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Personality and health.

• Sarah E. Hampson Sarah E. Hampson Department of Psychology and Health, Oregon Research Institute
• https://doi.org/10.1093/acrefore/9780190236557.013.121
• Published online: 19 December 2017

Although the belief that personality is linked to health goes back at least to Greek and Roman times, the scientific study of these links began in earnest only during the last century. The field of psychosomatic medicine, which grew out of psychoanalysis, accepted that the body and the mind were closely connected. By the end of the 20th century, the widespread adoption of the five-factor model of personality and the availability of reliable and valid measures of personality traits transformed the study of personality and health. Of the five broad domains of personality (extraversion, agreeableness, conscientiousness, emotional stability, and intellect/openness), the most consistent findings in relation to health have been obtained for conscientiousness (i.e., hard-working, reliable, self-controlled). People who are more conscientious have better health and live longer lives than those who are less conscientious. These advantages are partly explained by the better health behaviors, good social relationships, and less stress that tend to characterize those who are more conscientious. The causal relation between personality and health may run in both directions; that is, personality influences health, and health influences personality. In addition to disease diagnoses and longevity, changes on biomarkers such as inflammation, cortisol activity, and cellular aging are increasingly used to chart health in relation to personality traits and to test explanatory models. Recognizing that both personality and health change over the life course has promoted longitudinal studies and a life-span approach to the study of personality and health.

• personality
• conscientiousness
• health behaviors

Introduction

Does the kind of person we are affect whether or not we will succumb to disease? This question has been posed since Greek and Roman times, but it only began to attract serious scientific inquiry in the latter half of the 20th century . With the widespread adoption of the Big Five approach to personality-trait measurement, the pace of personality–health research quickened, and its remit was greatly expanded. Although personality is only one of the myriad factors that impact health, it is a central element in the psychology of health because personality influences many of those other factors. The variation in people’s educational attainment and socioeconomic position, their response to stress, their social connectedness, and their diligence in following health recommendations can all be attributed, in part, to personality.

Personality–health research establishes associations between personality traits and health outcomes, and examines the mechanisms to account for these associations. The results of personality–health research have the potential to improve human health and well-being. With greater understanding of the pathways between individual characteristics and health comes the opportunity to redirect those pathways. This knowledge can be used by us all to help keep on a more healthful course. It can be used by healthcare professionals to improve the tailoring of care to the individual. The results of personality–health research may even be used to guide interventions to support the development of health-enhancing personality characteristics.

In the study of personality and health, trait approaches have become pre-eminent. Personality traits are generally defined as a person’s characteristic thoughts, feelings, and behaviors (Funder, 2001 ). The widespread acceptance of the five-factor or Big Five approach to trait theory has been a boon for personality–health research. In this approach, personality traits are comprehensively organized in terms of five broad and relatively independent domains that include both positive and negative characteristics related to extraversion (sociable, energetic, withdrawn), agreeableness (helpful, cooperative, hostile), conscientiousness (hard-working, self-controlled, disorganized), emotional stability (calm, anxious, worrying), and openness to experience (curious, imaginative, unintelligent). By the early 21st century , there were reliable and valid measures of these broad domains and of the narrower groups of traits, known as facets, making up these domains (John, Naumann, & Soto, 2008 ; McCrae & Costa, 2008 ). With a common metric for personality measurement, personality–health research has become a cumulative, incremental science that is building a solid body of evidence.

In personality–health research, health outcomes include self-reports of general health and specific diseases, doctors’ reports of diagnosed conditions, clinically assessed biomarkers that are known risk factors for morbidity and mortality, and mortality itself. Self-reports of health are easy and inexpensive to collect, but they may be biased. This can be problematic for personality–health research; for example, people who are more neurotic (i.e., less emotionally stable in Big Five terms) tend to perceive themselves to be in poorer health (Chapman, Duberstein, Sörensen, Lyness, & Emery, 2006 ). In defense of self-reports, self-rated general health, measured by a single item (“Compared to others of your age and gender, would you say that in general your health is poor, fair, good, very good, or excellent?”) is a reliable predictor of risk of dying (DeSalvo, Bloser, Reynolds, He, & Muntner, 2006 ). Patients’ reports of diagnosed diseases tend to correlate moderately well with doctors’ diagnoses (Barr, Tonkin, Welborn, & Shaw, 2009 ). Objective outcomes, such as assay values and anthropometric measures, though free of subjective biases, can also suffer from unreliability, the “white coat” effect on blood pressure being a familiar example.

The Origins of Modern Personality and Health Research

Hippocrates (ca. 460–ca. 370 bc ) is known as the Father of Western medicine, but he was also a pioneer of personality and health because he recognized that the body and psyche are connected. His practice of medicine was based on the theory of the four body humors (blood, yellow bile, black bile, and phlegm), which were associated with four temperament types (sanguine, choleric, melancholic, phlegmatic). His was a biological model of personality and health; these bodily humors, which could be traced to different organs, were associated with temperament and disease. Although modern medicine has abandoned the four humors, the four temperaments resonate with modern personality-trait theory. For example, sanguine individuals are extraverted and emotionally stable, whereas melancholic individuals are introverted and neurotic. Western thought came to be dominated by Descartes’s mind-body dualism, but by the early 20th century , the link between the psyche and the soma was proving fascinating to psychoanalysts, particularly Sigmund Freud, who believed that mental illness could manifest itself in physical symptoms (Freud, 1955 ). The field of psychosomatic medicine developed from these traditions and formalized the idea that diseases can result from psychological problems.

Against this historical background, it was perhaps inevitable that late 20th century research focused on relating particular personality traits to specific diseases. The most prominent example of this work was on the association between the Type A and cardiovascular disease (M. Friedman & Rosenman, 1974 ). Type A individuals are characterized by behavior that is hostile and competitive, and they experience irritation and frustration because of a sense of time urgency (M. Friedman, 1996 ). Hippocrates would have called them choleric. Initially, studies appeared to demonstrate that Type A people were more likely to experience cardiovascular disease, but failures to replicate those finding and studies that included important control variables, such as diet, began to question the association (Houston & Snyder, 1988 ).

Instead of associating a particular type of person with a specific disease, H. S. Friedman and Booth-Kewley ( 1987 ) conducted a meta-analysis to see if the same traits in fact were associated with several diseases. They concluded that there may be a disease-prone personality, characterized by anxiety, hostility, and depression, a conclusion that has been confirmed by others (Suls & Bunde, 2005 ). With the Type A pattern reduced to its most active ingredient, hostility, Type A is no longer widely used as a measure of personality in personality–health research. But that body of work left behind the important legacy that certain personality traits are associated with diseases, if not in the one-to-one manner previously supposed.

The 1990s marked a turning point for the study of personality and health with the publication of findings on childhood personality predicting longevity using data from the Terman Life-Cycle study. The study began in 1921 with the goal of following a group of exceptionally gifted 11-year-olds to adulthood to see how their lives turned out (Terman & Oden, 1947 ). The sample was studied every 5 to 10 years, creating a uniquely valuable archive that included childhood personality and mortality data. H. S. Friedman and his colleagues used these data to test the hypothesis that childhood personality is associated with longevity. If supported, this would strongly indicate that personality influences health across the lifespan. Their first report caused quite a stir (H. S. Friedman et al., 1993 ). Children who were viewed as more conscientious by their teachers and parents lived longer than those who were less conscientious. They also observed that children who were more cheerful (optimistic, humorous) were at a slightly higher risk for mortality than those who were less cheerful, and that being emotionally stable may have been protective against mortality for men. The largest effect they observed was for conscientiousness. Having low childhood conscientiousness was equivalent to the mortality risk of having high blood pressure or high cholesterol (H. S. Friedman, Tucker, Schwartz, Tomlinson-Keasy et al., 1995 ). Naturally, the next question to investigate was why conscientiousness was related to longevity. The Terman archives included data on cause of death and on some health behaviors. However, the association was not fully accounted for by low-conscientious children dying from accidents and violence or by their histories of drinking alcohol, smoking, and obesity, which left many unanswered questions for the next era of personality–health research (H. S. Friedman, Tucker, Schwartz, Martin et al., 1995 ).

Twenty-First-Century Personality and Health Research

While H. S. Friedman and colleague’s findings provided the impetus for contemporary personality–health research, the emergence of the Big Five provided the necessary tools for describing and measuring personality. Now that a legitimate link between at least one of the Big Five traits and a hard health outcome (mortality) had been established, personality–health researchers began to take a closer look at all of the Big Five in relation to other health outcomes. They did so in cross-sectional and longitudinal studies, and they began to examine possible underlying mechanisms to explain these associations.

The large-scale study by Goodwin and Friedman ( 2006 ) illustrates the value of cross-sectional research. The authors related scores on the Big Five personality dimensions to self-reported mental and physical health and physical limitations in a representative sample of over 3,000 community-dwelling men and women participating in the Midlife Development in the United States (MIDUS) study. They found that that the Big Five traits of conscientiousness and emotional stability (vs. neuroticism) were consistently and strongly related to better health; whereas the associations for extraversion, agreeableness, and openness were less clear-cut. A limitation of this study was the reliance on self-reported health, which may have inflated the association between neuroticism and reporting mental and physical illness. The consistent findings for conscientiousness in this representative sample provided further support that this trait protects against poor health. The findings suggested that more-conscientious people were less likely to get sick and that for this reason, they were more likely to live longer lives. Yet, without longitudinal studies, even a tentative inference that conscientiousness is somehow causally related to better health could not be justified. Better health could cause higher levels of conscientiousness; or, both good health and conscientiousness could be spuriously associated because of a third underlying variable causing them both. Fortunately, several longitudinal studies that included personality and health data had been underway over a long enough period to permit researchers to ask whether personality traits measured years or even decades earlier were associated with subsequent health outcomes.

Longitudinal Personality–Health Research

Personality and mortality.

The association between childhood conscientiousness and longevity observed in the Terman Life-Cycle study spurred researchers to examine this association in other samples. The Terman participants were selected because of their high IQs. Would the association be observed in samples taken from the general population, and would it be observed when conscientiousness was assessed at an older age than childhood? A meta-analysis combining findings from 20 different samples including nearly 9,000 people, primarily adults but some adolescents as well, established an overall correlation of r = .11 between conscientiousness and mortality (Kern & Friedman, 2008 ). To provide some context for this correlation, it is of the same magnitude as the association between antihistamine use and reduced runny nose, and combat exposure and subsequent PTSD within 18 years (Meyer et al., 2001 ). Even more impressive was the conclusion drawn from a later meta-analysis of over 76,000 individuals, from seven different cohort studies, with a mean age of 51 years at the time of personality measurement. This meta-analysis compared the associations for all the Big Five and mortality and concluded that conscientiousness was the only Big Five trait to predict all-cause mortality (Jokela et al., 2013 ). Those in the lowest tertile for conscientiousness had a 34% increased risk of dying.

The case for conscientiousness and longevity was compelling, but what about the other Big Five traits? There is evidence that people who are higher on neuroticism (i.e., those who are low on emotional stability) are at greater risk of dying (Grossardt, Bower, Geda, Colligan, & Rocca, 2009 ; Wilson, Mendes de Leon, Bienias, Evans, & Bennett, 2004 ). In one study, older men with high and increasing levels of neuroticism over time were at higher risk of death (Mroczek & Spiro, 2007 ). However, neuroticism was not associated with mortality at eight years follow-up in a study of Medicare recipients (Costa, Weiss, Duberstein, Friedman, & Siegler, 2014 ). For some, neuroticism may be associated with better health because of greater vigilance to environmental and bodily cues related to health. As a consequence, neuroticism may actually be advantageous for health for some people (H. S. Friedman, 2000 ). Some studies have found that people higher on openness to experience have a lower mortality risk (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007 ; Turiano, Spiro, & Mroczek, 2012 ), and a meta-analysis indicated that there is a modest protective effect of openness even when controlling for standard mortality risk factors (Ferguson & Bibby, 2012 ). There is less evidence for extraversion and agreeableness. Extraverts may be at higher risk for mortality (Ploubidis & Grundy, 2009 ); whereas those with higher levels of agreeableness may enjoy protection from mortality risk (Costa et al., 2014 ). In sum, all of the Big Five have been associated with mortality, but the evidence is stronger and more consistent for conscientiousness than for the other traits.

Personality and Morbidity

Returning to a question asked by Hippocrates and Galen, personality researchers have used the Big Five framework to see whether there are traits that are predictive of the onset of particular diseases, and disease-specific mortality. To answer this question, meta-analyses have again proved helpful. In a meta-analysis of studies including nearly 35,000 participants who were diabetes-free at baseline, low conscientiousness was the only Big Five trait associated with risk of diabetes onset and diabetes mortality over the subsequent 5.7 years (Jokela, Elvainio et al., 2014 ). A meta-analysis of three cohort studies involving over 24,000 older participants examined death from coronary heart disease or stroke after 3–15 years (Jokela, Pulkki-Råback, Elovainio, & Kivimäki, 2014 ). Higher conscientiousness was protective against death from either heart disease or stroke, whereas higher extraversion increased the risk of stroke but not heart disease, and higher neuroticism was more strongly related to heart disease than stroke. Despite a common belief that personality is related to cancer, a meta-analysis of over 42,000 initially cancer-free individuals failed to relate any of the Big Five to increased risk of cancer or death by cancer after 5.4 years follow-up (Jokela, Batty et al., 2014 ).

Diabetes, cardiovascular disease, stroke, and cancer are leading causes of mortality in developed countries. Lack of conscientiousness may be implicated in the onset of all but cancer. The evidence for the involvement of the other Big Five is either less clear or nonexistent. These studies have used diagnosed disease, typically self-reported, as their end point. However, chronic diseases can take years to develop, and the risk of their onset is signaled by deterioration over time on measures, known as biomarkers, such as blood pressure, lipid profiles, and body mass index. Finding associations between personality traits and disease onset provided good justification to look for associations between traits and biomarkers of disease risk.

Personality and Biomarkers

A cross-sectional study of personality and health in a community-based sample of over 5,000 Sardinians has yielded some provocative findings. Participants who were lower on conscientiousness had less healthy lipid profiles (Sutin, Terraciano et al., 2010 ), and were more likely to display an unhealthy pattern of nighttime blood pressure changes (Terracciano et al., 2014 ). They also had higher levels of leptin, a hormone involved in appetite, suggesting they might be more resistant to leptin, which could be a mechanism involved in weight gain (Sutin et al., 2013 ). Those lower on conscientiousness were more likely to be obese (Terracciano et al., 2009 ). Also in this same sample, those who were more impulsive had higher white-blood-cell counts, and those who were more neurotic and less conscientious had higher levels of interleuken-6 (Sutin et al., 2012 ). Both of these biomarkers are indicators of inflammation, which has health-damaging effects and is associated with several chronic conditions. The link between low conscientiousness and higher levels of the inflammatory markers of C-reactive protein and interleukin-6 was confirmed in a meta-analysis involving several very large studies and many thousands of participants (Luchetti, Barkley, Stephan, Terracciano, & Sutin, 2014 ).

The associations between single biomarkers and personality traits can be quite small, particularly after controlling for covariates such as gender and education, necessitating large sample sizes to demonstrate statistically significant associations that may not be of clinical relevance. An alternative approach is to use a combination of biomarkers as the outcome. This has the advantage of summing across several small effects. It is also consistent with medical practice. For example, endocrinologists use the metabolic syndrome, which is derived from several biomarkers indicating cardiovascular and metabolic dysregulation, to evaluate risk for diabetes and heart disease. In another study using data from the Sardinian sample, Sutin, Costa et al. ( 2010 ) found that high neuroticism and low agreeableness were associated with the metabolic syndrome, whereas high conscientiousness was protective.

Cross-sectional findings such as those for the Sardinian sample are consistent with the interpretation that personality traits could lead to morbidity through deteriorating health, but longitudinal studies provide more compelling evidence for a causal pathway.

The ongoing Dunedin Multidisciplinary Health and Development study is following more than 1,000 people from their births in 1972–1973 in the town of Dunedin, New Zealand, to the present. The study did not include a measure of the Big Five in childhood but several measures of self-control were collected over the children’s first decade of life from parents, teachers, and self-reports. These were combined into a single, internally reliable measure that predicted an objectively assessed physical-health index at age 32. This index was composed of metabolic abnormalities (including overweight), airflow limitation, periodontal disease, sexually transmitted infection, and C-reactive protein level. Lower childhood self-control predicted poorer adult health on this index, even after controlling for social class in childhood and IQ (Moffitt et al., 2011 ).

The Hawaii Longitudinal Study of Personality and Health (Hawaii study) is notable for its detailed childhood personality assessment at age 10 conducted by teachers, from which scores on the Big Five have been derived (Goldberg, 2001 ). Forty years later, at a mean age of 50–51, childhood personality was related to a composite of cardiovascular and metabolic biomarkers of physiological dysregulation (blood pressure, lipid profile, obesity, urine protein, fasting blood glucose, and medication for blood pressure or cholesterol). The only childhood trait to predict dysregulation was conscientiousness. Those who were less conscientious as children were more likely to be more physiologically dysregulated as adults approximately 40 years later, controlling for gender, ethnicity, social class in childhood, and adult levels of conscientiousness (Hampson, Edmonds, Goldberg, Dubanoski, & Hillier, 2013 ).

The Cardiovascular Risk in Young Finns (CRYF) study is an ongoing investigation of risk factors for cardiovascular disease in a representative sample aged 3 to 18 when first recruited (Raitakari et al., 2008 ). Baseline measures of psychosocial risk factors (parents’ ratings of their child’s self-regulation, the socioeconomic environment, and stressful life events) were summed and used to predict a composite measure of cardiovascular health assessed by self-report and clinical measures 27 years later (Pulkki-Råback et al., 2015 ). Lower overall psychosocial risk predicted better cardiovascular health in a dose-response pattern, and the measure of children’s self-regulation alone predicted cardiovascular health.

Together, these three longitudinal studies that differ in location, time period, age of sample, and measures all converge on the same result: measures related to conscientiousness and self-control in childhood were associated prospectively with a composite measure of physical health decades later. These findings strongly suggest that this personality domain exerts a sustained influence on health status across adulthood. Paired with the findings for mortality, a convincing picture emerges of conscientiousness affecting health across the lifespan. Given the weight of evidence for an association, quite possibly causal, it becomes important to investigate plausible explanations for how personality could influence health.

Personality and Health Mechanisms

Three mechanisms to account for personality influences on health have received particular attention: health behaviors, social relationships, and stress. Personality traits may influence whether a person engages in health-enhancing or health-damaging behaviors, has a supportive social environment, is exposed to stress and can manage stress. These mechanisms are not mutually exclusive and most probably work together to produce biological changes that lead to health outcomes. Personality traits influence many of the factors that are involved in health, which makes studying a particular personality mechanism challenging. For example, when studying health behaviors, it is also necessary to consider that personality influences educational attainment, which affects socioeconomic position, which is strongly related to health. Those with higher educational attainment are likely to be more knowledgeable about health behaviors and to have more time and money to spend on a health-enhancing lifestyle, which will result in better health. Personality most likely affects health through a series of processes involving additional factors in a chain of influences that take place over time (Shanahan et al., 2014 ).

Health-Behavior Mechanisms

Several studies demonstrate that health-behavior mechanisms account for some of the association between personality, particularly conscientiousness, and mortality (e.g., Hagger-Johnson et al., 2012 ; Hill, Turiano, Hurd, Mroczek, & Roberts, 2011 ; Turiano, Chapman, Gruenewald, & Mroczek, 2015 ; Turiano, Hill, Roberts, Spiro, & Mroczek, 2012 ). According to a review by Turiano et al. ( 2015 ), the amount of variance in mortality attributable to conscientiousness that was mediated by health behaviors ranged from 0% to 21% across studies, with a mean of 12%. This suggests that health behaviors provide a partial explanation for the effects of conscientiousness on mortality; but other mechanisms are likely to be involved as well.

The health-behavior model has also been tested with morbidity as the outcome. In a cross-sectional study, Lodi-Smith et al. ( 2010 ) showed that conscientiousness influenced self-reported physical health through education, risky behaviors, and preventive health behaviors. Ideally, the mediation process implied by the health-behavior model should be tested prospectively by assessing personality prior to health behavior, which is assessed prior to the health outcome. Furthermore, measuring health behavior at one time point probably does not adequately capture the long-term health-behavior patterns that are required to produce biological effects on biomarkers, such as high cholesterol. To address this limitation, a cumulative measure of health-damaging behavior across the lifespan was developed in the Hawaii study that combined lifetime smoking, physical inactivity, and BMI summed across decades in adulthood (Hampson, Edmonds, Goldberg, Dubanoski, & Hillier, 2015 ). Childhood conscientiousness was related to objectively assessed health status at age 50–51 through this measure of life-span health-damaging behavior as well as educational attainment and cognitive ability, controlling for any effects of adult conscientiousness. Health-behavior mechanisms appear to underlie the association between both conscientiousness and mortality and conscientiousness and morbidity.

Social-Relationship Mechanisms

Individuals who are more socially integrated tend to have better health and to live longer (Seeman, 1996 , 2000 ). Social integration is typically defined in terms of being married, having children, working, and having other supportive social networks, including belonging to organizations such as churches or volunteer groups. Mechanisms to account for the association between social integration and better health include health behaviors, stress, and physiological effects. Better integrated individuals may be more likely to engage in health-enhancing behaviors and to avoid health-damaging ones. Because of their social relationships, they may be exposed to less stress, and have better social resources to cope with stress. The effects of social integration may also have a direct impact on physiological regulation. Given the importance of social relationships for health, they may be involved in personality–health mechanisms. Personality may influence a person’s degree of social integration, and social integration may serve as an explanatory mechanism for the association between personality and health.

Support for social integration as a personality–health mechanism was demonstrated in a study of marital history and mortality using the Terman Life-Cycle data (Tucker, Friedman, Wingard, & Schwartz, 1996 ). It is well-established that being married is associated with better health outcomes. However, current marital status may be too simplistic. Taking a lifespan perspective, Tucker et al. ( 1996 ) examined marital history, and showed that those who were married but had been divorced were at higher risk of dying than those who were consistently married (i.e., had not experienced a marital breakup). Those who were consistently married had higher childhood conscientiousness than those who were inconsistently married. Childhood conscientiousness accounted for some, but not all, of the difference in mortality between those who were consistently married versus inconsistently married. This pattern of results suggests that the childhood trait of conscientiousness directs individuals on a pathway to health in part through its influence on social relationships, in this case, marriage. A more conscientious individual may make a better spouse, perhaps initially by making a more considered choice of marriage partner and subsequently by being a more reliable and persevering partner.

Given that social integration is important for health, people who are more extraverted may enjoy better health because of their sociability. However, research on extraversion has produced findings both for and against the health benefits of extraverted traits (Hampson & Friedman, 2008 ). In an experimental study, participants with higher levels of sociability (in this case, a combination of extraversion and agreeableness) were less likely to succumb to the common cold when deliberately infected with the virus, controlling for baseline immunity. Those who were more sociable had larger social networks and more social contacts, yet the effects of sociability on disease susceptibility were not mediated by social integration (Cohen, Doyle, Turner, Alper, & Skoner, 2003 ). This study illustrates the complexity of personality–health mechanisms. The researchers could not identify the mediators of sociability in this particular study, and concluded that a common genetic basis for sociability and disease resistance may provide the explanation.

Trauma and Stress Mechanisms

Trauma exposure has been reliably shown to have long-term negative consequences for physical and mental health (Felitti et al., 1998 ; Freyd, Klest, & Allard, 2005 ). Personality may increase the likelihood of experiencing trauma through greater probability of trauma exposure, greater reactivity to trauma, or both. Trauma is a source of stress and thus may have a direct negative impact on the stress-response system, and dysregulation of this system has been linked with a variety of biomarkers of stress and inflammation (Fagundes, Glaser, & Kiecolt-Glaser, 2013 ; Schrepf, Markon, & Lutgendorf, 2014 ).

Personality is implicated in stress in several ways (Vollrath, 2001 ). Personality traits may moderate the stress response. For example, higher levels of neuroticism may be associated with greater sensitivity to stressful experiences so that a trauma may result in worse negative health outcomes for a more neurotic person compared to a less neurotic person. Higher levels of resilient traits, such as high conscientiousness, may dampen the stress response. In contrast, lack of self-control and low conscientiousness increase the probability that a person will be exposed to trauma and stress (Galla & Wood, 2015 ; Murphy, Miller, & Wrosch, 2013 ). In the Hawaii study, girls who were seen by their teachers as less agreeable and less conscientious at age 10 were more likely to retrospectively report trauma across three age periods (childhood, adolescence, and adulthood) 40 years later. In support of a personality–health mechanism, girls with lower levels of childhood conscientiousness reported more teen and adult trauma, which resulted in poorer objectively assessed health at age 50–51 (Hampson et al., 2016 ).

Multiple Mechanisms

Research indicates that personality–health mechanisms include health behaviors, social relationships, and trauma. These trait-related experiences have a biological impact that ultimately affects health, including disrupting the stress response and the immune system. The different kinds of mechanisms described here are interrelated and likely operate in combination. For example, personality is responsible, in part, for the stress resulting from a lack of close relationships, and for engaging in unhealthy behaviors, such as alcohol abuse and smoking. Stress and unhealthy behaviors compromise the immune system (Kiecolt-Glaser, Gouin, & Hantsu, 2010 ). Similarly, personality is implicated in experiencing trauma, such as child abuse, and childhood abuse is related to greater immune response to stressors in adulthood (Fagundes et al., 2013 ; Gouin, Glaser, Malarkey, Beversdorf, & Kiecolt-Glaser, 2012 ). While no one study can capture the full complexity of personality–health mechanisms, oversimplification can result in misleading conclusions. Studies that address the complexity of these interrelated mechanisms as they unfold over time are needed (Friedman & Kern, 2014 ).

A further complexity that needs to be considered is that while personality may influence health outcomes, it is also likely that health may influence personality. That is, if there is a causal relation between personality and health, it may run in both directions. Most of personality–health research is conducted from the perspective that personality may contribute causally to health outcomes, but there are also studies that have investigated the possibility that health affects personality and that there may be reciprocal effects.

Effects of Health on Personality

The experience of being in poor health is often associated with changes in mood and energy levels, and the changes that accompany illness may be associated with, and perhaps are causally related, to changes in personality. A disease is associated with altered biology, which may also affect the biological bases of personality traits. A diagnosis can lead to a new social identity, referred to by Goffman as the “sick” role (Goffman, 1990 ). When a person becomes a “cancer patient” or a “diabetic,” aspects of her lifestyle inevitably must change, and she is likely to be treated differently by others. As a consequence, she may come to see herself differently and undergo some degree of personality change. In addition, a life-altering illness is a stressful event, and stressful events appear to affect aspects of personality, such as increasing the level of neuroticism (Riese et al., 2014 ).

To demonstrate that a health event preceded a change in personality, the ideal research design would be to measure personality before and after the event. Comparing personality measured after the onset of illness with retrospective reports of personality prior to illness is less satisfactory. Recalling one’s personality at an earlier time is a difficult task, and it may be affected by one’s current health and personality. There have been recent studies that assessed personality pre- and post-illness, but the findings do not provide a consistent picture. In a Finnish study of young adults, the onset of a chronic illness was related to increased neuroticism and increased conscientiousness (Liekas & Salmela-Aro, 2015 ). In contrast, among older participants across three independent, large-scale cohort studies, conscientiousness decreased after the onset of chronic illness (Jokela, Hakulinen, Singh-Manoux, & Kivimäki, 2014 ). Data from the Baltimore Longitudinal Study of Aging, which include multiple assessments of health and personality over time, indicated that personality remained largely unchanged in response to disease onset (Sutin, Zonderman, Ferrucci, & Terracciano, 2013 ). It appears that disease onset may have both negative and positive consequences for personality change. Increases in neuroticism may be the result of health-related anxiety and vigilant disease monitoring, whereas increases in conscientiousness may be the result of improved health behavior that led to changes in self-perception. Among older people, becoming ill may lead to a “dolce vita” effect: enjoy life while you can at the expense of prudence (Marsh, Nagengast, & Morin, 2013 ).

One challenge for these studies is in distinguishing between a personality change associated with a change in health and the normative changes in personality that occur across the life course. Over time, health change may precipitate personality change, which may then influence health, and so on. These kinds of patterns of cross-lagged influences over time are studied in autoregressive models. Latent curve models represent the overall shape of the developmental trajectory of a variable, such as health or personality, over time. Both statistical approaches can be combined to capture the variable’s overall trajectory and its perturbations in autoregressive latent trajectories (Bollen & Curran, 2004 ). Although only a few existing studies in personality and health have the necessary longitudinal data, it is likely that more of this kind of modeling will be used in future studies.

Using Personality to Achieve Health Benefits

The evidence for a link between personality and health is by now quite substantial. The prospective evidence that personality measured years, even decades, earlier predicts health outcomes suggests that personality has a causal influence on health. Further support for personality as a cause of health outcomes comes from intervention studies in which the effects of deliberate changes in personality on health are studied. The logic here is that if personality is causally related to health, then changing personality should change health. There is growing interest in developing such interventions, given the mounting evidence that personality is implicated in health outcomes.

One of the earliest demonstrations that personality change can have beneficial health effects was provided by M. Friedman et al. ( 1984 ), who showed that counseling to increase psychological well-being among Type A patients reduced the risk of reoccurrence of cardiovascular events. This is an example of a top-down approach, where the intervention is designed to change personality and then leads to healthful behavior change. This is the principle behind many psychotherapeutic interventions that address behavioral and mental health issues (Chapman, Hampson, & Clarkin, 2014 ). However, people do not necessarily need external support to make personality changes. Volitional top-down trait change was observed in undergraduates, and an intervention to support volitional trait change was also effective in this population (Hudson & Fraley, 2015 ). An alternative is a bottom-up approach in which the intervention targets behavior change, which then leads to personality change (Magidson, Roberts, Collado-Rodriguez, & Lejuez, 2014 ).

Given the remarkably far-reaching influences of childhood conscientiousness on health, it may seem like a good idea to develop interventions to increase conscientiousness from an early age. Indeed, the experience of preschool and elementary school encourages children to develop self-control. However, a cautionary note must be sounded. There is some evidence that conscientiousness can have detrimental effects on health, so that universally increasing this trait could have unintended consequences. Highly conscientious individuals are verging on obsessional-compulsive, which is related to lower well-being (Carter, Guan, Maples, Williamson, & Miller, 2015 ). In a study of adolescent young women, those higher in conscientiousness were less likely to get into stressful situations, but when they did, they had more unhealthy stress responses than those who were lower in conscientiousness (Murphy et al., 2013 ). Situations that one cannot change through one’s own efforts may be more harmful for conscientious than for unconscientious individuals.

Despite the concerns about personality interventions, a number of researchers have proposed that the evidence for the association between personality and health justifies the wider dissemination of this knowledge to the medical community (Bogg & Roberts, 2013 ; Israel et al., 2014 ). For example, in this era of personalized medicine, a relatively brief personality assessment could provide insights that would be helpful for doctor–patient communication and selecting treatment regimens best suited to the individual.

Future Directions

Several aspects of personality–health research may attract increasing attention in the future. These are related to separate developments in the fields of both personality and health. In personality, there is increasing interest in moving away from studying the influence of broad traits, such as the Big Five, in favor of drilling down to narrower traits, such as the facets of the Big Five. The driver behind this trend is that close analysis can reveal an association between a broad personality domain and a specific health variable that is attributable to a particular facet of the domain. For example, a meta-analysis of the associations between facets of conscientiousness and various health behaviors indicated that physical activity was most strongly associated with the industriousness facet of conscientiousness, whereas absence of self-control was most strongly related to excessive alcohol use (Bogg & Roberts, 2004 ). Such findings have led to a renewed discussion of what is meant by causality in personality-trait-outcome research, and some have concluded that narrower traits or even individual personality test items should be used instead of broad traits (Mõttus, 2016 ).

Whether using broad traits or narrower facets, hypothesized models underlying personality–health associations are likely to become more complex. For example, a model that compares two or more multiple mediating mechanisms, such as health behavior and stress, instead of examining just one, permits inferences about their relative importance in predicting health outcomes. These findings would be useful for prioritizing targets for interventions (e.g., stress reduction vs. health-behavior change). Research has tended to focus on the independent influences of traits, but this is an oversimplification of personality processes. Researchers are now examining the possible influence of particular trait combinations, such as being both conscientious and neurotic, on health and health behavior. Neuroticism is typically associated with poor health, particularly self-reported health, while the reverse is true for conscientiousness. H. S. Friedman ( 2000 ) hypothesized that the combination of being both highly conscientious and neurotic may be associated with better health. These individuals, so-called healthy neurotics, would be attentive to their symptoms, seek medical advice promptly, and dutifully follow recommended preventive and treatment regimens. Although this hypothesis is intuitively appealing, it has yet to receive consistent empirical support (Weston & Jackson, 2015 ). Finally, the recognition that it is misleading to treat personality and health as static variables, when they in fact change across the life course, will likely lead to more studies involving multiple assessments of both personality and health over time (Takahashi, Edmonds, Jackson, & Roberts, 2012 ).

On the health side, the use of objective health indicators (biomarkers) is increasingly favored over reliance on self-reported health or diagnosed disease as health outcomes. Biomarkers have several advantages. They can signal changes that are early risk factors for later chronic diseases that may take years, perhaps decades, to develop. Relating personality traits to these biomarkers provides the possibility of early intervention to change the course of declining health before the onset of clinical disease. The number of relatively low-cost biomarkers available for researchers is expanding. For example, assays for leucocyte telomere length and for mitochondrial DNA (markers of different aspects of cellular aging) are becoming more readily available and less expensive. Biomarkers avoid the possible biases of self-reports but have their own issues that will need to be addressed, such as test-retest reliability.

Finally, with the explosion of research on genetics in the 21st century , it is likely that much will be discovered in the future about shared the genetic influences on personality and physical health. Given that both personality and health are influenced by genetic factors, associations between the two could be the result of shared genetic processes (Figueredo & Rushton, 2009 ). Research on the genetics of the neurotransmitters dopamine and serotonin suggests possible genetic links between personality and chronic illnesses (Delvecchio, Bellani, Altamura, & Brambilla, 2016 ). For example, chronic inflammation is associated with both depression (linked to neuroticism) and coronary artery disease, and may have a common genetic basis involving serotonin (McCaffery et al., 2006 ).

Over the past 25 years, the field of personality and health has moved from the margins to the mainstream. The evidence that personality traits are associated with health behaviors and health outcomes is overwhelming, and research is now focusing on explaining these associations. The field has only just begun to consider the implications of these advances for improving health at the individual and population levels. For individuals, it is quite possible to envisage that a visit to the doctor will eventually incorporate a brief personality assessment that will be used to tailor patient-centered self-management of health conditions. From a public health perspective, significant benefits could accrue from a higher mean level of a trait such as conscientiousness by shifting the entire distribution in a more healthful direction, for example, through universal school-based interventions. But until we better understand the possible costs as well as the benefits of using personality traits to improve health outcomes, we should proceed with care.

Acknowledgments

The preparation of this contribution was supported by a grant R01AG020048 from the National Institute on Aging, the National Institutes of Health.

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Journal of Management History

ISSN : 1751-1348

Article publication date: 19 August 2022

Issue publication date: 6 April 2023

The purpose of this study is to systematically examine and classify the multitude of personality traits that have emerged in the literature beyond the Big Five (Five Factor Model) since the turn of the 21st century. The authors argue that this represents a new phase of personality research that is characterized both by construct proliferation and a movement away from the Big Five and demonstrates how personality as a construct has substantially evolved in the 21st century.

Design/methodology/approach

The authors conducted a comprehensive, systematic review of personality research from 2000 to 2020 across 17 management and psychology journals. This search yielded 1,901 articles, of which 440 were relevant and subsequently coded for this review.

The review presented in this study uncovers 155 traits, beyond the Big Five, that have been explored, which the authors organize and analyze into 10 distinct categories. Each category comprises a definition, lists the included traits and highlights an exemplar construct. The authors also specify the significant research outcomes associated with each trait category.

Originality/value

This review categorizes the 155 personality traits that have emerged in the management and psychology literature that describe personality beyond the Big Five. Based on these findings, this study proposes new avenues for future research and offers insights into the future of the field as the concept of personality has shifted in the 21st century.

• Personality
• Systematic literature review

Medina-Craven, M.N. , Ostermeier, K. , Sigdyal, P. and McLarty, B.D. (2023), "Personality research in the 21st century: new developments and directions for the field", Journal of Management History , Vol. 29 No. 2, pp. 276-304. https://doi.org/10.1108/JMH-06-2022-0021

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How Personality Impacts Our Daily Lives

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

Verywell / Emily Roberts

Personality Characteristics

How personality develops, impact of personality, personality disorders.

Personality describes the unique patterns of thoughts, feelings, and behaviors that distinguish a person from others. A product of both biology and environment, it remains fairly consistent throughout life.

Examples of personality can be found in how we describe other people's traits. For instance, "She is generous, caring, and a bit of a perfectionist," or "They are loyal and protective of their friends."

The word "personality" stems from the Latin word persona , which refers to a theatrical mask worn by performers to play roles or disguise their identities.

Although there are many definitions of personality, most focus on the pattern of behaviors and characteristics that can help predict and explain a person's behavior.

Explanations for personality can focus on a variety of influences, ranging from genetic effects to the role of the environment and experience in shaping an individual's personality.

What exactly makes up a personality? Traits and patterns of thought and emotion play important roles, and so do these fundamental characteristics of personality:

• Consistency : There is generally a recognizable order and regularity to behaviors. Essentially, people act in the same way or in similar ways in a variety of situations.
• Both psychological and physiological : Personality is a psychological construct, but research suggests that it is also influenced by biological processes and needs.
• Affects behaviors and actions : Personality not only influences how we move and respond in our environment, but it also causes us to act in certain ways.
• Multiple expressions : Personality is displayed in more than just behavior. It can also be seen in our thoughts, feelings, close relationships, and other social interactions.

There are a number of theories about personality , and different schools of thought in psychology influence many of these theories. Some theories describe how personalities are expressed, and others focus more on how personality develops.

Type theories suggest that there are a limited number of personality types that are related to biological influences.

One theory suggests there are four types of personality. They are:

• Type A : Perfectionist, impatient, competitive, work-obsessed, achievement-oriented, aggressive, stressed
• Type B : Low stress, even- tempered , flexible, creative, adaptable to change, patient, tendency to procrastinate
• Type C : Highly conscientious, perfectionist, struggles to reveal emotions (positive and negative)
• Type D : Worrying, sad, irritable, pessimistic, negative self-talk, avoidance of social situations, lack of self-confidence, fear of rejection, appears gloomy, hopeless

There are other popular theories of personality types such as the Myers-Briggs theory. The Myers-Briggs Personality Type Indicator identifies a personality based on where someone is on four continuums: introversion-extraversion, sensing-intuition, thinking-feeling, and judging-perceiving.

After taking a Myers-Briggs personality test, you are assigned one of 16 personality types. Examples of these personality types are:

• ISTJ : Introverted, sensing, thinking, and judging. People with this personality type are logical and organized; they also tend to be judgmental.
• INFP : Introverted, intuitive, feeling, and perceiving. They tend to be idealists and sensitive to their feelings.
• ESTJ : Extroverted, sensing, thinking, and judging. They tend to be assertive and concerned with following the rules.
• ENFJ : Extroverted, intuitive, feeling, and judging. They are known as "givers" for being warm and loyal; they may also be overprotective.

Personality Tests

In addition to the MBTI, some of the most well-known personality inventories are:

• Minnesota Multiphasic Personality Inventory (MMPI)
• HEXACO Personality Inventory
• Caddell's 16PF Personality Questionnaire
• Enneagram Typology

Personality Traits

Trait theories tend to view personality as the result of internal characteristics that are genetically based and include:

• Agreeable : Cares about others, feels empathy, enjoys helping others
• Conscientiousness : High levels of thoughtfulness, good impulse control, goal-directed behaviors
• Eager-to-please : Accommodating, passive, and  conforming
• Extraversion : Excitability, sociability, talkativeness, assertiveness, and high amounts of emotional expressiveness
• Introversion : Quiet, reserved
• Neuroticism : Experiences stress and dramatic shifts in mood, feels anxious, worries about different things, gets upset easily, struggles to bounce back after stressful events
• Openness : Very creative , open to trying new things, focuses on tackling new challenges

Try Our Free Personality Test

Our fast and free personality test can help give you an idea of your dominant personality traits and how they may influence your behaviors.

Psychodynamic Theories

Psychodynamic theories of personality are heavily influenced by the work of Sigmund Freud and emphasize the influence of the unconscious  mind on personality. Psychodynamic theories include Sigmund Freud’s psychosexual stage theory and Erik Erikson’s stages of psychosocial development .

Behavioral Theories

Behavioral theories suggest that personality is a result of interaction between the individual and the environment. Behavioral theorists study observable and measurable behaviors, often ignoring the role of internal thoughts and feelings. Behavioral theorists include B.F. Skinner and John B. Watson .

Humanist theories emphasize the importance of free will and individual experience in developing ​a personality. Humanist theorists include Carl Rogers and Abraham Maslow .

Research on personality can yield fascinating insights into how personality develops and changes over the course of a lifetime. This research can also have important practical applications in the real world.

For example, people can use a personality assessment (also called a personality test or personality quiz) to learn more about themselves and their unique strengths, weaknesses, and preferences. Some assessments might look at how people rank on specific traits, such as whether they are high in extroversion , conscientiousness, or openness.

Other assessments might measure how specific aspects of personality change over time. Some assessments give people insight into how their personality affects many areas of their lives, including career, relationships, personal growth, and more.

Understanding your personality type can help you determine what career you might enjoy, how well you might perform in certain job roles, or how effective a form of psychotherapy could be for you.

Personality type can also have an impact on your health, including how often you visit the doctor and how you cope with stress. Researchers have found that certain personality characteristics may be linked to illness and health behaviors.

While personality determines what you think and how you behave, personality disorders are marked by thoughts and behavior that are disruptive and distressing in everyday life. Someone with a personality disorder may have trouble recognizing their condition because their symptoms are ingrained in their personality.

Personality disorders include paranoid personality disorder , schizoid personality disorder , antisocial personality disorder , borderline personality disorder (BPD), and narcissistic personality disorder (NPD).

While the symptoms of personality disorders vary based on the condition, some common signs include:

• Aggressive behavior
• Delusional thinking
• Distrust of others
• Flat emotions (no emotional range)
• Lack of interest in relationships
• Violating others' boundaries

Some people with BPD experience suicidal thoughts or behavior as well.

If you are having suicidal thoughts, contact the  National Suicide Prevention Lifeline  at  988  for support and assistance from a trained counselor. If you or a loved one are in immediate danger, call 911. 

For more mental health resources, see our  National Helpline Database .

If you are concerned that elements of your personality are contributing to stress, anxiety, confusion, or depression, it's important to talk to a doctor or mental health professional. They can help you understand any underlying conditions you may have.

It is often challenging to live with a personality disorder, but there are treatment options such as therapy and medication that can help.

Understanding the psychology of personality is much more than simply an academic exercise. The findings from personality research can have important applications in the world of medicine, health, business, economics, technology, among others. By building a better understanding of how personality works, we can look for new ways to improve both personal and public health.

The Myers & Briggs Foundation.  MBTI basics .

Bornstein RF. Personality assessment in the diagnostic manuals: On mindfulness, multiple methods, and test score discontinuities .  J Pers Assess . 2015;97(5):446-455. doi:10.1080/00223891.2015.1027346

Srivastava K, Das RC. Personality and health: Road to well-being .  Ind Psychiatry J . 2015;24(1):1–4. doi:10.4103/0972-6748.160905

Mayo Clinic. Personality disorders .

Carducci BJ. The Psychology of Personality: Viewpoints, Research, and Applications . Wiley Blackwell. 

John OP, Robins RW, Pervin LA. Handbook of Personality: Theory and Research . Guilford Press.

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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11.1: Personality and Behavior- Approaches and Measurement

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Learning Objectives

• Outline and critique the early approaches to assessing personality.
• Define and review the strengths and limitations of the trait approach to personality.
• Summarize the measures that have been used to assess psychological disorders.

Early theories assumed that personality was expressed in people’s physical appearance. One early approach, developed by the German physician Franz Joseph Gall (1758–1828) and known as phrenology , was based on the idea that we could measure personality by assessing the patterns of bumps on people’s skulls (Figure \(\PageIndex{1}\)). In the Victorian age, phrenology was taken seriously and many people promoted its use as a source of psychological insight and self-knowledge. Machines were even developed for helping people analyze skulls (Simpson, 2005). However, because careful scientific research did not validate the predictions of the theory, phrenology has now been discredited in contemporary psychology.

Another approach, known as somatology , championed by the psychologist William Herbert Sheldon (1898–1977), was based on the idea that we could determine personality from people’s body types (Figure \(\PageIndex{2}\)). Sheldon (1940) argued that people with more body fat and a rounder physique (“endomorphs”) were more likely to be assertive and bold, whereas thinner people (“ectomorphs”) were more likely to be introverted and intellectual. As with phrenology, scientific research did not validate the predictions of the theory, and somatology has now been discredited in contemporary psychology.

Another approach to detecting personality is known as physiognomy , or the idea that it is possible to assess personality from facial characteristics. In contrast to phrenology and somatology, for which no research support has been found, contemporary research has found that people are able to detect some aspects of a person’s character—for instance, whether they are gay or straight and whether they are Democrats or Republicans—at above chance levels by looking only at his or her face (Rule & Ambady, 2010; Rule, Ambady, Adams, & Macrae, 2008; Rule, Ambady, & Hallett, 2009).

Despite these results, the ability to detect personality from faces is not guaranteed. Olivola and Todorov (2010) recently studied the ability of thousands of people to guess the personality characteristics of hundreds of thousands of faces on the website What’s My Image? (www.whatsmyimage.com). In contrast to the predictions of physiognomy, the researchers found that these people would have made more accurate judgments about the strangers if they had just guessed, using their expectations about what people in general are like, rather than trying to use the particular facial features of individuals to help them. It seems then that the predictions of physiognomy may also, in the end, find little empirical support.

Personality as Traits

Personalities are characterized in terms of traits, which are relatively enduring characteristics that influence our behavior across many situations . Personality traits such as introversion, friendliness, conscientiousness, honesty, and helpfulness are important because they help explain consistencies in behavior.

The most popular way of measuring traits is by administering personality tests on which people self-report about their own characteristics. Psychologists have investigated hundreds of traits using the self-report approach, and this research has found many personality traits that have important implications for behavior. You can see some examples of the personality dimensions that have been studied by psychologists and their implications for behavior in Table \(\PageIndex{1}\).

Sources: Adorno, T. W., Frenkel-Brunswik, E., Levinson, D. J., & Sanford, R. N. (1950). The authoritarian personality . New York, NY: Harper; Triandis, H. (1989). The self and social behavior in differing cultural contexts. Psychological Review, 93 , 506–520; Rotter, J. (1966). Generalized expectancies of internal versus external locus of control of reinforcement. Psychological Monographs, 80 ; McClelland, D. C. (1958). Methods of measuring human motivation. In John W. Atkinson (Ed.), Motives in fantasy, action and society . Princeton, NJ: D. Van Nostrand; Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42 , 116–131; Shah, J., Higgins, T., & Friedman, R. S. (1998). Performance incentives and means: How regulatory focus influences goal attainment. Journal of Personality and Social Psychology, 74 (2), 285–293; Fenigstein, A., Scheier, M. F., & Buss, A. H. (1975). Public and private self-consciousness: Assessment and theory. Journal of Consulting and Clinical Psychology, 43 , 522–527; Rosenberg, M. (1965). Society and the adolescent self-image . Princeton, NJ: Princeton University Press; Zuckerman, M. (2007). Sensation seeking and risky behavior . Washington, DC: American Psychological Association.

Example of a Trait Measure

You can try completing a self-report measure of personality (a short form of the Five-Factor Personality Test) here. There are 120 questions and it should take you about 15–20 minutes to complete. You will receive feedback about your personality after you have finished the test.

www.personalitytest.net/ipip/ipipneo120.htm

As with intelligence tests, the utility of self-report measures of personality depends on their reliability and construct validity . Some popular measures of personality are not useful because they are unreliable or invalid. Perhaps you have heard of a personality test known as the Myers-Briggs Type Indicator (MBTI). If so, you are not alone, because the MBTI is the most widely administered personality test in the world, given millions of times a year to employees in thousands of companies. The MBTI categorizes people into one of four categories on each of four dimensions: introversion versus extraversion , sensing versus intuiting , thinking versus feeling , and judging versus perceiving .

Although completing the MBTI can be useful for helping people think about individual differences in personality, and for “breaking the ice” at meetings, the measure itself is not psychologically useful because it is not reliable or valid. People’s classifications change over time, and scores on the MBTI do not relate to other measures of personality or to behavior (Hunsley, Lee, & Wood, 2003). Measures such as the MBTI remind us that it is important to scientifically and empirically test the effectiveness of personality tests by assessing their stability over time and their ability to predict behavior.

One of the challenges of the trait approach to personality is that there are so many of them; there are at least 18,000 English words that can be used to describe people (Allport & Odbert, 1936). Thus a major goal of psychologists is to take this vast number of descriptors (many of which are very similar to each other) and to determine the underlying important or “core” traits among them (John, Angleitner, & Ostendorf, 1988).

The trait approach to personality was pioneered by early psychologists, including Gordon Allport (1897–1967), Raymond Cattell (1905–1998), and Hans Eysenck (1916–1997). Each of these psychologists believed in the idea of the trait as the stable unit of personality, and each attempted to provide a list or taxonomy of the most important trait dimensions. Their approach was to provide people with a self-report measure and then to use statistical analyses to look for the underlying “factors” or “clusters” of traits, according to the frequency and the co-occurrence of traits in the respondents.

Allport (1937) began his work by reducing the 18,000 traits to a set of about 4,500 traitlike words that he organized into three levels according to their importance. He called them “cardinal traits” (the most important traits), “central traits” (the basic and most useful traits), and “secondary traits” (the less obvious and less consistent ones). Cattell (1990) used a statistical procedure known as factor analysis to analyze the correlations among traits and to identify the most important ones. On the basis of his research he identified what he referred to as “source” (more important) and “surface” (less important) traits, and he developed a measure that assessed 16 dimensions of traits based on personality adjectives taken from everyday language.

Hans Eysenck was particularly interested in the biological and genetic origins of personality and made an important contribution to understanding the nature of a fundamental personality trait: extraversion versus introversion (Eysenck, 1998). Eysenck proposed that people who are extroverted (i.e., who enjoy socializing with others) have lower levels of naturally occurring arousal than do introverts (who are less likely to enjoy being with others). Eysenck argued that extroverts have a greater desire to socialize with others to increase their arousal level, which is naturally too low, whereas introverts, who have naturally high arousal, do not desire to engage in social activities because they are overly stimulating.

The fundamental work on trait dimensions conducted by Allport, Cattell, Eysenck, and many others has led to contemporary trait models, the most important and well-validated of which is the Five-Factor (Big Five) Model of Personality. According to this model, there are five fundamental underlying trait dimensions that are stable across time, cross-culturally shared, and explain a substantial proportion of behavior (Costa & McCrae, 1992; Goldberg, 1982). As you can see in Table \(\PageIndex{2}\), the five dimensions (sometimes known as the “Big Five”) are agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. (You can remember them using the watery acronyms CANOE or OCEAN.)

A large body of research evidence has supported the five-factor model. The Big Five dimensions seem to be cross-cultural, because the same five factors have been identified in participants in China, Japan, Italy, Hungary, Turkey, and many other countries (Triandis & Suh, 2002). The Big Five dimensions also accurately predict behavior. For instance, a pattern of high conscientiousness, low neuroticism, and high agreeableness predicts successful job performance (Tett, Jackson, & Rothstein, 1991). Scores on the Big Five dimensions also predict the performance of U.S. presidents; ratings of openness to experience are correlated positively with ratings of presidential success, whereas ratings of agreeableness are correlated negatively with success (Rubenzer, Faschingbauer, & Ones, 2000). The Big Five factors are also increasingly being used in helping researchers understand the dimensions of psychological disorders such as anxiety and depression (Oldham, 2010; Saulsman & Page, 2004).

An advantage of the five-factor approach is that it is parsimonious. Rather than studying hundreds of traits, researchers can focus on only five underlying dimensions. The Big Five may also capture other dimensions that have been of interest to psychologists. For instance, the trait dimension of need for achievement relates to the Big Five variable of conscientiousness, and self-esteem relates to low neuroticism. On the other hand, the Big Five factors do not seem to capture all the important dimensions of personality. For instance, the Big Five does not capture moral behavior, although this variable is important in many theories of personality. And there is evidence that the Big Five factors are not exactly the same across all cultures (Cheung & Leung, 1998).

Situational Influences on Personality

One challenge to the trait approach to personality is that traits may not be as stable as we think they are. When we say that Malik is friendly, we mean that Malik is friendly today and will be friendly tomorrow and even next week. And we mean that Malik is friendlier than average in all situations. But what if Malik were found to behave in a friendly way with his family members but to be unfriendly with his fellow classmates? This would clash with the idea that traits are stable across time and situation.

The psychologist Walter Mischel (1968) reviewed the existing literature on traits and found that there was only a relatively low correlation (about r = .30) between the traits that a person expressed in one situation and those that they expressed in other situations. In one relevant study, Hartshorne, May, Maller, & Shuttleworth (1928) examined the correlations among various behavioral indicators of honesty in children. They also enticed children to behave either honestly or dishonestly in different situations, for instance, by making it easy or difficult for them to steal and cheat. The correlations among children’s behavior was low, generally less than r = .30, showing that children who steal in one situation are not always the same children who steal in a different situation. And similar low correlations were found in adults on other measures, including dependency, friendliness, and conscientiousness (Bem & Allen, 1974).

Psychologists have proposed two possibilities for these low correlations. One possibility is that the natural tendency for people to see traits in others leads us to believe that people have stable personalities when they really do not. In short, perhaps traits are more in the heads of the people who are doing the judging than they are in the behaviors of the people being observed. The fact that people tend to use human personality traits, such as the Big Five, to judge animals in the same way that they use these traits to judge humans is consistent with this idea (Gosling, 2001). And this idea also fits with research showing that people use their knowledge representation (schemas) about people to help them interpret the world around them and that these schemas color their judgments of others’ personalities (Fiske & Taylor, 2007).

Research has also shown that people tend to see more traits in other people than they do in themselves. You might be able to get a feeling for this by taking the following short quiz. First, think about a person you know—your mom, your roommate, or a classmate—and choose which of the three responses on each of the four lines best describes him or her. Then answer the questions again, but this time about yourself.

Richard Nisbett and his colleagues (Nisbett, Caputo, Legant, & Marecek, 1973) had college students complete this same task for themselves, for their best friend, for their father, and for the (at the time well-known) newscaster Walter Cronkite. As you can see in Figure \(\PageIndex{3}\), the participants chose one of the two trait terms more often for other people than they did for themselves, and chose “depends on the situation” more frequently for themselves than they did for the other people. These results also suggest that people may perceive more consistent traits in others than they should.

The human tendency to perceive traits is so strong that it is very easy to convince people that trait descriptions of themselves are accurate. Imagine that you had completed a personality test and the psychologist administering the measure gave you this description of your personality:

I would imagine that you might find that it described you. You probably do criticize yourself at least sometimes, and you probably do sometimes worry about things. The problem is that you would most likely have found some truth in a personality description that was the opposite. Could this description fit you too?

The Barnum effect refers to the observation that people tend to believe in descriptions of their personality that supposedly are descriptive of them but could in fact describe almost anyone . The Barnum effect helps us understand why many people believe in astrology, horoscopes, fortune-telling, palm reading, tarot card reading, and even some personality tests. People are likely to accept descriptions of their personality if they think that they have been written for them, even though they cannot distinguish their own tarot card or horoscope readings from those of others at better than chance levels (Hines, 2003). Again, people seem to believe in traits more than they should.

A second way that psychologists responded to Mischel’s findings was by searching even more carefully for the existence of traits. One insight was that the relationship between a trait and a behavior is less than perfect because people can express their traits in different ways (Mischel & Shoda, 2008). People high in extraversion, for instance, may become teachers, salesmen, actors, or even criminals. Although the behaviors are very different, they nevertheless all fit with the meaning of the underlying trait.

Psychologists also found that, because people do behave differently in different situations, personality will only predict behavior when the behaviors are aggregated or averaged across different situations. We might not be able to use the personality trait of openness to experience to determine what Saul will do on Friday night, but we can use it to predict what he will do over the next year in a variety of situations. When many measurements of behavior are combined, there is much clearer evidence for the stability of traits and for the effects of traits on behavior (Roberts & DelVecchio, 2000; Srivastava, John, Gosling, & Potter, 2003).

Taken together, these findings make a very important point about personality, which is that it not only comes from inside us but is also shaped by the situations that we are exposed to. Personality is derived from our interactions with and observations of others, from our interpretations of those interactions and observations, and from our choices of which social situations we prefer to enter or avoid (Bandura, 1986). In fact, behaviorists such as B. F. Skinner explain personality entirely in terms of the environmental influences that the person has experienced. Because we are profoundly influenced by the situations that we are exposed to, our behavior does change from situation to situation, making personality less stable than we might expect. And yet personality does matter—we can, in many cases, use personality measures to predict behavior across situations.

The MMPI and Projective Tests

One of the most important measures of personality (which is used primarily to assess deviations from a “normal” or “average” personality) is the Minnesota Multiphasic Personality Inventory (MMPI), a test used around the world to identify personality and psychological disorders (Tellegen et al., 2003). The MMPI was developed by creating a list of more than 1,000 true-false questions and choosing those that best differentiated patients with different psychological disorders from other people. The current version (the MMPI-2) has more than 500 questions, and the items can be combined into a large number of different subscales. Some of the most important of these are shown in Table \(\PageIndex{3}\), but there are also scales that represent family problems, work attitudes, and many other dimensions. The MMPI also has questions that are designed to detect the tendency of the respondents to lie, fake, or simply not answer the questions.

To interpret the results, the clinician looks at the pattern of responses across the different subscales and makes a diagnosis about the potential psychological problems facing the patient. Although clinicians prefer to interpret the patterns themselves, a variety of research has demonstrated that computers can often interpret the results as well as can clinicians (Garb, 1998; Karon, 2000). Extensive research has found that the MMPI-2 can accurately predict which of many different psychological disorders a person suffers from (Graham, 2006).

One potential problem with a measure like the MMPI is that it asks people to consciously report on their inner experiences. But much of our personality is determined by unconscious processes of which we are only vaguely or not at all aware. Projective measures are measures of personality in which unstructured stimuli, such as inkblots, drawings of social situations, or incomplete sentences, are shown to participants, who are asked to freely list what comes to mind as they think about the stimuli . Experts then score the responses for clues to personality. The proposed advantage of these tests is that they are more indirect—they allow the respondent to freely express whatever comes to mind, including perhaps the contents of their unconscious experiences.

One commonly used projective test is the Rorschach Inkblot Test , developed by the Swiss psychiatrist Hermann Rorschach (1884–1922). The Rorschach Inkblot Test is a projective measure of personality in which the respondent indicates his or her thoughts about a series of 10 symmetrical inkblots (Figure \(\PageIndex{5}\)). The Rorschach is administered millions of time every year. The participants are asked to respond to the inkblots, and their responses are systematically scored in terms of what, where, and why they saw what they saw. For example, people who focus on the details of the inkblots may have obsessive-compulsive tendencies, whereas those who talk about sex or aggression may have sexual or aggressive problems.

Another frequently administered projective test is the Thematic Apperception Test (TAT) , developed by the psychologist Henry Murray (1893–1988). The Thematic Apperception Test (TAT) is a projective measure of personality in which the respondent is asked to create stories about sketches of ambiguous situations, most of them of people, either alone or with others (Figure \(\PageIndex{6}\)). The sketches are shown to individuals, who are asked to tell a story about what is happening in the picture. The TAT assumes that people may be unwilling or unable to admit their true feelings when asked directly but that these feelings will show up in the stories about the pictures. Trained coders read the stories and use them to develop a personality profile of the respondent.

Other popular projective tests include those that ask the respondent to draw pictures, such as the Draw-A-Person test (Machover, 1949), and free association tests in which the respondent quickly responds with the first word that comes to mind when the examiner says a test word. Another approach is the use of “anatomically correct” dolls that feature representations of the male and female genitals. Investigators allow children to play with the dolls and then try to determine on the basis of the play if the children may have been sexually abused.

The advantage of projective tests is that they are less direct, allowing people to avoid using their defense mechanisms and therefore show their “true” personality. The idea is that when people view ambiguous stimuli they will describe them according to the aspects of personality that are most important to them, and therefore bypass some of the limitations of more conscious responding.

Despite their widespread use, however, the empirical evidence supporting the use of projective tests is mixed (Karon, 2000; Wood, Nezworski, Lilienfeld, & Garb, 2003). The reliability of the measures is low because people often produce very different responses on different occasions. The construct validity of the measures is also suspect because there are very few consistent associations between Rorschach scores or TAT scores and most personality traits. The projective tests often fail to distinguish between people with psychological disorders and those without or to correlate with other measures of personality or with behavior.

In sum, projective tests are more useful as icebreakers to get to know a person better, to make the person feel comfortable, and to get some ideas about topics that may be of importance to that person than for accurately diagnosing personality.

Psychology in Everyday Life: Leaders and Leadership

One trait that has been studied in thousands of studies is leadership, the ability to direct or inspire others to achieve goals . Trait theories of leadership are theories based on the idea that some people are simply “natural leaders” because they possess personality characteristics that make them effective (Zaccaro, 2007). Consider Bill Gates, the founder of the Microsoft Corporation, shown in Figure \(\PageIndex{7}\). What characteristics do you think he possessed that allowed him to create such a strong company, even though many similar companies failed?

Research has found that being intelligent is an important characteristic of leaders, as long as the leader communicates to others in a way that is easily understood by his or her followers (Simonton, 1994, 1995). Other research has found that people with good social skills, such as the ability to accurately perceive the needs and goals of the group members and to communicate with others, also tend to make good leaders (Kenny & Zaccaro, 1983).

Because so many characteristics seem to be related to leader skills, some researchers have attempted to account for leadership not in terms of individual traits, but rather in terms of a package of traits that successful leaders seem to have. Some have considered this in terms of charisma (Sternberg & Lubart, 1995; Sternberg, 2002). Charismatic leaders are leaders who are enthusiastic, committed, and self-confident; who tend to talk about the importance of group goals at a broad level; and who make personal sacrifices for the group . Charismatic leaders express views that support and validate existing group norms but that also contain a vision of what the group could or should be. Charismatic leaders use their referent power to motivate, uplift, and inspire others. And research has found a positive relationship between a leader’s charisma and effective leadership performance (Simonton, 1988).

Another trait-based approach to leadership is based on the idea that leaders take either transactional or transformational leadership styles with their subordinates (Bass, 1999; Pieterse, Van Knippenberg, Schippers, & Stam, 2010). Transactional leaders are the more regular leaders, who work with their subordinates to help them understand what is required of them and to get the job done. Transformational leaders, on the other hand, are more like charismatic leaders—they have a vision of where the group is going, and attempt to stimulate and inspire their workers to move beyond their present status and to create a new and better future.

Despite the fact that there appear to be at least some personality traits that relate to leadership ability, the most important approaches to understanding leadership take into consideration both the personality characteristics of the leader as well as the situation in which the leader is operating. In some cases the situation itself is important. For instance, you might remember that President George W. Bush’s ratings as a leader increased dramatically after the September 11, 2001, terrorist attacks on the World Trade Center. This is a classic example of how a situation can influence the perceptions of a leader’s skill.

In still other cases, different types of leaders may perform differently in different situations. Leaders whose personalities lead them to be more focused on fostering harmonious social relationships among the members of the group, for instance, are particularly effective in situations in which the group is already functioning well and yet it is important to keep the group members engaged in the task and committed to the group outcomes. Leaders who are more task-oriented and directive, on the other hand, are more effective when the group is not functioning well and needs a firm hand to guide it (Ayman, Chemers, & Fiedler, 1995).

Key Takeaways

• Personality is an individual’s consistent patterns of feeling, thinking, and behaving.
• Personality is driven in large part by underlying individual motivations, where motivation refers to a need or desire that directs behavior.
• Early theories assumed that personality was expressed in people’s physical appearance. One of these approaches, known as physiognomy, has been validated by current research.
• Personalities are characterized in terms of traits—relatively enduring characteristics that influence our behavior across many situations.
• The most important and well-validated theory about the traits of normal personality is the Five-Factor Model of Personality.
• There is often only a low correlation between the specific traits that a person expresses in one situation and those that he expresses in other situations. This is in part because people tend to see more traits in other people than they do in themselves. Personality predicts behavior better when the behaviors are aggregated or averaged across different situations.
• The Minnesota Multiphasic Personality Inventory (MMPI) is the most important measure of psychological disorders.
• Projective measures are measures of personality in which unstructured stimuli, such as inkblots, drawings of social situations, or incomplete sentences are shown to participants, who are asked to freely list what comes to mind as they think about the stimuli. Despite their widespread use, however, the empirical evidence supporting the use of projective tests is mixed.

Exercises and Critical Thinking

• Consider your own personality and those of people you know. What traits do you enjoy in other people, and what traits do you dislike?
• Consider some of the people who have had an important influence on you. What were the personality characteristics of these people that made them so influential?

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Personality Psychology Explained: 7 Theories and Assessments

The search to understand human nature has been a journey lasting thousands of years, taking us from ancient Greece to modern gene labs.

During that time, there have been many theories of personality, yet they all attempt to answer the same questions.

What makes us who we are, and how do we differ from those around us?

This article introduces a brief history of personality theory before exploring the present, including models, fascinating findings, and how to perform assessments.

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This Article Contains:

What is personality psychology, a brief history of the field, 3 scientifically proven personality theories, 4 fascinating facts and research findings, assessing personality: 2 valid tests and questions, 3 books on the topic, positivepsychology.com’s related resources, a take-home message.

Our personality is the sum of the psychological qualities that impact our enduring thinking, behavior, and feelings. It is very much part of who we are and how others see us (Allen, Greenlees, & Jones, 2011).

Our personality traits not only define us, but identify how we differ from others (Larsen, Buss, Wismeijer, & Song, 2017). And despite their persistence, every trait and process of personality is not equally active all the time. While aggression may be appropriate when defending oneself from attack, it is not suitable for handing a book in at the library (Nabi et al., 2005).

Personality traits are not separate from who we are; they affect how we act, see ourselves, feel, and interact with others. Not only that, they shape how we view life and the goals we pursue (Larsen et al., 2017).

Therefore, a comprehensive personality theory has several challenges. It must explain how every human is, to some degree (Kluckhohn & Murray, 1948; Larsen et al., 2017):

• Like all others We all have the capacity to feel love and a need for companionship (known as universals or human nature ).
• Like some others We each vary in our need for belonging (known as particulars or individual differences ).
• Like no others We are unique in how we express our feelings (known as our individual uniqueness ).

As therapists working with clients, it can be useful to walk through the elements of personality theory with our clients. An understanding that they share much of who they are with other humans and, yet, remain unique and special may offer both comfort during difficult times and encouragement when attempting to grow.

After all, much of psychology’s current thinking owes a considerable debt to the long, complex, and interweaving trail of ideas, thinkers, and research into personality.

While we could begin much earlier, we will instead start in the early years of the 20th century.

Psychoanalysis and the early theories of personality

In 1921 German psychiatrist Ernst Kretschmer suggested that body shape was linked to personality. In his view, a slim, delicate person is much more likely to be introverted than someone strong and muscular. Despite a lack of empirical evidence, this idea was further developed in the 1930s by William Sheldon. He created a scoring system that linked body appearance to a set of personality traits (Holzman, 2020).

Around that time was also the rise of psychoanalysis , driven by the Austrian neurologist Sigmund Freud. He began by focusing on psychopathologies – such as hysteria and phobic conditions – before moving into psychoanalysis and personality development and functioning.

Freud believed that neurotic conditions were rooted in distressing episodes from the past – mostly real or imagined sexual fantasies – that were incompatible with the person’s current moral standards. Such conflicts between human drives (the id) and the primarily unconscious structures that control them (the ego) were thought to lead to lasting self-criticism (Freud, 1922; Holzman, 2020).

Swiss psychiatrist Carl Jung arrived at a theory of personality development that was less driven by sexual desires, yet more abstract, and at times, even spiritual (Holzman, 2020).

Austrian psychiatrist Alfred Adler, a contemporary of Jung, also challenged the importance of sexual motives in defining who we are. Instead, he suggested that our behavior continually hangs in a balance; we exaggerate one behavior to compensate for a deficiency in another (McAdams, 1997).

Erik Erikson, an American psychoanalyst, proposed eight stages of psychosocial growth – or personality transformation. The next phase only emerges upon successful completion of the existing one.

But what does this mean for personality?

Psychoanalytic theory – borne out of psychoanalysts’ consulting rooms – was largely untestable but did provide a starting point for the subsequent development of frameworks for personality research and was the beginning of client-focused therapy.

Earlier personality theories tend to focus on the human nature level, providing a universal account for humans as a species. While such grand theories are historically interesting, they fail to explain what makes us unique (Larsen et al., 2017).

Trait theories of personality

From the 1940s onwards, several investigators including Gordon Allport, Henry Murray, and Raymond Cattell began exploring the personality traits’ stability and hierarchy. Rather than based on single key characteristics, they found personality to be a “unified and organized totality” (McAdams, 1997).

And yet, by 1971, Rae Carlson claimed that the field had lost its way, misplacing the person in personality research (McAdams, 1997).

By the 1980s, many researchers had accepted that personality was a function of both traits and situation – the interaction between person and environment. Over this time, personality psychologists were spending a great deal of time attempting to “reach a single systematic taxonomy for personality traits” (McAdams, 1997).

Perhaps the most celebrated trait psychologist, Hans Eysenck, produced a taxonomy of personality firmly rooted in biology (Eysenck & Eysenck, 1975). Not only was it highly heritable, but it also helped to explain how individuals differed.

Eysenck organized personality into three main traits, psychoticism (P), extraversion–introversion (E), and neuroticism–emotional stability (N), memorable as the acronym PEN. For example, we might typically think of extroverts as liking parties and having many friends; neurotics as anxious, irritable worriers; and the psychotic as egocentric or aggressive.

However, despite the theory’s strengths, the model has since been considered limited in its number of traits. And while Raymond Cattell created an extended taxonomy of 16 personality factors, he could not replicate how he found them (Larsen et al., 2017).

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Contemporary personality research has a strong focus on individual and group differences (such as between men and women, and across cultures, etc.). It typically sets out to answer the following questions (Larsen et al., 2017):

• How many personality traits are there?
• How are they organized?
• What is their origin?
• What are their consequences?

By answering the questions, psychologists can explain how we differ, behave, and perform (Laajaj et al., 2019).

Several of the personality models receiving the most attention at present include:

Five-factor model

The five-factor model of personality, also known as the Big Five , is a suggested taxonomy of personality consisting of the following traits (McCrae & John, 1992):

• Conscientiousness
• Extraversion
• Agreeableness
• Neuroticism

The creation of the model and agreement of its traits resulted from a statistical and linguistic analysis of natural language.

Crucially, the five-factor model has proven highly reliable at describing the basic personality dimensions and how we differ. Indeed, the model has been successfully proven over the last five decades across multiple languages (Larsen et al., 2017).

However, despite its replicability, there have been challenges to labeling some of the existing traits and the suggestion of a sixth factor known as Honesty–Humility (Hilbig & Zettler, 2015).

HEXACO model of personality

Canadian psychologists Michael Ashton and Kibeom Lee presented their six-factor model known as HEXACO in 1994 (Larsen et al., 2017). It consists of:

• Honesty-Humility – sincerity, fairness, modesty, etc.
• Emotionality – fearfulness, anxiety, dependence, etc.
• eXtraversion – social self-esteem, social boldness, liveliness, etc.
• Agreeableness – forgiveness, flexibility, gentleness, etc.
• Conscientiousness – organization, diligence, perfectionism, etc
• Openness to experience – creativity, curiosity, etc

While similar to the Big Five, there are several significant differences. For example, in the Big Five model, irritability and a quick temper fall under neuroticism; in HEXACO, they are found in agreeableness.

Although the Big Five model remains the dominant model, over 150 published studies use HEXACO, and the body of research continues to grow (Larsen et al., 2017).

Evolutionary theory

In 1848, Charles Darwin wrote On the Origin of Species. Its impact was phenomenal (Darwin, 1859). Not only was its effect on explaining the diversity of species profound, but more recently, it has had a significant impact on psychology in the form of evolutionary psychology .

Research into twins’ personalities has found that between 30% and 50% of people’s variation is genetic. However, this also means that between 50% and 70% of our personality is environmental . Such factors include (Workman & Reader, 2015):

• Treatment as a child
• Mother’s lifestyle during pregnancy
• Birth trauma, such as oxygen starvation
• Childhood diseases
• Good and bad experiences throughout life

For evolutionary theory to successfully explain personality, it will need to answer the following two questions:

• Why do particular personality traits pass down through the generations (30–50%)?
• Why is the remaining 50–70% of personality left to chance?

Evolutionary theory may ultimately offer psychology a grand unifying theory, but it isn’t there yet (Buss, 2016).

Research continues in this exciting and fruitful area and will undoubtedly lead to further understanding of the effect of both nature and nurture on who we are and how we behave.

While the above three theories (and others) continue to develop and be challenged, and as new ones arise, it is crucial to remember that a personality theory should be judged by its ability to:

• Explain the empirical data (observations of personality and behavior)
• Offer a guide to new and important discoveries
• Be precise enough to be testable
• Contain few assumptions and premises; it should be possible to explain findings based on the theory, rather than relying on a complex set of caveats
• Be consistent with well-established thinking inside and outside psychology

With many more personality theories abounding, it is interesting to investigate the research results which either prove, or disprove, such theories. We share a few interesting research results.

Sensation seekers

Research has shown that people who inherit a variation of the D4DR gene do not feel the effect of dopamine – involved in motivation and arousal – as much as others.

Studies suggest that such differences can lead to potentially dangerous, thrill-seeking behavior as individuals attempt to feel the dopamine pathways’ full effect (Eichhammer et al., 2005).

Shyness, anxiety, and eating disorders

Research has linked variations of another gene, 5-HTT, to shyness.

Several studies suggest that differences in the gene expression impact coping with anxiety and the prevalence of eating disorders (Monteleone, Tortorella, Castaldo, & Maj, 2005; Workman & Reader, 2015).

Personality research is WEIRD

Research into personalities of Americans and other WEIRD populations (Western, educated, industrialized, rich, and democratic) may not be as representative as previously thought (Henrich, 2020).

According to Joseph Henrich, the evolutionary approach suggests that our dispositions are calibrated based on the social and economic environments with which we are confronted throughout our lives.

Our personality is influenced by where  we are brought up.

Therefore, we should be careful about how we ascribe the findings of personality research in restricted populations to those culturally very different from ourselves.

Personality impacts sports

Personality plays a crucial role in sporting success and can even impact the sports people play.

Athletes are typically more extroverted and less neurotic than non-athletes, and individual athletes are more conscientious than team players. However, team players tend to be more agreeable than lone sportspeople (Allen et al., 2011; Nia & Besharat, 2010).

Such findings are significant as they can influence coping and the choice of subsequent coaching interventions.

Measuring personality: crash course psychology #22

The following personality tests are widely used, and two have repeatedly passed scientific scrutiny. They also offer useful insights into clients’ personalities for purposes of coaching and therapy.

Big Five personality inventory

The popular Big Five personality inventory, based on the five-factor model, has been widely validated.

The test consists of 44 questions answered on a scale between 1 and 5 (1 – disagree strongly, 5 – agree strongly) (Kaiseler, Polman, & Nicholls, 2012; Sutton, 2019).

Questions include:

I am a person who is talkative. I am a person who finds fault in others. I am someone who is reserved.

HEXACO Personality Inventory

The HEXACO-PI-R is growing in popularity and is a scientifically validated assessment.

Each domain is scored on four different facets. For example, extraversion is measured based on social self-esteem, social boldness, sociability, and liveliness (Larsen et al., 2017).

Each person is typically scored on their response to 100 statements (though a 60-item test is also available) such as:

I wouldn’t pretend to like someone to get that person to do favors for me. I feel like crying when I see other people crying. On most days I feel cheerful and optimistic.

Myers-Briggs Type Indicator

This test is worth mentioning briefly as it is popular in business but has been challenged repeatedly by academic research.

Despite academic misgivings, the Myers-Briggs Type Indicator (MBTI) is often used to determine staff appropriateness for positions (Myers, McCaulley, Quenk, & Hammer, 1998).

Each person is scored on eight fundamental preferences:

• Introversion

Despite broad appeal and utility, the MBTI has been strongly criticized by researchers for its failure to meet rigorous theoretical criteria. It not only lacks testability, but also has internal contradictions (Stein & Swan, 2019).

According to Randy Larsen, the test may have some limited value in getting people to think about their personality but should not solely decide a person’s fit for a job (Larsen et al., 2017).

The following three books approach the exciting field of personality from very different (yet overlapping) angles to offer a more complete view of the subject matter.

1. Personality Psychology: Domains of Knowledge About Human Nature – R. Larsen, D. Buss, A Wismeijer, and J. Song

This first book is an incredibly well-put-together and comprehensive guide to personality, covering all key knowledge domains in the field.

Larsen and colleagues review the scientific basis for classical and contemporary theories and the latest developments in intelligence, genetics, and personality disorders.

Find the book on Amazon .

2. The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous –  Joseph Henrich

Joseph Henrich’s engaging book is an interesting challenge to psychology and its community of researchers and academics.

How can we map our understanding of who we are on to other cultures when our research is often restricted to WEIRD (Western, educated, industrialized, rich, and democratic) cultures?

Henrich also provides a unique standpoint that helps us look at how WEIRD populations became so psychologically peculiar.

3. Evolutionary Psychology: An Introduction – Lance Workman and Will Reader

This comprehensive volume is the ideal introduction and companion to evolutionary psychology for students, practitioners, and the interested public.

The book is full of the latest research, and Lance Workman and Will Reader introduce the challenges, issues, and advances that have faced evolutionary psychology in recent years.

Try out some of the tools and worksheets below with your clients to help them understand how they emotionally react to situations and can know themselves better.

• Getting To Know Yourself is a great place to start. Clients can use the boxes to help them remind themselves of who they are.
• Signs of Emotional Discomfort can help them to spot the signals that suggest they are becoming more agitated and responding poorly to life events.
• Exploring Our Feelings helps your clients reflect on the frequency and content of their emotions.
• Emotional Awareness can increase emotional intelligence by encouraging clients to track their emotional states throughout the day.
• Mapping Emotions directs your clients’ attention to bodily experiences of emotion to reach a greater acceptance of feelings.

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Ultimately, we all share a set of common needs, wishes, and desires with our fellow humans, including the desire to know who we are. Yet, we are all unique. We vary in our strengths and weaknesses and have individual differences in our personalities.

As individuals (and clients), we can each be encouraged to celebrate what we share with others – to gain empathy and understanding – while recognizing and valuing our uniqueness.

After all, this is what it means to be human and defines who we are as individuals.

A sense of our personality – knowing who we are – can help give us that understanding.

Genetics, the study of evolutionary psychology, and ongoing trait analysis will continue to offer more significant insights into our personality. And rather than limit our choices, improved understanding will open the door to showing us what we should accept and treasure in ourselves and where we may need support from professionals.

For the therapist, such knowledge of our client and their self-awareness can be empowering, leading to bigger, more appropriate, and long-lasting changes that are individual and specific, yet considerate of others.

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

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• Buss, D. M. (2016). Evolutionary psychology: The new science of the mind . Routledge, Taylor & Francis Group.
• Carlson, R. (1971). Where is the person in personality research? Psychological Bulletin , 75 (3), 203–219.
• Darwin, C. (1859). On the origin of species . Murray.
• Eichhammer, P., Sand, P. G., Stoertebecker, P., Langguth, B., Zowe, M., & Hajak, G. (2005). Variation at the DRD4 promoter modulates extraversion in Caucasians. Molecular Psychiatry , 10 (6), 520–522.
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Research Article

Differential personality change earlier and later in the coronavirus pandemic in a longitudinal sample of adults in the United States

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing – original draft

* E-mail: [email protected]

Affiliation Florida State University College of Medicine, Tallahassee, FL, United States of America

Roles Conceptualization, Writing – review & editing

Affiliation University of Montpellier, Montpellier, France

Roles Methodology, Writing – review & editing

Affiliation University of Michigan, Ann Arbor, MI, United States of America

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – review & editing

• Angelina R. Sutin, 
• Yannick Stephan, 
• Martina Luchetti, 
• Damaris Aschwanden, 
• Ji Hyun Lee, 
• Amanda A. Sesker, 
• Antonio Terracciano

• Published: September 28, 2022
• https://doi.org/10.1371/journal.pone.0274542
• Peer Review
• Reader Comments

Five-factor model personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness) are thought to be relatively impervious to environmental demands in adulthood. The coronavirus pandemic is an unprecedented opportunity to examine whether personality changed during a stressful global event. Surprisingly, two previous studies found that neuroticism decreased early in the pandemic, whereas there was less evidence for change in the other four traits during this period. The present research used longitudinal assessments of personality from the Understanding America Study (N = 7,109; 18,623 assessments) to examine personality changes relatively earlier (2020) and later (2021–2022) in the pandemic compared to pre-pandemic levels. Replicating the two previous studies, neuroticism declined very slightly in 2020 compared to pre-pandemic levels; there were no changes in the other four traits. When personality was measured in 2021–2022, however, there was no significant change in neuroticism compared to pre-pandemic levels, but there were significant small declines in extraversion, openness, agreeableness, and conscientiousness. The changes were about one-tenth of a standard deviation, which is equivalent to about one decade of normative personality change. These changes were moderated by age and Hispanic/Latino ethnicity, but not race or education. Strikingly, younger adults showed disrupted maturity in that they increased in neuroticism and declined in agreeableness and conscientiousness. Current evidence suggests the slight decrease in neuroticism early in the pandemic was short-lived and detrimental changes in the other traits emerged over time. If these changes are enduring, this evidence suggests population-wide stressful events can slightly bend the trajectory of personality, especially in younger adults.

Citation: Sutin AR, Stephan Y, Luchetti M, Aschwanden D, Lee JH, Sesker AA, et al. (2022) Differential personality change earlier and later in the coronavirus pandemic in a longitudinal sample of adults in the United States. PLoS ONE 17(9): e0274542. https://doi.org/10.1371/journal.pone.0274542

Editor: Baogui Xin, Shandong University of Science and Technology, CHINA

Received: April 18, 2022; Accepted: August 28, 2022; Published: September 28, 2022

Copyright: © 2022 Sutin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in the current analyses can be downloaded from: https://uasdata.usc.edu/index.php?r=eNpLtDKyqi62MrFSKkhMT1WyLrYyNAeyS5NyMpP1UhJLEvUSU1Ly80ASQDWJKZkpIKaxlZKlhYmSdS1cMG0-Euo . Note that data are available but users must first register for a free account with UAS before the link will direct them to the dataset for download. The analytic scripts are in supplementary material .

Funding: Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG053297 to ARS. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Introduction

Since the beginning of the coronavirus pandemic, there has been interest in tracking its effect on psychological outcomes [ 1 ]. This published work has focused understandably on factors related to mental health. Many studies, for example, examined how symptoms of depression and anxiety [ 2 ], loneliness [ 3 , 4 ], and social support [ 5 ] changed compared to before the pandemic. In addition to aspects of mental and social well-being, the pandemic may have had an impact on more general ways of thinking, feeling, and behaving (i.e., personality). The five-factor model (FFM) [ 6 ] of personality operationalizes trait psychological function along five broad dimensions: neuroticism (the tendency to experience negative emotions and vulnerability to stress), extraversion (the tendency to be talkative and outgoing), openness (the tendency to be creative and unconventional), agreeableness (the tendency to be trusting and straightforward), and conscientiousness (the tendency to be organized, disciplined, and responsible). These traits are relatively stable over time [ 7 ] but are theoretically thought to be responsive to environmental pressures [ 8 ], including stressful events. The coronavirus pandemic has offered the unique opportunity to examine how a global stressful event experienced by the whole population may change personality.

Previous research suggests that personal, but not collective, stressful events may be associated with personality change. Neuroticism, for example, has been found to increase after personal stressful [ 9 , 10 ] or traumatic [ 11 ] events. In contrast, collective stressful events, such as natural disasters, seem to be unrelated to personality change [ 12 , 13 ]. A study that examined personality change from before to after the 2011 earthquake in Christchurch, New Zealand, for example, found no change in any of the five traits from before to after the disaster (there was a slight increase in neuroticism among participants directly affected by the quake; [ 12 ]). In addition, in a sample measured twice after exposure to Hurricane Harvey, there was no evidence of mean-level change in any of the five traits, even among participants with the most exposure [ 13 ]. This literature thus suggests that personality traits are not responsive to natural disasters.

In contrast to natural disasters, which tend to be limited in geographic area, the coronavirus pandemic has affected the entire globe and nearly every aspect of life. There is a developing literature on how the pandemic might be shaping personality change. Early in the pandemic, during the acute phase, we examined personality change in a sample of adults from across the United States (ages 18–90). We hypothesized that neuroticism would increase because of pandemic-related stressors and the accompanying fear and uncertainty would lead to more feelings of emotional instability [ 14 ]. Surprisingly, however, neuroticism declined slightly between January/February 2020 and March 2020. Although surprising, it is consistent with anecdotal evidence that anxiety (one core aspect of neuroticism) declined early in the pandemic among individuals who typically suffer from anxiety [ 15 ]. Further, a small sample from Germany found that neuroticism was slightly lower among university students during the first coronavirus lockdown compared to their neuroticism measured before the pandemic [ 16 ]. Although modest, this current evidence suggests that, at least early in the pandemic, during the acute phase, there was a decline in neuroticism.

There is less evidence for change in the other traits from pre- to during the pandemic. Although extraversion was hypothesized to decline because pandemic restrictions (e.g., lockdowns, social distancing, event cancellations) reduced the ability to be sociable, the evidence is mixed: Extraversion decreased slightly in a sample of university students in Germany [ 16 ], whereas it did not change in a nationwide sample of adults in the United States accounting for sociodemographic characteristics [ 14 ]. No change was found for Openness, Agreeableness, and Conscientiousness in the American sample [ 14 ], and these traits were not measured in the German sample [ 16 ].

These two studies provided important insights into the early effect of the pandemic on personality. The present research builds on these initial findings in four critical ways. First, we seek to replicate the finding that neuroticism declined early in the pandemic in a larger national sample of adults in the United States. Second, we address whether the other traits changed in this larger and more diverse sample than the previous samples. Third, with assessments of personality in both 2020 and in 2021–2022, we evaluate differential patterns of personality change across the acute (2020) and adaptation (2021–2022) phases of the pandemic. Finally, with a relatively diverse sample, we test whether personality change was moderated by age, gender, race, Hispanic/Latino ethnicity, or education.

To put any potential change in personality in context, previous research has found that personality changes, on average, about one-tenth of a standard deviation per decade of adulthood [ 17 ]. Regarding direction, neuroticism, extraversion, and openness tend to decline from younger to older adulthood, and agreeableness and conscientiousness tend to increase, although neuroticism and conscientiousness may change direction and increase and decrease, respectively, in older adulthood [ 17 ]. Although personality traits may change more in younger and older adulthood, compared to middle adulthood, we do not make specific predictions about age differences in personality change during the pandemic because the virus and the response to it has been unprecedented and its effects significant but different across age groups. Older adults, for example, faced a greater threat of severe disease and death, whereas younger adults faced more restriction on age-normative activities. If any differences are found, it would suggest a fruitful future direction to pursue to identify theoretical and empirical reasons for differential personality change by age. If changes are similar across age, it would suggest that personality is reactive to a global stressful event regardless of specific age-related stressors.

The purpose of this research is to examine personality change during the coronavirus pandemic compared to pre-pandemic levels using longitudinal assessments of personality from the Understanding America Study (UAS) [ 18 ]. We construe these analyses as exploratory because this study will be the first study of change in personality measured relatively earlier (acute phase) and relatively later (adaptation phase) in the pandemic (pandemic assessments in 2020 and 2021–2022), and because previous findings were not consistent with theoretical expectations. We do expect, however, that neuroticism declined early in the pandemic because of the two previous studies. If this decline is apparent in the UAS sample, it will provide robust evidence that neuroticism was reactive to the pandemic. We do not expect change in the other four traits early in the pandemic based on our previous findings [ 14 ]. We do not make predictions about change in personality later in the pandemic or how change may differ by sociodemographic characteristics.

Materials and methods

Participants and procedure.

UAS is an internet panel study of participants across the United States administered by the University of Southern California [ 18 ]. Participants completed surveys through the device of their choice (desktop, laptop, mobile, etc.) and, when necessary, were provided with a device and internet access to participate. To date, the UAS has administered the same personality measure three times (UAS1, UAS121, UAS237). Personality in UAS1 was collected between May 2014-March 2018, personality in UAS121 was collected between January 2018-April 2020, and personality in UAS237 was collected between April 2020-February 2022 (see COVID section below for how assessments were categorized for analysis). All participants had personality measured at least once prior to the pandemic. Because of the sampling structure of UAS, participants reported on their personality again in either 2020 or 2021–2022, but did not report on their personality in both years. As such, for all participants there is one assessment of personality during the pandemic; all available personality data was used in the analyses. Documentation for each wave can be found at the UAS website: https://uasdata.usc.edu/index.php under “Surveys” and UAS1, UAS121, and UAS237. Participants were included in the analytic sample if they had personality data reported during the pandemic and at least one personality assessment prior to the pandemic. Participants also needed to have sociodemographic information available. A total of 7,109 participants met these criteria, for a total of 18,623 assessments (Mean = 2.62 assessments/participant, SD = .48; range = 2–3; n = 4,495 at UAS1, n = 7,019 at UAS121, n = 7,109 at UAS 237). The current analyses were based on publicly-available, de-identified data and thus did not require approval from the local IRB. The primary data collection was overseen by the IRB at the University of Southern California and written informed consent was obtained from participants. Detailed information about the original data collection, ethical oversight, and consent process can be found in Laith and colleagues [ 18 ].

The analyses in this paper were not preregistered and are exploratory. The data used in the current analyses can be downloaded from: https://uasdata.usc.edu/index.php?r=eNpLtDKyqi62MrFSKkhMT1WyLrYyNAeyS5NyMpP1UhJLEvUSU1Ly80ASQDWJKZkpIKaxlZKlhYmSdS1cMG0-Euo . Note that data are available but users must first register for a free account with UAS before the link will direct them to the dataset for download. The analytic scripts are in supplementary material.

Personality traits.

Personality was measured with the 44-item Big Five Inventory (BFI) [ 19 ] at each personality assessment. Participants rated items that measured neuroticism (e.g., can be moody; eight items), extraversion (e.g., is talkative; eight items), openness (e.g., has an active imagination; ten items), agreeableness (e.g., is generally trusting; nine items), and conscientiousness (e.g., is a reliable worker; nine items). Items were rated from 1 ( strongly disagree ) to 5 ( strongly agree ), reverse scored when necessary, and the sum taken in the direction of the domain label (e.g., higher scores on neuroticism indicated greater neuroticism). Although sum scores can sometimes be problematic for missing data, at each personality assessment, more than 99% of participants who completed the assessment had personality scores, which indicated that missing data were not a problem in this study. Scores on neuroticism and extraversion could range from 8 to 40, scores on openness could range from 10 to 50, and scores on agreeableness and conscientiousness could range from 9 to 45. The test-retest correlation between the first and last personality assessment was .72 for neuroticism, .78 for extraversion, .73 for openness, .65 for agreeableness, and .69 for conscientiousness, indicating relatively high rank-order stability, which is similar to test-retest correlations reported during non-pandemic times: .71 for neuroticism, .79 for extraversion, .79 for openness, .70 for agreeableness, and .70 for conscientiousness (Hampson & Goldberg, 2006) [ 20 ]. There were some differences between participants who reported on their personality in 2021–22 versus 2020. Specifically, participants who reported on their personality in 2021–2022 were younger at baseline ( d = .28, p < .01), had more years of education ( d = .12, p < .01), were less likely to be men (χ 2 = 10.51, p < .01) or Hispanic ethnicity (χ 2 = 307.99, p < .01), and more likely to be Asian (χ 2 = 111.25, p < .01) than participants who reported on their personality in 2020. After accounting for sociodemographic differences, participants who reported on their personality in 2021–2022 were lower on baseline neuroticism ( d = .08, p < .01) and baseline conscientiousness ( d = .10, p < .01) compared to participants who reported on their personality in 2020.

Sociodemographic covariates.

Sociodemographic factors were age in years at the first personality assessment, gender (0 = women, 1 = men), race (three dummy-coded variables that compared Black = 1, Asian = 1, and Otherwise-identified = 1 to white = 0), Hispanic/Latino ethnicity (1 = Hispanic or Latino ethnicity, 0 = not Hispanic or Latino ethnicity) and education, reported on a scale from 1 ( less than first grade ) to 16 ( doctorate degree ). These covariates were selected because of potential age, gender, and education differences in personality and the differential effect of the pandemic across sociodemographic groups.

A variable was created that indicated whether each personality assessment occurred before or during the coronavirus pandemic. We set March 1, 2020 as the start of the pandemic for this sample because widescale closures and cancellations started to occur in the United States in early March 2020. Because patterns of personality change might be different depending on phase of the pandemic, we categorized the COVID personality assessments into two time periods: personality assessments during 2020 (March 1, 2020-December 31, 2020; the acute phase) and personality assessments after 2020 (January 1, 2021-February 16, 2022; the adaptation phase).

Statistical approach

The trajectory of each personality trait was modeled on time. Time in years was calculated from the first personality assessment to each subsequent assessment. Multilevel modeling (MLM) was used to estimate the trajectory of personality over time (which represents normative developmental/age-related change over time), with random effects for intercept and slope. Level 1 was repeated assessments of personality nested within-person. Socio-demographic variables were entered at level 2 to control for age, gender, race, Hispanic/Latino ethnicity, and education. Following previous studies on pandemic-induced change in subjective age [ 21 ] and well-being [ 22 ] compared to pre-pandemic levels, we specified a change component that was a time-varying dummy variable that compared all personality assessments prior to the pandemic (May 2014-February 2020; pre-pandemic personality) to the personality assessments during the pandemic (March 2020-February 2022; which reflect normative history-related change during the pandemic). Two dummy-coded variables were created. The first coded personality measured between March 1, 2020 and December 31, 2020 during the acute phase of the pandemic as 1 and others as 0. The second coded personality measured between January 1, 2021 and February 16, 2022 as 1 and others as 0. All participants in the analytic sample had one or two personality assessments prior to the pandemic and one personality assessment in either 2020 or 2021–2022 (because of the structure of UAS, personality assessments were not from the same participants in both 2020 and 2021–2022). The dummy-coded variable indicated whether personality measured during the pandemic increased or decreased during the pandemic compared to pre-pandemic levels.

We also tested whether personality change was moderated by sociodemographic factors (age, gender, race, Hispanic/Latino ethnicity, education) by including an interaction term between each dummy-coded COVID variable and the sociodemographic factor in separate regressions for each interaction. For age, we also ran the same MLM analysis separately for three age groups: younger adults (<30 years old), middle-aged adults (30–64 years old), and older adults (≥65 years old) because each age group had different challenges at different points in the pandemic. Due to the large number of tests and difficulty replicating interaction effects, the p-value was set to < .01 for the moderation analysis.

Descriptive statistics for study variables are in Table 1 . Table 2 reports results of the multilevel models that show personality change during the pandemic, accounting for the effect of time over the three assessments. Consistent with the previous studies on change in neuroticism early in the pandemic [ 14 , 16 ], neuroticism was lower (approximately one-tenth of a standard deviation) in 2020 compared to pre-pandemic levels. This decline, however, was not apparent in the next phase of the pandemic; neuroticism measured in 2021–2022 was not statistically different than neuroticism measured prior to the pandemic. Note that the time trend for neuroticism was positive, which indicated that neuroticism increased over time. The negative coefficient for COVID indicated a decrease in neuroticism during the pandemic, despite the time trend of increases over time. A different pattern emerged for the other four traits. For these traits, there was no difference in 2020 compared to their pre-pandemic levels. Extraversion, openness, agreeableness, and conscientiousness, however, declined in 2021–2022 compared to their level pre-pandemic.

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A different pattern of personality change was apparent when the sample was split into three age groups ( Table 3 ). The divergence by age was largest for neuroticism ( Fig 1 ). When was measured in 2020, older adults had the greatest decline in neuroticism. Middle-aged adults also declined in neuroticism, with an effect size about half that of older adults. Younger adults showed this initial decline, but it was not statistically significant. The bigger discrepancy across age groups occurred for personality measured in 2021–2022. In this case, middle-aged adults continued to decline in neuroticism at this later stage of the pandemic, as did older adults, albeit the decline was not statistically significant. In contrast, younger adults had a significant increase in neuroticism in 2021–2022 compared to prior to the pandemic. The pattern that emerged for the remaining traits was similar across the four traits, with declines for both younger and middle-aged adults in 2021–2022. There were two patterns particularly worth noting. First, the coefficients for agreeableness and conscientiousness were at least twice as large among younger than middle-aged adults, which indicated larger declines in this age group. Second, there was no significant change in these traits among older adults in either 2020 or 2021–2022: Extraversion, openness, agreeableness, and conscientiousness during the pandemic for participants over 65 were similar to pre-pandemic levels. The continuous interactions with age supported the overall pattern of age differences in personality change during the pandemic ( S1 Table ). Specifically, there was a negative interaction between COVID year and age on neuroticism for both 2020 and 2021–2022, which indicated the decline in neuroticism was larger at older ages in 2020 and the increase was larger at younger ages in 2021–2022, respectively. Likewise, the age interactions for agreeableness and conscientiousness indicated the decline in these two traits in 2021–2022 was stronger among relatively younger than relatively older participants. The interaction with age for 2020 for agreeableness was also significant.

Age differences in the effect of the pandemic on personality change in 2020 and in 2021–2022 for neuroticism (Panel A), extraversion (Panel B), openness (Panel C), agreeableness (Panel D), and conscientiousness (Panel E). Asterisks indicate significant personality changes from pre-pandemic levels.

https://doi.org/10.1371/journal.pone.0274542.g001

https://doi.org/10.1371/journal.pone.0274542.t003

Personality change during COVID was also moderated by Hispanic/Latino ethnicity ( S1 Table ). Hispanic/Latino participants did not experience the decline in neuroticism apparent among non-Hispanic/Latino participants. Hispanic/Latino participants also decreased more in agreeableness earlier in the pandemic than non-Hispanic/Latino participants. Both Hispanic/Latino and non-Hispanic/Latino participants declined in extraversion, openness, and conscientiousness in 2021–2022, but this decline was larger for Hispanic/Latino participants. There was less evidence for differences by the other sociodemographic groups ( S1 Table ).

Replicating previous work on personality change in the acute phase [ 14 , 16 ], the present research found a significant decrease in neuroticism in 2020 compared to neuroticism prior to the pandemic. There was no significant change in the other traits in 2020. There was, however, a different pattern of change when personality was measured in 2021–2022: The beneficial effect of the pandemic on neuroticism dissipated, whereas there was significant decline in the other four traits compared to before the pandemic. Importantly, significant age differences also emerged that indicated that the decline in neuroticism in 2020 was largest for older adults, whereas the decline in the other four traits in 2021 was apparent in middle-aged and particularly younger adults. The present research thus suggests differential acute and longer-term time of measurement effects of the pandemic on personality change.

At the sample level, change in personality from before to during the pandemic was approximately one-tenth of a standard deviation. Although modest in absolute terms, it can be put in the perspective of developmental changes that occur over adulthood. Normative personality change has been estimated to be approximately one-tenth a standard deviation per decade in adulthood [ 17 ]. Given our analyses accounted for these normative age-related changes, the change observed during the short time of the pandemic approximated the degree of change usually observed over a decade. In addition, the changes were much larger for some demographic groups, including the decline in neuroticism for older adults, the decline in conscientiousness for younger adults, and the decline in extraversion for Hispanic/Latino participants, which were about one-fifth of a standard deviation.

The present research adds to the replicated evidence that neuroticism declined early in the pandemic [ 14 , 16 ]. This decline is particularly surprising against the backdrop of other longitudinal research on mental health that found symptoms of depression, anxiety, and psychological distress increased during the first year of the pandemic [ 1 , 2 , 23 ]. These findings appear contradictory, particularly because symptoms of depression and anxiety are expressions of neuroticism [ 24 ]. Both changes, however, may occur simultaneously. It may be that, prior to the pandemic, individuals higher in neuroticism ascribed feelings of distress to this dispositional aspect of themselves. The fear and uncertainty caused by the pandemic, however, may have provided a reason for such feelings, leading to declines in perceptions of dispositional neuroticism. Further, prior to the pandemic, there were no behavioral suggestions to express or cope with neuroticism, but pandemic guidance (washing hands, social distancing, masking) gave people a preventive behavior to engage in against the external stressor. The messaging around taking care of one’s mental health may also have contributed to decreases in neuroticism, especially for older adults since so much of the messaging was around taking care of this age group. It is also possible that the greater social cohesion early in the pandemic brought a sense of belonging that lessened a general disposition toward distress and/or observing the distress in the world had individuals re-evaluate their own tendency towards fear and anxiety. Further, there may be social comparison processes that shape how individuals perceive themselves. That is, ratings of personality are based, in part, on comparisons to other people. Early in the pandemic, when there was a lot of reporting on fear and anxiety about the virus in the media and on social media, individuals may have viewed themselves as less fearful and anxious than those around them: Individuals may have viewed themselves as less neurotic because the social norms around neuroticism shifted. Three studies now document this decline in neuroticism early in the pandemic.

A completely different pattern of change emerged during the adaptation phase of the pandemic. Neuroticism did not continue to decline, but rather was not statistically different from prior to the pandemic, which suggests the beneficial decline in neuroticism due to the pandemic was temporary. In addition, the other four traits, which did not change in the acute phase, all declined significantly in 2021–2022 compared to before the pandemic. This pattern suggests that for extraversion, openness, agreeableness, and conscientiousness, there was either a delayed effect that took longer to become apparent and/or different stressors and strains later in the pandemic contributed to these changes rather than the stressors and strains earlier in the pandemic. One possibility is that the social cohesion apparent early in the pandemic helped support stability of these traits. That is, in the acute phase, despite fear and uncertainty, the increase in social support [ 3 ] and sense of community [ 25 ] may have helped maintain personality. The decline in social support [ 26 ] and increase in social conflict on pandemic-related protective measures [ 27 ], may explain at least part of change observed in 2021–2022.

In our first paper on personality change very early in pandemic, we hypothesized a decrease in extraversion and conscientiousness because of restrictions on social gatherings and the loss of daily routines that often give structure to one’s life, respectively. We did not, however, find any support for these declines [ 14 ]. The present analyses suggest a delayed or longer-term effect on these traits. Early in the pandemic, there were anecdotal stories of long-lost connections being reestablished as old friends and acquaintances reached out to one another [ 28 , 29 ]. Such connections may have helped support extraversion in the acute phase of the pandemic. Over a year of restrictions on social gatherings–either mandated or self-imposed over safety concerns–may have culminated in feeling less temperamentally outgoing than prior to the pandemic. Likewise, it might have taken more time for the lack of structure and fewer immediate responsibilities to consolidate into declines in conscientiousness. It may also be the case that, prior to the pandemic, external structures that supported schedules and routines were perceived as the individual’s own level of conscientiousness. Without this stability and structure, it may be harder to be organized and follow through on responsibilities. The changes observed in 2021–2022 may be the accumulation of changes in daily life that took more time to culminate in trait decline.

There were also significant declines in openness and agreeableness. These declines may have been, in part, a response to the social upheaval in response to the pandemic that was sharper in 2021–2022. The continued uncertainty around the pandemic, particularly as it dragged into a second year [ 30 ], as well as the decline in mobility [ 31 ], may have led individuals to narrow their activities and worldviews. Likewise, there may have been a decrease in interest in art and artistic experiences because of less ability to engage in art due to closures of concert venues, museums, theaters, etc. The move to online communication and entertainment and reliance on social media may have decreased exposure to new ideas. Such changes may have contributed to declines in openness. There has been a decline in trust apparent for decades [ 17 , 32 ]. Although there was an increase in confidence in science and the medical community early in the pandemic, this increase was short-lived and the decline precipitous during the second year of the pandemic [ 33 ]. The decline in agreeableness observed later in the pandemic is consistent with this trend. It is notable that this decline is apparent controlling for the general time trend of declines in agreeableness. This decline might have been partly fueled by amplification of mis/disinformation that undermines trust and may also highlight benefits to not being straightforward.

Two sociodemographic factors were significant moderators of personality change during the pandemic: age and Hispanic/Latino ethnicity. Compared to middle-aged and older adults, the personality of younger adults seemed particularly sensitive to change. Personality tends to develop most and consolidate during young adulthood [ 34 ], with the pattern of development toward greater maturity in the form of declines in neuroticism and increases in agreeableness and conscientiousness [ 35 ]. Over a year into the pandemic, however, young adults show the opposite of this developmental trend. The personality of older adults, in contrast, is thought to be more impervious to change (at least until very old age or cognitive impairment [ 36 – 38 ]); and, indeed, four of the five traits were relatively impervious to change among older adults. There may also be other reasons for the age differences in personality change. We cannot, for example, distinguish between age and cohort because they are confounded in the current sample; it is possible that the differences are due to cohort rather than age. It is also possible that different age groups faced different challenges in the second year of the pandemic, such as instability in the job market and school-related stressors (e.g., continued school closures, quarantining of the self or one’s children after exposure). Such stressors may be more impactful for younger and middle-aged adults than older adults, who also may both be less likely to experience and have more resources to handle pandemic-related stressors that did occur.

Personality change during the pandemic was also moderated by Hispanic/Latino ethnicity. In 2020, Hispanic/Latino participants did not decrease in neuroticism but did decrease in agreeableness earlier than non-Hispanic/Latino participants. This pattern could be due, in part, to the strain of the pandemic not equally distributed across the population. The financial cost of the pandemic was larger for Hispanic/Latino adults compared to their counterparts [ 39 ], and, at the same time, this population had higher rates of hospitalization and death due to COVID than non-Hispanic white adults [ 40 ]. Further, the decline in extraversion, openness, and conscientiousness in 2021–2022 was stronger for Hispanic/Latino participants than non-Hispanic/Latino participants. Perhaps these declines were because of processes that may have been apparent across the population were amplified by ongoing stressors of high-risk work situations and risk of COVID for themselves and their families. Surprisingly, although Black adults faced similar stressors, in this sample, Black participants did not show a similar pattern of personality change (i.e., the moderation analysis indicated no difference in change between Black and white participants).

The present research focused on personality change during the coronavirus pandemic. It is important to note other significant collective events in the United States during this time. The death of George Floyd, the subsequent social justice protests, the backlash to the protests, and the January 6, 2021 insurrection at the U.S. Capitol are significant events that occurred during this time that may also have shaped the observed changes. More research needs to tease apart whether/how different events may have shaped personality change.

Implications for models of personality

There are several theoretical accounts to explain personality development across the lifespan. Biologically-oriented models indicate personality in adulthood is relatively impervious to environmental pressures and changes that are not biologically-based should rebound [ 34 ]. Environmental models, in contrast, highlight life events in the trajectory of adult personality, although evidence on specific life events tends to be mixed and sometimes conflicting [ 8 ]. The neuroticism finding represents a significant time of measurement effect that replicated across three studies. We are not aware of similar population-wide effects that have replicated across independent studies. The findings suggest that a large scale, global event had an impact on personality at the population level. It appears that this decline was transitory; it is too early to determine whether the changes observed in 2021–2022 will endure or dissipate with time. It is also possible personal experiences and perceptions of collective events may be more impactful on personality than the event itself [ 41 ].

Personality traits go through most development in adolescence and early adulthood and tend to reach stability about age 30 [ 34 ]. At the other end of adulthood, personality tends to remain stable until cognitive impairment reduces stability [ 36 ]. It is notable, but perhaps not surprising, that most significant personality change during the pandemic occurred in younger adulthood, with most traits showing no change among older adults. It is further of note that middle-aged adults were more similar to younger adults than older adults (except for neuroticism). It is unclear whether this pattern is due to greater malleability of traits earlier than later in adulthood or whether the stressors and strains of the pandemic, which differed across age groups, led to more personality change.

These findings may have implications for long-term outcomes associated with personality. Individuals higher in conscientiousness, for example, tend to achieve more education [ 42 ] and income [ 43 ], develop fewer chronic diseases [ 44 ], are at lower risk of dementia [ 45 ], and ultimately live longer [ 46 ]. The decline in conscientiousness, particularly for younger adults, may have consequences for these outcomes, especially if the decline is not transitory. Higher neuroticism is associated with engagement in health-risk behaviors [ 47 , 48 ] and is a risk factor for poor mental health outcomes [ 24 ]. This increase may make some individuals more vulnerable to poor outcomes. It is especially worrying that the largest changes in these two traits were among younger adults, as the implications of these changes may ripple throughout their adult lives.

Although there should be some confidence in the decline in neuroticism, given that it has replicated, the other findings need to be interpreted with caution until replicated. It is of note that the lack of personality change in 2020 replicated our previous study on personality change in the acute phase of the pandemic (but Krautter and colleagues [ 16 ] found a decrease in extraversion during this time in a sample from Germany). Most importantly, the changes that occurred in 2021–2022 need to be replicated and put in the context of the sample. The sample was large and used a well-established measure of personality. The sample, however, was from the United States. No part of the world escaped the pandemic, but the course and response to the virus varied considerably across countries, and even within the same country. More research is needed to evaluate personality change during the pandemic in other cultural contexts and populations. In addition, although our sample was fairly diverse, the percentage of people of color was relatively low. The sample may have been underpowered to detect different patterns of personality change for people of color (the sample of Hispanic/Latino participants was larger and thus more powered to detect differences). This research documents personality traits over the first two years of the pandemic but the changes cannot be attributed solely to the pandemic. As discussed above, political and social upheaval co-experienced with the pandemic may have also contributed to the observed changes. We identified a time of measurement effect on change but were unable to distinguish the specific reasons for the changes. Pandemic-related policies and restrictions may be an additional contextual factor that is important for personality change. In the present research, participants were from around the United States who experienced very different state government responses to the pandemic (e.g., California versus Florida). Future research could address whether specific policy differences across states or countries have different impacts on change. Also, with few assessments of personality per participant, it was not possible to test for nonlinear changes over time. Future research would benefit from more assessments of personality to be able to test for such change. Further, there may be personality change related to infection with SARS-CoV-2, particularly for individuals with severe cases and/or long COVID. Recent evidence indicates significant changes in brain structure and cognitive function associated with SARS-CoV-2 infection (Douaud et al., 2022) [ 49 ]. Personality change could be one outcome of such alterations in neurological structure. The present research could not address this possibility. Finally, it would be worthwhile to take a “nuance” approach [ 50 ] and analyze change in specific items of the BFI to determine whether changes were driven by specific components of each trait.

Despite these limitations, the present research offers new evidence for longitudinal change in personality across the pandemic. This research highlights the need to continue to assess longitudinal changes, as the pandemic may have cumulative effects that were not apparent in the first few months. This research also highlights the differential impact on personality change across demographic groups (e.g., young adults, Hispanic/Latino). Future research needs to continue to track trends in personality change to evaluate potential longer-term outcomes associated with this change, particularly for groups impacted the most.

Supporting information

S1 table. interaction terms between sociodemographic factors and the pandemic on personality change..

https://doi.org/10.1371/journal.pone.0274542.s001

S2 Table. Syntax for reported analyses.

https://doi.org/10.1371/journal.pone.0274542.s002

Acknowledgments

The project described in this paper relies on data from survey(s) administered by the Understanding America Study (UAS), which is maintained by the Center for Economic and Social Research (CESR) at the University of Southern California (USC). The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of USC or UAS.

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• 19. John OP, Srivastava S. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In: Pervin LA, John OP, editors. Handbook of personality: Theory and research. 2nd ed. New York: Guilford; 1999. p. 102–38.
• 34. McCrae RR, Costa PT. Personality in adulthood: A Five-Factor Theory perspective. 2nd ed. New York: Guilford Press; 2003.

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.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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The Big Five Personality Traits, also known as OCEAN or CANOE, are a psychological model that describes five broad dimensions of personality: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. These traits are believed to be relatively stable throughout an individual’s lifetime.

• 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:

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 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 the actions of women in individualistic countries would be more likely to be attributed to their 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.

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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

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The Link Between Borderline Personality Disorder and Anger

What factors contribute to the tendency for such anger arousal.

Posted April 9, 2024 | Reviewed by Abigail Fagan

• How Can I Manage My Anger?
• Find a therapist to heal from anger
• Borderline Personality Disorder (BPD) involves instability of self-image, relationships and emotions.
• The above symptoms contribute to a heightened sense of threat and related anger.
• A number of biosocial developmental models explain the development of borderline personality disorder.

Borderline personality disorder (BPD) is a severe and complex personality disorder associated with instability in interpersonal relations, behavior, and emotions. Emotional dysregulation is a key contributor to BPD, involving difficulties in regulating emotions in order to support an individual’s pursuit of goals and behaving effectively in a variety of contexts.

Anger and aggression are key features associated with BPD — most often related to fears of abandonment, unstable relationships, an unstable self-image , emotional instability, and chronic feelings of emptiness. As such anger may be sudden, intense and difficult to calm down. When more intense, it is often described as “borderline rage”.

The character Alex Forrest (played by Glenn Close) offers a powerful representation of a woman suffering from borderline personality disorder in Fatal Attraction . In the pursuit of her relationship with Dan Gallagher (played by Michael Douglas) she exhibits many of the symptoms of BPD including rage, unstable self-image, fears of abandonment and violence.

The symptoms of borderline personality most often first appear during teenage years and early twenties worked with one young man early in my practice who clearly articulated the impact of “feeling empty”. On one occasion he stated, “How am I supposed to know what career I wish to pursue? I have no idea what I like. I don’t know who I am!” Another client reported getting drunk on weekends and seeking to have a physical altercation. It was as if this gave him some meaning against a blank slate presented by the weekend.

Factors that trigger anger

The fear of abandonment or rejection as well as sensitivity to frustration and stress can easily lead to a heightened sense of threat. The lack of a solid identity combined with feelings of emptiness leaves one with BPD vulnerable to feeling criticized, slighted or rejected, which consequently intensifies fears of being alone. The lack of a more solid self-image, coupled with a pattern of unstable interpersonal relationships combine to undermine the resilience to stress and challenging emotions.

Contributing factors to BPD anger

There are a number of biosocial developmental models regarding the development of borderline personality disorder. These emphasize developing in an environment that is invalidating and having adverse childhood experiences in combination with genetically linked vulnerabilities. Specifically, these might include impulsivity and emotional instability.

Some of these theories highlight specific areas of functioning as providing an understanding of emotional triggering for individuals with BPD. Some focus on emotional regulation . Others focus on cognitive aspects, including cognitive-emotional patterns that cause those with BPD to have greater expectations of rejection than those without BPD (Cavicchioli and Maffer, 2019).

In one study of individuals with BPD, it was found that half of the group also had suffered from two or more anxiety disorders (Quenneville, Kalogeropoulou, Lise-Kung, et. al., 2019). Additionally, greater childhood mistreatment was associated with greater severity of illness, impulsivity and trait anger.

Some researchers have found that those with BPD exhibit a negative bias in decoding social cues (Kleindienst, Hauschild, Liebke, et. al., 2019) They found that those who exhibited more chronic symptoms of BPD were more likely to assess happy facial expressions as being less happy than those without BPD.

Exploring the neuroscience of BPD, one group of researchers has found through magnetic resonance scanning with males, that those with BPD showed less activation of the prefrontal area of the brain during the viewing of happy and angry faces (Bertsch, Krauch, Roelofs, et. al., 2019). Additionally, they found that reduced functioning of this area of the brain was associated with impaired emotional control and with greater acting out of anger.

One study found that anger rumination was greater for those with BPD when compared to a healthier population (Oliva, Ferracini, Amoia, et. al., 2023). Anger rumination has been found to be positively associated with anger feelings and aggressive/impulsive behaviors.

Research regarding intimate partner violence (IPV) found that male and female perpetrators are more often found to have BPD than others (Johnson, Leone, and Xu, 2014). Another study found that individuals with BPD are more likely to be victims of IPV (Jackson, Sippel, Mota, et. al., 2015). It was hypothesized that emotional dysregulation may trigger such a reaction by the partner.

Trait anger (chronic) has been found to be higher for individuals with BPD than for others (Armenti and Babcock, 2018). Additionally, those with BPD, having higher trait anger, were more likely to engage in IPV than others.

It’s important to note that aggression associated with borderline personality disorder may be directed outward or inward. In one expansive study (36,309 respondents) exploring this issue, it was found that violence toward others was more associated with identity disturbance, impulsivity and intense anger, while violence directed inward was more associated with avoidance of abandonment, self-mutilating behavior, and feelings of emptiness (Hartford, Chen, Kerridge, et. al., 2018).

In the late 80s I worked in an inpatient program for women who self-injure. Most were diagnosed with BPD. While there were often several key contributing factors to such behavior, the self-injury served as a distraction from feeling empty and a way of feeling connected with oneself.

Research has also shown that there is a strong association between having an insecure attachment style and borderline personality disorder (Critchfield, Levy, Clarking, et. al., 2007). And when this is of the anxious insecure attachment style, there is a greater likelihood of reactive aggression. Additionally, self-harm was found to be associated with relational avoidance while anger and irritability were associated with anxiety.

For some disorders, symptoms tend to remit as an individual ages. While this may be true in general, one study of BPD found that, while younger adults with BPD were more likely to have emotional dysregulation, be impulsive, aggressive and self-injurious and have intense feelings of emptiness, older patients were still impaired primarily with regard to impulsiveness, emotional regulation and social functioning (Martino, Gammino, Sanza, et. al., 2020).

It’s reported that 45% of people treated for borderline personality disorder do not respond well to current psychological treatments (Woodridge, Reis, Townsend, et. al., 2021). The symptom complex associated with BPD undermines the capacity to form a trusting relationship with a therapist. In general, an integrated approach seems to be the most effective with this population.

A meta-analysis regarding such treatment, published between 1989 and 2019, identifies the most effective approaches as dialectical behavior therapy (DBT), schema therapy, psychoeducation, system training of emotional predictability and problem solving, and treatment using mentalization (Mungo, Hein, Hubain, et. al., 20.

DBT involves both a cognitive behavioral emphasis in conjunction with mindfulness , which includes individual therapy and skills training groups. Schema-Focused Therapy is an integrative approach that combines strategies from CBT, experiential, interpersonal and psychoanalytic therapy.

Individuals with BPD have difficulties forming relationships, managing their emotions and stress in general, and have an unstable self-image. Borderline personality disorder entails a constellation of symptoms that reflect and create an increased tendency for anger arousal. As such, it is essential that any psychotherapy for BPD also address anger management . Additionally, fears of abandonment and rejection undermine not only personal relationships but therapeutic relationships as well. However, there are a variety of well-researched therapeutic treatments that can help those with BPD live a more stable and fulfilling life.

Cavicchioli, M. & Maffei, C. (2020). Rejection sensitivity in borderline personality disorder and the cognitive-affective personality system: A meta-analytic review. Personality Disorders: Theory, Research, and Treatment, Vol.11 (1), 1-12. doi.org/10.1037/per0000359

Qenneville, A., Kalogeropoulou, E., Lise Kung, A., et. al. (2020). Childhood maltreatment, anxiety disorders and outcome in borderline personality disorder. Psychiatry Research, Vol 284, (2)

Kleindienst, N., Hauschild, S., Liebke, L., et. al. (2019). A negative bias in decoding positive social cues characterizes emotion processing in patients with symptom-remitted Borderline Personality Disorder. Borderline Personality Disorder and Emotion Dysregulation. Vol. 6, (17). https://doi.org/10.1186/s40479-019-0114-3

Bertsch, K., Krauch, M., Roelofs, K., et. al. (2019). Out of control? Acting out anger is associated with deficient prefrontal emotional action control in male patients with borderline personality disorder. Neuropharmacology, Vol. 156, (15)

Oliva, A., Ferracini, S., Amoia, R., et. al. (2023) The association between anger rumination and emotional dysregulation in borderline personality disorder: a review. Journal of Affective Disorders, Vol. 338 (1) 546-553

Johnson MP, Leone JM, Xu Y. ( 2014). Intimate terrorism and situational couple violence in general surveys: Ex-spouses required. Violence Against Women ;20:186. http://dx.doi.org/10.1177/1077801214521324 (originally published online 6 February 2014).

Jackson, M., Sippel, L., Mota, N., et. al. (2015). Borderline personality disorder and related constructs as rick factors for intimate partner violence perpetration. Aggressive Violent Behavior, Sep-Oct: Vol. 24: 95-106.

Armenti, N. & Babcock, J., et. al. (2018). Borderline personality features, anger and intimate partner violence: an experimental manipulation of rejection. Journal of Interpersonal Violence, Vol. 36, (5-6).

Harford, T, Chen, C., Kerridge, B., et. al. (2018). Borderline personality disorder an violence toward self and others: a national study. Home Journal of Personality Disorders, Vol.33 (5). Published Online:October 2019. doi.org/10.1521/pedi_2018_32_361

Critchfield, K., Levy, K, Clarkin, J, et. al. (2008). The relational context of aggression in borderline personality disorder: using adult attachment style to predict forms of hostility. Journal of Clinical Psychooogy, Vol 64, (1) 67-82

Martino, F., Gammino, L, Sanza, M., et. al. (2020). Impulsiveness and emotional dysregulation as stable features in borderline personality disorder outpatients over time. The Journal of Nervous and. Mental Disease, Vol. 208, (9) 715-720.

Woodbridge, J., Reis, S., Townsend, M., et. al. (2021) Searching in the dark: shining a light on some predictors of non-response to psychotherapy for borderline personality disorder. Plus One: doi.org/10.1371/journal.pone.0255055

Mungo, A., Hein, M., Hubain, P. et al. (2020). Impulsivity and its Therapeutic Management in Borderline Personality Disorder: a Systematic Review. Psychiatry Q 91, 1333–1362 (2020). https://doi.org/10.1007/s11126-020-09845-zReview Article

Bernard Golden, Ph.D., is the founder of Anger Management Education and author of Overcoming Destructive Anger: Strategies That Work .

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• Published: 25 July 2022

A prediction-focused approach to personality modeling

• Gal Lavi 1 ,
• Jonathan Rosenblatt 2 &
• Michael Gilead 3  

Scientific Reports volume  12 , Article number:  12650 ( 2022 ) Cite this article

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• Human behaviour
• Social neuroscience

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people’s personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room for improved predictive accuracy. We propose a novel approach to modeling human personality that is based on the maximization of the model’s predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized a supervised machine learning technique for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five-dimensional personality summary, which we term the “Predictive Five” (PF), provides predictive performance that is better than the FFM on two independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. The approach described herein has the promise of eventually providing an interpretable, low-dimensional personality representation, which is also highly predictive of behavior.

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Introduction.

Humans significantly differ from each other. Some people’s idea of fun is partying all night long, and others enjoy binging on a TV series while eating snacks; some are extremely intelligent, and others less so; some are hot-headed, and others remain cool, no matter what. Because of this variety, predicting humans’ thoughts, feelings, and behaviors is a cumbersome task; nonetheless, we attempt to solve this task on a daily basis. For example, when we decide who to marry, we try to predict whether we can depend on the other person till death do us part; when we choose a career, we must do our best to predict whether we will be successful and fulfilled in a given profession.

In order to predict a person’s thoughts, feelings, and behaviors, people often have no other option but to generate something akin to a scientific theory 1 —a parsimonious model that attempts to capture the unique characteristics of individuals, and that could be used to predict their behavior in novel circumstances. Indeed, research shows that people employ such theories when predicting their own 2 and others’ behaviors. Unfortunately, theories based strictly on intuition are often highly inaccurate 3 , even if produced by professional psychological theoreticians 4 . In light of this, ever since the early days of psychology research, scholars have been attempting to devise personality models using the scientific method, giving rise to the longstanding field of personality science.

Personality, when used as a scientific term, refers to the mental features of individuals that characterize them across different situations, and thus can be used to predict their behavior. In the early years of personality research, scientists generated numerous competing theories and measures, but struggled to arrive at a scientific consensus regarding the core structure of human personality. In recent decades, a consensus theory of the core dimensions of human personality has emerged—the Five Factor Model (FFM).

The FMM emerged from the so-called “lexical paradigm”, which assumed that if people regularly exhibit a form of behavior that is meaningful to human life, then language will produce a term to describe it 5 . Given this assumption, personality psychologists performed research wherein they asked individuals to rate themselves on lists of common English language trait words (e.g., friendly, upbeat), and then developed and used early dimensionality-reduction methods to find a parsimonious model that can account for much of the variability in each person’s trait ratings 5 .

Much research shows that these five factors, often termed the “Big Five” are relatively stable over time and have convergent and discriminant validity across methods and observers 6 . Moreover, research into the FFM has replicated the dimensional structure in different samples, languages, and cultures 7 , 8 (but see 9 for a recent criticism). In light of this, the FFM is taken by some to reflect a comprehensive ontology of the psychological makeup of human beings 10 according to Mccrae and Costa 11 the five factors are “both necessary and reasonably sufficient for describing at a global level the major features of personality’’.

Surely, human beings are complex entities, and their personality is not fully captured by five dimensions; however, the importance of having a parsimonious model of humans’ psychological diversity cannot be overstated. As noted by John and Srivasta 12 , a parsimonious taxonomy permits researchers to study “ specified domains of personality characteristics, rather than examining separately the thousands of particular attributes that make human beings individual and unique.” Moreover, as they note, such a taxonomy greatly facilitate s “ the accumulation and communication of empirical findings by offering a standard vocabulary, or nomenclature”.

An additional consequence of having a parsimonious model of the core dimensions of human personality, is that such an abstraction enables the acquisition of novel knowledge via statistical learning (see 13 for a discussion of the importance of abstract representations in learning); namely, whereas the estimation of covariances between high-dimensional vectors is often highly unreliable (i.e., the so-called “curse of dimensionality” 14 ), learning the statistical correlates of a low-dimensional structure is a more tractable problem. For example, research has shown that participants’ self-reported ratings on the FFM dimensions can be reliably estimated based on their digital footprint 15 .

This ability to infer individuals’ personality traits using machine learning also raises serious concerns, as it may be used for effective psychological manipulation of the public. In 2013, a private company named Cambridge Analytica harvested the data of Facebook users, and used statistical methods to infer the personality characteristics of hundreds of millions of Americans 16 . This psychological profile of the American population was supposedly used by the Trump campaign in an attempt to tailor political advertisements based on an individuals’ specific personality profile. While the success of these methods remains unclear, given the vast amount of data accumulated by companies such as Alphabet and Meta, the potential dangers of machine-learning based psychological profiling is taken by many to be a serious threat to democracy 17 .

Even if dubious entities indeed manage to acquire the Big Five personality profile of entire populations, it is far from obvious that such information could be used to generate actionable predictions. Indeed, the FFM was criticized by some researchers for its somewhat limited contribution to predicting outcomes on meaningful dimensions 18 , 19 , 20 . In light of such claims, some have argued that the public concern over the Cambridge Analytica scandal was overblown 21 (but see 22 for evidence for potential reasons for concern).

Roberts et al. 23 present counter-argument for critical stances against the predictive accuracy of the FFM and note that: “As research on the relative magnitude of effects has documented, personality psychologists should not apologize for correlations between 0.10 and 0.30, given that the effect sizes found in personality psychology are no different than those found in other fields of inquiry.” While this claim is clearly true, there is also no doubt that such correlations (that translate to explained variance in the range of 1%-9%) potentially leave room for improvements in terms of predictive accuracy.

If one’s goal is to find a parsimonious representation of personality that has better predictive accuracy than the FFM, it could be instructive to remember that the statistical method by which the FFM was produced—namely, Factor Analysis—is not geared towards prediction. Factor analysis is an unsupervised dimensionality-reduction method (i.e., a method that maps original data to a new lower dimensional space without utilizing information regarding outcomes) aimed at maximizing explanatory coherence and semantic interpretability, rather than maximizing predictive ability. It does so by finding a parsimonious, low-dimension representation (e.g., the five Big Five factors: extraversion, neuroticism and so on) that maximizes the variance explained in the higher-dimension domain (e.g., hundreds of responses to questionnaire items; for example, “I am lazy”; “I enjoy meeting new people”). Advances in statistics and machine learning have opened up new techniques for supervised dimensionality-reduction. Namely, methods that reduce the dimensionality of a source domain (i.e., predictor variables, \({X}_{1},...{,X}_{n}\) ; in the case of personality, hundreds of questionnaire items) by focusing on the objective of maximizing the capacity of the lower-dimensional representation to predict outcomes of a target domain (outcome variables, \({Y}_{1},...{,Y}_{m}\) , for example, depression, risky behavior, workplace performance).

Such techniques where dimensionality-reduction is achieved via maximization of predictive accuracy across a host of target-domain outcomes hold the potential of providing psychologists with parsimonious models of a psychological feature space that serve as relatively “generalizable predictors” of important aspects of human behavior. Moreover, it may demonstrate that privacy leaks, a-lá Cambridge-Analytica, are indeed a serious threat to democracy, despite being dismissed by some as science fiction.

In light of this, we investigated whether a supervised dimensionality-reduction approach that takes into account a host of meaningful can potentially improve the predictive performance of personality models. Such an approach could pave the way to a new family of personality models and could advance the study of personality. Alternatively, it may very well be the case that the FFM indeed “carves nature at its joints” and provides the most accurate ontology of the psychological proclivities of humans. In such a case, the FFM may remain the best predictive model of personality, and our approach will not provide improvements in predictions.

In order to examine this question, we conducted three studies. In Study 1, we built a supervised learning model using big data of personality questionnaire items and diverse, important life outcomes. We reduced the dimensionality of 100 questionnaire items into a set of five dimensions, with the objective of simultaneously minimizing prediction errors across ten meaningful life outcomes. We hypothesized that the resulting five-dimensional representation will outperform the FFM representation–when fitting a new model and attempting to predict the ten important outcomes on a held-out dataset. Next, in Studies 2 and 3, we explored the performance of the resulting model on new outcome variables.

Participants

The analyses relied on the myPersonality dataset that was collected between 2007 and 2012 via the myPersonality Facebook application. The myPersonality database is no longer shared by its creators for additional use. We received approval to download that data from the administrators of myPersonality on January 7th, 2018, and downloaded the data shortly thereafter. After the myPersonality database was taken down in 2018, we sent an email to the administrators (on June 8th, 2018), and received confirmation that we can use the data we have already downloaded. The application enabled its users to take various validated psychological and psychometric tests, such as different versions of the International Personality Item Pool (IPIP) questionnaire. Many participants also provided informed consent for researchers to access their Facebook usage details (e.g., liked pages). Participation was voluntary and likely motivated by people’s desire for self-knowledge 24 . The Participants in the myPersonality database are relatively representative of the overall population 25 . All participants provided informed consent for the data they provided to be used in subsequent psychological studies. We used data from 397,851 participants (210,279 females, 142,497 males, and 44,805 did not identify) who answered all of the questions on the 100-item IPIP representation of Goldberg’s 26 markers for the FFM which are freely available for all types of use. Participants’ mean age was 25.7 years ( SD  = 8.84). The study was approved by the Institutional Review Board of Ben-Gurion University, and was conducted in accordance with relevant guidelines and regulations.

Dependent variables

We sought to use supervised learning in order to find a low-dimensional representation of personality that can be used to predict psychological consequences across a diverse set of domains. We thus focused on ten meaningful outcome variables that were available in the myPersonality database, that cover many dimensions of human life which psychologists care about:

(1) Intelligence Quotient (IQ), measured with a brief 20 items version of the Raven’s Standard Progressive Matrices test 27 .

(2) Well-being, measured with the Satisfaction with Life scale 28 .

Personal values, measured using two scores representing the two axes from the Schwartz's Values Survey:

(3) Self-transcendence vs. Self-enhancement values and

(4) Openness to Change vs. Conservation values 29 .

(5) Empathy, measured with the Empathy Quotient Scale 30 .

(6) Depression, measured with The Center for Epidemiologic Study Depression (CES-D) scale 31 .

(7) Risky behavior, measured with a single-item question concerning illegal drug use.

(8) Self-reports of legal, yet unhealthy behavior (measured as averaging two single-item questions concerning alcohol consumption and smoking).

(9) Single item self-report of political ideology.

(10) The number of friends of participants’ had on the social network Facebook.

Independent variables

Our independent variables were the participants’ answers to the 100 questions included in the IPIP-100 questionnaire 32 . In this questionnaire, the participants are asked to rate their agreement with various statements related to different behaviors in their life and their general characteristics and competencies, on a scale from 1 (strongly disagree) to 5 (strongly agree). The original use of this questionnaire is to reliably gauge participants' scores on each of the FFM dimensions. It includes five subscales, each containing 20 items; the factor score for each FFM dimension can be calculated as a simple average of these 20 questions (after reverse coding some items). In the current research we treat each item from this list of 100 questions as a separate independent variable, and seek to reduce the dimensionality of this vector using supervised learning.

Model construction

The problem we set out to solve is to find a good predictive model that is: (a) based on the 100 questions of the existing IPIP-100 questionnaire, and (b) uses five variables only, so we can fairly compare it with the FFM. Reduced Rank Regression (RRR) is a tool that allows just that: it can be used to compress the original 100 IPIP items, to a set of five new variables. These new variables are constructed so that they are good predictors, on average, of a large set of outcomes. Unlike Principal Component Analysis (PCA) or Factor Analysis, RRR reduces data dimensionality by optimizing predictive accuracy.

We randomly divided our data into an independent train and test sets. Each subject in the train and test set had 100 scores of the IPIP questionnaire ( \({X}_{1},{X}_{2},...{,X}_{100}\) ), as well as their score in each of the ten dependent variables ( \({Y}_{1},{Y}_{2},...{,Y}_{10}\) ).

X ( n × 100) and Y ( n × 100) have been centered and scaled. We fitted a linear predictor, with coefficient vector:

And in matrix notation:

Our linear predictors were fully characterized by the matrix C. We wanted these predictors to satisfy the following criteria: (a) minimize the squared prediction loss (b) consist of 5 predictors, i.e., rank ( C ) =  r  = 5. Criterion (a) ensures the goodness of fit of the model, and criterion (b) ensures a fair comparison with the FFM. The RRR problem amounts to finding a set of predictors, \(\hat{C}\) , so that:

where || \(\cdot \) || denotes the Frobenius matrix norm. The matrix \(C\) can be expressed as a product of two rank-constrained matrices:

where \(B\) is of has p rows and r columns, denoted, p  ×  r , and \({A}\) is of dimension q  ×  r . The model (2) may thus be rewritten as:

The n  ×  r matrix \(X\hat{B}\) , which we noted \(\tilde{X}\) , may be interpreted as our new low-dimension personality representation. Crucially for our purposes, the same set of r predictors is used for all dependent variables. By choosing dependent variables from different domains, we dare argue that this set of predictors can serve as a set of “generalizable predictors”, which we call henceforth the Predictive Five (PF). For the details of the estimation of \(\hat{B}\) see the attached code. For a good description of the RRR algorithm see 33 .

Model assessment

To assess the predictive performance of the PF, and compare it to the predictive properties of the classical FFM, we used a fourfold cross validation scheme. The validation worked as follows: we learned \(\hat{B}\) from a train set (397,851 participants) using RRR; we then divided the independent test set (800 participants) into 4 subsets; we learned \(\hat{A}\) from a three-quarters part of the test set (600 participants), and computed the R 2 on the holdout test set (200 participants); we iterated this process over the 4-test subsets. The rationale of this scheme is that: (a) predictive performance is assessed using R 2 on a completely novel dataset ; (b) when learning the predictive model, we wanted to treat the personality attributes as known. We thus learned \(\hat{B}\) and \(\hat{A}\) from different sets. The size of the holdout set was selected so that R 2 estimates will have low variance. The details of the process can be found in the accompanying code ( https://github.com/GalBenY/Predictive-Five ).

To examine the performance of the RRR algorithm against another candidate reference model we also performed Principal Component Regression (PCR), where we reduced the IPIP questionnaire to its 5 leading principal components, which were then used to predict the outcome variables. We used the resulting model as a point of comparison in follow-up assessment of predictive accuracy. Like the RRR case, we learned the principal components from the train-set (397,851 participants). Next we divided the independent test set (800 participants) into 4 subsets and used a fourfold cross validation: ¾ to learn 5 coefficients, and ¼ to compute.

In order to calculate the significance of the difference in the predictive accuracy of the models we took the following approach: predictions are essentially paired, since they originate from the same participant. For each participant, we thus computed the (holdout) difference between the (absolute) error of the PF and FFM models: \(|{{\widehat{y}}_{i}}^{PF}|-|{{\widehat{y}}_{i}}^{FFM}|\) . Given a sample of such differences, comparing the models collapses to a univariate t-test allowing us to reject the null hypothesis that the mean of the differences is 0.

PF loadings

Each of the resulting PF dimensions were a weighted linear combination of IPIP-100 item responses. Despite the fact that the resulting model was based on a questionnaire meant to reliably gauge the FFM, the resulting outcome did not fully recapitulate the FFM structure. The detailed loadings for each of the resulting five dimensions appears in the supplementary materials (Fig.  1 , Supplementary Materials), can be examined in an online application we have created ( https://predictivefive.shinyapps.io/PredictiveFive ), and can be easily gleaned by examining the correlation of PF scores to the FFM scores (Fig.  2 ). None of the PF dimensions strongly correlated with demographic variables (Table 1 , Supplementary Materials). In Fig.  1 , we display the correlations between the ten outcome variables, five principle components of these outcome variables (capturing 86% of the total variance), and the five PF dimensions. For example, it can be observed the PF 3 is inversely related to performance on the intelligence test and to empathy.

Correlations between the 10 outcome variables, 5 principle components of outcome variables, and the 5 PF dimensions.

Correlations between the PF and FFM scale scores.

Predictive performance

The out-of-sample R 2 of the three models is reported in Table 1 . From this figure, we learn that the PF-based regression model is a better predictor of the outcome variables. This holds true on average (over behavioral outcomes), but also for nine of the ten outcomes individually. On 5 of the 10 comparisons, the PF-based model significantly outperformed the FFM, and in a single case the FFM-based model significantly outperformed the PF. The average improvement across all 10 measures was 40.8%.

Reproducibility analysis

If it were the case that our model discovery process produces very different loadings when run on different samples of participants, then the ontological status of the PF representation should be called into question.

In order to assess the reproducibility of the PF we split the training dataset from Study 1 into two datasets; sample A with 198,850 participants and sample B with 198,851 participants. We then learned the rotation matrix, B, on each data part, and applied it. Equipped with two independent copies of the PF, \({X}_{l }{\widehat{B}}_{l}, l=\{A,B\}\) replicability is measured by the correlation between data-parts, over participants. Table 2 reports this correlation, averaged over the 5 PFs (column “Correlation between replications”). As can be seen, the correlation between the replications is satisfactory-to-high and ranges from 0.7 to 0.98. This suggests that PF representation replicates well across samples.

Reliability analysis

If the same individuals, tested on different occasions, receive markedly different scores on the PF dimensions, then the ontological status of the PF representation should be called into question. To this end, we exploit the fact that 96,682 users answered the IPIP questionnaire twice. The test–retest correlation between these two answers is reported in Table 2 (column “Test–retest correlation”). It varied from 0.69 for the Dimension 3, to 0.79 for both Dimensions 1 and 5, suggesting that the variance captured by these dimensions is indeed (relatively) stable.

Divergence from the FFM

The superior predictive performance of the PF representation provides evidence that it differs from the FFM. Additionally, as can be gleaned from Fig.  2 (and from the detailed factor loadings’ Supplemental Material), Dimensions 3 and 4 reflect a relatively even combination of several FFM dimensions.

However, these observations do not provide us with an estimate of the degree of agreement between the two multidimensional spaces. Prevalent statistical methods of assessment of discriminant validity 34 are also not suitable to answer our question regarding the convergence\divergence between the PF and FFM spaces. These various methods only provide researchers with estimates of the agreement between unidimensional constructs .

Nonetheless, the underlying logic behind these methods (i.e., a formalization of a multitrait-multimethod matrix 35 ) is still applicable to our case. We calculated an estimate of agreement between the FFM and the PF spaces using cosine similarity , which gauges the angle between two points in a multidimensional space (the smaller the angle, the closer are the points). Our rationale is that if the FFM scores differ from the PF, they should span different spaces. The cosine similarity within measures (in our case, first and second measurements, denoted T1 and T2) should thus be larger than the similarity between measures (FFM to PF).

We used the data from the 96,682 participants for which we had test–retest data. Instead of computing standard test–retest correlations, we calculated a multidimensional test-rest score as the cosine similarity of participants’ scores on the first and second measurement, for both the FFM and PF. These estimates are expected to be highly similar and provide an upper bound on the similarity measure, partially analogous to the diameter of the multitrait-multimethod matrix. In a second stage, for each T1 and T2 vector, we measured the extent to which participants’ FFM scores are similar to their PF score, thereby calculating a magnitude that is analogous to measures of divergent validity . Because cosine similarity is sensitive to the sign and order of dimensions, we extracted the maximal possible similarity between the two spaces, providing the most conservative estimate of divergent validity.

As can be seen in Fig.  3 , the T1-T2 similarity of the FFM is nearly maximal ( M  = 0.994, SD  = 0.011); the T1-T2 similarity of PF is also very high ( M  = 0.969, SD  = 0.100). The similarity between the FFM and the PF on both T1 and T2 is much lower ( M  = 0.730, SD  = 0.111). The minimal difference between the convergence measures and divergence measures is on the magnitude of Hedge's g of 2.217, clearly representing a substantial divergence between the FFM and PF spaces. In other words, while the PF representation bears some resemblance, it is clearly a different representation.

Distribution, over participants, of the multidimensional similarity between the FFM and PF representations.

The results of Study 1 provide evidence that a supervised dimensionality reduction method can yield a low-dimensional representation that is simultaneously predictive of a set of psychological outcome variables. We demonstrate that by using a standard personality questionnaire and supervised learning methods, it is possible to improve the overall prediction of a set of 10 important psychological outcomes, even when restricting ourselves to 5 dimensions of personality. RRR allowed us to compress the 100 questions of the personality questionnaire to a new quintet of attributes that optimize prediction across a large set of psychological outcomes. The resulting set of five dimensions differs from the FFM, and has better predictive power on the held-out sample than the classical FFM and an additional comparison benchmark of five dimensions generated using Principal Component Analysis.

A theory of personality should strive to predict humans’ thoughts, feelings, and behaviors across different life contexts. Indeed, the representation we discovered in Study 1 was superior to the FFM in terms of its ability to predict a diverse set of psychological outcomes on a set of novel observations. The fact that the same low-dimensional representation was applicable across a set of important outcomes of human psychology suggests that it is a relatively generalizable model, in the sense that it simultaneously applies to several important domains. However, despite the diversity of the outcome measures examined in Study 1, it remains possible that the PF representation is only effective for the prediction of the set of outcome measures on which it was trained. Such a finding would not negate the usefulness of this model, given the wide variety of outcomes captured by the PF. However, it is interesting to see whether the resulting representation can improve prediction on additional sets of outcomes. In light of this, in Study 2 we sought to examine the performance of the PF on a set of novel outcome measures that were present in the myPersonality database, but that were held-out from the model generation process. Specifically, in this study we sought to see whether the PF representation outperforms the FFM in its ability to predict participants’ experiences during their childhood .

Unlike the outcome measures used in Study 1, this dependent variable does not pertain to participants’ lives in the present, rather, it is a measure of their past experiences. As such, “retrodiction” of remote history may be especially challenging. Nonetheless, it is widely held that individuals’ psychological properties are shaped, at least to some extent, by the degree to which they were raised in a loving household 36 , 37 . Indeed, there is evidence to the fact that many specific psychological attributes are shaped by experiences with primary caregivers (e.g., shared environmental effects on topics such as food preference 38 , substance abuse 39 , and agression 40 ). In light of this, we reasoned that it is reasonable to expect that one's personality profile should contain information that is predictive of individuals' retrospective reports of their upbringing.

We used data from 3869 participants who answered all of the questions on the 100-item IPIP representation of 26 markers for the Big Five factor structure, and answered the short form “My Memories of Upbringing” (EMBU) questionnaire 41 .

The short form of the EMBU includes a total of six subscales: three subscales that contain questions to measure the extent to which the participants' father was a warm , rejecting , and overprotecting parent, and three subscales that measure the extent to which the participants' mother was warm , rejecting , and overprotecting .

As can be seen in Table 1 , for all six variables, prediction accuracy was relatively low; however, importantly, in all six cases the PF-based model outperformed the FFM-based model, and was significantly better for four out of the six outcome variables. The average improvement across the six outcome measures was 49.2%.

The results of Study 2 further support the idea that the PF representation that was built using the 10 meaningful outcome measures present in the myPersonality database is at least somewhat generalizable. However, Study 2 again relied on myPersonality participants, upon which the PF was built. In light of this, in Study 3 we sought to further test the generality of the PF by examining whether it outperforms the FFM-based model on a set of new participants. Furthermore, we wanted to see whether our model can outperform the FFM-based model on a set of new outcome measures selected by an independent group of professional psychologists, blind to our model-generation procedure.

We collected new data using Amazon’s Mechanical Turk ( www.MTurk.com ). M-Turk is an online marketplace that enables data collection from a diverse workforce who are paid upon successful completion of each task. Our target sample size was 500 participants, which is double the size of what is considered a standard, adequate sample size in individual differences research 42 . In practice, 582 participants participated in the study, 35 of them were omitted for failing attention checks, leaving 547 participants in the final dataset (243 females and 304 men). This number exceed a sample size of 470 participants which provides 95% confidence that a small effect (⍴ = 0.1) will be estimated with narrow (w = 0.1) Corridor of Stability 42 .

In order to make sure that the PF generalize across different domains of psychological interest, it was important to generate the list of outcome variables in a way that is not biased by our knowledge of the original ten outcome variables on which the PF was designed (i.e., intelligence, well-being, and so on). Therefore, on January 3rd, 2019, we gathered a list of 12 new outcome measures by posting a call on the Facebook group PsychMAP ( https://www.facebook.com/groups/psychmap ) asking researchers: “to name psychological outcome measures that you find interesting, important, and that can be measured on M-Turk using a single questionnaire item on a Likert scale.” Once we arrived at the target number of questions we closed the discussion and stopped collecting additional variables. The 12 items were suggested by eight different psychologists, six of which had a PhD in psychology and five were principal investigators. By using this variable elicitation method, we had no control over the outcome measures, and could be certain that we have gathered a randomly-chosen sample of outcomes that are of interest to psychologists.

This arbitrariness of the outcome generation process (selecting the first 12 outcomes nominated by psychologists, without any consideration of consensus views regarding variable importance)—and the likely low psychometric reliability of single-item measures–can be seen as a limitation of this study. However, our reasoning was that such a situation best approximates the "messiness" of the unexpected, noisy, real-world scenarios wherein prediction may be of interest–and as such, provides a good test of predictive performance of the FFM and PF.

In the M-turk study, participants rated their agreement with 12 statements (1- Strongly Disagree to 7- Strongly Agree). The elicited items were:

(1) “I care deeply about being a good person at heart”.

(2) “I value following my heart/intuition over carefully reasoning about problems in my life”.

(3) “Other people's pain is very real to me”.

(4) “It is important to me to have power over other people”.

(5) “I have always been an honest person”.

(6) “When someone reveals that s/he is lonely I want to keep my distance from him/her”.

(7) “Before an important decision, I ask myself what my parents would think”.

(8) “I have math anxiety”.

(9) “I am typically very anxious”.

(10) “I enjoy playing with fire”.

(11) “I am a hardcore sports fan”.

(12) “Politically speaking, I consider myself to be very conservative”.

The independent variables were participants’ answers to the 100 questions of the IPIP questionnaire.

Similarly to Study 1, we use a fourfold cross validation scheme in order to assess the predictive performance of the PF on new data set and outcome variables. Next, we compared it to the predictive performance of the FFM. The validation worked as follows: we had \(\hat{B}\) from Study 1, we learned \(\hat{A}\) from a part of the new sample (400 ~ participants) and computed the R 2 on the holdout test set (130 ~ participants). In the spirit of the fourfold cross-validation, we iterated this process over the 4-test sets and calculated the average test R 2 for each model.

Similarly to Studies 1–2, the results showed that the predictive performance of the PF was again better than that of the Big Five, although the improvements were more modest (average 30% improvement across the 12 measures). In 5 out of 12 cases, the PF-based model was significantly better than the FFM-based model, and the opposite was true in 2 cases.

The out-of-sample R 2 of the two models (PF\Big Five) in Study 3 show a consistent trend with the results presented earlier in Study 1 and Study 2, that is, a somewhat higher percentage of explained variance in the models with the PF as predictors. This improvement observed in Study 3 was more modest than that observed earlier, but is nonetheless non-trivial—given that the set of outcome variables was different from the one the PF representation was trained on, and given that the PF representation was trained on items from questionnaires designed to measure the FFM. As such, the results of Studies 1–3 clearly demonstrate the generalizability of the PF.

A potential criticism of these findings is that the success of the PF model was more prominent on variables that were more similar to the 10 dependent measures upon which the PF was trained. However, it is important to keep in mind that the 12 outcome measures in this study were selected at random by an external group of psychologists. As such, this primarily means that the 10 psychological outcomes used to train the PF indeed provide good coverage of psychological processes that are of interest to psychologists, and thereby, overall, generalize well to novel prediction challenges.

General discussion

In this contribution, we set out to examine the viability of a novel approach to modeling human personality. Unlike the prevailing Five-Factor Model (FFM) of personality, which was developed by relying on unsupervised dimensionality reduction techniques (i.e., Factor Analysis), we utilized supervised machine learning techniques for dimensionality reduction, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). Whereas the FFM is optimized towards discovering an ontology that explains most of the variance on self-report measures of psychological traits, our new approach devised a low-dimensional representation of human trait statements that is optimized towards prediction of life outcomes. Indeed, the results showed that our model, which we term the Predictive Five (PF), provides predictive performance that is better than the one achieved by the FFM in independent validation datasets (Study 1–2), and on a new set of outcome variables, selected independently of the first study (Study 3). The main contribution of the current work is explicating and demonstrating a methodological approach of generating a personality representation. However, the results of this work is a specific representation that is of interest and of potential use in and of itself. We now turn to discuss both our general approach and the resulting representation.

Interpreting the PF

The dimensional structure that emerged when using our supervised-dimensionality reduction approach differed from the FFM. Two dimensions (Dimension 1 and 2) largely reproduced the original FFM factors of Extraversion and Neuroticism. Interestingly, these two dimensions are the ones that were highlighted in early psychological research as the “Big Two” factors of personality (Wiggins, 1966). Dimension 5 was also highly related to an existing FFM dimension, namely, Openness to Experience .

The third and fourth dimensions in the model did not correspond to a single FFM trait, but were composed of a mixture of various items. An inspection of the loadings suggests that Dimension 4 is related to some sort of a combative attitude, perhaps captured best by the construct of Dominance 43 , 44 , 45 . The items that loaded highly on this dimension related to hostility (“Do not sympathize with others”; “Insult people”), a right-wing political orientation (“Do not vote for liberal political candidates”), and an approach-oriented 46 stance (“Get chores done right away”; “Find it easy to get down to work”).

Like PF Dimension 4, Dimension 3 also seemed to capture approach-oriented characteristics (with high loadings for the items “Get chores done right away” and “Find it easy to get down to work”), however, this dimension differed from Dimension 4 in that it represented a harmony-seeking phenotype 47 . The items highly loaded on this dimension were those associated with low levels of narcissism (“keep in the background”, “do not believe I am better than others”) but with a stable self-worth (“am pleased with myself”). Additional items that were highly loaded on this dimension were those that reflect cooperativity (“concerned with others” and “sympathize with others”).

These two dimensions may seem like dialectical opposites. Indeed, the item “sympathize with others” strongly loaded on both factors, but with a different sign. However, the additional items that strongly loaded on these two dimensions appear to have provided a context that altered the meaning of this item. This is evident in the fact that Dimensions 3 and 4 are not correlated with each other. A possible speculative interpretation is that the two phenotypes captured by Dimensions 3 and 4 can be thought of as two strategies that may have been adaptive throughout human evolution. The first, captured by Dimension 4 seems to represent aggressive traits that may have been especially useful in the context of inter -group competition and conflict; the second, captured by Dimension 3, seems to represent traits that may be associated with intra -group cooperation and peace.

In general, the interpretability of the PF representation is lower than that of the FFM, with some surprising items loaded together on the same dimension. For example, the two agreeableness items that “do not believe I am better than others” and “respect others” that are strongly correlated with each other were highly loaded onto Dimension 1 (that is related to introversion), but with opposite signs. To a certain extent, this is a limitation of the predictive approach in psychology. However, such confusing associations may lead us towards identifying novel insights. For example, it is possible that some individuals adopt an irreverent stance towards both self and others, and such a stance could be predictive of various psychological outcomes, and correlated with introversion.

Towards a more predictive science of personality

As noted, the reasons that people seek models of personality are twofold: first, we want models that allow us to understand, discuss and study the differences between people; second, we need these models in order to be able to predict and affect people’s choices, feelings and behaviors 48 . Current approaches to personality modeling succeeded on the former, providing highly comprehensible dimensions of individual differences (e.g., we can easily understand and communicate the contents of the dimension of “Neuroticism” by using this sparse semantic label). However, the ability of the FFM to accurately predict outcomes in people’s lives is at least somewhat limited 19 , 20 , 20 , 49 .

The significance of the current work is that it describes a new approach to modeling human personality, that makes the prediction of behavior an explicit and fundamental goal. Our research shows that supervised dimensionality reduction methods can generate relatively generalizable, low-dimensional models of personality with somewhat improved predictive accuracy. Such an approach could complement the unsupervised dimensionality reduction models that have prevailed for decades in personality research. Moreover, this research can complement attempts to improve the predictive validity of psychology by using non-parsimonious (i.e., facets and item-level) questionnaire-based predictive models 50 .

Aside from providing a general approach for the generation of personality models, the current research also provides a potentially useful instrument for psychologists across different domains of psychological investigation. Our findings suggest that psychologists who are interested in predicting meaningful consequences (e.g., workplace or romantic compatibility) or in optimizing interventions on the basis of individuals’ characteristics (e.g., finding out which individuals will best respond to a given therapeutic technique)—may benefit from incorporating the PF dimensions in their predictive models. To facilitate such future research, we provide the R code that calculates the five dimensions based on answers on the freely available IPIP-100 questionnaire ( https://github.com/GalBenY/Predictive-Five ). The use of an existing, open-access, widely-used questionnaire means that researchers can now easily apply the PF coding scheme alongside with the FFM coding scheme to their data, and compare the utility of the two models in their own specific research domains.

One avenue of potential use of the PF representation is in clinical research. The PF showed improved prediction of depression and well-being; moreover, the PF substantially outperformed the FFM in the prediction of two known resilience factors (intelligence and empathy). Specifically, PF Dimension 3 (which, as noted above, seems to represent some harmony-seeking phenotype) significantly contributed to the prediction of all of four outcomes. As such, future work could further investigate the incremental validity of this dimension (and the PF representation more generally) as a global resilience indicator.

Across a set of 28 comparisons, the predictions derived from the PF-based model were significantly better in 15 cases, and significantly worse in 3 cases. The average improvement in R 2 across the 28 outcomes was 37.7%. However, it is important to note that the PF representation described herein is just a first proof of concept of this general approach, and it is likely that future attempts that are untethered to the constraints undertaken in the current study can provide models of greater predictive accuracy. Specifically, in the current research we relied on the IPIP-100, a questionnaire designed by researchers specifically in order to reliability measure the factors of the FFM, and limited ourselves to a five-dimension solution, to allow comparison with the FFMs. The PF representation outperformed the FFM representation despite these constraints. These results provide a very conservative test for the utility of our approach.

Future directions

Future attempts to generate generalizable predictive models will likely produce even stronger predictive performance if they relax the constraint of finding exactly five dimensions and perform dimensionality-reduction based on the raw data used to generate the FFM itself—namely, the long list of trait adjectives that exist in human language, and that were reduced into the five dimensions of the FFM.

For the sake of simplicity comparability to the FFM, the current work employed a linear method for supervised dimensionality reduction. Recent work in machine learning has demonstrated the power of Deep Neural Networks as tools for dimensionality reduction (e.g., language embedding models). In light of this, it is likely that future work that utilizes non-linear methods for supervised dimensionality reduction could generate ever more predictive representations (i.e., “personality embeddings”).

A limitation of the current work is that the PF was trained on a relatively limited set of 10 important life outcomes (e.g., IQ, well-being, etc.). While these outcome measures seem to cover many of the important consequences humans care about (as evident by the predictive performance on Study 3), it is likely that training a PF model on a larger set of outcome variables will improve the coverage and generalizability of future (supervised) personality models. A potential downside of extending the set of outcome measures used for training, is that at some point (e.g., 20, 100 outcomes) it is possible that the “blanket will become too short”: namely, that it will be difficult to find a low-dimensional representation that arrives at satisfactory prediction performance simultaneously across all outcomes. Thus, future research aiming at generating more predictive personality models may need to find a “sweet spot” that allows the model to fit to a sufficiently comprehensive array of target outcomes.

What may be the most important consequence of the current approach is that whereas previous attempts of modeling human personality necessarily limited by their reliance on the subjective products of the human mind (i.e., were predicated on human-made psychological theories, or subjective ratings of trait words), our approach holds the unique potential of generating personality representations that are based on objective inputs.

A final question concerning predictive models of personality is whether we even want to generate such models, given the potential of their misuse. While the current results still show the majority of variance in psychological outcomes remain unexplained–in the era of social networks and commercial genetic testing, the predictive approach to personality modeling could theoretically lead to models that render human behavior highly predictable. Such models give rise to both ethical concerns (e.g., unethical use by governments and private companies, as in the Cambridge-Analytica scandal) and moral qualms (e.g., if behavior becomes highly predictable, what will it mean for notions of free will and personal responsibility?). While these are all valid concerns, we believe that like all other scientific advancements, personality models are tools that can provide a meaningful contribution to human life (e.g., predicting suicide in order to avoid it; predicting which occupation will make a person happiest). As such, the important, inescapable quest towards generating even more effective models that will allow us to predict and intervene in human behavior is only just the beginning.

Data availability

The data for Study 1, 3 and 4 rely on the myPersonality database ( www.mypersonality.org ) which is an unprecedented big-data repository for psychological research, used in more than a hundred publications. We achieved permission from the owners of the data to use it for the current research—but we do not have their permission to share it for wider use. The data for Study 2 is available upon request. We also share the complete code and the full model with factor loadings ( https://github.com/GalBenY/Predictive-Five ).

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The study was supported by ISF grant 1113/18 to M.G.

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Jonathan Rosenblatt

School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

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Lavi, G., Rosenblatt, J. & Gilead, M. A prediction-focused approach to personality modeling. Sci Rep 12 , 12650 (2022). https://doi.org/10.1038/s41598-022-16108-3

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What Your Personality Says About Your Risk Of Heart Disease

H eart disease is responsible for one death every 33 seconds across the country, according to the U.S. Centers for Disease Control and Prevention  (CDC). A number of factors can increase one's risk for heart disease, including smoking, hypertension, obesity, a lack of exercise, overconsumption of alcohol, and much more.

Certain emotions may also be tied to a greater likelihood of cardiovascular disease — predominantly those related to stress. Researchers from a 2018 study published in the scientific journal In Vivo highlight how studies over the years have suggested strong emotions such as anger, depression, hostility, and anxiety may be risk factors for heart disease and other cardiovascular conditions.

Of course, we're only human, and all of us experience these emotions at one time or another. However, some research has found particularly strong correlations between heart disease risk and those with Type A personalities. Often thought of as being productivity-focused, behavioral traits often attributed to Type A personalities include ambitiousness, aggressiveness, competitiveness, and being work-driven, according to 2012 research published in the American Journal of Public Health.

Read more: Lies Your Doctor Knows You're Telling

Type A Personalities May Be More Susceptible To Stress

Type A personalities have been the subject of study for decades. Follow-up research from 1975 published in the Journal of the American Medical Association (JAMA) outlines how over the course of more than eight years, 257 study subjects had developed clinical coronary heart disease. A strong relationship between Type A behavior and rates of heart disease was found. All subjects were middle-aged men.

So what's the explanation for why more work-centric people may be at risk for heart disease? Researchers from a 2018 scientific review published in the Indian Heart Journal point out how some studies have shown that people with Type A personalities are more susceptible to stress. Stress can impact the body in a number of ways, including boosting one's risk of heart disease. The researchers also point out that Type A behaviors have been linked with elevated blood cholesterol levels and coronary artery disease rates compared to those who have more go-with-the-flow personality traits.

Type D Personalities May Also Be At Risk For Heart Disease

Over time, experts have expanded their research on personality traits and heart disease risk to include the lesser-known personality type Type D. With the "D" standing for "distressed", this personality type is largely characterized by negative emotional states and social inhibition (per Indian Heart Journal). Similar to Type A personalities, these individuals also tend to experience heightened stress levels, particularly in the workplace and other social settings.

Research has suggested a connection between people with Type D personalities and more severe cases of disease, poorer health, exaggerated blood pressure and heart responses to stress, and an increased risk of cardiac death. Because these individuals find interpersonal exchanges challenging, those with Type D personalities who have cardiovascular diseases often delay seeking medical help.

Regardless of whether you're more of a Type A or Type D personality, there are numerous factors that influence one's risk for heart disease outside of emotional behaviors. Experts have also explored the opposite relationship, in which heart disease may increase one's risk of Type A or Type D-like personality traits . Research has also shown that hope and other positive emotional states may offer protection against coronary heart disease (per In Vivo).

Read the original article on Health Digest .

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Talk about a highway to hell.

New research has found that people who commonly text and drive are concerningly associated with psychopathic behavior.

The new data — from interviews with nearly 1,000 German drivers, about 73% of them women found a shocking 600-plus participants, or about 61%, admitted to “problematic” use of their devices while driving.

Along with connections to fear of missing out and anti-social behavior, problematic smartphone users (PSU) evoked three negative traits known as the “dark triad” — narcissism, Machiavellianism and psychopathy, which unrelated research also recently connected to drivers with deliberately loud cars .

“[Problematic smartphone use] is an excellent predictor regardless of the Dark Triad personality traits,” the study authors wrote, adding a potential prescription for addressing the issue.

“Since this factor can be changed more easily than personality, PSU should be targeted in public safety interventions, driving training and court-mandated medical-psychological assessment of driver fitness.”

The research team also proposed other ways to curb the habitual use of cell phones.

“It might be a good strategy to help people reduce their PSU in everyday life, which should indirectly decrease the chances of using their phones on the road and prevent accidents and fatal crashes.”

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Cluster analysis in fibromyalgia: a systematic review

• Systematic Review
• Published: 15 May 2024

Cite this article

• Anna Carolyna Gianlorenço   ORCID: orcid.org/0000-0002-7334-3835 1 , 2 ,
• Valton Costa   ORCID: orcid.org/0000-0002-2356-7523 1 , 2 ,
• Walter Fabris-Moraes   ORCID: orcid.org/0000-0002-8467-8454 2 ,
• Maryela Menacho   ORCID: orcid.org/0000-0001-9507-2215 1 , 2 ,
• Luana Gola Alves   ORCID: orcid.org/0009-0008-7428-8090 2 ,
• Daniela Martinez-Magallanes   ORCID: orcid.org/0000-0002-8428-9037 2 &
• Felipe Fregni   ORCID: orcid.org/0000-0002-1703-7526 2  

The multifaceted nature of Fibromyalgia syndrome (FM) symptoms has been explored through clusters analysis.

To synthesize the cluster research on FM (variables, methods, patient subgroups, and evaluation metrics).

We performed a systematic review following the PRISMA recommendations. Independent searches were performed on PubMed, Embase, Web of Science, and Cochrane Central, employing the terms “fibromyalgia” and “cluster analysis”. We included studies dated to January 2024, using the cluster analysis to assess any physical, psychological, clinical, or biomedical variables in FM subjects, and descriptively synthesized the studies in terms of design, cluster method, and resulting patient profiles.

We included 39 studies. Most with a cross-sectional design aiming to classify subsets based on the severity, adjustment, symptomatic manifestations, psychological profiles, and response to treatment, based on demographic and clinical variables. Two to four different profiles were found according to the levels of severity and adjustment to FMS. According to symptom manifestation, two to three clusters described the predominance of pain versus fatigue, and thermal pain sensitivity (less versus more sensitive). Other clusters revealed profiles of personality (pathological versus non-pathological) and psychological vulnerability (suicidal ideation). Additionally, studies identified different responses to treatment (pharmacological and multimodal).

Several profiles exist within FMS population, which point out to the need for specific treatment options given the different profiles and an efficient allocation of healthcare resources. We notice a need towards more objective measures, and the validation of the cluster results. Further research might investigate some of the assumptions of these findings, which are further discussed in this paper.

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Acknowledgements

Valton Costa and Maryela Menacho are fellows of the Institutional Internationalization Program (CAPES/PrInt/UFScar) funded by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES)/Ministry of Education of Brazil.

FF and ACG were supported by a NIH grant (1R01AT009491-01A1).

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Neuroscience and Neurological Rehabilitation Laboratory, Physical Therapy Department, Federal University of Sao Carlos, Sao Carlos, SP, Brazil

Anna Carolyna Gianlorenço, Valton Costa & Maryela Menacho

Spaulding Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital, Harvard Medical School, 1575 Cambridge Street, Cambridge, MA, USA

Anna Carolyna Gianlorenço, Valton Costa, Walter Fabris-Moraes, Maryela Menacho, Luana Gola Alves, Daniela Martinez-Magallanes & Felipe Fregni

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ACG, VC, WFM, and FF conceived and designed the work, acquired, and analyzed the data; MM, LG, and DMM acquired and analyzed the data; All authors drafted and revised the work critically for important intellectual content, and finally approved the version to be published. All authors agreed to be accountable for all aspects of the work, including the accuracy and integrity.

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Big Five personality traits in the workplace: Investigating personality differences between employees, supervisors, managers, and entrepreneurs

1 Imperial College London, London, United Kingdom

Kreisha Lou Guzman

2 R&D, Macro Health Research Organization Inc., Quezon City, Philippines

Antonio Malvaso

3 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

Associated Data

Publicly available datasets were analyzed in this study. This data can be found here: https://www.understandingsociety.ac.uk .

Personality relates to employment status. Previous studies have mainly compared the difference between entrepreneurs and managers. It remains unknown how personalities differ in entrepreneurs, managers, supervisors, and employees. In this research, we answer the questions by analyzing data from Understanding Society: the UK Household Longitudinal Study (UKHLS) that consisted of 2,415 entrepreneurs, 3,822 managers, 2,446 supervisors, and 10,897 employees. By using a multivariate analysis of variance (MANOVA) and ANOVA, we found that employment status has a significant multivariate effect on personality traits ( F (5, 17,159) = 172.51, p < 0.001) after taking account into demographics. Moreover, there were also significant univariate effects for Neuroticism ( F (3,19502) = 16.61, P < 0.001), Openness ( F (3,19502) = 3.53, P < 0.05), Agreeableness ( F (3,19502) = 66.57, P < 0.001), Conscientiousness ( F (3,19502) = 16.39, P < 0.001), and Extraversion ( F (3,19502) = 31.61, P < 0.001) after controlling for demographics. Multiple comparisons revealed that entrepreneurs are characterized by low Neuroticism, high Openness, high Conscientiousness, and high Extraversion while managers had low Neuroticism, low Agreeableness, high Openness, high Conscientiousness, and high Extraversion. Finally, supervisors are associated with high Conscientiousness. Implications and limitations are discussed.

Introduction

Criterion-related validity studies strongly supported the role of personality in predicting employee job performance ( Ones et al., 2007 ; Chamorro-Premuzic and Furnham, 2010 ). Literature agrees that there is a significant relationship between personality and job performance across all occupational groups, managerial levels, and performance outcomes ( Barrick and Mount, 1991 ; Hurtz and Donovan, 2000 ; Barrick et al., 2001 ). Although higher Conscientiousness and lower Neuroticism were associated with higher job performance across most types of jobs, the relationship between Extraversion, Openness, and Agreeableness with job performance was found to be more context-dependent ( Barrick et al., 2001 ). Thus, it is important to understand how personality differs in different job positions.

Over the years, more and more people have found success in creating their businesses and working on their terms. With the number of successful entrepreneurs on the rise, researchers have become more interested in specific characteristics of entrepreneurs and how they affect their performance ( Kerr et al., 2018 ). A notable number of studies comparing the differences in Big Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) between entrepreneurs and managers emerged between 1960 and 2000 ( Kerr et al., 2018 ). Managers were often compared to entrepreneurs (e.g., Zhao and Seibert, 2006 ), given the need of both groups to direct workers and manage multiple tasks. Both are crucial positions crucial in the company’s operations, but their roles are completely different. An entrepreneur is described as an individual who is “instrumental to the conception of the idea of an enterprise and its implementation” ( Kets de Vries, 1996 ) and “an innovator and a catalyst of change who continuously does things that were not done before and do not fit established societal patterns” ( Schumpeter, 1965 ). Meanwhile, a manager is defined as “the one who sets goals, plans and organizes the activities, motivates human resources, and controls the overall procedures.” ( Tovmasyan, 2017 ). Another important player in the organizational structure is the supervisor. According to Stevens and Ash (2001) , the supervisor is responsible for ensuring that the work of his subordinates is completed on time and at a satisfactory level of quality. Although the terms’ manager and supervisor are sometimes used interchangeably with managers, they are not the same. Managers are higher-level and higher-paid leaders whereas supervisors are closer to day-to-day activities of their teams to ensure the manager’s goals are met.

These observed characteristic differences in employment status are attributed to the “attraction-selection-attrition model” by Schneider (1987) . According to this model, “first, individuals are attracted to jobs commensurate with their personality traits (i.e., attraction). Second, organizational selection procedures result in the selection of individuals with similar personality scale scores for a particular job (i.e., selection). Finally, individuals who take jobs to which personality traits are not suited are more likely to leave their jobs (i.e., attrition)” ( Ones and Viswesvaran, 2003 ).

Specifically, combined evidence from the meta-analysis conducted by Zhao and Seibert (2006) reported that entrepreneurs were more open to experience, more conscientious, less agreeable, less neurotic, and but have similar levels of Extraversion compared to managers. However, many individual studies showed different patterns. One example is from a Canadian survey of 218 entrepreneurs and managers by Envick and Langford (2000) , and they found that entrepreneurs were significantly less conscientious, less agreeable, and less extroverted than managers.

Entrepreneurs were also consistently found to be more open than managers. Researchers hypothesized that an entrepreneur is likely to be attracted to constantly changing environments and the novelty of new challenges in a business venture ( Zhao and Seibert, 2006 ; Kerr et al., 2018 ). Individuals who thrived on challenges and novel environments presented creative solutions, business models, and products, and the Openness of entrepreneurs may help these functions ( Kerr et al., 2018 ). Meanwhile, managers are usually chosen by their superiors to execute and deliver high-quality results for a set of directives rather than for seeking novel solutions. Thus, it is hypothesized that an entrepreneur’s environment and job requirements might be more suitable for those who were more open ( Kerr et al., 2018 ).

Zhao and Seibert (2006) suggested that higher Conscientiousness, which is a composite of achievement motivation and dependability, is the most significant difference between entrepreneurs and managers. Their study also found that entrepreneurs and managers are similar in dependability, but entrepreneurs score significantly higher than managers in the achievement motivation facet. A meta-analysis by Collins et al. (2004) concluded that individuals who pursue entrepreneurial careers were significantly higher in achievement motivation than individuals who pursue other types of careers. Stewart and Roth (2007) similarly concluded that entrepreneurs have a higher need for achievement than managers. It is often hypothesized that achievement-oriented individuals set goals, maintain high standards, and have a strong sense of ownership. In contrast, there is insufficient evidence on whether entrepreneurs score higher than managers on Extraversion. Extraversion is a trait that measures the extent to which one is dominant, energetic, active, talkative, and enthusiastic ( Costa and McCrae, 1992 ). Several studies found that Extraversion is more fundamental for entrepreneurs than managers since entrepreneurs act as salespeople for their ideas to investors, partners, employees, and customers. However, no reliable difference was observed in the literature according to Zhao and Seibert (2006) . Further, Envick and Langford (2000) found that entrepreneurs are less extroverted than managers, suggesting that many entrepreneurs may run small businesses from their homes to be away from large bureaucracies that demand one to be relentlessly sociable.

Thus, although the personality differences between entrepreneurs and managers have been extensively studied and compared, much less is known about how personality would differ in entrepreneurs, managers, and supervisors from normal employees. Moreover, previous studies used a small sample size, which could be biased due to their reduced power. Understanding the personality differences between different employment statuses is important because understanding the personality trait differences between different employment statuses may have the potential to contribute to the established personality-job choice-job performance relationship, and thus contribute to employee selection. The aim of our study is to understand the personality difference between them by analyzing data on a large scale. We hypothesized that Openness and Conscientiousness are positively related to the employment status hierarchy (i.e., employee- > supervisor- > manager- > entrepreneur), Neuroticism and Agreeableness are negatively related to the employment status hierarchy, and Extraversion has little association with the employment status hierarchy.

Materials and methods

Sample and data collection.

Data were from Understanding Society: the UK Household Longitudinal Study (UKHLS), which has been collecting annual information from the original sample of UK households since 1991 (when it was previously known as The British Household Panel Study (BHPS). Data were ethically collected from this sample from 2011 to 2012. This data collection has been approved by the University of Essex Ethical Committee by letter dated 17 December 2010. Samples included (1) The General Population Sample (GPS), which is a clustered and stratified probability sample of approximately 24, 000 households living in the Great Britain and a sample of approximately 2000 households in the Northern Ireland in 2009, (2) The Ethnic Minority Boost Sample (EMBS), which consists of approximately 4000 households chosen from areas with high ethnic minorities, and (3) The British Household Panel Survey sample (BHPS), which is consisted of around 8000 households. Please refer to Lynn (2009) for more details. Each household is visited each year to collect relevant information. Interviews are conducted face-to-face in participants’ homes by trained interviewers or they completed a survey online. We excluded participants who were under the age of 18 or who were above the age of 99, and those who had missing fields in relevant variables. Thus, a total number of 19,580 participants remained in our analysis from the original 49,693 participants.

Measurement and analysis

Personality was measured using the 15-item version (3 items for each personality trait) of the Big Five Inventory with a Likert scale ranging from 1 (“disagree strongly”) to 5 (“agree strongly”). Personality scores were reversed when appropriate. The mean scores averaged across the three items for assessing each personality trait were used to represent scores for each personality trait. These shorter forms of personality measures have been approved to have good internal consistency, test-rest reliability, and convergent and discriminant validity ( Hahn et al., 2012 ; Soto and John, 2017 ). Participants also responded to questions regarding if they are entrepreneurs, managers, supervisors, or employees if they were workers. Demographics information was collected from participants as well ( Table 1 ). All analyses were conducted using a customized script on MATLAB 2018a. We used the mean scores of relevant items to represent each personality trait. A multivariate analysis of variance (MANOVA) and ANOVA were used to see the effect of employment status on personality traits in general and in detail with employment status and demographics as predictors. A multiple comparison test was used to assess the specific differences in each personality trait in different employment statuses.

Descriptive statistics of sociodemographic variables and personality traits.

Demographics can be found in Table 1 . Employment status had a significant multivariate effect on personality traits ( F (5, 17159) = 172.51, p < 0.001) after taking account into demographics. Moreover, there were also significant univariate effects for Neuroticism ( F (3,19502) = 16.61, P < 0.001), Openness ( F (3,19502) = 3.53, P < 0.05), Agreeableness ( F (3,19502) = 66.57, P < 0.001), Conscientiousness ( F (3,19502) = 16.39, P < 0.001), and Extraversion ( F (3,19502) = 31.61, P < 0.001) after controlling for demographics ( Table 2 ).

The results of the ANOVA for A. Neuroticism, B. Agreeableness, C. Openness, D. Conscientiousness, and E. Extraversion respectively.

Multiple comparison tests showed that entrepreneurs are less neurotic than normal employees (mean difference = −0.16, [95% CI: −0.24, −0.08], p < 0.001). Managers had lower Neuroticism scores than employees (mean difference = −0.16, [95% CI: −0.24, −0.08], p < 0.001) and supervisors (mean difference = −0.09, [95% CI: −0.18, 0.00], p < 0.05). Managers were less agreeable than supervisors (mean difference = −0.07, [95% CI: −0.14, 0.00], p < 0.05) and employees (mean difference = −0.06, [95% CI: −0.12, 0.00], p < 0.05). Regarding Openness, entrepreneurs were more open than managers (mean difference = 0.17, [95% CI: 0.09, 0.25], p < 0.001), supervisors (mean difference = 0.29, [95% CI: 0.20, 0.38], p < 0.001), and employees (mean difference = 0.37, [95% CI: 0.30, 0.45], p < 0.001). Similarly, managers had higher Openness scores than supervisors (mean difference = 0.20, [95% CI: 0.14, 0.27], p < 0.001) and employees (mean difference = 0.08, [95% CI: 0.01, 0.15], p < 0.05). Conscientiousness scores in entrepreneurs (mean difference = 0.11, [95% CI: 0.05, 0.16], p < 0.001), in managers (mean difference = 0.11, [95% CI: 0.06, 0.16], p < 0.001), in supervisors (mean difference = 0.11, [95% CI: 0.05, 0.17], p < 0.001) were significantly higher than that of in employees. Finally, entrepreneurs were more extroverted than supervisors (mean difference = 0.23, [95% CI: 0.14, 0.33], p < 0.001) and employees (mean difference = 0.25, [95% CI: 0.17, 0.32], p < 0.001). Managers were also more extraverted than supervisors (mean difference = 0.15, [95% CI: 0.07, 0.24], p < 0.001) and employees (mean difference = 0.17, [95% CI: 0.10, 0.24], p < 0.001; Figure 1 ).

The bar graph shows differences in personality traits between different employment statuses with standard error.

Token together, our study compared the personality differences between employees, supervisors, managers, and entrepreneurs using multivariate and univariate ANOVA after controlling for demographics with multiple comparison tests to assess specific differences between groups. Our study is the first study that compared the Big Five personality differences between these groups according to the best of our knowledge although previous studies have compared this difference between entrepreneurs and managers. A detailed discussion is provided in the following paragraphs.

Results showed that entrepreneurs and managers exhibit lower Neuroticism compared to employees. These findings were consistent with existing studies suggesting entrepreneurs are less neurotic ( Zhao and Seibert, 2006 ; Kerr et al., 2018 ). Lower levels of Neuroticism are described as having emotional stability that allows entrepreneurs to deal with stress and uncertainty, and develop a good working relationship with others ( Etemad et al., 2013 ). Another study done by Yitshaki (2021) also highlighted the need for entrepreneurs to keep their emotions in control because their firm’s growth might depend on how they manage these. Similarly, managers have to be emotionally stable to fulfill management duties. However, we did not find a significant difference in Neuroticism between entrepreneurs and managers ( Zhao and Seibert, 2006 ).

Similarly, we found a significant effect of employment status on Agreeableness. People with high Agreeableness were found to be more prosocial ( Costa and McCrae, 1992 ), and it seems to be crucial for the success of entrepreneurs to gain external resources from other organizations with the help of maintained relationships ( Street and Cameron, 2007 ). Specifically, we found that managers were less agreeableness than supervisors and employees. Indeed, although high Agreeableness may lead one to be considered trustworthy and build positive work relationships, it may prevent managers to drive hard bargains, look out for one’s own self-interest, and influence other people for one’s own advantage. All of these characteristics made it not desirable for managers because they may interfere with the manager’s ability to make difficult decisions which may affect subordinates and coworkers ( Zhao and Seibert, 2006 ).

Similarly, we found a significant effect of employment status on Openness, which is a trait that has been often characterized by creativity, being attracted to changing environments, and prefer variety over routine ( Kerr et al., 2018 ). Specifically, we found that managers were less open than supervisors and employees. Indeed, the goal of a manager is to control the whole procedure and ensure goals are met rather than being very creative and innovative, which requires less degree of Openness although managers’ Openness may be positively associated with organizational success ( Kay and Christophel, 1995 ).

This study also found that Openness in entrepreneurs is higher than that of managers, supervisors, and employees. Specifically, entrepreneurs were more open than managers, supervisors, and employees. Similarly, managers were more open than supervisors and employees. Entrepreneurs are characterized by their emphasis on innovation ( Zhao and Seibert, 2006 ). Creating a new venture may require the entrepreneur to come up with new or novel ideas, use creativities to solve problems that have not been encountered before, and make innovative products, business models, or strategies. Interestingly, we also found that managers are more open than supervisors and employees, which may indicate that even though enforcing the rules is important, being innovative in establishing policies and making strategies is also critical for the success of the manager as well.

Conscientiousness is described as a person’s ability to control their impulses, develop long-term goals, and consistently work on these goals to achieve them. In this study, we found that entrepreneurs, managers, and supervisors have higher Conscientiousness scores than normal employees. Despite mixed results of previous studies ( Envick and Langford, 2000 ; Collins et al., 2004 ; Stewart and Roth, 2007 ; Cantner et al., 2011 ), the role of Conscientiousness is generally considered important in entrepreneurship which was stressed by Ciavarella et al. (2004) as the positive link between long-term venture survival. Additionally, Hough and Oswald (2000) reported that Conscientiousness is the strongest predictor of managerial performance. Ülgen et al. (2016) discussed the relationship between Conscientiousness and management styles and found significant effects of Conscientiousness on management styles that require rational decision-making like authoritarian, protective, supporter, and laissez-faire styles but not on the unionized styles.

We also found that Extraversion scores in entrepreneurs and managers are significantly higher than that of supervisors or employees. Individuals with Extraversion tend to be dominant, energetic, talkative, and enthusiastic ( Costa and McCrae, 1992 ). Entrepreneurs are most likely to get involved in activities that require a high level of social skills, it is expected that they exhibit higher levels of Extraversion, which is heavily supported by our results. Thus, having jobs not requiring much interaction with other people could explain why average employees had the lower level of Extraversion among the other statuses of employment. The finding that entrepreneurs do not have higher Extraversion scores than managers seemed to be consistent with one previous study ( Awwad and Al-Aseer, 2021 ) but contradictory to others (e.g., Zhao and Seibert, 2006 ).

There are some limitations in this study. First, we used cross-sectional data and all the relationships in the current study were associative, which makes it hard to identify the causal effect. Thus, it remains unclear regarding if certain personality traits cause people to be in certain employment status or if employment status causes changes in personality traits. Second, we measured employment status in general, it is unclear how personality in a different occupation and in different employment statuses would differ. For instance, a salesman’s personality could totally differ from an assembly line worker as the main activity of a salesman is to engage with other people, which requires more social skill and thus have different personality traits. Moreover, compared to personality traits, characteristics such as general or emotional intelligence, temperament or motivation, or interests and aspirations may be more important in differentiating occupational positions ( McManus et al., 2003 ; Cheng and Furnham, 2012 ; Stoll et al., 2017 ).

This study provided novel insights and further understanding of how the Big Five personality traits vary across different employment statuses. A deeper comprehension of the connection between personality and employment status has the possibility to be useful in several practical fields. Although theories of vocational choice have found considerable application in the context of career counseling, different employment status as a career path has received less consideration in this literature. Our findings offer proof of the personality traits that set someone who is likely to be drawn to, chosen for, and stay in a different employment status. With this knowledge, people will be better able to match their strengths to the risks and opportunities presented by a professional career. The decisions made by venture capitalists, government funding organizations, and others on their support for certain employment status may be influenced, at least in part, by their own theories and models of employment status and personality. Decision-makers may become more realistic and modest in the implementation of their own implicit ideas if they are aware of the true relationship between personality and employment status. Large firms frequently work to foster innovation by choosing staff members who will act as internal entrepreneurs (intrapreneurs) and elevating them to important positions. The study’s findings can be used to create suitable selection and placement standards for such choices. Furthermore, this study has consequences for how people interested in entrepreneurship should be trained. Even though the Big Five fundamental personality traits are generally stable, many of the behaviors connected to them can be learned with experience and effort. For instance, research by Barrick et al. (1993) revealed that people who scored highly on Conscientiousness were more likely to develop and stick to goals, which was then linked to their better job performance. Both the person seeking to pursue different positions and society at large may find training intended to promote the behaviors associated with employment status to be very useful. We don’t believe that personality theory offers a comprehensive theory of employment status or even covers all the possible themes. Instead, our findings demonstrate that personality must be taken into account as one significant element in a multidimensional model of the variables, processes, and contextual factors influencing employment status and the establishment of new ventures.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by University of Essex. The patients/participants provided their written informed consent to participate in this study.

Author contributions

WK: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, writing – original draft, and writing – review and editing. KG: writing – original draft. AM: writing – review and editing. All authors contributed to the article and approved the submitted version.

Funding Statement

This work was supported by the Imperial Open Access Fund.

Conflict of interest

KG was employed by the company Macro Health Research Organization Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Female labor force participation

Across the globe, women face inferior income opportunities compared with men. Women are less likely to work for income or actively seek work. The global labor force participation rate for women is just over 50% compared to 80% for men. Women are less likely to work in formal employment and have fewer opportunities for business expansion or career progression. When women do work, they earn less. Emerging evidence from recent household survey data suggests that these gender gaps are heightened due to the COVID-19 pandemic.

Women’s work and GDP

Women’s work is posited to be related to development through the process of economic transformation.

Levels of female labor force participation are high for the poorest economies generally, where agriculture is the dominant sector and women often participate in small-holder agricultural work. Women’s participation in the workforce is lower in middle-income economies which have much smaller shares of agricultural activities. Finally, among high-income economies, female labor force participation is again higher, accompanied by a shift towards a service sector-based economy and higher education levels among women.

This describes the posited  U-shaped relationship  between development (proxied by GDP per capita) and female labor force participation where women’s work participation is high for the poorest economies, lower for middle income economies, and then rises again among high income economies.

This theory of the U-shape is observed globally across economies of different income levels. But this global picture may be misleading. As more recent studies have found, this pattern does not hold within regions or when looking within a specific economy over time as their income levels rise.

In no region do we observe a U-shape pattern in female participation and GDP per capita over the past three decades.

Structural transformation, declining fertility, and increasing female education in many parts of the world have not resulted in significant increases in women’s participation as was theorized. Rather, rigid historic, economic, and social structures and norms factor into stagnant female labor force participation.

Historical view of women’s participation and GDP

Taking a historical view of female participation and GDP, we ask another question: Do lower income economies today have levels of participation that mirror levels that high-income economies had decades earlier?

The answer is no.

This suggests that the relationship of female labor force participation to GDP for lower-income economies today is different than was the case decades past. This could be driven by numerous factors -- changing social norms, demographics, technology, urbanization, to name a few possible drivers.

Gendered patterns in type of employment

Gender equality is not just about equal access to jobs but also equal access for men and women to good jobs. The type of work that women do can be very different from the type of work that men do. Here we divide work into two broad categories: vulnerable work and wage work.

The Gender gap in vulnerable and wage work by GDP per capita

Vulnerable employment is closely related to GDP per capita. Economies with high rates of vulnerable employment are low-income contexts with a large agricultural sector. In these economies, women tend to make up the higher share of the vulnerably employed. As economy income levels rise, the gender gap also flips, with men being more likely to be in vulnerable work when they have a job than women.

From COVID-19 crisis to recovery

The COVID-19 crisis has exacerbated these gender gaps in employment. Although comprehensive official statistics from labor force surveys are not yet available for all economies,  emerging studies  have consistently documented that working women are taking a harder hit from the crisis. Different patterns by sector and vulnerable work do not explain this. That is, this result is not driven by the sectors in which women work or their higher rates of vulnerable work—within specific work categories, women fared worse than men in terms of COVID-19 impacts on jobs.

Among other explanations is that women have borne the brunt of the increase in the demand for care work (especially for children). A strong and inclusive recovery will require efforts which address this and other underlying drivers of gender gaps in employment opportunities.

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