research on personality traits has demonstrated that

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• Published: 27 February 2023

Disentangling the personality pathways to well-being

• Paulo A. S. Moreira 1 , 2 , 5 ,
• Richard A. Inman 1 , 2 &
• C. Robert Cloninger 3 , 4  

Scientific Reports volume  13 , Article number:  3353 ( 2023 ) Cite this article

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• Human behaviour

An Author Correction to this article was published on 19 October 2023

This article has been updated

Recent genomic, psychological, and developmental research shows that human personality is organized as a complex hierarchy that ascends from individual traits in many specific situations to multi-trait profiles in two domains that regulate emotional reactivity (temperament) or goals and values (character), and finally to three integrated temperament-character networks that regulate learning to maintain well-being in changing conditions. We carried out person-centered analyses of the components of subjective well-being (positive affect, negative affect, and life satisfaction) to personality in both adolescents (N = 1739) and adults (N = 897). Personality was considered at each level of its organization (trait, temperament or character profiles, and joint temperament-character networks). We show for the first time that negative affect and life satisfaction are dependent on the personality network for intentional self-control, whereas positive affect is dependent on the personality network for self-awareness that underlies the human capacities for healthy longevity, creativity, and prosocial values.

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Introduction.

A long tradition of research has shown that people tend to lead longer and healthier lives, and behave more prosocially, when they subjectively experience their lives positively rather than negatively 1 , 2 . Such subjective experiences, referred to as subjective well-being (henceforth SWB), capture the cognitive and emotional aspects of the subjective feelings a person has about their own life circumstances 3 , 4 , 5 . SWB is said to be higher when a person experiences a high level of positive emotions alongside a low level of negative emotionality, and when they also evaluate their life circumstances to be satisfactory according to relevant criteria and standards (e.g., values, goals, norms, and cultural variables). Thus, SWB is widely considered to have a tripartite structure comprising positive affect, negative affect, and life satisfaction. Findings from various empirical studies have suggested these three primary components are dissociable (e.g., low negative affect does not assure high positive affect), and so ought to be assessed and studied independently 6 , 7 , 8 . However, studies have also shown that positive affect, negative affect, and life satisfaction are highly correlated 9 , and tend to load on a general SWB factor 10 , 11 , 12 leading many authors to postulate a higher-order SWB factor. Consequently, it is common in the literature to encounter studies that use composite SWB scores as a way to examine global SWB.

Given the strongly-established link between SWB and positive outcomes, scholars have invested substantial effort in understanding why some people experience higher SWB than others. Toward this goal, research on the association between SWB and personality is highly relevant because it has the potential to uncover the basic biopsychosocial systems and processes that influence the nature of human subjective experience 13 . However, this depends on using models of personality for which there is strong evidence that individual personality traits correspond to unitary latent constructs with specified underlying psychological systems 14 . Presently, lexical models of personality traits such as the ‘Big Five’ 15 , 16 and HEXACO 17 models are dominant in research on personality and SWB, with studies often revealing weak to moderate associations (i.e., about 20% of variance) with extraversion and neuroticism 18 . However, to advance the state of the art it is important to consider alternative models like Cloninger’s psychobiological model of personality as measured by the Temperament and Character Inventory (TCI) 19 , 20 . This model is at least as good or better than other models in terms of predictive validity 21 and the TCI has the benefit of measuring traits that are regulated by genetically, functionally, and developmentally distinct psychobiological systems of learning and memory 22 , 23 , 24 , 25 . However, despite the potential of this model for providing insights into SWB, it has not been as widely considered in research on this topic as the lexical models derived by the restrictive and questionable assumptions of linear factor analysis (although see 26 , 27 , 28 ).”

Therefore, the overarching aim of this study was to examine the relationship between personality and SWB from the perspective of Cloninger’s psychobiological model. In particular, we aimed to add to current knowledge by using an approach that fully acknowledges the complex organizational hierarchy of human personality 25 . To this end, it is useful to briefly outline the features of Cloninger’s psychobiological model and recent evidence that validates its assumptions.

The psychobiological model of personality

Cloninger’s psychobiological model of human personality is based on genomic and neuroimaging work that was not possible when the lexical tradition of personality assessment was developed using factor analytical techniques. It accounts well for the phenotypic variation measured by alternative models, and also provides a robust foundation in the genomics and neurobiology of learning and memory. According to Cloninger’s model of personality 19 , 20 , subjective experiences are dependent on organizations of psychobiological processes that underlie three distinct but interacting systems of learning and memory that evolved sequentially in human evolution: associative conditioning, intentionality, and self-awareness 24 , 29 , 30 . A concise summary of the genetics, neuroscience, and psychology of this research is available 31 , 32 .

Temperament and character traits

Empirical findings robustly demonstrate a distinct domain of heritable and relatively stable aspects of personality that underlie and modulate the expression of basic emotions (i.e. temperament) 31 , 33 . Temperament involves individual differences in prelogical brain functions for associative conditioning of habits, attachments, and emotional reactivity. Individual differences in these brain functions are quantified by the TCI in terms of four empirically distinct dimensions: novelty seeking (impulsive, exploratory vs. deliberate, reserved), harm avoidance (fearful, pessimistic vs. risk taking, optimistic), reward dependence (friendly, sentimental vs. detached, objective), and persistence (determined, ambitious vs. easily discouraged, underachieving). Studies using functional neuroimaging have confirmed how individual differences in these four dissociable traits are associated with individual differences in the structure and function of brain regions involved in emotional functioning and associative conditioning 34 . Other studies provide strong neurophysiological, neuroanatomical, and biochemical evidence for the distinct psychobiological origins of these temperament traits 22 , 31 , 35 , 36 , 37 .

The psychobiological model of personality conceptualizes human personality also has a regulatory and cognitive domain in addition to the temperament domain. This self-regulatory domain of human character involves processes of intentionality and self-awareness that have been shown empirically to be genetically, psychologically, and developmentally distinct from temperament 31 . This domain of mental self-government includes executive functions that are intrapersonal (e.g., planning and foresight), legislative functions that are interpersonal (e.g., empathy and norms for cooperation), and judicial functions that are transpersonal (e.g., insight and intuitive evaluation of what is meaningful and good) 38 . Thus, the TCI quantifies individual differences in these processes in terms of three character dimensions: self-directedness (i.e., resourceful, purposeful, and responsible), cooperativeness (i.e. tolerant, helpful, and empathic) and self-transcendence (easily absorbed in flow states, meditative, and identifying with other people, nature, and what is sacred). Research indicates that character traits are as heritable as temperament traits 39 , and brain imaging studies have indicated that individual differences in character are reflected in differences in brain structure and function. Structurally, TCI character traits are correlated with local gray and white matter volumes in brain regions that are involved in self-reflection (self-directedness), empathizing (cooperation), and religious belief (self-transcendence) 40 .

Various past studies provide evidence that harm avoidance, persistence, and self-directedness may be particularly important for understanding SWB. For example, harm avoidance has shown to be significantly higher in participants with mood or anxiety disorders compared to those without, with the opposite pattern evident for self-directedness and persistence 41 . Prior work has also shown that adolescents reporting high positive affect and low negative affect have significantly lower harm avoidance and higher self-directedness than those reporting low positive affect and high negative affect 42 . More indirectly, a systematic review has indicated that well-being is most consistently associated with brain activation in the anterior cingulate cortex 43 , which serves to emotional and cognitive functions, such as regulating emotion in accord with goals and values, and is strongly correlated with individual differences in persistence 44 . However, it is worth noting that average associations estimated with linear methods can be weak or inconsistent because TCI traits are nonlinear in their functional effects.

Multi-trait temperament and character profiles

Recent genomic studies using deep machine learning algorithms have uncovered that human subjective experience depends on complex interactions among the temperament traits, among the character traits, and between temperament and character, rather than on individual traits acting independently 22 , 23 , 25 . For example, there is extensive empirical support that genes code for different temperament configurations that describe the whole person 31 , 45 . Such temperament configurations include the ‘reliable’ profile (defined by high persistence and reward dependence, and low novelty seeking and harm avoidance), the ‘sensitive’ profile (high harm avoidance, reward dependence, and novelty seeking), and the ‘antisocial’ profile (high novelty seeking, low reward dependence and persistence) 22 . Similar latent profiles have been identified in independent samples 46 , 47 .

Beyond these major temperament types, recent studies using artificial classification methods (e.g., median split) have identified 16 configurations of high and low values for the four temperaments 48 . Studies have also identified and tested how multi-trait character profiles differ in well-being. Notably, a recent study by Zwir et al. 23 identified distinct sets of genes that coded for five configurations of high and low values for character traits: three reflecting healthy personalities and two reflecting unhealthy personalities. Other studies have used artificial classification methods to cluster participants into the eight possible configurations of high and low values for the three character traits 26 , 28 , 49 , 50 . Research using latent profile analysis to identify naturally occurring characters has also identified these eight theoretical configurations 47 .

A growing number of studies have examined how multi-trait personality profiles relate to indicators of well-being, although most have focused on character because of its prominent self-regulatory role. This work has consistently show that physical, mental, and social well-being is strongest when all three character traits are high, and lowest when all three character traits are low 23 , 26 , 28 , 50 , 51 . These studies have also shown that the three TCI character traits make different non-linear contributions to positive affect, negative affect, and life satisfaction 26 , 27 . When only changing one character dimension in the configuration, higher life satisfaction was shown to be linked consistently to higher self-directedness; higher negative affect was linked to lower self-directedness (and sometimes lower cooperativeness); while higher positive affect was linked to higher levels of all three character traits (i.e. the creative character).

A smaller body of prior work has also shown that distinct temperament configurations also differ markedly in well-being. In particular, a temperament configuration of high reward dependence, high persistence, low novelty seeking, and low harm avoidance (the reliable temperament) has been linked to increased probability for well-being 22 . However, it is noteworthy that this work used a single composite index of well-being derived from each participant’s configuration of character dimensions to facilitate cross-cultural replication. While character profile is a valid indicator of overall physical, mental, and social well-being 26 , 52 , this approach does not distinguish between the three separable aspects of SWB. Therefore, there is a specific need to examine how people with distinct temperament profiles differ in positive affect, negative affect, and life satisfaction.

Joint temperament-character networks

Research has shown that the full range of possible temperament profiles can occur with each character profile, although the probabilities of the different temperament-character profile combinations differ on average 25 , 53 , 54 . Recent behavioral-genetic research has identified three nearly disjoint phenotypic networks that account for the complex relations of temperament profiles with character profiles 25 . These three networks have been labelled as (a) the emotional-unreliable network, (b) the organized-reliable network, and (c) the creative-reliable network.

The first of these personality networks, the emotional-unreliable network, is strongly associated with a genotypic network for emotion regulation and social attachment (associative conditioning), and individuals classified in this network are typically emotionally reactive with weak capacity for rational self-government. Studies have shown this network is primarily comprised of people with apathetic or dependent character (low in self-directedness and cooperativeness) associated with sensitive or antisocial temperaments (high novelty seeking, low in persistence) 25 , 47 , 55 . The second personality network, the organized-reliable network, is strongly associated with genes for the regulation of intentional goal-setting (intentionality). People in this network are capable of resourceful productivity but are conventional, materialistic and practical, and not always compassionate or empathetic. Studies have found this network is mostly comprised of people with a reliable temperament associated with characters that are high in self-directedness and/or in cooperativeness 25 , 47 , 55 . Finally, the creative-reliable network is strongly associated with genes for episodic learning and autobiographic memory that allow for intuitive insight and creative imagination in the appraisal of values and theories (self-awareness). People in this third network primarily have a reliable temperament associated with a creative character 25 , 47 , 55 , are therefore capable of resourceful productivity and are more compassionate, helpful, intuitive, meditative, and creative. Because of reciprocal interactions among temperament and character–character regulates temperament while temperament biases perception and behavior–these adaptive networks are self-organizing 56 . Such self-organization implies that all individuals have the potential to develop a mature and coherent character that can regulate habits, attachments, and innate emotional tendencies to maintain a subjective state of calm awareness, resilience, and positive emotionality; that is, in other words, to experience SWB 19 , 57 .

These three phenotypic networks are each strongly correlated with a distinct genotypic network that regulates a different system of learning and memory, each of which is linked to distinct brain circuits. Specifically, the emotional-unreliable network is associated with clusters of 249 genes involved in emotion regulation and social attachment by associative conditioning, with the habitual responses to extracellular stimuli regulated by the ERP and PI3K molecular pathways. The organized-reliable network is associated with clusters of 438 genes for the regulation of intentional goal-setting (intentionality). Specifically, intentional self-control of the seeking of food and other goals involves the phospho-inositol/ Calcium second-messenger signaling system within cells. The creative-reliable network is associated with genes for episodic learning and autobiographic memory that allow for intuitive insight and creative imagination that extends a person’s perspective beyond their present place, time, and identity. Thus, creative self-awareness is associated with clusters of 574 genes, including 267 genes in modern human beings (mostly long-non-coding RNAs and microRNAs) that are not found in chimpanzee or Neanderthal genomes. The genes for self-awareness allow regulation of epigenetic modification of brain circuitry and co-expression of genes in particular brain regions that comprise networks for awareness and evaluation of life as a narrative that gives a person meaning and satisfaction. In this way, the personality features of each network can be considered prototypical of a major system of learning and memory. The structure of the personality traits, genetic clusters, and environmental influences are nearly separate from one another, but there is sufficient overlap to allow collaborative interactions to facilitate the integration of the three learning networks in a way that allows a person to bring their person’s habits in accord with their goals and values despite changing internal conditions and external situations. Detailed descriptions of the identification and replication of these networks and their evolution are presented in various articles 22 , 23 , 24 , 25 , 32 . The application of these findings in person-centered psychotherapy has been described using Plato’s Allegory of the Cave and his metaphors for rational self-government elsewhere 58 , 59 , 60 , 61 .

Prior work has shown that the three temperament-character networks–reflecting different integrated configurations of brain circuits for associative conditioning, intentionality and self-awareness–are highly correlated with indicators of well-being. For example, Zwir et al. found that the creative-reliable network was associated with the highest probability well-being, followed by the organized-reliable network and finally the emotional-unreliable network 25 . Prior works have also shown that the three networks differ in comic style (people in the creative-reliable network had a ‘lighter’ style) 47 , and virtues and character strengths (people in the creative-reliable network were more self-controlled, caring and inquisitive) 62 , among other indicators of adaptive functioning 55 .

Study aims and hypotheses

Despite this current knowledge, researchers still know relatively little about how SWB is influenced (a) by non-linear and dynamic interactions among configurations of personality features in specific situations; (b) by the multidimensional configurations of temperament and of character domains; or (c) by the personality networks that maintain health and SWB despite environmental changes. Moreover, within the growing number of studies that are addressing these issues, most have focused on the multi-trait domain of character because of its prominent self-regulatory role 23 , 26 , 27 , 28 , 50 . These studies have consistently show that the physical, mental, and social well-being is strongest when all three character traits are high, and lowest when all three character traits are low. Beyond this focus on character, studies by Zwir et al. have shown that higher well-being was linked to the specific (“reliable”) configuration of high reward dependence and persistence, and low novelty seeking and harm avoidance 22 , and highest in the creative-reliable phenotypic network 25 . However, it is noteworthy that in order to facilitate cross-cultural replication these studies used a single composite index of well-being derived from each participant’s configuration of character dimensions. While character profiles are a valid indicator of overall physical, mental, and social well-being 26 , 52 , this approach does not distinguish between the individual components of SWB; namely, positive affect, negative affect, and life satisfaction. Therefore, work is needed to evaluate how configurations of temperament and character are related to the individual components of SWB.

By examining how positive affect, negative affect, and life satisfaction are associated with personality at these increasing levels of descriptive complexity, we aimed to provide richer insights into the psychobiological systems and processes underpinning the multidimensional phenomenon of SWB 22 , 23 , 24 , 25 .

Toward this goal, a first aim was to use a person-centered approach to explore how interactions among distinct combinations of (a) temperament traits, and (b) character traits are associated with positive affect, negative affect, and life satisfaction. By using non-linear statistical methods 26 we aimed to add to a growing body of research by highlighting the need to recognize the complex nonlinearity of developmental processes. We did not make explicit predictions on which specific temperament or character profiles would be most strongly linked to the SWB components, or on how specific pairs of temperament or character profiles might differ, instead exploring the results to provide a broader understanding of the multidimensional nature of human adaptive functioning. However, based on various prior works we expected harm avoidance, persistence and self-directedness to present the strongest associations on average across SWB dimensions. We also anticipated that higher self-transcendence would be uniquely associated with higher positive affect (because people high in this trait are able to manifest joy from transpersonal identification with something greater than themselves 19 ). Additionally, we broadly expected that the reliable temperament and the creative character would be associated with the highest levels of SWB dimensions overall.

A second major aim of this study was to test the associations of the individual components of SWB with three temperament-character networks that are distinct in their evolution, genetics, development, biopsychosocial functions, and phenotypes 24 , 25 , and that regulate the three major systems of learning and memory that have evolved in modern human beings in addition to general intelligence. Studies have shown that these networks differ markedly in composite measures of physical, mental, and social well-being. However, no study has considered whether these differences are consistent across the three separable aspects of SWB. Based on current evidence we were able to formulate two specific hypotheses. Firstly, we expected that the emotional-unreliable network would be associated with the lowest levels of positive affect and life satisfaction, and the highest negative affect. This is because research has often shown that the emotional reactivity of an unregulated temperament from low intentional self-control is linked to ill-health, low well-being, and maladaptive functioning 23 , 63 .

Secondly, we postulated that the creative-reliable network would generally be associated with higher SWB, but particularly with positive affect. We formulated this hypothesis because research shows the capacity for self-awareness through reflection and meditation activates molecular processes that promote an upward spiral of plasticity, virtue and effective functioning that are beneficial to the self and others, allowing for the manifestation of joy and positive emotions 23 , 64 , 65 , 66 . Indeed, studies have shown that capacity for self-awareness is linked to greater parasympathetic activity allowing individuals to operate in a state of calm awareness, and to function flexibly when confronted with challenges, thus maintaining state of positive emotionality 67 .

Hypothesis 1

The emotional-unreliable network will be associated with the lowest positive affect and life satisfaction, and highest negative affect.

Hypothesis 2

The creative-reliable network will be unique in its association with higher positive affect.

Participants

Adult sample.

The first sample (Sample 1) comprised two moderately sized independent data sets that were pooled into one. Pooling two samples has the benefit of increasing statistical power and sample heterogeneity (due to design characteristics and sampling methods etc.) 68 . To obtain this convenience sample we approached undergraduate university students from several degree programs at the lead authors’ institution. Students agreeing to partake in the study were given survey packs to distribute to friends and family. Data were obtained for 767 individuals. Because of the sampling strategy, the sample age was skewed toward younger adults. The second data set comprised 400 adults who were recruited as part of a study on personality and religiousness. These adults were also recruited using a non-probabilistic chain referral sampling technique.

The initial pooled sample comprised 1167 adults. For data to be included in the final analysis we determined that participants must be ≥ 18 years (which excluded 12 individuals), to have responded to > 25% items from the study measures (which excluded 46 individuals), and to have responded correctly to all five attention-check items within the TCI-R (which excluded 212 individuals). Thus, the final sample comprised 897 adults (30% male, 70% female) aged between 18 and 88 years ( M  = 35.7, SD  = 16.8).

Adolescent sample

The second sample (Sample 2) was a sample of ninth graders participating in the third wave of a longitudinal study on student engagement in sustainable development. In Portugal, the 9th grade corresponds to the final year of basic education. Data collection for this third wave occurred during the COVID-19 pandemic between November 2020 and January 2021, which was period of widespread stress. In total, we obtained data for 1,823 individuals. However, for data to be included in the final analysis participants had to be < 18 years (excluding 8 individuals) and to have respond to > 25% items from the study measures (excluding 78 individuals). The JTCI does not include attention-check items. After exclusions, the final sample comprised 1,739 adults from 57 schools. Within this sample there were 756 boys (43%) and 849 girls (49%) with a mean age of 14.2 years ( SD  = 0.7).

Positive and negative affect

The emotional component of SWB was measured in Portuguese adults using the Positive and Negative Affect Schedule (PANAS) 69 , 70 . This instrument comprises 20 adjectives that describe emotional experiences: 10 positive (example item: “Excited”) and 10 negative (example item: “Afraid”). For each item, participants are asked to rate the extent to which they have felt the emotion over the last weeks on a scale from 1 (not at all or very little) to 5 (extremely). Omega coefficients were 0.89 and 0.90 for the positive affect and negative affect scales, respectively.

In adolescents, the emotional component of SWB was measured using the Emotional Tonality Scale , itself an adaptation of the PANAS. This instrument comprises 27 adjectives that describe emotional experiences: 12 positive (example item: “Excited”) and 15 negative (example item: “Afraid”). Like the PANAS, participants are asked to rate the extent to which they have felt the emotion over the last weeks on a scale from 1 (not at all or very little) to 5 (extremely). Omega coefficients were 0.88 and 0.91 for the positive affect and negative affect scales, respectively.

Life satisfaction

The cognitive component of SWB was measured in adults using the World Health Organization Quality-of-Life Scale (WHOQOL-BREF). This 26-item scale was developed to assess “individuals’ perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” 71 . In total, 24 of the 26 items are grouped within four domains: physical health, psychological health, social relationships, and physical environment. The remaining two items reflect global assessments of life satisfaction quality and health (example item: “How would you rate your quality of life”). All items are rated on a scale from 1 to 5, with higher scores reflecting increased well-being. We calculated a mean average score for this scale excluding item 26 because this item “How often do you have negative feelings such as blue mood, despair, anxiety, depression?” refers to the emotional, rather than cognitive, component of SWB. The omega coefficient for this scale was 0.91.

In adolescents, the cognitive component of SWB was measured in this sample using the six-item Brief Multidimensional Students’ Life Satisfaction Scale 72 . These items require respondents to judge their satisfaction with five specific life domains—family life, friends, school, oneself, and home–as well as life in general. Satisfaction is rated on a six-point scale from 0 (terrible) to 6 (fantastic). Research showed this scale has measurement invariance across 23 diverse populations 73 . The omega coefficient for this scale was 0.91.

Personality

In adults, personality was measured using the European Portuguese Temperament and Character Inventory (TCI-R) 74 , 75 . The 240 items of this scale measure the behavioral and subjective aspects of how people respond in different situations. Four temperament dimensions quantify individual differences in associative conditioning and habitual behaviors: Novelty Seeking, Harm Avoidance, Reward Dependence, and Persistence. Three character dimensions capture the rational self-regulatory domain of personality: Self-Directedness, Cooperativeness, and Self-Transcendence. Omega coefficients for the seven scales ranged from 0.76 to 0.90.

Adolescents responded to the European Portuguese Junior Temperament and Character Inventory (JTCI) 76 , an adaptation of the TCI intended for youth aged 9 years and older 77 . This version of the JTCI has 127 items to capture the four temperament dimensions and three character dimensions of Cloninger’s psychobiological model of personality. All items are rated on a five-point scale from 1 (completely false) to 5 (completely true). Omega coefficients for the seven scales ranged from 0.68 to 0.89.

Statistical analyses

We began by calculating correlations between all study variables. Because there was some indication that not all variables were normally distributed, we opted to calculate Spearman’s correlations coefficients. We considered correlation values ≥ 0.20 as being “practically” significant in terms of effect size 78 .

Next, to describe how dynamic intra-individual organizations of biopsychosocial processes relate to SWB we used a person-centered approach to analysis. We formed temperament profiles by dividing participants into groups reflecting those above and below the normative median 79 for each of the four temperament traits, after excluding participants who were in the center of the distribution (45th to 55th percentile) for all four traits. This resulted in the 16 possible combinations of high and low values for novelty seeking, harm avoidance, reward dependence, and persistence shown in Table 1 . The same procedure was repeated to group participants into the eight possible combinations of high and low character score on self-directedness, cooperativeness, and self-transcendence, again after excluding participants who were in the center of the distribution (45th to 55th percentile) for all three traits. To represent the three temperament-character networks we grouped participants into clusters of people expected to have low overall health (i.e., the emotional-unreliable cluster of people with character profiles including low self-directedness), intermediate health (i.e., the organized reliable cluster of people with characters that are high in self-directedness but not always high in cooperativeness and/or self-transcendence ), and high well-being (i.e., the creative reliable cluster of people with high self-directedness, cooperativeness and self-transcendence), as in prior works. The average age in years and gender distribution for each personality profile are in Supplementary Table 1 .

After forming personality profiles, we performed as series of robust ANOVAs 80 , 81 based on 20% trimmed means to test differences in SWB dimensions across temperament profiles, character profiles, and temperament-character networks. Effect sizes for robust ANOVAs were assessed by calculating an explanatory measure of effect size (ξ) 82 that does not require equal variance. Values of \(\hat{\xi }\)  = 0.10, 0.30, and 0.50 were set as thresholds for small, medium and large effect sizes 80 . Each robust ANOVA was followed by robust post-hoc tests, also based on 20% trimmed means. For these robust post-hoc comparisons, 95% confidence intervals for the possible effect sizes were corrected for the number of tests performed. All analyses were performed using R (version 4.1.2) 83 .

Missing data

For adults, 91% of participants had no missing data for the PANAS and 93% had no missing data for the WHOQOL-BREF. Moreover, 95% of participants had < 1% missing data for the TCI-R. For adolescents, 99% of participants had no missing data for the PANAS and > 99% of participants had no missing data for the BMSLSS. 98% of adolescents had no missing data for the JTCI. We imputed all missing data using a predictive mean matching single imputation method.

Ethical declarations

To ensure adherence to the Declaration of Helsinki, the study protocols for each sample were approved by the ethics committee of the Centro de Investigação em Psicologia para o Desenvolvimento (Ref: CIPD/2122/PERS/3) or the ethics committee at Universidade Lusíada Porto (Ref: UL/CE/CIPD/2207). All research methods were performed in accordance with relevant guidelines/regulations. Informed consent was obtained from all participants prior to the study. For participants under the age of 18 years, informed consent was also obtained from a legal guardian.

Descriptive statistics

A detailed summary of descriptive statistics for both samples is presented in Supplementary Materials . However, while none of the variables presented strong skew or kurtosis (all values <|1|), it was apparent that scores for negative affect were skewed toward the lower end of the scale (skew = 0.91).

Correlational analyses

Table 2 presents the correlations among all study variables in both samples. Because the samples were large, it was unsurprising that most TCI traits correlated significantly with SWB dimensions. It was also notable that the findings were generally consistent across the two samples.

Positive affect

Consistent with our expectations the correlation coefficients indicated harm avoidance had an inverse negative relationship with positive affect in both samples (R = −0.20 and −0.39) thus contributing to 4% and 15% of the variance in adolescents and adults, respectively. Next, we found that persistence had a positive relationship with positive affect (R = 0.30 and 0.41), contributing to more variation than harm avoidance (9% and 17% respectively). Finally, we observed that self-directedness was the TCI trait most strongly correlated with positive affect in adolescents (R = 0.33), contributing to 11% of the variance. However, in adults self-directedness has a less strong linear relationship with positive affect (R = 0.19) than self-transcendence (R = 0.26) and reward dependence (R = 0.22).

Negative affect

It was evident from Table 2 that in both samples harm avoidance ( R  = 0.42 and 0.40) and self-directedness ( R  = −0.46 and −0.48) had the strongest linear associations with negative affect. Thus, self-directedness appeared to make a moderate contribution to negative affect, accounting for 21% and 23% of the variance, respectively. An interesting finding was that for adolescents novelty seeking had a positive linear association with negative affect ( R  = 0.32) while in adults this relationship was close to zero ( R  = 0.05). It was also noteworthy that persistence was negatively correlated with negative affect in adolescents ( R  = −0.24) but close to uncorrelated in adults ( R  = −0.05).

Mirroring the results for negative affect, it was evident in Table 2 that life satisfaction was most strongly associated with self-directedness ( R  = 0.53 and 0.46), accounting for 28% and 21% of the variance, respectively. In both samples life satisfaction was also negatively associated with harm avoidance ( R  = −0.33 and -0.44), and positively associated with persistence ( R  = 0.33 and 0.31). Unlike for negative affect, life satisfaction was shown to have a positive association with cooperativeness ( R  = 0.31 and 0.24) and reward dependence ( R  = 0.34 and 0.21).

Temperament results

Differences among temperament profiles.

Mean scores for positive affect, negative affect, and life satisfaction (see Fig.  1 ) were compared among people in the 16 temperament profiles using robust ANOVAs. These indicated the between-subjects effect of temperament profile was significant for positive affect (adults: p  < 0.001, \(\hat{\xi }\)  = 0.52; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.46); negative affect (adults: p  < 0.001, \(\hat{\xi }\)  = 0.49; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.52); and life satisfaction (adults: p  < 0.001, \(\hat{\xi }\)  = 0.51; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.51).

Boxplots with superimposed 20% trimmed means (black dots) for positive affect, negative affect, and life satisfaction across temperament profiles. Note. ( A, B ) Differences for Positive Affect. ( C, D ) Differences for Negative Affect. ( E, F ) Differences for Life satisfaction. Horizontal lines represent the grand mean for each variable.

Non-linear analysis of temperament dimensions

We evaluated the non-linear associations between temperament traits and SWB dimensions by examining selected paired comparisons from robust post-hoc tests, which are summarized in Table 3 (Adults sample) and Table 4 (Adolescent sample). These profiles capture the 16 possible configurations of novelty seeking (N = high novelty seeking; n = low novelty seeking), harm avoidance (H = high harm avoidance; h = low harm avoidance), reward dependence (R = high reward dependence; r = low reward dependence), and persistence (P = high persistence; p = low persistence). Mean differences between pairs of temperament profiles (e.g. nhrp vs. nhrP) were interpreted as statistically significant when their associated confidence intervals (CIs) did not include the value zero.

Overall, the two tables indicated that most paired comparisons were non-significant. However, an important observation was a trend for higher positive affect with higher persistence; with three significant contrasts in the adolescent sample and one significant contrast in the adult sample. Notably, in both samples, persistence was significantly associated with higher positive affect when comparing the higher vs. lower persistence variants of the nHR “Cautious” temperament.

The non-linear interactions of temperament traits for negative affect were not simply the opposite of those observed for positive affect. The most relevant finding was that higher harm avoidance was associated with higher negative affect for seven of the eight possible configurations of novelty seeking, reward dependence and persistence in adolescents. Fewer contrasts were significant for the adult sample, although the trend was in the same direct as the adolescents. A second noteworthy finding was that most comparisons between profiles differing in persistence were non-significant. Thirdly, for adolescents it was evident that higher novelty seeking was associated with higher negative affect in three contrasts where harm avoidance was high (nHrp vs. NHrp; nHRp vs. NHRp; and nHRP vs. NHRP).

Finally, there was a trend in both adults and adolescents for lower life satisfaction in profiles with higher harm avoidance. For adolescents, five of the eight comparisons were significant, with mean differences ranging from −0.50 to −0.97, while for adults two comparisons were significant (mean differences of −0.32 and −0.46). For adolescents only, there was an indication that higher reward dependence was associated with higher life satisfaction. In particular, this association was significant in three of the four comparisons where novelty seeking was low (although also in one comparison where novelty seeking was high).

Character results

Differences among character profiles.

Figure  2 . presents participants’ scores for positive affect, negative affect, and life satisfaction grouped by the 8 distinct character profiles formed of self-directedness (S = high self-directedness; s = low self-directedness), cooperativeness (C = high cooperativeness; c = low cooperativeness), and self-transcendence (T = high self-transcendence; t = low self-transcendence). Robust ANOVAs indicated the between-subjects effect of character profile was significant for positive affect (adults: p  < 0.001, \(\hat{\xi }\)  = 0.37; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.43); negative affect (adults: p  < 0.001, \(\hat{\xi }\)  = 0.50; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.46); and life satisfaction (adults: p  < 0.001, \(\hat{\xi }\)  = 0.50; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.50).

Boxplots with superimposed 20% trimmed means (black dots) for positive affect, negative affect, and life satisfaction across character profiles. Note. ( A, B ) Differences for Positive Affect. ( C, D ) Differences for Negative Affect. ( E, F ) Differences for Life satisfaction. Horizontal lines represent the grand mean for each variable.

Non-linear analysis of character dimensions

Paired comparisons to test the associations of each character dimension with SWB dimensions while controlling for the others (e.g. sct vs. scT) are summarized in Table 5 (Adult sample) and Table 6 (Adolescent sample).

A first major finding from the two tables was that higher positive affect was associated with higher self-directedness for three of the four possible configurations of cooperativeness and self-transcendence. For both samples, higher self-directedness was not significantly associated with higher positive affect when cooperativeness and self-transcendence were both low (sct vs. Sct). A second noteworthy result was that higher positive affect was associated with higher self-transcendence, with both samples showing significant differences when self-directedness and cooperativeness were both low (sct vs. scT), and both high (SCt vs. SCT).

Most notably, we observed that higher self-directedness was consistently associated with lower negative affect, with all contrasts significantly different in both samples. Cooperativeness and self-transcendence, in contrast, did not appear to show strong associations with negative affect. That said, an interesting finding was that for adolescents higher self-transcendence was linked to higher negative affect for the SCt vs. SCT contrast.

Like negative affect, the most notable finding for life satisfaction was the strong consistent association with self-directedness in both samples. The results in Tables 5 and 6 also indicated that life satisfaction was largely not associated with cooperativeness or self-transcendence.

Differences among personality networks

Figure  3 presents participants’ scores for positive affect, negative affect, and life satisfaction grouped by the three personality networks. There was a large convergence between the results for adults and adolescents. Robust ANOVAs showed significant differences among the networks for positive affect (adults: p  < 0.001, \(\hat{\xi }\)  = 0.34; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.41); negative affect (adults: p  < 0.001, \(\hat{\xi }\)  = 0.62; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.54); and life satisfaction (adults: p  < 0.001, \(\hat{\xi }\)  = 0.53; adolescents: p  < 0.001, \(\hat{\xi }\)  = 0.63).

Boxplots with superimposed 20% trimmed means (black dots) for positive affect, negative affect, and life satisfaction across temperament-character networks. Note. ( A, B ) Differences for Positive Affect. ( C, D ) Differences for Negative Affect. ( E, F ) Differences for Life satisfaction. Horizontal lines represent the grand mean for each variable.

An inspection of Fig.  3 a and b indicated that positive affect was lowest for the Emotional-Unreliable network and highest for the Creative-Reliable network. The robust post-hoc analyses presented in Table 7 generally supported this observation. However, in adults it was questionable whether the Emotional-Unreliable network differed significantly in positive affect from the Organized-Reliable network ( \(\hat{\varphi }\)  = −0.13, 95% CI [−0.29, 0.02], p  = 0.037).

From Fig.  3 c and d it was evident that the Emotional-Unreliable network was associated with higher than average negative affect, while the Organized-Reliable and Creative-Reliable networks were associated with lower than average negative affect. Supporting this, post-hoc tests displayed in Table 7 showed the Emotional-Unreliable network differed significantly from the other two networks. In adults, it was questionable whether the Organized-Reliable network differed significantly in positive affect from the Creative-Reliable network ( \(\hat{\varphi }\)  = 0.11, 95% CI [−0.02, 0.25], p  = 0.047). However, in adolescents the paired-comparison showed the Creative-Reliable network had higher negative affect than the Organized-Reliable network ( \(\hat{\varphi }\)  = −0.23, 95% CI [−0.33, −0.13], p  < 0.001).

Figure  3 e and f appeared to show the reverse pattern of Fig.  3 c and d, with the Emotional-Unreliable network associated with lower life satisfaction than the Organized-Reliable and Creative-Reliable networks. In both samples, post-hoc comparisons confirmed these differences. Additionally, post-hoc analyses (Table 7 ) indicated that the Organized-Reliable and Creative-Reliable networks did not differ significantly from one another.

In this study, we explored the associations between the three dimensions of SWB–positive affect, negative affect, and life satisfaction–and components of personality that are shown by rigorous research to be regulated by genetically, functionally, and developmentally distinct psychobiological processes 22 , 23 , 24 , 25 , 34 , 45 . Adopting a similar procedure to past studies 47 , 62 we tested how personality at various levels of descriptive complexity relates to SWB. Specifically, this involved testing how the three SWB dimensions relate to (a) individual temperament and character traits, (b) multi-trait temperament profiles and multi-trait character profiles, and (c) integrated temperament-character networks.

Using an analytical approach from prior works 26 that allowed us to evaluate associations of individual temperament and character traits, we found that positive affect seemed to be largely dependent on persistence, self-directedness and self-transcendence. This finding aligns with others works that have demonstrated how human flourishing occurs when habits are persistently regulated to be in congruence with self-transcendent goals and values, thus resulting in a well-integrated personality 62 . Other constructs related to motivational persistence and perseverance, such as grit , have also been consistently associated with positive emotional experiences 84 .

Supporting current understanding that negative affect is not simply the absence of positive affect; we found that negative affect was linked to different personality traits. Specifically, negative affect was positively associated with high harm avoidance (which has specific subscales for fearfulness and shyness) and low self-directedness. This finding aligns with past works that have consistently shown how harm avoidance and low self-directedness are indicators of neuroticism and frequently associated with psychopathology and emotional/behavioral problems 85 , 86 , 87 , 88 , 89 . We also found that participants high in novelty seeking (and particularly adolescents) reported higher negative affect. Indeed, clinical research that has demonstrated that high novelty seeking is a precursor to emotional and behavioral problems 87 , 90 , such as substance abuse 91 , and Cluster B personality disorders 89 . The fact that negative affect was more strongly influenced by novelty seeking in adolescents than adults may reflect the relevance of this trait for the developmental process of identity formation and emancipation from adult authority, and immature capacity to self-regulate emotional impulses by character 92 . Within the sample of adolescents, one possibility is that unregulated novelty seeking, reflecting an impulsive eagerness to explore and try new things, may lead to more friction with adult authority and frustration at not being fully autonomous, and thus prompt negative affect.

As with negative affect, we found that life satisfaction was associated with harm avoidance and self-directedness (although with the opposite pattern of association). However, life satisfaction did not appear to be dependent on novelty seeking, instead revealing a trend for association with reward dependence in adolescents. Like novelty seeking, research shows reward dependence peaks in mid-adolescence, reflecting a high desire for peer approval 92 . The fact that satisfaction with life was more strongly associated reward dependence in adolescents than adults may again be because of their immature capacity to self-regulate emotional impulses by their own character, which results in a greater need for social support 92 . Within adolescents, one possibility is that reward dependence promotes higher satisfaction with life, particularly regarding friends and school, because it allows individuals to be more receptive to the norms and expectations of their peers 92 .

Robust evidence indicates that genes code for distinct temperament profiles rather than individual temperament traits 22 , 45 . In this person-centered study, we grouped participants into all theoretically possible configurations of the four temperaments. A first finding was that these temperament profiles were associated with differences in SWB. For example, participants with an explosive temperament (NHrp) typically reported lower than average positive affect and life satisfaction, and higher than average negative affect, suggesting that this temperament configuration is the least adaptive in terms of SWB. In contrast, adults and adolescents with a reliable temperament (nhRP) tended to report higher than average positive affect and life satisfaction, and lower than average negative affect. These findings are consistent with an emerging body of work that shows a reliable temperament is particularly adaptive for human functioning, with positive associations with student engagement 55 and an overall ‘good character’ 62 .

We also grouped participants into all theoretically possible character profiles. As predicted, the study indicated that SWB was strongly related to overall character development, with the ‘creative’ character (SCT) associated with the highest positive affect, lowest negative affect, and most life satisfaction. This finding aligns fully with evidence that the synergistic development of the three character traits allows an individual to cultivate the healthy practices of letting go (self-directedness), working in the service of others (cooperativeness), and growing in awareness of what is beyond the individual self (self-transcendence) 64 , 93 , which are key practices of virtue in action that promote SWB 26 , 28 . Indeed, this is why “third-wave psychotherapies” that aim to promote self-transcendence alongside self-directedness and cooperativeness (e.g., through mindfulness training), are more effective than more narrow cognitive-behavioral approaches 94 , 95 .

A major contribution of the study was that we explored, for the first time, how the three components of SWB differed between the personality networks that reflect differences in three major systems of human learning and memory 25 . Our results demonstrated that the capacity for self-control of emotional conflicts and goals, as is characteristic of people in the organized-reliable and creative-reliable networks, was beneficial for reducing negative affect and elevating hopeful cognitive appraisals about one’s circumstances. However, we found that only the combination of intentional self-control and self-awareness was associated with elevations in positive affect. This finding extends and clarifies earlier work showing that the emergence of self-awareness in behaviorally modern Homo sapiens around 100,000 years ago provided human beings the capacity for healthy longevity, creativity, and prosocial values and behavior 24 .

Our current findings align with previous conceptualizations of positive emotions as tools selected for their adaptive functions of broadening one’s horizon, leading to more flexible creative responses and the building of personal psychological and social resources 96 . Indeed, it has been demonstrated that the emergence of positive emotions has cascade effects into socio-cognitive processes such as attention 97 , interpersonal cognition 98 , logical reasoning 99 and creativity 100 . In turn, while positive emotions energize people to explore and to think outside of the box, intentional self-control and self-awareness provide the flexibility needed for growth, and innovative problem resolution, as well as the structure needed for effective, management of the available resources. Therefore, based on our findings, as well as the latest genetic, neurobehavioral, and evolutionary evidence presented throughout this study, we argue that the psychobiological model offers an explanatory holistic framework that is capable of adequately addressing the prominent non-linear and dynamic links between emotion and cognition, which promote positive human functioning and flourishing.

A major implication of the study results for clinical practice is that they add to a growing recognition that an understanding of a whole person’s adaptive functioning and well-being requires an integration of interdependent brain networks for emotional reactivity, rational self-government, and self-awareness. Put differently, the study highlights that the path to a happy and healthy life depends not on one key feature, but rather the integration of the physical (biological), mental, and spiritual aspects of human functioning 101 . Therefore, clinical practitioners need to adopt person-centered interventions that aim to cultivate growth of the whole person 64 . Additionally, the study shows that higher positive affect is dependent on different psychobiological processes than life satisfaction and negative affect, namely those associated with the capacity for self-awareness such as insight and creativity. As such, the cultivation of human flourishing via positive emotionality specifically requires the growing in awareness of oneself, via reflective and meditative practices, as an entangled aspect of something greater than one’s individual self (that is, extending to human communities, nature, the universe, and possibly what is sacred and divine 64 ).

Despite the growing number of empirical studies on SWB–including recently on the association between psychobiological personality dimensions and SWB 102 –there remain various research questions that require further investigation. Firstly, most research on this topic has used cross-sectional designs. However, research has demonstrated that temperament and character dimensions develop across the lifespan 51 , 92 , and therefore it is important to explore with longitudinal designs how intraindividual change in personality over time relates to changes in SWB. Second, while studies strongly support the association between personality and SWB dimensions, it remains necessary to explicate how these associations interact with societal, environmental, and cultural variables. A reasonable hypothesis, for example, might be that factors like income vary in their association with SWB as a function of personality. As a related issue, it is important to conduct empirical studies to develop understanding of how human personality relates to outcomes such as health and longevity via its association with SWB.

It is important to note limitations that may constrain the generality of our findings 103 . We acknowledge that specific characteristics of the study samples (e.g., adolescent sample comprising only 9th graders) may limit generalizability of our findings. We also note our exclusive use of self-report instruments, although argue that the TCI-R and JTCI have been validated extensively, and therefore expect our results to be reproducible. Finally, we draw attention to the fact that data collection for the adolescent sample occurred during the COVID19 pandemic, meaning we cannot fully discount whether the same findings would be found outside of the pandemic.

Conclusions

The study findings indicate that individual differences in positive affect, negative affect and life satisfaction are dependent on distinct organizations of psychobiological systems and processes. Most notably, we found that the organization of the relations among temperament and character profiles, captured by more-or-less integrated personality networks that take into account the intentional and creative self-regulatory processes within individuals was strongly related to all aspects of SWB. Specifically, negative affect and life satisfaction were dependent on a personality network for intentional self-control, while positive affect was uniquely dependent on the personality network for self-awareness. This finding empirically demonstrates, for the first time, an association between positive affect and a genetically distinct personality network linked to the unique human capacities for creativity, healthy longevity, prosocial behavior, and self-awareness.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available because the TCI-R is copyrighted and associated data is proprietary. Non TCI-R data are however available from the Corresponding author upon reasonable request.

Change history

19 october 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-023-45105-3

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Changes in Personality Functioning and Pathological Personality Traits as a Function of Treatment: A Feasibility Study

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With the dimensional shift, personality pathology is now commonly conceptualized using a combination of personality functioning and (pathological) personality traits. Personality functioning has been deemed more sensitive to treatment than the specific trait combination of personality problems. To empirically examine just that, the goal of this pilot study was to simultaneously compare changes in personality functioning (LPFS-BF 2.0), pathological traits (PID-5-BF), and normal-range traits (BFI-2) among individuals receiving integrative, dynamic-relational psychotherapy (baseline n  = 52, follow-up n  = 31) and a matched control group ( n  = 31). The results showed that clients had stronger changes in personality functioning than in traits when compared to the control group. In addition, clients lower on personality functioning were more inclined to drop-out of therapy. This study points to the unique clinical utility of personality functioning and provides a foundation for future research focusing on the sensitivity of personality functioning and personality traits to changes within the context of psychotherapy.

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Introduction

Following the transition from a categorical to a dimensional understanding of personality pathology (Clark, 2007 ; Widiger & Simonsen, 2005 ), and psychopathology in general (Kotov et al., 2021 ), personality pathology is now commonly conceptualized using a combination of personality functioning and pathological personality traits (Ofrat et al., 2018 ; Skodol, 2012 ). This model structures both the Alternative Model of Personality Disorders (AMPD, APA, 2013a ) and the 11 th edition of the International Classification of Diseases (ICD-11, World Health Organization, 2022 ).

In the AMPD, personality functioning is defined in terms of specific disturbances related to the self (identity and self-direction) and in relating to other people (empathy and intimacy) on a continuum from no disturbance to severe disturbance (Bender et al., 2011 ; Morey et al., 2022 ). AMPD pathological traits capture relative stable patterns of dysfunctional thoughts, feelings, and behaviors organized within five broad domains constituting maladaptive extremes of normal-range five factor trait domains: Negative Affectivity (low Emotional Stability), Detachment (low Extraversion), Antagonism (low Agreeableness), Disinhibition (low Conscientiousness), and Psychoticism (low Openness, Krueger, 2019 ; Krueger et al., 2012 ). In this model, personality functioning defines personality pathology in terms of a general level of impaired psychological capacity whereas pathological traits depict variation in the behavioral expressions of personality dysfunction (Bender et al., 2011 ; Zimmermann, 2022 ). For instance, a person having an impaired capacity for Empathy may be more prone to act callously (a facet of Antagonism). Or a person struggling with Intimacy may be more likely to show withdrawal (a facet of Detachment) or separation insecurity (a facet of Negative Affectivity). The model is intended to facilitate identification of personality related problems and their severity in general clinical practice through assessment of personality functioning. It also provides descriptions of the style of difficulties that can be used to plan intervention strategies in specialized settings based on the constellation of maladaptive personality trait levels (Skodol, 2012 ; Swales, 2022 ).

Personality functioning can be operationalized with the Level of Personality Functioning Scale (LPFS, APA, 2022 ; Bender et al., 2011 ) and pathological traits with the Personality Inventory for DSM-5 (PID-5, APA, 2022 ; Krueger et al., 2012 ).

This model has been widely studied throughout the world and is becoming the standard model for personality disorder diagnosis (Bach & Tracy, 2022 ; Zimmermann et al., 2019 ). While developed in the context of personality pathology, research has repeatedly shown that self-report measures of personality functioning seem to capture impairments that are relevant across mental disorders (Zimmermann et al., in press ), and pathological traits are manifested across different types of psychopathology (Kotov et al., 2010 ; Widiger et al., 2019 ). This is in line with the original AMPD suggestion, that assessment of personality related problems are relevant across patient groups (Skodol, 2012 ). Insofar as personality functioning and pathological traits have a cross-diagnostic impact they are potentially relevant for individuals even without a formal personality disorder diagnosis (APA, 2013a , p. 774; Newton-Howes et al., 2022 ). However, significant gaps remain regarding applied questions about how to use this model for clinical practice. For instance, how can the distinction between personality functioning and pathological traits help guide therapy? It has been suggested that personality functioning should be more sensitive to treatment effects and thus should serve as a treatment target because it reflects the problems a person is having, whereas pathological and normal-range traits reflect aspects of the person that may be less likely or desirable to change (Bach & Simonsen, 2021 ; Sharp, 2022 ; Sharp & Wall, 2021 ). It follows that personality functioning should change more than both pathological and normal-range traits as a function of treatment, however thus far evidence for this effect has been limited. The goal of this pilot study is to provide initial evidence regarding the sensitivity of personality functioning and pathological traits to change in psychotherapy.

Changes in Personality Functioning and Traits as a Function of Psychotherapy

Some longitudinal studies have suggested that personality functioning changes more rapidly compared to traits (Wright et al., 2016 ), although other research has suggested hat that this distinction does not apply to Negative Affectivity (Haehner et al., 2023 ). It has been hypothesized that severity of personality functioning should be more sensitive to treatment than the specific trait combination of personality problems (Crawford et al., 2011 ; Hopwood et al., 2011 ; Skodol, 2018 ). Existing research support the sensitivity of personality functioning to psychotherapy. Kraus et al. ( 2021 ) assessed personality functioning in a diverse sample of psychiatric clients who received integrative psychoanalytic therapy, with an average treatment duration of 94 days, and found significant improvements from pre- to posttreatment. Kvarstein et al. ( 2023 ) investigated longitudinal improvement of personality functioning in a diverse clinical sample assigned to treatment for personality disorder. The study showed overall significant improvement of personality functioning with a moderate effect size ( d  = 0.7) irrespective of treatment duration. There is also evidence that these changes are associated with other clinically significant outcomes. Huber et al. ( 2017 ) examined changes in clinician-rated personality functioning (using the Scales of Psychological Capacities) among 67 clients with a current episode of moderate or severe depression. The study showed that changes in personality functioning from pre- to post-treatment predicted depression and general psychiatric distress at the three-year follow-up.

Personality traits show relatively high levels of absolute stability, with pathological traits being somewhat less stable (Bleidorn et al., 2022 ; Hopwood & Bleidorn, 2018 ). It has been suggested that traits may be relatively more stable than personality functioning (Wright et al., 2015 ; Zimmermann et al., 2017 ) and that psychotherapeutic treatment should focus on helping a person to cope with one’s maladaptive trait expressions rather than trying to change the basic traits themselves (Bach & Presnall-Shvorin, 2020 ). However, there is significant evidence that normal range traits, and particularly neuroticism or negative affect, change in psychotherapy as well (Roberts et al., 2017 ). Research examining changes in pathological traits in a psychotherapeutic setting is still limited. Niemeijer et al. ( 2023 ) found small but significant decreases in both Negative Affectivity and Detachment across 8–14 weeks of cognitive behavioral treatment. Similarly, Rek et al. ( 2022 ) found significant decreases in Negative Affectivity, Detachment, and Disinhibition across 7 weeks of treatment for depression. Torres-Soto et al. ( 2021 ) also found significant changes in Negative Affectivity, Detachment, Disinhibition, and Psychoticism over a treatment period ranging 3 to 12 months in a sample of inpatients with personality disorder diagnoses.

The Current Study

To summarize, while research studying changes in personality functioning and pathological traits as a function of psychotherapy is scarce, existing studies indicate that different methods of psychotherapy in diverse clinical samples can have positive effects on both constructs. However, three major limitations of existing studies should be noted: (1) Most studies on personality functioning have not applied measures that specifically operationalize the construct as conceptualized in the AMPD or ICD-11. Instead, related measures from different theoretical traditions have been used, which can complicate comparison of findings. (2) None of the studies applied a control group, which makes it impossible to determine whether the reported changes are due to the psychotherapy provided or primarily is caused by natural temporal changes and fluctuations. (3) To this date, no study has concurrently investigated changes in personality functioning and traits in psychotherapy. In this study, we compared changes in personality functioning, pathological traits, and normal-range traits among individuals receiving psychotherapy and a control group matched on age and gender.

A convenience sample of clients (i.e., participants who received psychotherapy) were recruited as part of the standard intake procedure of a non-governmental organization (NGO) that provided free counseling and psychotherapy to socially deprived adults in Denmark. Lower socioeconomic status and social deprivation is associated with increased prevalence of personality related problems (Grant et al., 2004 ; Newton-Howes et al., 2021 ; O’Donoghue et al., 2023 ) making it reasonable to assume more pronounced levels of personality dysfunction and pathological traits in socially deprived community samples. Structured clinical assessment was not performed, but treatment applicants were screened by NGO employees with respect to information on demographics, suicidality, and psychiatric history including current diagnoses.

As part of the standard intake phone interview, interested clients were informed about the research project. At the beginning of the first consultation clients were reminded about the research project and invited to participate in the study. Participants then completed a baseline questionnaire during the first half of the initial consultation before the therapy began. After finishing the last treatment session participants were asked to complete the follow-up questionnaire. Therapists were allowed to provide clarification if some of the items were unclear to the participants, but not allowed to assist the client in choosing specific answers for the individual items. Based on the age and gender composition of the client sample, we systematically recruited a nonclinical convenience control sample via social media announcements. Control participants fulfilled a baseline survey online and were invited by email to complete an identical online follow-up survey after eight weeks. Individuals who did not complete the follow-up survey within 7 days was sent a reminder.

Psychotherapy was delivered in one-hour sessions once per week for 8–12 weeks, by fifteen master’s students in psychology, two psychotherapists, and two psychologists. Consequently, a total of nineteen therapists ( M  = 28.10 years, SD  = 8.34, range = 22–50) worked with the clients enrolled in the present study. The therapists were not restricted to specific techniques, but everyone participated in the NGO’s training courses focusing on an integrative, relational-dynamic theory framework and short-term intervention. The aim of this treatment framework is to improve current distress severity and support the client in navigating present interpersonal conflicts (e.g., by focusing on improvement in understanding one’s own mind and those of others, as well as addressing the client’s reenactment of specific positions in social interactions, Allen, 2013 ; Jørgensen, 2019 ). Therapists received continuous group supervision from three licensed psychologists, who were certified psychotherapists. The majority of therapists were novices. However, research indicates that the amount of professional experience is not necessarily identical with therapist effectiveness (Berglar et al., 2016 ; Walsh et al., 2019 ).

The study was exempt from notification by The Central Denmark Region Committees on Health Research Ethics (c.nr. 1-10-71-1-21). All participants provided written consent and had the opportunity to withdraw from the study at any time without losing their right to receive treatment at the NGO.

Participants

The target group for the NGO was vulnerable adults with severe functional impairment. Inclusion criteria required that clients were not currently receiving public psychiatric treatment and did not have the finances to afford private psychological counseling, often due to unemployment. The study mirrored the existing exclusion criteria of the NGO: Applicants were excluded from receiving therapy if they had a psychotic disorder, autism spectrum disorder, active substance use disorder, or if they were currently on a public psychiatric treatment waiting list. A total of 52 clients participated at baseline and 21 of these did not complete follow-up measures: one was hospitalized during the course of treatment, and twenty dropped out of treatment. The final client sample, therefore, consisted of 31 individuals ( M  = 43.2 years, SD  = 11.91, range = 29–78). The average duration of time from baseline to follow-up was 65.03 days ( SD  = 13.26). Sociodemographic information (see Table  1 ) showed that more than half of the participants were diagnosed with one or more mental disorders. More than half of the participants were either single or divorced, and while the majority had a high-school level education or above, two-thirds of them were unemployed.

Control Group

A total of 39 individuals provided contact information and completed the baseline survey for the study using a secure online software platform (REDCap, Harris et al., 2019 ). Of these, the first 31 control participants whose age and gender matched a client participant were invited to complete the follow-up survey. Participants were matched 100% on gender with only small differences in age. The final control sample consisted of 31 individuals ( M  = 42.6 years, SD  = 13.26, range = 29 to 78). The average duration of time from baseline to follow-up was 60.94 days ( SD  = 6.69).

The Level of Personality Functioning Scale – Brief Form 2.0 (LPFS-BF 2.0, Weekers et al., 2019 ) is a 12 item self-report questionnaire assessing self- and interpersonal functioning. Respondents are asked to rate each item on a 4-point scale (from 1 “ very false or often false” to 4 “ very true or often true” ) with higher scores indicating more severe problems with personality functioning. The Danish version of the LPFS-BF 2.0 used in this study has formerly been validated (Bach & Hutsebaut, 2018 ). Normative data has been derived from the Danish general population based on total scale scores (Weekers et al., 2022 ). Cronbach’s α for the total score was .89.

The Personality Inventory for DSM-5 Brief Form (PID-5-BF, APA, 2013b ; Krueger et al., 2012 ) is a 25 item self-report inventory measuring the five pathological trait domains from the DSM-5 Section III Alternative Model for Personality Disorders: Detachment, Antagonism, Disinhibition, Negative Affect, and Psychoticism. Each trait domain is measured by five items, each scored on a 4-point scale (from 0 “very false or often false” to 3 “very true or often true” ). The official algorithm was used for scoring the 5 domain scales (APA, 2013b ). A validated Danish translation of the PID-5 was used (Bach et al., 2016 ; Bo et al., 2016 ). Internal consistency ranged from a Cronbach’s α of .62 for Antagonism to .76 for Psychoticism.

The Big Five Inventory-2 (BFI-2) (BFI-2, Soto & John, 2017 ) is a 60-item self-report questionnaire measuring the five-factor trait domains: Extraversion, Agreeableness, Conscientiousness, Negative Emotionality, and Openness to Experience. Each domain is measured by 12 items that are rated on a 5-point scale (from 1 “Disagree strongly” to 5 “ Agree strongly” ). For this study the validated Danish version of the BFI-2 was used, which has shown measurement properties comparable to the English-language version (Vedel et al., 2021 ). Internal consistency ranged from a Cronbach’s α of .83 for Agreeableness to .89 for Negative Emotionality.

Data Analysis

Statistical analyses were conducted using SPSS version 28.0.1. (IBM, 2021 ). Data quality was evaluated within each group by examining skew and kurtosis of scales and inspection of P-P plots.

Independent t-tests were conducted to examine whether the clients and controls who did not participate in the follow-up differed from those that did. Next, independent t-tests were conducted to examine, whether the clients and controls who completed follow-up differed on age and duration of days between completing baseline and follow-up questionnaires.

To test for group-by-time effects in changes of personality functioning, pathological- and normal-range traits from baseline to follow-up, a sequence of Two-Way Mixed Analyses of Variances was conducted. Group (clients and controls) was entered as the between-subjects factor and time (baseline and follow-up) was entered as the within-subjects factor. Levels of significance was set as α = .05. Given the small sample size, the findings were fleshed out using pairwise comparisons of estimated marginal means focusing on descriptive effect size differences between groups.

As depicted in Table  2 , the 21 clients (40.4%) who did not participate in the follow-up assessment scored significantly higher on baseline LPFS, Negative Affectivity, Psychoticism, and Negative Emotionality while scoring lower on Extraversion. There were no other significant differences between these two client groups. No significant differences were found between those control participants who were not invited to participate in the follow-up assessment and those who were.

There were no differences between groups in age (Client M  = 43.23, SD  = 11.91; Control M =  42.55, SD  = 13.26), t (60) = .21, p  = .83 or duration of days between assessments (Client M  = 65.03, SD  = 13.26; Control M  = 60.94, SD  = 6.69), t (60) = 1.54, p  = .13.

Results for group-by-time effects are depicted in Table  3 . At baseline, clients were higher in personality dysfunction, Detachment, Disinhibition, and Negative Emotionality compared to the control group. Over time, clients’ levels of personality dysfunction declined significantly, they became less Antagonistic, and showed small, non-significant declines in Detachment, Disinhibition, and Negative Affect. Interestingly, the control group had significant and unexpected decreases in Negative Affectivity as well as small, non-significant declines in Detachment, Antagonism, and Disinhibition. Personality functioning was the only variable with a significant group, time, and interaction effect. Personality functioning changed significantly from baseline to follow-up in the clients (d = .40) but was stable in the control group (d = .01).

The goal of this study was to simultaneously examine changes in personality functioning, pathological traits, and normal-range traits during short-term, integrative psychotherapy for vulnerable adults. Personality functioning changed more among clients in psychotherapy than traits. Moreover, the difference in level of change between clinical and control groups was only significant for personality functioning, although this might have been due in part to mild improvements in traits in the control group. Furthermore, dropouts had higher levels of personality dysfunction at baseline, suggesting that personality functioning may serve as a useful indicator for dropout risk. These results add empirical evidence for the clinical utility of personality functioning.

Relative Sensitivity of Personality Functioning and Pathological Traits to Change

The results of the current study expand upon extant research (cf., Huber et al., 2017 ; Kraus et al., 2021 ; Kvarstein et al., 2023 ) by indicating that short-term psychotherapy for socially deprived adults stimulates changes in personality functioning, and is consistent with the view that personality functioning may be more sensitive to change than traits.

This sensitivity can be understood in terms of theoretical models of personality dysfunction. One point of view suggest that personality functioning encompasses the subjective experience and mental representation of oneself and others (Sharp & Wall, 2021 ). Building upon this, Zimmermann (2022) proposed that pathological traits may be expressions or consequences of impaired personality functioning, such as reduced capacity for empathy leading to callous actions. Based on this reasoning, changes in personality functioning should be detectable more rapidly than changes in pathological traits, given that alterations in trait expressions rely upon increased personality functioning. In contrast, Clark and Ro ( 2014 ; Ro & Clark, 2013 ) suggested that personality traits are the cause of functioning problems, and personality dysfunction should be viewed as one of the manifestations of these problems. The authors describe personality functioning as a construct that reflect the consequences resulting from the interplay between a person’s traits and other factors in their life. Therefore, without assuming that changes in personality functioning must precede changes in traits, more rapid changes in personality functioning may occur as a result of more adaptive transactions between the person and their environment.

From a measurement-based perspective, personality and maladaptive psychopathology constructs have both stable and dynamic aspects (Hopwood et al., 2022 ). Personality functioning encompasses a strong affective component (i.e., items about internal states and feelings; Nuzum et al., 2019 ). Affect-related constructs are generally less stable than behavioral (i.e., observable action tendencies) and cognition-related (i.e., thought patterns) constructs. Therefore, measures of personality functioning should capture changes more rapidly than measures of traits, given the latter has a high focus on general behavioral and cognitive tendencies (Anusic & Schimmack, 2016 ; Nuzum et al., 2019 ). Consistent with the results of the current study, Haehner et al. ( 2023 ), applying a longitudinal design with a non-clinical sample, recently found that the level of both personality functioning and Neuroticism were less stable than traits over a 24-week period following a negative life event (e.g., a friendship breakup, a job loss, etc.). Haehner et al. ( 2023 ) suggest that the higher changeability of personality functioning and Neuroticism was related to its affect-related nature and its close association with personal distress and dissatisfaction. Consequently, in the context of our study, the higher sensitivity to change in self-reported personality functioning compared to pathological traits may be attributed primarily to it capturing (negative) affect-related self-experiences of non-specific psychological distress. These experiences are generally expected to change more in psychotherapy than stable aspects of personality (Connor & Walton, 2011 ; Noordhof et al., 2018 ).

Obviously, the results do not rule out the possibility that the therapy did indeed induce changes in pathological traits. However, the changes reported by the clients could not be distinguished from changes reported by control participants within the study’s time period. The potential for long-term effects of psychotherapy on pathological traits was detected by Niemeijer et al. ( 2023 ), who found that Negative Affectivity and Detachment showed small yet significant decreases over 8–14 weeks of CBT, with reductions continuing over six month after therapy. It is notable that the control group showed mild improvements in pathological traits, and particularly Negative Affectivity. In contrast to the current findings, former research (Bleidorn et al., 2022 ) has shown relatively high stability in pathological traits across both short-term (i.e., two weeks, Somma et al., 2020 ) and long term durations (Stricker et al., 2022 ; Wright et al., 2015 ). Based on existing studies and the small sample size, it seems most cautious to interpret the decline in Negative Affectivity traits in the control as caused by sampling error. However, the association between changes in personality functioning and Negative Affectivity was weaker in the control group ( r  = .24) compared to the client group ( r  = .39), perhaps suggesting that changes in traits are linked to improvements in personality functioning for those in treatment more than for those not in treatment. Nevertheless, to better distinguish between the sources of change in both personality functioning and personality traits, the results of our study should be replicated in larger samples with more control groups and long-term follow ups.

Personality Functioning Increases the risk of Dropout from Treatment

The clients of the current study were vulnerable and socially deprived adults. Given that decreases in personality functioning severity are related to heightened well-being and reduced functional impairment (Huber et al., 2017 ; Skodol, 2018 ; Wright et al., 2016 ), it is reasonable to assume that changes in personality functioning hold valuable potential for helping this group of individuals towards a more thriving life. Notably, the clients who dropped out of treatment showed more severe personality dysfunction at baseline, as well as more maladaptive trait scores, compared to the clients who completed treatment. These findings are in line with previous research (Bach & Simonsen, 2021 ) indicating that people with higher levels of personality dysfunction have a more difficult time staying in treatment. On one side, this may indicate that a certain level of personality functioning is required to participate in psychotherapy (cf., Busmann et al., 2019 ). On the other side, a treatment should be responsive to the individuals it serves, and among other factors, it is important that the treatment is delivered in a way that suits the client’s personality (McMurran et al., 2010 ). Research suggests that while the number of years a therapists has been practicing does not directly relate to treatment effectiveness, experience and therapeutic flexibility play a more important role when dealing with more severe pathology and personality-related difficulties (Berglar et al., 2016 ; Busmann et al., 2019 ; Jørgensen, 2019 ). This suggests that the influence of severity of personality functioning on dropout rates may be more pronounced in settings where therapists are relatively inexperienced. Typically, more experienced therapists are better equipped to repair and maintain the therapeutic relationship and address the most urgent client needs, thus enhancing client satisfaction and reducing the risk of dropout (Walsh et al., 2019 ). However, socially deprived adults not only struggle with psychological difficulties but typically also with economic, occupational, and other social factors. Therefore, psychotherapeutic work with this group of people might require a heightened focus on both personality functioning as well as social resources and occupational status. Nevertheless, the higher level of personality dysfunction among clients who dropped out indicates the importance of addressing personality-related issues at the beginning of therapy to enhance treatment engagement and outcomes.

Limitations

The primary limitation of this pilot study was our use of a small sample of vulnerable adults, limiting power to find group differences in change. Firm conclusions cannot be drawn given this limitation, but as a feasibility study, the results have outlined potential effect size estimates to be used for power analyses in future research. While sampling a vulnerable population is a study strength, the specificity of the sample raises questions about generalizability to other clinical settings, patient groups and cultures. Future studies should use multimethod measures and approaches to provide a more comprehensive assessment of patient personality and functioning. Finally, the follow-up interval was relatively brief. Ideally, future research would assess clients and control groups multiple times over the course of a more extended time period before, during, and after treatment.

This study investigated the sensitivity to change of personality functioning and personality traits in short-term psychotherapy with socially deprived adults and a matched control group. There were three main findings. First, personality functioning significantly distinguished clients who dropped out of treatment. Second, clients showed stronger changes in personality functioning than traits. Third, changes over time across client and control groups were stronger for personality functioning than traits. These findings are suggestive of the unique clinical utility of personality functioning for psychotherapy. However, conclusions are limited by the use of a small and specific sample. This research provides a foundation for future research on the sensitivity of personality functioning and personality traits to changes as a function of psychotherapy.

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Kiel, L., Hopwood, C.J. & Lind, M. Changes in Personality Functioning and Pathological Personality Traits as a Function of Treatment: A Feasibility Study. J Psychopathol Behav Assess (2024). https://doi.org/10.1007/s10862-024-10138-z

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Lewis R. Goldberg

Oregon Research Institute

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

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

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

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

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

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

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

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

PERSONALITY EFFECTS ON MORTALITY, DIVORCE, AND OCCUPATIONAL ATTAINMENT

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

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

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

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

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

The Measurement of Effect Sizes in Prospective Longitudinal Studies

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

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

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

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

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

The Predictive Validity of Personality Traits for Mortality

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

SES and IQ Effects on Mortality/Longevity

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

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

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

Personality Traits and Mortality

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

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

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

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

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

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

The Predictive Validity of Personality Traits for Divorce

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

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

SES and IQ Effects on Divorce

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

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

Personality Traits and Marital Outcomes

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

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

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

The Predictive Validity of Personality Traits for Educational and Occupational Attainment

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

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

SES, IQ, and Status Attainment

Note. SES = socioeconomic status.

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

Personality Traits and Occupational Attainment

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

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

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

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

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

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

GENERAL DISCUSSION

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

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

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

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

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

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

Recommendations

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

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

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

Conclusions

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

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

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

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

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

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

Acknowledgments

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

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

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

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

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

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

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

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

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

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