her personality whereas actions of women in collectivistic countries would be more likely to be attributed to their compliance with gender role norms.

Factors that Influence the Big 5

Like with all theories of personality , the Big Five is influenced by both nature and nurture . Twin studies have found that the heritability (the amount of variance that can be attributed to genes) of the Big Five traits is 40-60%.

Jang et al. (1996) conducted a study with 123 pairs of identical twins and 127 pairs of fraternal twins. They estimated the heritability of conscientiousness, agreeableness, neuroticism, openness to experience, and extraversion to be 44%, 41%, 41%, 61%, and 53%, respectively. This finding was similar to the findings of another study, where the heritability of conscientiousness, agreeableness, neuroticism, openness to experience and extraversion were estimated to be 49%, 48%, 49%, 48%, and 50%, respectively (Jang et al., 1998).

Such twin studies demonstrate that the Big Five personality traits are significantly influenced by genes and that all five traits are equally heritable. Heritability for males and females does not seem to differ significantly (Leohlin et al., 1998).

Studies from different countries also support the idea of a strong genetic basis for the Big Five personality traits (Riemann et al., 1997; Yamagata et al., 2006).

Roehrick et al. (2023) examined how Big Five traits (extraversion, agreeableness, conscientiousness, neuroticism, openness) and context relate to smartphone use. The study used surveys, experience sampling, and smartphone sensing to track college students’ personality, context, and hourly smartphone behaviors over one week.

They found extraverts used their phones more frequently once checked, but conscientious people were less likely to use their phone and used them for shorter durations. Smartphones were used in public, with weaker social ties, and during class/work activities. They were used less with close ties. Perceived situations didn’t relate much to use.

Most variability in use was within-person, suggesting context matters more than personality for smartphone behaviors. Comparisons showed context-explained duration of use over traits and demographics, but not frequency.

The key implication is that both personality and context are important to understanding digital behavior. Extraversion and conscientiousness were the most relevant of the Big Five for smartphone use versus non-use and degree of use. Contextual factors like location, social ties, and activities provided additional explanatory power, especially for the duration of smartphone use.

Stability of the Traits

People’s scores of the Big Five remain relatively stable for most of their life with some slight changes from childhood to adulthood. A study by Soto & John (2012) attempted to track the developmental trends of the Big Five traits.

They found that overall agreeableness and conscientiousness increased with age. There was no significant trend for extraversion overall although gregariousness decreased and assertiveness increased.

Openness to experience and neuroticism decreased slightly from adolescence to middle adulthood. The researchers concluded that there were more significant trends in specific facets (i.e. adventurousness and depression) rather than in the Big Five traits overall.

History and Background

The Big Five model resulted from the contributions of many independent researchers. Gordon Allport and Henry Odbert first formed a list of 4,500 terms relating to personality traits in 1936 (Vinney, 2018). Their work provided the foundation for other psychologists to begin determining the basic dimensions of personality.

In the 1940s, Raymond Cattell and his colleagues used factor analysis (a statistical method) to narrow down Allport’s list to sixteen traits.

However, numerous psychologists examined Cattell’s list and found that it could be further reduced to five traits. Among these psychologists were Donald Fiske, Norman, Smith, Goldberg, and McCrae & Costa (Cherry, 2019).

In particular, Lewis Goldberg advocated heavily for five primary factors of personality (Ackerman, 2017). His work was expanded upon by McCrae & Costa, who confirmed the model’s validity and provided the model used today: conscientiousness, agreeableness, neuroticism, openness to experience, and extraversion.

The model became known as the “Big Five” and has seen received much attention. It has been researched across many populations and cultures and continues to be the most widely accepted theory of personality today.

Each of the Big Five personality traits represents extremely broad categories which cover many personality-related terms. Each trait encompasses a multitude of other facets.

For example, the trait of Extraversion is a category that contains labels such as Gregariousness (sociable), Assertiveness (forceful), Activity (energetic), Excitement-seeking (adventurous), Positive emotions (enthusiastic), and Warmth (outgoing) (John & Srivastava, 1999).

Therefore, the Big Five, while not completely exhaustive, cover virtually all personality-related terms.

Another important aspect of the Big Five Model is its approach to measuring personality. It focuses on conceptualizing traits as a spectrum rather than black-and-white categories (see Figure 1). It recognizes that most individuals are not on the polar ends of the spectrum but rather somewhere in between.

Frequently Asked Questions

Is 5 really the magic number.

A common criticism of the Big Five is that each trait is too broad. Although the Big Five is useful in terms of providing a rough overview of personality, more specific traits are required to be of use for predicting outcomes (John & Srivastava, 1999).

There is also an argument from psychologists that more than five traits are required to encompass the entirety of personality.

A new model, HEXACO, was developed by Kibeom Lee and Michael Ashton, and expands upon the Big Five Model. HEXACO retains the original traits from the Big Five Model but contains one additional trait: Honesty-Humility, which they describe as the extent to which one places others’ interests above their own.

What are the differences between the Big Five and the Myers-Briggs Type Indicator?

The Big Five personality traits and the Myers-Briggs Type Indicator (MBTI) are both popular models used to understand personality. However, they differ in several ways.

The Big Five traits represent five broad dimensions of personality. Each trait is measured along a continuum, and individuals can fall anywhere along that spectrum.

In contrast, the MBTI categorizes individuals into one of 16 personality types based on their preferences for four dichotomies: extraversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving. This model assumes that people are either one type or another rather than being on a continuum.

Overall, while both models aim to describe and categorize personality, the Big Five is thought to have more empirical research and more scientific support, while the MBTI is more of a theory and often lacks strong empirical evidence.

Is it possible to improve certain Big Five traits through therapy or other interventions?

It can be possible to improve certain Big Five traits through therapy or other interventions.

For example, individuals who score low in conscientiousness may benefit from therapy that focuses on developing planning, organizational, and time-management skills. Those with high neuroticism may benefit from cognitive-behavioral therapy, which helps individuals manage negative thoughts and emotions.

Additionally, therapy such as mindfulness-based interventions may increase scores in traits such as openness and agreeableness. However, the extent to which these interventions can change personality traits long-term is still a topic of debate among psychologists.

Is it possible to have a high score in more than one Big Five trait?

Yes, it is possible to have a high score in more than one Big Five trait. Each trait is independent of the others, meaning that an individual can score high on openness, extraversion, and conscientiousness, for example, all at the same time.

Similarly, an individual can also score low on one trait and high on another. The Big Five traits are measured along a continuum, so individuals can fall anywhere along that spectrum for each trait.

Therefore, it is common for individuals to have a unique combination of high and low scores across the Big Five personality traits.

Ackerman, C. (2017, June 23). Big Five Personality Traits: The OCEAN Model Explained . PositivePsychology.com. https://positivepsychology.com/big-five-personality-theory

Cherry, K. (2019, October 14). What Are the Big 5 Personality Traits? Verywell Mind . Retrieved 12 June 2020, from https://www.verywellmind.com/the-big-five-personality-dimensions-2795422

Costa, P., Terracciano, A., & McCrae, R. (2001). Gender Differences in Personality Traits Across Cultures: Robust and Surprising Findings . Journal of Personality and Social Psychology, 81 (2), 322-331. https://doi.org/10.1037/0022-3514.81.2.322

Fiske, D. W. (1949). Consistency of the factorial structures of personality ratings from different sources. The Journal of Abnormal and Social Psychology, 44 (3), 329-344. https://doi.org/10.1037/h0057198

Grohol, J. M. (2019, May 30). The Big Five Personality Traits . PsychCentral. Retrieved 10 June 2020, from https://psychcentral.com/lib/the-big-five-personality-traits

Gurven, M., von Rueden, C., Massenkoff, M., Kaplan, H., & Lero Vie, M. (2013). How universal is the Big Five? Testing the five-factor model of personality variation among forager-farmers in the Bolivian Amazon . Journal of personality and social psychology, 104 (2), 354–370. https://doi.org/10.1037/a0030841

Jang, K. L., Livesley, W. J., & Vemon, P. A. (1996). Heritability of the Big Five Personality Dimensions and Their Facets: A Twin Study . Journal of Personality, 64 (3), 577–592. https://doi.org/10.1111/j.1467-6494.1996.tb00522.x

Jang, K. L., McCrae, R. R., Angleitner, A., Riemann, R., & Livesley, W. J. (1998). Heritability of facet-level traits in a cross-cultural twin sample: Support for a hierarchical model of personality. Journal of Personality and Social Psychology, 74 (6), 1556–1565.

John, O. P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (Vol. 2, pp. 102–138). New York: Guilford Press.

Lahey B. B. (2009). Public health significance of neuroticism. The American psychologist, 64 (4), 241–256. https://doi.org/10.1037/a0015309

Loehlin, J. C., McCrae, R. R., Costa, P. T., & John, O. P. (1998). Heritabilities of Common and Measure-Specific Components of the Big Five Personality Factors . Journal of Research in Personality, 32 (4), 431–453. https://doi.org/10.1006/jrpe.1998.2225

Manolika, M. (2023). The Big Five and beyond: Which personality traits do predict movie and reading preferences?  Psychology of Popular Media, 12 (2), 197–206

McCrae, R. R. (2002). Cross-Cultural Research on the Five-Factor Model of Personality . Online Readings in Psychology and Culture, 4 (4). https://doi.org/10.9707/2307-0919.1038

Myers, David G. (2011). Psychology (10th ed.) . Worth Publishers.

Neal, A., Yeo, G., Koy, A., & Xiao, T. (2012). Predicting the form and direction of work role performance from the Big 5 model of personality traits . Journal of Organizational Behavior, 33 (2), 175–192. https://doi.org/10.1002/job.742

Riemann, R., Angleitner, A., & Strelau, J. (1997). Genetic and Environmental Influences on Personality: A Study of Twins Reared Together Using the Self‐ and Peer Report NEO‐FFI Scales . Journal of Personality, 65 (3), 449-475.

Roehrick, K. C., Vaid, S. S., & Harari, G. M. (2023). Situating smartphones in daily life: Big Five traits and contexts associated with young adults’ smartphone use. Journal of Personality and Social Psychology, 125 (5), 1096–1118.

Soto, C. J., & John, O. P. (2012). Development of Big Five Domains and Facets in Adulthood: Mean-Level Age Trends and Broadly Versus Narrowly Acting Mechanism . Journal of Personality, 80 (4), 881–914. https://doi.org/10.1111/j.1467-6494.2011.00752.x

Vinney, C. (2018, September 27). Understanding the Big Five Personality Traits . ThoughtCo. Retrieved 12 June 2020, from https://www.thoughtco.com/big-five-personality-traits-4176097

Yamagata, S., Suzuki, A., Ando, J., Ono, Y., Kijima, N., Yoshimura, K., . . . Jang, K. (2006). Is the Genetic Structure of Human Personality Universal? A Cross-Cultural Twin Study From North America, Europe, and Asia. Journal of Personality and Social Psychology, 90 (6), 987-998. https://doi.org/10.1037/0022-3514.90.6.987

Keep Learning

  • Minnesota Multiphasic Personality Inventory (MMPI)
  • McCrae, R. R., & Terracciano, A. (2005). Universal features of personality traits from the observer’s perspective: data from 50 cultures. Journal of Personality and Social Psychology, 88 (3), 547.
  • Cobb-Clark, DA & Schurer, S. The stability of big-five personality traits. Economics Letters. 2012; 115 (2): 11–15.
  • Marsh, H. W., Nagengast, B., & Morin, A. J. (2013). Measurement invariance of big-five factors over the life span: ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects. Developmental psychology, 49 (6), 1194.
  • Power RA, Pluess M. Heritability estimates of the Big Five personality traits based on common genetic variants. Transl Psychiatry. 2015;5 :e604.
  • Personality Theories Book Chapter
  • The Cambridge Handbook of Personality Psychology

big five personality

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Big 5 Personality Traits

Reviewed by Psychology Today Staff

The differences between people’s personalities can be broken down in terms of five major traits—often called the “Big Five.” Each one reflects a key part of how a person thinks, feels, and behaves. The Big Five traits are:

  • Openness to experience (includes aspects such as intellectual curiosity and creative imagination )
  • Conscientiousness (organization, productiveness, responsibility)
  • Extroversion (sociability, assertiveness ; its opposite is Introversion )
  • Agreeableness (compassion, respectfulness, trust in others)
  • Neuroticism (tendencies toward anxiety and depression )

Individual personalities are thought to feature each of these five broad traits to some degree. When the traits are measured, some people rate higher and others rate lower: Someone can be more conscientious and less agreeable than most people, for instance, while scoring about average on the other traits. These traits remain fairly stable during adulthood.

People can also differ on the more specific facets that make up each of the Big Five traits. A relatively extroverted person might be highly sociable but not especially assertive .

The five-factor model is widely used by personality researchers, but it is not the only model. A more recently introduced six-factor model known as HEXACO adds the factor of honesty-humility to the original five traits.

  • Measuring the Big Five
  • Why the Big Five Matter
  • Other Personality Tests

Photo by Min An from Pexels

The Big Five traits are typically assessed using one of multiple questionnaires. While these tests vary in the exact terms they use for each trait, they essentially cover the same broad dimensions, providing high-to-low scores on each: openness to experience (also called open-mindedness or just openness), conscientiousness , extroversion (the reverse of which is introversion ), agreeableness , and neuroticism (sometimes negative emotionality or emotional stability).

One test, the latest version of the Big Five Inventory, asks how much a person agrees or disagrees that he or she is someone who exemplifies various specific statements, such as:

  • “Is curious about many different things” (for openness, or open-mindedness)
  • “Is systematic, likes to keep things in order” (for conscientiousness)
  • “Is outgoing, sociable” (for extroversion)
  • “Is compassionate, has a soft heart” (for agreeableness)
  • “Is moody, has up and down mood swings” (for neuroticism, or negative emotionality)

Based on a person’s ratings for dozens of these statements (or fewer, for other tests), an average score can be calculated for each of the five traits.

Scores on a Big Five questionnaire provide a sense of how low or high a person rates on a continuum for each trait. Comparing those scores to a large sample of test takers—as some online tests do—offers a picture of how open, conscientious, extroverted (or introverted), agreeable, and neurotic one is relative to others. 

Analyzing English words used to describe personality traits, researchers used statistical techniques to identify clusters of related characteristics . This led to a small number of overarching trait dimensions that personality psychologists have scientifically tested in large population samples.

The Big Five were not determined by any one person—they have roots in the work of various researchers going back to the 1930s. In 1961, Ernest Tupes and Raymond Christal identified five personality factors that others would reanalyze and rename. Lewis Goldberg used the term Big Five in 1981 to describe these broad factors. 

Some Big Five questionnaires break the five main traits down into smaller sub-components or “facets,” which are correlated with each other but can be independently measured. In the Big Five Inventory, for instance, “sociability” and “ assertiveness ” are distinct facets of extroversion, while “organization” and “responsibility” are facets of conscientiousness.

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The five-factor model not only helps people better understand how they compare to others and to put names to their characteristics. It’s also used to explore relationships between personality and many other life indicators. These include consequential outcomes such as physical health and well-being as well as success in social, academic, and professional contexts. Personality psychologists have observed reliable associations between how people rate on trait scales and how they fare or feel, on average, in various aspects of their lives.

Quite a lot , at least in Western samples. There is reliable evidence, for example, that extroversion is associated with subjective well-being, neuroticism with lower work commitment, and agreeableness with religiousness. Certain traits have been linked to mortality risk. However, these are overall patterns and don’t mean that a trait necessarily causes any of these outcomes.

Yes. While personality trait measures tend to be fairly consistent over short periods of time in adulthood, they do change over the course of a lifetime. There’s also reason to believe that deliberate personality change is possible.

Flora Westbrook/Pexels

Various ways of representing major traits have been proposed, and personality researchers continue to disagree on the number of distinct characteristics that can be measured. The five-factor model dominates the rest, as far as psychologists are concerned, although multiple types of assessments exist to measure the five traits.

Outside of academic psychology, tests that aim to sort people into personality types—including the Myers-Briggs/MBTI and Enneagram—are highly popular, though many experts take issue with such tests on scientific grounds. The five-factor model has conceptual and empirical strengths that others lack.

For a number of reasons , many personality psychologists consider Big Five tests superior to the Myers-Briggs Type Indicator . These include concerns about the reliability of the types assigned by the Myers-Briggs and the validity of the test—though there is some overlap between its dimensions (which include extroversion-introversion) and the Big Five.

It depends on how strictly you define a “type.” Research indicates that for any given trait, people fall at various points along a continuum rather than fitting neatly into categories. While some identify wholeheartedly as a total extrovert or introvert, for example, there are many shades in between, and most of us would score somewhere in the middle.

Yes. Some have criticized the five-factor model for its origins in data rather than in theory and argued that it does not encompass all fundamental traits (see HEXACO ). There is also evidence that current tests provide less reliable results outside of Western, industrialized countries.

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December 17, 2008

The "Big Five" Personality Traits

By Nikolas Westerhoff

In the 1970s two research teams led by Paul Costa and Robert R. McCrae of the National Institutes of Health and Warren Norman and Lewis Goldberg of the University of Michigan at Ann Arbor and the University of Oregon, respectively, discovered that most human character traits can be described using five dimensions. Surveys of thousands of people yielded these largely independent traits:

Extroversion: The most broadly defined of the Big Five factors measures cheerfulness, initiative and communicativeness. Those who score high for extroversion are companionable, sociable and able to accomplish what they set out to do. Those with low scores tend to be introverted, reserved and more submissive to authority.

Openness: People with high scores here love novelty and are generally creative. At the other end of the scale are those who are more conventional in their thinking, prefer routines, and have a pronounced sense of right and wrong.

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Agreeableness: This trait describes how we deal with others. High values show that someone is friendly, empathetic and warm. Shy, suspicious and egocentric individuals score low on the spectrum.

Conscientiousness: This dimension measures a person’s degree of organization. Those with high scores are motivated, disciplined and trustworthy. Irresponsible and easily distracted people are found at the low end of the scale.

Neuroticism: This scale measures emotional stability. People with high scores are anxious, inhibited, moody and less self-assured. Those at the lower end are calm, confident and contented.

Where are you on the Big Five scale? You can find out by taking a free personality test at www.outofservice.com/bigfive

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.

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  1. Trajectories of Big Five Personality Traits: A Coordinated Analysis of 16 Longitudinal Samples

    This study assessed change in self-reported Big Five personality traits. We conducted a coordinated integrative data analysis using data from 16 longitudinal samples, comprising a total sample of over 60 000 participants.

  2. New study throws into doubt the universality of the 'Big Five'

    Few psychology theories have as much support as the "Big Five" personality traits — the finding that people's personalities can be described by variations across five basic dimensions: openness to new experience, conscientiousness, extroversion/introversion, agreeableness and neuroticism.

  3. (PDF) Big Five personality traits

    Big Five personality traits In book: The SAGE encyclopedia of lifespan human development (pp.240-241) Authors: Christopher J Soto Colby College Abstract A personality trait is a characteristic...

  4. Assessing the Big Five personality traits using real-life ...

    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...

  5. Looking beyond the Big Five: A selective review of alternatives to the

    Numerous studies utilize the Big 5 personality traits as the measure of personality, thus establishing it as central to our understanding of what personality is. ... While there is still limited research investigating Big 5 personality amongst members who do not fit into these categories, one study by Gurven, Von Rueden, Massenkoff, Kaplan, ...

  6. (PDF) The Big Five Personality Traits and Academic ...

    Objective and method: This meta-analysis reports the most comprehensive assessment to date of the strength of the relationships between the Big Five personality traits and academic...

  7. Big Five personality traits and academic performance: A meta‐analysis

    PDF Tools Share Abstract Objective and Method This meta-analysis reports the most comprehensive assessment to date of the strength of the relationships between the Big Five personality traits and academic performance by synthesizing 267 independent samples ( N = 413,074) in 228 unique studies.

  8. The Discovery and Evolution of the Big Five of Personality Traits: A

    The Big Five construct of personality traits is a taxonomy of five higher-order personality traits that are believed to be responsible for people's differences and is considered the...

  9. Big Data Gives the "Big 5" Personality Traits a Makeover

    The "Big Five" traits (extroversion, neuroticism, openness, conscientiousness and agreeableness) emerged in the 1940s through studies of the English language for descriptive terms. Those...

  10. Big 5 Personality Traits: The 5-Factor Model of Personality

    The Big 5 personality traits are extraversion (also often spelled extroversion), agreeableness, openness, conscientiousness, and neuroticism . Extraversion is sociability, agreeableness is kindness, openness is creativity and intrigue, conscientiousness is thoughtfulness, and neuroticism often involves sadness or emotional instability.

  11. Big 5 Personality Traits: Psychology & Research Behind The Test

    The Big Five personality traits are openness, conscientiousness, extroversion, agreeableness, and neuroticism. These five fundamental traits attempt to summarize the human personality on a comparative scale. "Personality is defined as someone's usual patterns of behaviors, feelings, and thoughts.

  12. Big Five Personality Traits: The 5-Factor Model of Personality

    Neuroticism Behavioral Outcomes Critical Evaluation The Big Five Model, also known as the Five-Factor Model, is the most widely accepted personality theory held by psychologists today. The theory states that personality can be boiled down to five core factors, known by the acronym CANOE or OCEAN.

  13. Big 5 Personality Traits

    The Big Five traits are: Openness to experience (includes aspects such as intellectual curiosity and creative imagination) Conscientiousness (organization, productiveness, responsibility)...

  14. Full article: The Big Five Personality Traits as predictors of life

    The Big Five personality traits are the most widely used and recognized model as a comprehensive taxonomy of individual differences in human personality (John & Srivastava, Citation 1999). Stake and Eisele ( Citation 2010 ), considered the Big Five to provide a comprehensive map of universal personality traits.

  15. Full article: Association Between the Big Five and Trait Emotional

    Measurement of Big Five Personality Traits. Big five Inventory Citation 58 is a well-known measure of personality and consists of 44 short phrases describing the different traits. It assesses the personality on the big five traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness.

  16. The "Big Five" Personality Traits

    Mind & Brain In the 1970s two research teams led by Paul Costa and Robert R. McCrae of the National Institutes of Health and Warren Norman and Lewis Goldberg of the University of Michigan at Ann...

  17. Big Five Personality Traits

    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.

  18. The Big 5 Personality Traits

    Discover What Are the Big 5 Personality Traits? The 'big 5' personality traits Extraversion Agreeableness Emotional stability Recap Personality can be described by five distinct...

  19. Big Five personality traits

    Psychology portal v t e The Big Five personality traits, sometimes known as "the five-factor model of personality " or " OCEAN model ", is a grouping of five unique characteristics used to study personality. [1] It has been developed from the 1980s onward in psychological trait theory .

  20. Meta-analysis of personality trait differences between omnivores

    Vegetarian and vegan diets have been increasing in the Western world. Recent research has focused on personality trait differences between dietary groups, in part because personality traits are broad characteristics that can integrate findings about different factors that motivate vegetarian or vegan diets. Previous research on personality predictors of vegetarian and vegan (veg*n) diet ...