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Personality Survey: Top 25 Questions, Types, Steps & Tips

5 steps to design a good personality surveyPersonality surveys have become a powerful tool for understanding human behavior, personality tests, traits, research, and preferences.

Whether used in academic research, organizational settings, personal careers or personal development, A personality test provides a structured approach to measure the complexities of a personality test.

A personality survey is a systematic assessment that measures various aspects of an individual’s personality test, including traits, career, future, attitudes, and behaviors.

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These surveys help individuals gain self-awareness, identify strengths and weaknesses, and explore areas for personal growth and future.

Personality surveys facilitate effective team-building, personality tests, reliability, talent selection, and career development initiatives in organizational contexts.

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What is a Personality Survey?

A personality survey is defined as a survey that consists of multiple question types that aim to collect insights into the personality of respondents or tests. This survey is mostly introspective that measures life reports in the form of rating scales by using a questionnaire . The data collection from a personality survey provides insights into a human-being and the decision making process as well as the rationale behind that process.

These personality test survey questions are used in the survey software to help distinguish ability from personality. It primarily helps to understand how you relate with others feelings, approach your problems, deal with feelings and understand your personal life.

Surveys have uses in multiple fields but the primary use is in a professional environment where a prospective employee is administered this online survey to gauge temperament, decision making process, business acumen etc. It helps in understanding how motivated to work hard and driven an employee is and the factors that drive the individual.

The other times a personality test survey is used to self-introspect, to match compatibility, assess theories, determine in work, evaluate change in a person after a certain process, diagnosing psychological problems, student evaluation and also sometimes in forensic settings. A student interest survey helps customize teaching methods and curriculum to make learning more engaging and relevant to students’ lives.

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Personality Survey Types

Several types of personality surveys are commonly used to assess and understand individuals’ personalities. Here are some recognized personality type surveys:

Myers Briggs Type Indicator:

Myers Briggs personality test is a widely used personality test or assessment that categorizes individuals into 16 different personality types based on preferences in four key areas: extraversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving. Myers Briggs test provides valuable insights into how individuals perceive the industries, make decisions, and interact with others. It’s important to note that while the Myers Briggs Type Indicator can offer valuable insights, the Myers Briggs test is just one tool among many for understanding human personality and has its limitations.

Big Five Personality Traits:

The Big Five system assesses personality test across five dimensions, including openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. These traits provide a broad framework for understanding personality variation.

DISC Assessment:

The DISC assessment categorizes individuals into four primary behavioral styles: dominance, interest, influence, steadiness, and conscientiousness. It helps identify how individuals interact, communicate, and respond in various situations.

The Enneagram test is a typing system that helps to describe nine interconnected personality types of research, each associated with distinct motivations, fears, and coping mechanisms. It provides a deeper understanding of an individual’s core desires and fears.

Hogan Personality Inventory:

The HPI assesses personality traits related to social effectiveness, work approach, and interpersonal interactions. It provides insights into an individual’s typical behavior patterns and potential strengths and challenges in the workplace.

StrengthsFinder:

The StrengthsFinder assessment test identifies an individual’s top strengths from 34 talent themes. It focuses on identifying and leveraging an individual’s natural talents to enhance personal and professional development.

These are a few personality types, each offering unique and interesting data insights and characteristics into an individual’s projective tests. Selecting the most appropriate type of survey depends on the specific objectives and context in which the assessment will be used.

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Top 25 Questions to Ask in Your Personality Survey

The key to getting accurate responses and a good survey response rate is the survey design . The survey platform consists of an online survey with multiple question types. The personality test survey can be broken down into 2 major sections, the demographic survey and the core personality type question survey.

Personality Survey Questions for Demographic Assessment

The demographic survey questions one of the most important aspects of the personality survey in a questionnaire since they help profile the respondents and in turn help understand how different sets of people have different types of personalities.

Some of the most important demographic questions are:

• Please select your gender for the survey
• What is your highest education completed?
• What is your marital status?
• What is your ethnicity?
• Please select your employment type

Survey Questions for Personality Test

These survey questions are a deep-dive into the personality tests of an individual. The questions in this section are set-up to understand how a certain individual behaves, the decisions describe the making process and temperament of most people.

• Do you like meeting new people?
• Do you like helping people out?
• What do you do if you have been unjustly blamed for something you didn’t do?
• How long does it take you to calm down when you’ve been angry?
• Are you easily disappointed?
• Do you help people only if you think you’d get something in return?
• Do you set up long term goals?
• Are you easily fazed?
• How often do you prefer to go out into a social environment or a public place?
• Are you considerate of other people’s feelings?
• Are you always busy?
• Do you like solving complex problems?
• Do you make people feel welcome?
• Is your go-to reaction in a problem to cheat your way out of it?
• Do you feel overwhelmed often?
• How often do you travel?
• Do you prefer familiarity over unfamiliarity?
• Are you generally passionate about social causes?
• Do you like being pushy?
• Do you tend to always see the good in people, no matter what the circumstance is?

Survey questions for a personality test serve as the foundation for gathering insights into an individual’s unique traits and behaviors. By carefully designing these questions, administrators can unlock valuable information about personality tests or dimensions.

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5 Steps to Design a Good Personality Survey

A personality survey is effective only when set-up right. It is important to have the survey collect the insights that you need to gauge the respondent in the manner that’s most effective to you. Hence, the survey design is extremely important for the projective test.

The 5 steps to designing a good personality survey with abstract ideas, are:

• Define the survey objective: It is imperative to define the survey objective before creating the survey or deploying it to potential respondents. The “why” and “how” is important to be put in the proper place before beginning the study. This ensures that actionable insights can be derived from the data collection .
• Ask the right questions: To get a good survey response rate and to collect deep level personality insights, it is important that the right questions are asked in the survey. This helps the researcher collect personality test data information about a person or a set of people. This is mainly important when a new employee is being on boarded or is being considered for a promotion or role hike .
• Build the survey flow: Most respondents drop out of a survey as the questions if the survey questions are not relevant to them. This makes it very important to effectively use survey skip logic and branching so the preceding answer defines the next question displayed to a survey taker .
• Take the survey for a spin: Before the survey is deployed or administered to potential respondents, it needs to be thoroughly tested. This is to ensure that the survey renders properly and the logic of the survey is correctly developed.
• Analyze and report the survey responses : The data collection is only half of the survey objective. Analyzing and reporting those responses is the other important part of the data collection. Analysis helps draw trend lines and parallels between personalities and how different people react under certain circumstances.

However, it’s essential to recognize that personality is complex and multifaceted, and no survey can capture the entirety of an individual’s personality. Therefore, while personality surveys can be helpful tools, they should be used with other methods and approaches to understand human behavior and traits comprehensively.

Tips for a Good Personality Survey

Creating a good personality survey requires careful planning and consideration. By following these essential tips, you can design a survey that provides valuable insights into individuals’ traits and behaviors.

Let’s explore the key tips for conducting a successful personality survey. Listed below are some tips for a good personality survey:

Ask unbiased questions:

When you design a survey, make sure that the survey questions are unbiased. Driving respondents towards responding in a certain manner skews the survey results.

Personality Tests:

Consider incorporating established personality tests into your survey. Personality tests provide a standardized and structured approach to assessing individual traits, behaviors, and preferences. Additionally, the results from projective techniques tests can offer valuable data for analyzing trends, patterns, and correlations among different personality types within your survey population.

Be respectful with your questions:

In a personality survey, it is very important to be respectful with questions. If respondents feel personally attacked with survey questions, it increases the survey dropout rate .

Keep consistent answer options:

If the answer options of a survey in the survey software aren’t uniform, it may confuse the respondents and the survey results could subsequently get skewed. Hence, it is important to have consistent answer questions options in the survey.

Keep questions optional:

Due to the nature of the personality survey, complex or personal questions could form the basis of the survey. It is therefore important to allow the respondents the flexibility to skip questions or they would feel uncomfortable continuing and subsequently drop out of the survey.

A well-designed personality survey can unlock valuable insights into human behavior, personality type, vivid imagination, preferences, and traits. By following the tips, you can create a survey that delivers reliable results and facilitates a deeper understanding of personality.

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Personality surveys are an invaluable tool for gaining insights into human behavior and understanding the complexities of personality. With the help of advanced survey software platforms like QuestionPro, survey administrators can design and implement effective personality surveys that deliver meaningful and reliable results.

QuestionPro offers a range of features and tools that enable survey administrators to create engaging and personalized surveys, such as interactive question types, custom branding, and conditional logic.

By leveraging the power of QuestionPro’s survey software, organizations can design personality surveys that facilitate effective team-building, talent selection, and career development initiatives. Similarly, individuals can use personality surveys to gain self-awareness, identify strengths and weaknesses, and explore areas for personal growth.

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Big Five Personality Traits

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

View All Term Definitions

Breakdown by Domain

Key features, context & culture.

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

Developmental Perspective

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

Associated Outcomes

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

Available Resources

Support materials.

• No materials provided

Programs & Strategies

• No programs or strategies provided

Measurement Tools

Personality traits are often measured through questionnaire scales such as:

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

Key Publications

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

Multiple researchers

Developer Type

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

Common Uses

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

Key Parameters

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

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Psychology Questions About Personality

Personality Psychology Research Topics

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

List of Personality Topics

• Before You Begin
• Starting Your Research

Personality is a popular subject in psychology, so it's no surprise that this broad area is rife with fascinating research topics. There are many psychology questions about personality that can be a great topic for a paper, or just help you get to know others a little better.

Are you looking for a great topic for a paper , presentation, or experiment for your personality psychology class? Here are just a few ideas that might help kick-start your imagination.

At a Glance

If you are writing a paper, doing an experiment, or just curious about why people do the things they do, exploring some different psychology questions about personality can be a great place to start. Topics you might choose to explore include different personality traits, personality tests, and how different aspects of personality influence behavior.

Possible Topics for Personality Psychology Research

The type of psychology questions about personality that you might want to explore depend on what you are interested in and what you want to know. Some topics you might opt to explore include:

Personality Traits

• How do personality traits relate to creativity? Are people with certain traits more or less creative? For your project, you might try administering scales measuring temperament and creativity to a group of participants.
• Are certain personality traits linked to prosocial behaviors ? Consider how traits such as kindness, generosity, and empathy might be associated with altruism and heroism .
• How does Type A behavior influence success in school? Are people who exhibit Type A characteristics more likely to succeed?
• Is there a connection between a person's personality type and the kind of art they like? For example, are extroverts more drawn to brighter colors or art that depicts people vs. abstract, non-representational art?
• Do people tend to choose pets based on their personality types? How do the personalities of dog owners compare to those of cat owners?

Personality Tests

• How do personality assessments compare? Consider comparing common assessments such as the Myers-Briggs Temperament Indicator , the Keirsey Temperament Sorter, and the 16PF Questionnaire.
• How reliable are personality test results? If you give someone the same test weeks later, will their results be the same?

Family and Relationships

• Do people tend to marry individuals with similar personalities? Do people who marry partners with personalities similar to their own have more satisfying relationships?
• What impact does birth order have on personality? Are first-born children more responsible, and are last-borns less responsible?

Personality and Behaviors

• Is there a connection between personality types and musical tastes ? Do people who share certain personality traits prefer the same types of music?
• Are people who participate in athletics more likely to have certain personality characteristics? Compare the personality types of athletes versus non-athletes.
• Are individuals with high self-esteem more competitive than those with low self-esteem? Do those with high self-esteem perform better than those who have lower self-esteem?
• Is there a correlation between personality type and the tendency to cheat on exams? Are people low in conscientiousness more likely to cheat? Are extroverts or introverts more liable to cheat?
• How do personality factors influence a person's use of social media? For example, are people high in certain traits more likely to use Facebook, Instagram, and Twitter? Are individuals who use social media frequently more or less extroverted?

You can also come up with questions about your own about different topics in personality psychology. Some that you might explore include:

• Big 5 personality traits
• The id, ego, and superego
• Psychosocial development
• Hierarchy of needs
• Myers-Briggs Type Indicator
• Personality disorders

What to Do Before You Begin Your Research

Once you find a suitable research topic, you might be tempted just to dive right in and get started. However, there are a few important steps you need to take first.

Most importantly, be sure to run your topic idea past your instructor. This is particularly important if you are planning to conduct an actual experiment with human participants.

In most cases, you will need to gain your instructor's permission and possibly submit your plan to your school's human subjects committee to gain approval.

How to Get Started With Your Research

Whether you are doing an experiment, writing a paper , or developing a presentation, background research should always be your next step.

Consider what research already exists on the topic. Look into what other researchers have discovered. By spending some time reviewing the existing literature, you will be better able to develop your topic further.

What This Means For You

Asking psychology questions about personality can help you figure out what you want to research or write about. It can also be a way to think about your own personality or the characteristics of other people. If you're stumped for an idea, consider talking to your instructor or think about some questions you've had about people in your own life.

Atherton OE, Chung JM, Harris K, et al. Why has personality psychology played an outsized role in the credibility revolution ?  Personal Sci . 2021;2:e6001. doi:10.5964/ps.6001

American Psychological Association. Frequently asked questions about institutional review boards .

Leite DFB, Padilha MAS, Cecatti JG. Approaching literature review for academic purposes: The Literature Review Checklist .  Clinics (Sao Paulo) . 2019;74:e1403. doi:10.6061/clinics/2019/e1403

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

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• Open access
• Published: 25 July 2022

A prediction-focused approach to personality modeling

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

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

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• Human behaviour
• Social neuroscience

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

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Introduction.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Participants

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

Dependent variables

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

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

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

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

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

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

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

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

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

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

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

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

Independent variables

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

Model construction

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

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

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

And in matrix notation:

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

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

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

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

Model assessment

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

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

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

PF loadings

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

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

Correlations between the PF and FFM scale scores.

Predictive performance

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

Reproducibility analysis

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

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

Reliability analysis

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

Divergence from the FFM

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(8) “I have math anxiety”.

(9) “I am typically very anxious”.

(10) “I enjoy playing with fire”.

(11) “I am a hardcore sports fan”.

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

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

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

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

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

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

General discussion

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

Interpreting the PF

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

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

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

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

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

Towards a more predictive science of personality

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

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

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

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

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

Future directions

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

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

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

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

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

Data availability

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

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Personality traits, emotional intelligence and decision-making styles in Lebanese universities medical students

• Radwan El Othman 1 ,
• Rola El Othman 2 ,
• Rabih Hallit 1 , 3 , 4   na1 ,
• Sahar Obeid 5 , 6 , 7   na1 &
• Souheil Hallit 1 , 5 , 7   na1  

BMC Psychology volume  8 , Article number:  46 ( 2020 ) Cite this article

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This study aims to assess the impact of personality traits on emotional intelligence (EI) and decision-making among medical students in Lebanese Universities and to evaluate the potential mediating role-played by emotional intelligence between personality traits and decision-making styles in this population.

This cross-sectional study was conducted between June and December 2019 on 296 general medicine students.

Higher extroversion was associated with lower rational decision-making style, whereas higher agreeableness and conscientiousness were significantly associated with a higher rational decision-making style. More extroversion and openness to experience were significantly associated with a higher intuitive style, whereas higher agreeableness and conscientiousness were significantly associated with lower intuitive style. More agreeableness and conscientiousness were significantly associated with a higher dependent decision-making style, whereas more openness to experience was significantly associated with less dependent decision-making style. More agreeableness, conscientiousness, and neuroticism were significantly associated with less spontaneous decision-making style. None of the personality traits was significantly associated with the avoidant decision-making style. Emotional intelligence seemed to fully mediate the association between conscientiousness and intuitive decision-making style by 38% and partially mediate the association between extroversion and openness to experience with intuitive decision-making style by 49.82 and 57.93% respectively.

Our study suggests an association between personality traits and decision-making styles. The results suggest that EI showed a significant positive effect on intuitive decision-making style and a negative effect on avoidant and dependent decision-making styles. Additionally, our study underlined the role of emotional intelligence as a mediator factor between personality traits (namely conscientiousness, openness, and extroversion) and decision-making styles.

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Decision-making is a central part of daily interactions; it was defined by Scott and Bruce in 1995 as «the learned habitual response pattern exhibited by an individual when confronted with a decision situation. It is not a personality trait, but a habit-based propensity to react in a certain way in a specific decision context» [ 1 ]. Understanding how people make decisions within the moral domain is of great importance theoretically and practically. Its theoretical value is related to the importance of understanding the moral mind to further deepen our knowledge on how the mind works, thus understanding the role of moral considerations in our cognitive life. Practically, this understanding is important because we are highly influenced by the moral decisions of people around us [ 2 ]. According to Scott and Bruce (1995), there are five distinct decision-making styles (dependent, avoidant, spontaneous, rational, intuitive) [ 1 ] and each individuals’ decision-making style has traits from these different styles with one dominant style [ 3 ].

The dependent decision-making style can be regarded as requiring support, advice, and guidance from others when making decisions. Avoidant style is characterized by its tendency to procrastinate and postpone decisions if possible. On the other hand, spontaneous decision-making style is hallmarked by making snap and impulsive decisions as a way to quickly bypass the decision-making process. In other words, spontaneous decision-makers are characterized by the feeling of immediacy favoring to bypass the decision-making process rapidly without employing much effort in considering their options analytically or relying on their instinct. Rational decision-making style is characterized by the use of a structured rational approach to analyze information and options to make decision [ 1 ]. In contrast, intuitive style is highly dependent upon premonitions, instinct, and feelings when it comes to making decisions driving focus toward the flow of information rather than systematic procession and analysis of information, thus relying on hunches and gut feelings. Several studies have evaluated the factors that would influence an individual’s intuition and judgment. Rand et al. (2016) discussed the social heuristics theory and showed that women and not men tend to internalize altruism _ the selfless concern for the well-being of others_ in their intuition and thus in their intuitive decision-making process [ 4 ]. Additionally, intuitive behavior honesty is influenced by the degree of social relationships with individuals affected by the outcome of our decision: when dishonesty harms abstract others, intuition promotion causes more dishonesty. On the contrary, when dishonesty harms concrete others, intuition promotion has no significant effect on dishonesty. Hence, the intuitive appeal of pro-sociality may cancel out the intuitive selfish appeal of dishonesty [ 5 ]. Moreover, the decision-making process and styles have been largely evaluated in previous literature. Greene et al. (2008) and Rand (2016) showed that utilitarian moral judgments aiming to minimize cost and maximize benefits across concerned individuals are driven by controlled cognitive process (i.e. rational); whereas, deontological moral judgments _where rights and duties supersede utilitarian considerations_ are dictated by an automatic emotional response (e.g. spontaneous decision-making) [ 6 , 7 ]. Trémolière et al. (2012) found that mortality salience makes people less utilitarian [ 8 ].

Another valuable element influencing our relationships and career success [ 9 ] is emotional intelligence (EI) a cardinal factor to positive patient experience in the medical field [ 10 ]. EI was defined by Goleman as «the capacity of recognizing our feelings and those of others, for motivating ourselves, and for managing emotions both in us and in our relationships» [ 11 ]. Hence, an important part of our success in life nowadays is dependent on our ability to develop and preserve social relationships, depict ourselves positively, and control the way people descry us rather than our cognitive abilities and traditional intelligence measured by IQ tests [ 12 ]. In other words, emotional intelligence is a subtype of social intelligence involving observation and analyses of emotions to guide thoughts and actions. Communication is a pillar of modern medicine; thus, emotional intelligence should be a cornerstone in the education and evaluation of medical students’ communication and interpersonal skills.

An important predictor of EI is personality [ 13 ] defined as individual differences in characteristic patterns of thinking, feeling and behaving [ 14 ]. An important property of personality traits is being stable across time [ 15 ] and situations [ 16 ], which makes it characteristic of each individual. One of the most widely used assessment tools for personality traits is the Five-Factor model referring to «extroversion, openness to experience, agreeableness, conscientiousness, neuroticism». In fact, personality traits have an important impact on individuals’ life, students’ academic performance [ 17 ] and decision-making [ 18 ].

Extroversion is characterized by higher levels of self-confidence, positive emotions, enthusiasm, energy, excitement seeking, and social interactions. Openness to experience individuals are creative, imaginative, intellectually curious, impulsive, and original, open to new experiences and ideas [ 19 ]. Agreeableness is characterized by cooperation, morality, sympathy, low self-confidence, high levels of trust in others, and tend to be happy and satisfied because of their close interrelationships [ 19 ]. Conscientiousness is characterized by competence, hard work, self-discipline, organization, strive for achievement and goal orientation [ 20 ] with a high level of deliberation making conscientious individuals capable of analyzing the pros and cons of a given situation [ 21 ]. Neuroticism is characterized by anxiety, anger, insecurity, impulsiveness, self-consciousness,and vulnerability [ 20 ]. High neurotic individuals have higher levels of negative affect, are easily irritated, and more likely to turn to inappropriate coping responses, such as interpersonal hostility [ 22 ].

Multiple studies have evaluated the impact of personality traits on decision-making styles. Narooi and Karazee (2015) studied personality traits, attitude to life, and decision-making styles among university students in Iran [ 23 ]. They deduced the presence of a strong relationship between personality traits and decision-making styles [ 23 ]. Riaz and Batool (2012) evaluated the relationship between personality traits and decision-making among a group of university students (Fig. 1 ). They concluded that «15.4 to 28.1% variance in decision-making styles is related to personality traits» [ 24 ]. Similarly, Bajwa et al. (2016) studied the relationship between personality traits and decision-making among students. They concluded that conscientiousness personality trait is associated with rational decision-making style [ 25 ]. Bayram and Aydemir (2017) studied the relationship between personality traits and decision-making styles among a group of university students in Turkey [ 26 ]. Their work yielded to multiple conclusion namely a significant association between rational and intuitive decision-making styles and extroversion, openness to experience, conscientiousness, and agreeableness personality traits [ 26 ]. The dependent decision-making style had a positive relation with both neuroticism and agreeableness. The spontaneous style had a positive relation with neuroticism and significant negative relation with agreeableness and conscientiousness. Extroversion personality traits had a positive effect on spontaneous style. Agreeableness personality had a positive effect on the intuitive and dependent decision-making style. Conscientiousness personality had a negative effect on avoidant and spontaneous decision-making style and a positive effect on rational style. Neuroticism trait had a positive effect on intuitive, dependent and spontaneous decision-making style. Openness to experience personality traits had a positive effect on rational style [ 26 ].

Schematic representation of the effect of the big five personality types on decision-making styles [ 24 ]

Furthermore, several studies have evaluated the relationship between personality traits and emotional intelligence. Dawda and Hart (2000) found a significant relationship between emotional intelligence and all Big Five personality traits [ 27 ]. Day and al. (2005) found a high correlation between emotional intelligence and extroversion and conscientiousness personality traits [ 28 ]. A study realized by Avsec and al. (2009) revealed that emotional intelligence is a predictor of the Big Five personality traits [ 29 ]. Alghamdi and al. (2017) investigated the predictive role of EI on personality traits among university advisors in Saudi Arabia. They found that extroversion, agreeableness, and openness to experience emerged as significant predictors of EI. The study also concluded that conscientiousness and neuroticism have no impact on EI [ 13 ].

Nonetheless, decision-making is highly influenced by emotion making it an emotional process. The degree of emotional involvement in a decision may influence our choices [ 30 ] especially that emotions serve as a motivational process for decision-making [ 31 ]. For instance, patients suffering from bilateral lesions of the ventromedial prefrontal cortex (interfering with normal processing of emotional signals) develop severe impairments in personal and social decision-making despite normal cognitive capabilities (intelligence and creativity); highlighting the guidance role played by emotions in the decision-making process [ 32 ]. Furthermore, EI affects attention, memory, and cognitive intelligence [ 33 , 34 ] with higher levels of EI indicating a more efficient decision-making [ 33 ]. In one study, Khan and al. concluded that EI had a significant positive effect on rational and intuitive decision-making styles and negative effect on dependent and spontaneous decision-making styles among a group of university students in Pakistan [ 35 ].

This study aims to assess the impact of personality traits on both emotional intelligence and decision-making among medical students in Lebanese Universities and to test the potential mediating role played by emotional intelligence between personality and decision-making styles in this yet unstudied population to our knowledge. The goal of the present research is to evaluate the usefulness of implementing such tools in the selection process of future physicians. It also aimed at assessing the need for developing targeted measures, aiming to ameliorate the psychosocial profile of Lebanese medical students, in order to have a positive impact on patients experience and on medical students’ career success.

Study design

This cross-sectional study was conducted between June and December 2019. A total of 296 participants were recruited from all the 7 faculties of medicine in Lebanon. Data collection was done through filling an anonymous online or paper-based self-administered English questionnaire upon the participant choice. All participants were aware of the purpose of the study, the quality of data collected and gave prior informed consent. Participation in this study was voluntary and no incentive was given to the participants. All participants were General medicine students registered as full-time students in one of the 7 national schools of medicine aged 18 years and above regardless of their nationality. The questionnaire was only available in English since the 7 faculties of medicine in Lebanon require a minimum level of good English knowledge in their admission criteria. A pilot test was conducted on 15 students to check the clarity of the questionnaire. To note that these 15 questionnaires related data was not entered in the final database. The methodology used in similar to the one used in a previous paper [ 36 ]

Questionnaire and variables

The questionnaire assessed demographic and health characteristics of participants, including age, gender, region, university, current year in medical education, academic performance (assessed using the current cumulative GPA), parental highest level of education, and health questions regarding the personal history of somatic, and psychiatric illnesses.

The personality traits were evaluated using the Big Five Personality Test, a commonly used test in clinical psychology. Since its creation by John, Donahue, and Kentle (1991) [ 37 ], the five factor model was widely used in different countries including Lebanon [ 38 ]; it describes personality in terms of five board factors: extroversion, openness to experience, agreeableness, conscientiousness and neuroticism according to an individual’s response to a set of 50 questions on a 5-point Likert scale: 1 (disagree) to 5 (agree). A score for each personality trait is calculated in order to determine the major trait(s) in an individual personality (i.e. the trait with the highest score). The Cronbach’s alpha values were as follows: total scale (0.885), extroversion (0.880), openness to experience (0.718), agreeableness (0.668), conscientiousness (0.640), and neuroticism (0.761).

Emotional intelligence was assessed using the Quick Emotional Intelligence Self-Assessment scale [ 38 ]. The scale is divided into four domains: «emotional alertness, emotional control, social-emotional awareness, and relationship management». Each domain is composed of 10 questions, with answers measured on a 5-point Likert scale: 0 (never) to 4 (always). Higher scores indicate higher emotional intelligence [ 38 ] (α Cronbach  = 0.950).

The decision-making style was assessed using the Scott and Bruce General Decision-Making Style Inventory commonly used worldwide since its creation in 1995 for this purpose [ 1 ]. The inventory consists of 25 questions answered according to a 5-point Likert scale: 1 (strongly disagree) to 5 (strongly agree) intended to evaluate the importance of each decision-making style among the 5 styles proposed by Scott and Bruce: dependent, avoidant, spontaneous, rational and intuitive. The score for each decision-making style is computed in order to determine the major style for each responder (α Cronbach total scale  = 0.744; α Cronbach dependent style  = 0.925; α Cronbach avoidant style  = 0.927; α Cronbach spontaneous style  = 0.935; α Cronbach rational style  = 0.933; α Cronbach intuitive style  = 0.919).

Sample size calculation

The Epi info program (Centers for Disease Control and Prevention (CDC), Epi Info™) was employed for the calculation of the minimal sample size needed for our study, with an acceptable margin of error of 5% and an expected variance of decision-making styles that is related to personality types estimated by 15.4 to 28.1% [ 24 ] for 5531 general medicine student in Lebanon [ 39 ]. The result showed that 294 participants are needed.

Statistical analysis

Statistical Package for Social Science (SPSS) version 23 was used for the statistical analysis. The Student t-test and ANOVA test were used to assess the association between each continuous independent variable (decision-making style scores) and dichotomous and categorical variables respectively. The Pearson correlation test was used to evaluate the association between two continuous variables. Reliability of all scales and subscales was assessed using Cronbach’s alpha.

Mediation analysis

The PROCESS SPSS Macro version 3.4, model four [ 40 ] was used to calculate five pathways (Fig.  2 ). Pathway A determined the regression coefficient for the effect of each personality trait on emotional intelligence, Pathway B examined the association between EI and each decision-making style, independent of the personality trait, and Pathway C′ estimated the total and direct effect of each personality trait on each decision-making style respectively. Pathway AB calculated the indirect intervention effects. To test the significance of the indirect effect, the macro generated bias-corrected bootstrapped 95% confidence intervals (CI) [ 40 ]. A significant mediation was determined if the CI around the indirect effect did not include zero [ 40 ]. The covariates that were included in the mediation model were those that showed significant associations with each decision-making style in the bivariate analysis.

Summary of the pathways followed during the mediation analysis

Sociodemographic and other characteristics of the participants

The mean age of the participants was 22.41 ± 2.20 years, with 166 (56.1%) females. The mean scores of the scales used were as follows: emotional intelligence (108.27 ± 24.90), decision-making: rationale style (13.07 ± 3.17), intuitive style (16.04 ± 3.94), dependent style (15.53 ± 4.26), spontaneous style (13.52 ± 4.22), avoidant style (12.44 ± 4.11), personality trait: extroversion (21.18 ± 8.96), agreeableness (28.01 ± 7.48), conscientiousness (25.20 ± 7.06), neuroticism (19.29 ± 8.94) and openness (27.36 ± 7.81). Other characteristics of the participants are summarized in Table  1 .

Bivariate analysis

Males vs females, having chronic pain compared to not, originating from South Lebanon compared to other governorates, having an intermediate income compared to other categories, those whose mothers had a primary/complementary education level and those whose fathers had an undergraduate diploma vs all other categories had higher mean rationale style scores. Those fathers, who had a postgraduate diploma, had a higher mean intuitive style scores compared to all other education levels. Those who have chronic pain compared to not and living in South Lebanon compared to other governorates had higher dependent style scores. Those who have chronic pain compared to not, those who take medications for a mental illness whose mothers had a primary/complementary education level vs all other categories and those whose fathers had a postgraduate diploma vs all other categories had higher spontaneous style scores (Table  2 ).

Higher agreeableness and conscientiousness scores were significantly associated with higher rational style scores, whereas higher extroversion and neuroticism scores were significantly associated with lower rational style scores. Higher extroversion, openness and emotional intelligence scores were significantly associated with higher intuitive scores, whereas higher agreeableness, conscientiousness and neuroticism scores were significantly associated with lower intuitive style scores. Higher agreeableness and conscientiousness were associated with higher dependent style scores, whereas higher openness and emotional intelligence scores were significantly associated with lower dependent styles scores. Higher agreeableness, conscientiousness, neuroticism, and emotional intelligence scores were significantly associated with lower spontaneous style scores. Finally, higher extroversion, neuroticism and emotional intelligence scores were significantly associated with lower avoidant style scores (Table  3 ).

Post hoc analysis: rationale style: governorate (Beirut vs Mount Lebanon p  = 0.022; Beirut vs South p  < 0.001; Mount Lebanon vs South p  = 0.004; South vs North p  = 0.001; South vs Bekaa p  = 0.047); monthly income (intermediate vs high p  = 0.024); mother’s educational level (high school vs undergraduate diploma p  = 0.048); father’s education level (undergraduate vs graduate diploma p = 0.01).

Intuitive style: father’s education level (high school vs postgraduate diploma p  = 0.046).

Dependent style: governorate (Beirut vs Mount Lebanon p  = 0.006; Beirut vs South p  = 0.003);

Avoidant style: mother’s educational level (high school vs undergraduate diploma p  = 0.008; undergraduate vs graduate diploma p  = 0.004; undergraduate vs postgraduate diploma p  = 0.001).

Mediation analysis was run to check if emotional intelligence would have a mediating role between each personality trait and each decision-making style, after adjusting overall covariates that showed a p  < 0.05 with each decision-making style in the bivariate analysis.

Rational decision-making style (Table  4 , model 1)

Higher extroversion was significantly associated with higher EI, b = 0.91, 95% BCa CI [0.60, 1.23], t = 5.71, p  < 0.001 (R2 = 0.31). Higher extroversion was significantly associated with lower rational decision-making even with EI in the model, b = − 0.06, 95% BCa CI [− 0.11, − 0.02], t = − 2.81, p  = 0.003; EI was not significantly associated with rational decision-making, b = 0.02, 95% BCa CI [− 0.0003, 0.03], t = 1.93, p  = 0.054 (R2 = 0.29). When EI was not in the model, higher extroversion was significantly associated with lower rational decision-making, b = − 0.05, 95% BCa CI [− 0.09, − 0.01], t = − 2.43, p  = 0.015 (R2 = 0.28). The mediating effect of EI was 21.22%.

Higher agreeableness was not significantly associated with EI, b = − 0.05, 95% BCa CI [− 0.40, 0.31], t = − 0.26, p  = 0.798 (R2 = 0.31). Higher agreeableness was significantly associated with higher rational decision-making style even with EI in the model, b = 0.07, 95% BCa CI [0.02, 0.11], t = 2.89, p  = 0.004; EI was not significantly associated with the rational decision-making, b = 0.01, 95% BCa CI [− 0.0003, 0.03], t = 1.92, p  = 0.055 (R2 = 0.29). When EI was not in the model, higher agreeableness was significantly associated with higher rational decision-making, b = 0.07, 95% BCa CI [0.02, 0.11], t = 2.86, p = 0.004 (R2 = 0.28). The mediating effect of EI was 0.10%.

Higher conscientiousness was significantly associated with higher EI, b = 1.40, 95% BCa CI [1.04, 1.76], t = 7.62, p  < 0.001 (R2 = 0.31). Higher conscientiousness was significantly associated with the rational decision-making style even with EI in the model, b = 0.09, 95% BCa CI [0.04, 0.14], t = 3.55, p < 0.001; EI was not significantly associated with the rational decision-making, b = 0.01, 95% BCa CI [− 0.0003, 0.03], t = 1.93, p  = 0.055 (R2 = 0.29). When EI was not in the model, conscientiousness was significantly associated with the rational decision-making style, b = 0.11, 95% BCa CI [0.07, 0.16], t = 4.76, p < 0.001 (R2 = 0.28). The mediating effect of EI was 22.47%.

Higher neuroticism was significantly associated with lower EI, b = − 0.50, 95% BCa CI [− 0.80, − 0.20], t = − 3.26, p  = 0.001 (R2 = 0.31). Neuroticism was not significantly associated with rational decision-making style with EI in the model, b = − 0.09, 95% BCa CI [− 0.05, 0.03], t = − 0.43, p  = 0.668; EI was not significantly associated with rational decision-making, b = 0.01, 95% BCa CI [− 0.0003, 0.03], t = 1.93, p  = 0.055 (R2 = 0.29). When EI was not in the model, neuroticism was not significantly associated with the rational decision-making style, b = − 0.02, 95% BCa CI [− 0.06, 0.02], t = − 0.81, p  = 0.418 (R2 = 0.28).

No calculations were done for the openness to experience personality traits since it was not significantly associated with the rational decision-making style in the bivariate analysis.

Intuitive decision-making style (Table 4 , model 2)

Higher extroversion was significantly associated with higher EI, b = 0.86, 95% BCa CI [0.59, 1.13], t = 6.28, p  < 0.001 (R2 = 0.41). Higher extroversion was significantly associated with higher intuitive decision-making even with EI in the model, b = 0.05, 95% BCa CI [0.002, 0.11], t = 2.03, p  = 0.043; EI was significantly associated with intuitive decision-making style, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.003 (R2 = 0.21). When EI was not in the model, higher extroversion was significantly associated with higher intuitive decision-making, b = 0.08, 95% BCa CI [0.03, 0.13], t = 3.21, p  = 0.001 (R2 = 0.18). The mediating effect of EI was 49.82%.

Higher agreeableness was significantly associated with EI, b = − 0.33, 95% BCa CI [− 0.65, − 0.02], t = − 2.06, p  = 0.039 (R2 = 0.41). Higher agreeableness was significantly associated with lower intuitive decision-making style even with EI in the model, b = − 0.15, 95% BCa CI [− 0.21, − 0.10], t = − 5.16, p  < 0.001; higher EI was significantly associated with higher intuitive decision-making, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.004 (R2 = 0.21). When EI was not in the model, higher agreeableness was significantly associated with lower intuitive decision-making, b = − 0.17, 95% BCa CI [− 0.22, − 0.11], t = − 5.48, p < 0.001 (R2 = 0.18). The mediating effect of EI was 6.80%.

Higher conscientiousness was significantly associated with higher EI, b = 1.18, 95% BCa CI [0.85, 1.51], t = 7.06, p < 0.001 (R2 = 0.41). Higher conscientiousness was significantly associated with lower intuitive decision-making style even with EI in the model, b = − 0.10, 95% BCa CI [− 0.16, − 0.03], t = − 2.95, p  = 0.003; higher EI was also significantly associated with higher intuitive decision-making, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.004 (R2 = 0.21). When EI was not in the model, conscientiousness was not significantly associated with the intuitive decision-making style, b = − 0.06, 95% BCa CI [− 0.12, 0.0004], t = − 1.95, p  = 0.051 (R2 = 0.18). The mediating effect of EI was 38%.

Higher openness to experience was significantly associated with higher EI, b = 1.44, 95% BCa CI [1.13, 1.75], t = 9.11, p  < 0.001 (R2 = 0.41). Higher openness to experience was significantly associated with higher intuitive decision-making style with EI in the model, b = 0.08, 95% BCa CI [0.01, 0.14], t = 2.38, p  = 0.017; higher EI was also significantly associated with intuitive decision-making style, b = 0.03, 95% BCa CI [0.01, 0.05], t = 2.91, p  = 0.004 (R2 = 0.21). When EI was not in the model, higher openness to experience was significantly associated with intuitive decision-making style, b = 0.12, 95% BCa CI [0.06, 0.18], t = 4.22, p  < 0.001 (R2 = 0.18). The mediating effect of EI was 57.93%.

No calculations were done for neuroticism personality trait since it was not significantly associated with the intuitive decision-making style in the bivariate analysis.

Dependent decision-making style (Table 4 , model 3)

Agreeableness was not significantly associated with EI, b = − 0.15, 95% BCa CI [− 0.49, 0.17], t = − 0.94, p  = 0.345 (R2 = 0.32). Higher agreeableness was significantly associated with higher dependent decision-making style even with EI in the model, b = 0.29, 95% BCa CI [0.23, 0.34], t = 10.51, p  < 0.001; higher EI was significantly associated with lower dependent decision-making, b = − 0.04, 95% BCa CI [− 0.06, − 0.02], t = − 4.50, p  < 0.001 (R2 = 0.40). When EI was not in the model, higher agreeableness was significantly associated with higher dependent decision-making, b = 0.29, 95% BCa CI [0.24, 0.35], t = 10.44, p  < 0.001 (R2 = 0.18). The mediating effect of EI was 2.38%.

Higher conscientiousness was significantly associated with higher EI, b = 1.04, 95% BCa CI [0.69, 1.38], t = 5.93, p  < 0.001 (R2 = 0.32). Higher conscientiousness was significantly associated with higher dependent decision-making style even with EI in the model, b = 0.15, 95% BCa CI [0.09, 0.20], t = 4.88, p  < 0.001; higher EI was also significantly associated with lower dependent decision-making, b = − 0.04, 95% BCa CI [− 0.06, − 0.02], t = − 4.50, p  < 0.001 (R2 = 0.40). When EI was not in the model, higher conscientiousness was significantly associated with a higher dependent decision-making style, b = 0.10, 95% BCa CI [0.04, 0.16], t = 3.49, p  < 0.001 (R2 = 0.36). The mediating effect of EI was 30.25%.

Higher openness to experience was significantly associated with higher EI, b = 1.37, 95% BCa CI [1.05, 1.69], t = 8.41, p  < 0.001 (R2 = 0.32). Higher openness to experience was significantly associated with lower dependent decision-making style even with EI in the model, b = − 0.13, 95% BCa CI [− 0.19, − 0.08], t = − 4.55, p < 0.001; higher EI was also significantly associated with dependent decision-making style, b = − 0.04, 95% BCa CI [− 0.19, − 0.08], t = − 4.50, p < 0.001 (R2 = 0.40). When EI was not in the model, higher openness to experience was significantly associated with lower dependent decision-making style, b = − 0.19, 95% BCa CI [− 0.24, − 0.14], t = − 7.06, p < 0.001 (R2 = 0.36). The mediating effect of EI was 43.69%.

No calculations were done for neuroticism and extroversion personality traits since they were not significantly associated with the dependent decision-making style in the bivariate analysis.

Spontaneous decision-making style (Table 4 , model 4)

Agreeableness was not significantly associated with EI, b = 0.17, 95% BCa CI [− 0.19, 0.53], t = 0.91, p  = 0.364 (R2 = 0.17). Higher agreeableness was significantly associated with lower spontaneous decision-making style even with EI in the model, b = − 0.10, 95% BCa CI [− 0.16, − 0.03], t = − 3.07, p  = 0.002; EI was not significantly associated with spontaneous decision-making, b = − 0.01, 95% BCa CI [− 0.03, 0.01], t = − 0.71, p  = 0.476 (R2 = 0.15). When EI was not in the model, higher agreeableness was significantly associated with lower spontaneous decision-making, b = − 0.10, 95% BCa CI [− 0.16, − 0.04], t = − 3.11, p = 0.002 (R2 = 0.15). The mediating effect of EI was 1.25%.

Higher conscientiousness was significantly associated with higher EI, b = 1.26, 95% BCa CI [0.88, 1.64], t = 6.56, p  < 0.001 (R2 = 0.17). Higher conscientiousness was significantly associated with lower spontaneous decision-making style even with EI in the model, b = − 0.16, 95% BCa CI [− 0.23, − 0.09], t = − 4.51, p  < 0.001; EI was not significantly associated with spontaneous decision-making style, b = − 0.01, 95% BCa CI [− 0.03, 0.01], t = − 0.71, p  = 0.476 (R2 = 0.15). When EI was not in the model, higher conscientiousness was significantly associated with lower spontaneous decision-making style, b = − 0.17, 95% BCa CI [− 0.23, − 0.10], t = − 5.11, p  < 0.001 (R2 = 0.15). The mediating effect of EI was 5.64%.

Neuroticism was not significantly associated with EI, b = − 0.22, 95% BCa CI [− 0.53, 0.08], t = − 1.43, p  = 0.153 (R2 = 0.17). Higher neuroticism was significantly associated with lower spontaneous decision-making style even with EI in the model, b = − 0.11, 95% BCa CI [− 0.16, − 0.06], t = − 4.05, p  < 0.001; EI was not significantly associated with spontaneous decision-making style, b = − 0.01, 95% BCa CI [− 0.03, 0.01], t = − 0.71, p = 0.476 (R2 = 0.15). When EI was not in the model, higher neuroticism was significantly associated with lower spontaneous decision-making style, b = − 0.11, 95% BCa CI [− 0.16, − 0.05], t = − 4.01, p  < 0.001 (R2 = 0.15). The mediating effect of EI was 1.49%.

No calculations were done for openness to experience and extroversion personality traits since they were not significantly associated with the spontaneous decision-making style in the bivariate analysis .

Avoidant decision-making style (Table 4 , model 5)

Higher extroversion was significantly associated with higher EI, b = 0.88, 95% BCa CI [0.54, 1.21], t = 5.18, p  < 0.001 (R2 = 0.15). Extroversion was not significantly associated with avoidant decision-making style even with EI in the model, b = − 0.01, 95% BCa CI [− 0.06, 0.05], t = − 0.27, p  = 0.790; higher EI was significantly associated with avoidant decision-making style, b = − 0.04, 95% BCa CI [− 0.06, 0.03], t = − 4.79, p  < 0.001 (R2 = 0.25). When EI was not in the model, extroversion was not significantly associated with avoidant decision-making style, b = − 0.05, 95% BCa CI [− 0.1, 0.08], t = − 1.69, p  = 0.092 (R2 = 0.19).

Higher neuroticism was significantly associated with lower EI, b = − 0.59, 95% BCa CI [− 0.91, − 0.27], t = − 3.60, p < 0.001 (R2 = 0.15). Neuroticism was not significantly associated with avoidant decision-making style even with EI in the model, b = − 0.03, 95% BCa CI [− 0.09, 0.02], t = − 1.34, p  = 0.182; higher EI was significantly associated with lower avoidant decision-making style, b = − 0.04, 95% BCa CI [− 0.06, − 0.03], t = − 4.79, p < 0.001 (R2 = 0.25). When EI was not in the model, neuroticism was not significantly associated with avoidant decision-making style, b = − 0.09, 95% BCa CI [− 0.06, 0.04], t = − 0.33, p  = 0.739 (R2 = 0.19).

No calculations were done for openness to experience, agreeableness, and conscientiousness personality traits since they were not significantly associated with the avoidant decision-making style in the bivariate analysis.

This study examined the relationship between personality traits and decision-making styles, and the mediation role of emotional intelligence in a sample of general medicine students from different medical schools in Lebanon.

Agreeableness is characterized by cooperation, morality, sympathy, low self-confidence, high levels of trust in others and agreeable individuals tend to be happy and satisfied because of their close interrelationships [ 19 , 20 ]. Likewise, dependent decision-making style is characterized by extreme dependence on others when it comes to making decisions [ 1 ]. Our study confirmed this relationship similarly to Wood (2012) [ 41 ] and Bayram and Aydemir (2017) [ 26 ] findings of a positive relationship between dependent decision-making style and agreeableness personality trait and a negative correlation between this same personality trait and spontaneous decision-making style. In fact, this negative correlation can be explained by the reliance and trust accorded by agreeable individuals to their surroundings, making them highly influenced by others opinions when it comes to making a decision; hence, avoiding making rapid and snap decisions on the spur of the moment (i.e. spontaneous decision-making style); in order to explore the point of view of their surrounding before deciding on their own.

Conscientiousness is characterized by competence, hard work, self-discipline, organization, strive for achievement, and goal orientation [ 20 ]. Besides, conscientious individuals have a high level of deliberation making them capable of analyzing the pros and cons of a given situation [ 21 ]. Similarly, rational decision-makers strive for achievements by searching for information and logically evaluating alternatives before making decisions; making them high achievement-oriented [ 20 , 42 ]. This positive relationship between rational decision-making style and conscientiousness was established by Nygren and White (2005) [ 43 ] and Bajwa et al. (2016) [ 25 ]; thus, solidifying our current findings. Furthermore, we found that conscientiousness was positively associated with dependent decision-making; this relationship was not described in previous literature to our knowledge and remained statistically significant after adding EI to the analysis model. This relationship may be explained by the fact that conscientious individuals tend to take into consideration the opinions of their surrounding in their efforts to analyze the pros and cons of a situation. Further investigations in similar populations should be conducted in order to confirm this association. Moreover, we found a positive relationship between conscientiousness and intuitive decision-making that lost significance when EI was removed from the model. Thus, solidifying evidence of the mediating role played by EI between personality trait and decision-making style with an estimated mediation effect of 38%.

Extroversion is characterized by higher levels of self-confidence, positive emotions, enthusiasm, energy, excitement seeking, and social interactions. Similarly, intuitive decision-making is highly influenced by emotions and instinct. The positive relationship between extroversion and intuitive decision-making style was supported by Wood (2012) [ 41 ], Riaz et al. (2012) [ 24 ] and Narooi and Karazee (2015) [ 23 ] findings and by our present study.

Neuroticism is characterized by anxiety, anger, self-consciousness, and vulnerability [ 20 ]. High neurotic individuals have higher levels of negative affect, depression, are easily irritated, and more likely to turn to inappropriate coping responses, such as interpersonal hostility [ 22 ]. Our study results showed a negative relationship between neuroticism and spontaneous decision-making style.

Openness to experience individuals are creative, imaginative, intellectually curious, impulsive and original, open to new experiences and ideas [ 19 , 20 ]. One important characteristic of intuitive decision-making style is tolerance for ambiguity and the ability to picture the problem and its potential solution [ 44 ]. The positive relationship between openness to experience and intuitive decision-making style was established by Riaz and Batool (2012) [ 24 ] and came in concordance with our study findings. Additionally, our results suggest that openness personality trait is negatively associated with dependent decision-making style similar to previous findings [ 23 ]. Openness to experience individuals are impulsive and continuously seek intellectual pursuits and new experiences; hence, they tend to depend to a lesser extent on others’ opinions when making decisions since they consider the decision-making process a way to uncover new experiences and opportunities.

Our study results showed that EI had a significant positive effect on intuitive decision-making style. Intuition can be regarded as an interplay between cognitive and affective processes highly influenced by tactic knowledge [ 45 ]; hence, intuitive decision-making style is the result of personal and environmental awareness [ 46 , 47 , 48 ] in which individuals rely on the overall context without much concentration on details. In other words, they depend on premonitions, instinct, and predications of possibilities focusing on designing the overall plan [ 49 ] and take responsibility for their decisions [ 46 ]. Our study finding supports the results of Khan and al. (2016) who concluded that EI and intuitive decision-making had a positive relationship [ 35 ]. On the other hand, our study showed a negative relationship between EI and avoidant and dependent decision-making styles. Avoidant decision-making style is defined as a continuous attempt to avoid decision-making when possible [ 1 ] since they find it difficult to act upon their intentions and lack personal and environmental awareness [ 50 ]. Similarly to our findings, Khan and al. (2016) found that avoidant style is negatively influenced by EI [ 35 ]. The dependent decision-making style can be regarded as requiring support, advice, and guidance from others when making decisions. In other words, it can be described as an avoidance of responsibility and adherence to cultural norms; thus, dependent decision-makers tend to be less influenced by their EI in the decision-making process. Our conclusion supports Avsec’s (2012) findings [ 51 ] on the negative relationship between EI and dependent decision-making style.

Practical implications

The present study helps in determining which sort of decision is made by which type of people. This study also represents a valuable contribution to the Lebanese medical society in order to implement such variables in the selection methods of future physicians thus recruiting individuals with positively evaluated decision-making styles and higher levels of emotional intelligence; implying better communication skills and positively impacting patients’ experience. Also, the present study may serve as a valuable tool for the medical school administration to develop targeted measures to improve students’ interpersonal skills.

Limitations

Even though the current study is an important tool in order to understand the complex relationship between personality traits, decision-making styles and emotional intelligence among medical students; however, it still carries some limitations. This study is a descriptive cross-sectional study thus having a lower internal validity in comparison with experimental studies. The Scott and Bruce General Decision-Making Style Inventory has been widely used internationally for assessing decision-making styles since 1995 but has not been previously validated in the Lebanese population. In addition, the questionnaire was only available in English taking into consideration the mandatory good English knowledge in all the Lebanese medical schools; however, translation, and cross-language validation should be conducted in other categories of Lebanese population. Furthermore, self-reported measures were employed in the present research where participants self-reported themselves on personality types, decision-making styles and emotional intelligence. Although, all used scales are intended to be self-administered; however, this caries risk of common method variance; hence, cross-ratings may be employed in the future researches in order to limit this variance.

The results suggest that EI showed a significant positive effect on intuitive decision-making style and a negative effect on avoidant and dependent decision-making styles. In addition, our study showed a positive relationship between agreeableness and dependent decision-making style and a negative correlation with spontaneous decision-making style. Furthermore, conscientiousness had a positive relationship with rational and dependent decision-making style and extroversion showed a positive relationship with intuitive decision-making style. Neuroticism had a negative relationship with spontaneous style and openness to experience showed a positive relationship with intuitive decision-making style and a negative relationship with dependent style. Additionally, our study underlined the role of emotional intelligence as a mediation factor between personality traits and decision-making styles namely openness to experience, extroversion, and conscientiousness personality traits with intuitive decision-making style. Personality traits are universal [ 20 ]; beginning in adulthood and remaining stable with time [ 52 ]. Comparably, decision-making styles are stable across situations [ 1 ]. The present findings further solidify a previously established relationship between personality traits and decision-making and describes the effect of emotional intelligence on this relationship.

Availability of data and materials

All data generated or analyzed during this study are not publicly available to maintain the privacy of the individuals’ identities. The dataset supporting the conclusions is available upon request to the corresponding author.

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Acknowledgements

We would like to thank all students who agreed to participate in this study.

Author information

Rabih Hallit, Sahar Obeid and Souheil Hallit are last co-authors.

Authors and Affiliations

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

Radwan El Othman, Rabih Hallit & Souheil Hallit

Department of Pediatrics, Bahman Hospital, Beirut, Lebanon

Rola El Othman

Department of Infectious Disease, Bellevue Medical Center, Mansourieh, Lebanon

Rabih Hallit

Department of Infectious Disease, Notre Dame des Secours University Hospital Center, Byblos, Lebanon

Research and Psychology departments, Psychiatric Hospital of the Cross, P.O. Box 60096, Jal Eddib, Lebanon

Sahar Obeid & Souheil Hallit

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

Sahar Obeid

INSPECT-LB: Institut National de Santé Publique, Epidémiologie Clinique et Toxicologie – Liban, Beirut, Lebanon

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REO and REO were responsible for the data collection and entry and drafted the manuscript. SH and SO designed the study; SH carried out the analysis and interpreted the results; RH assisted in drafting and reviewing the manuscript; All authors reviewed the final manuscript and gave their consent; SO, SH and RH were the project supervisors.

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

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Big Five Personality Traits and Second Language Learning: a Meta-analysis of 40 Years’ Research

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• Volume 34 , pages 851–887, ( 2022 )

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• Xinjie Chen 1 ,
• Jinbo He   ORCID: orcid.org/0000-0002-2785-9371 2 ,
• Elizabeth Swanson 1 ,
• Zhihui Cai 3 &
• Xitao Fan 4  

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Despite numerous studies involving personality traits and second language (L2) learning achievement over many years, there is a lack of an overall picture about how personality traits are related to L2 learning achievement. This study aims to conduct a systematic quantitative synthesis of the studies that examined the relationships between the Big Five personality traits and L2 learning achievement. A total of 137 correlation coefficients from 31 primary studies conducted in 24 countries, with a total cumulative sample size of 8853 and published between 1982 and 2020, were included in this synthesis. The findings showed that openness to experience ( r  = .23; 95% CI: .15, .30; p  < .001), conscientiousness ( r  = .18; 95% CI: .08, .28; p  = .002), extraversion ( r  = .12; 95% CI: .02, .21; p  = .017), and agreeableness ( r  = .10; 95% CI: .01, .18; p  = .025) had positive correlations with L2 learning achievement, while neuroticism ( r  =  − .04; 95% CI: − .09, .02; p  = .227) had a negative yet statistically non-significant correlation with L2 learning achievement. More specifically, openness to experience and conscientiousness were the stronger correlates with L2 learning achievement, followed by more moderate correlates of extraversion and agreeableness, while neuroticism was the weakest among the five. Furthermore, several study features (i.e., study region, age of participants, L1 and L2 similarities, and schooling levels) were shown to explain the variations in the relationships between the Big Five personality traits and L2 learning achievement across individual studies. Implications for L2 teaching and future research are discussed.

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Empirical Verification of the Relationship Between Personality Traits and EFL Attainments

Disentangling the Association Between the Big Five Personality Traits and Student Achievement: Meta-Analytic Evidence on the Role of Domain Specificity and Achievement Measures

The Bidirectional Relationship Between Self-Concept and Self-Efficacy and Their Relative Importance to Foreign Language Learning Achievement

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The current study was partly supported by the Presidential Fund of the Chinese University of Hong Kong, Shenzhen to Jinbo He (Grant Number: PF. 01.001428).

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Xinjie Chen & Elizabeth Swanson

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Outlier detection—Baujat plots.

References of 31 papers included in the meta‐analysis.

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Alavinia, P., & Sameei, A. (2012). Potential bonds between extroversion/introversion and Iranian EFL learners’ listening comprehension ability.  English Language Teaching ,  5 (5), 19–30.

Alavinia, P., & Hassanlou, A. (2014). On the viable linkages between extroversion/introversion and academic Iranian EFL learners’ writing proficiency.  English Language Teaching ,  7 (3), 167–175.

Arispe, K., & Blake, R. J. (2012). Individual factors and successful learning in a hybrid course.  System ,  40 (4), 449–465.

Bagheri, M. S., & Faghih, M. (2012). The relationship between self-esteem, personality type and reading comprehension of Iranian EFL students.  Theory and Practice in Language Studies ,  2 (8), 1641–1650.

Baker‐Smemoe, W., Dewey, D. P., Bown, J., & Martinsen, R. A. (2014). Variables affecting L2 gains during study abroad.  Foreign Language Annals ,  47 (3), 464–486.

Boroujeni, A. A. J., Roohani, A., & Hasanimanesh, A. (2015). The impact of extroversion and introversion personality types on EFL learners’ writing ability.  Theory and Practice in Language Studies ,  5 (1), 212–218.

Busch, D. (1982). Introversion‐extraversion and the EFL proficiency of Japanese students.  Language learning ,  32 (1), 109–132.

Cao, C., & Meng, Q. (2020). Exploring personality traits as predictors of English achievement and global competence among Chinese university students: English learning motivation as the moderator.  Learning and Individual Differences, 77 , 101,814.

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Chen, X., He, J., Swanson, E. et al. Big Five Personality Traits and Second Language Learning: a Meta-analysis of 40 Years’ Research. Educ Psychol Rev 34 , 851–887 (2022). https://doi.org/10.1007/s10648-021-09641-6

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ORIGINAL RESEARCH article

Moderator role of type d personality traits between depressive symptoms and job satisfaction among teachers.

• 1 Adıyaman University, Faculty of Education, Psychological Counseling and Guidance Department, Adıyaman, Türkiye
• 2 SBI. Van Education and Research Hospital, Van, Van, Türkiye

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Background: Type D personality is characterized by negative affect (NA) and social suppression (SI). It has been indicated Type D personality is associated with depression, anxiety, and burnout. Depressive complaints and social inhibition negatively affect job satisfaction. The aim of this study is to investigate the moderating role of Type D personality structure between the severity of depressive complaints and job satisfaction in teachers. Methods: 939 teachers, who constitute the sample of the study, completed the sociodemographic form, Type D personality scale (DS-14), Beck Depression Inventory (BDI) and Minnesota Satisfaction Scale Short Form with an online survey.Results: While a negative relationship was found between teachers' NA scores and their intrinsic and extrinsic job satisfaction (r=-.28 and r=-.19, respectively), a negative relationship was detected between SI scores and intrinsic and extrinsic job satisfaction (r=-.22 and r=-.21, respectively). NA and SI had partial moderating roles in the relationship between BDI score and intrinsic job satisfaction. SI played a partial moderating role in the relationship between BDI and extrinsic job satisfaction.It can be said Type D personality traits has a moderating role between the severity of teachers' depressive complaints and job satisfaction.

Keywords: depressive symptoms, Job Satisfaction, negative affect, Social inhibition, teachers, Type D personality

Received: 17 Mar 2024; Accepted: 22 Apr 2024.

Copyright: © 2024 Yetkin Tekin and Karadağ. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ayşegül Yetkin Tekin, Adıyaman University, Faculty of Education, Psychological Counseling and Guidance Department, Adıyaman, Türkiye

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

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Personality Measurement and Assessment in Large Panel Surveys*

Brent roberts.

* University of Illinois, Urbana-Champaign

Joshua J. Jackson

† University of Illinois, Urbana-Champaign

Angela L. Duckworth

‡ University of Pennsylvania

Katherine Von Culin

** University of Pennsylvania

Personality tests are being added to large panel studies with increasing regularity, such as the Health and Retirement Study (HRS). To facilitate the inclusion and interpretation of these tests, we provide some general background on personality psychology, personality assessment, and the validity of personality tests. In this review, we provide background on definitions of personality, the strengths and weaknesses of the self-report approaches to personality testing typically used in large panel studies, and the validity of personality tests for three outcomes: genetics, income, and health. We conclude with recommendations on how to improve personality assessment in future panel studies.

Personality psychology concerns itself with variation across individuals and how the individual differences shape people’s lives and society’s structures. Personality is clearly a multifaceted system and can be conceptualized at many different levels of analysis (John, Robins, & Pervin, 2008). We prefer to simplify personality by dividing it into four correlated, but conceptually distinct categories. Specifically, most individual differences can be thought of as falling into the domains of Abilities, Traits, Motives, and Narratives ( Roberts & Wood, 2006 ). In a nutshell, abilities concern what people are capable of, traits reflect what people typically think, feel, and do, motives subsume what people want or desire, and narratives reflect the particular stories of peoples’ lives. For the remainder of this paper, we will concentrate on personality traits. This is not to say the other domains of personality are irrelevant. Rather, it is tacit acknowledgement that personality traits are the domain most often discussed when considering the inclusion of personality variables into any study, such as the HRS. It is also an acknowledgment that with the exception of cognitive ability, we know more about personality traits than any of the domains of personality described above.

Personality traits are defined as the relatively enduring patterns of thoughts, feelings, and behaviors that differentiate individuals from one another and are elicited in trait affording situations ( Roberts, 2009 ). Currently, most personality psychologists accept the Big Five (Extraversion, Agreeableness, Conscientiousness, Emotional Stability/Neuroticism, and Openness to Experience) as an adequate working taxonomy of personality traits. According to three recent reviews, personality traits matter for many important life outcomes, but in particular for outcomes related to health, work, and relationships (e.g., Caspi, Roberts, & Shiner, 2005 ; Ozer & Benet-Martinez, 2006 ; Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007 ).

In terms of health and longevity, personality traits predict objective health outcomes, such as life expectancy, and the range of mechanisms thought to affect health and life expectancy (Adler & Matthews, 1994). Traits such as conscientiousness are critical for the health-related behaviors that are known to undermine or promote health, such as tobacco, alcohol, and drug consumption, risky sexual activities, risky driving, healthy eating and activity level ( Bogg & Roberts, 2004 ). Numerous studies of hostility have shown that it not only predicts problematic health behaviors, but also physiological factors such as cardiovascular reactivity, which plays a significant role in heart disease ( Smith, 2006 ). And, in a testament to the fact that personality traits predict multiple health factors, most personality traits are associated with mortality at levels similar to or higher than socioeconomic status ( Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007 ).

Achievement outcomes were long thought to be the exclusive purview of cognitive ability ( Heckman, Stixrud, & Urzua, 2006 ). Numerous studies have shown that personality traits, such as conscientiousness, predict grades and overall GPA above and beyond cognitive ability in elementary school (Abe, 2005), secondary school (Duckworth & Seligman, 2005), and college (Chamorro-Premuzic & Furnham, 2003; Conard, 2005; Noftle & Robins, 2008). Similarly, personality traits predict a whole suite of work-related outcomes. Personality traits predict how individuals go about the job search process (Wanberg, Glomb, Song, & Sorenson, 2005), and which types of jobs people are interested in, therefore helping to shape the career pathways people choose (Mount, Barrick, Scullen, & Rounds, 2005). Moreover, personality traits predict work behaviors such as absenteeism (Ones, Viswesvaran, & Schmidt, 2003), job satisfaction (Judge, Heller, & Mount, 2002), leadership ( Judge, Bono, Ilies, & Gerhardt, 2002 ), and counterproductive work behaviors (Roberts, Harms, Caspi, & Moffitt, 2008). Finally, personality traits predict immediate outcomes, such as job performance ( Hogan & Holland, 2003 ), as well as long-term occupational attainment and income above and beyond cognitive ability ( Heckman et al., 2006 ; Judge, Higgins, Thoresen, & Barrick, 1999 ).

Personality traits also play an important role in shaping relationships and marriages. People who are more neurotic are less securely attached ( Noftle & Shaver, 2006 ), and tend to be less satisfied with their partners—regardless of who their partners are ( Robins, Caspi, & Moffitt, 2000 ). In contrast, people who are more agreeable and open to experience have more satisfied partners ( Donnellan, Conger, & Bryant, 2004 ). Given these findings, it is not too surprising to note that people who are more agreeable, open, and less neurotic are perceived as more desirable mates ( Botwin, Buss, & Shackelford, 1997 ). It turns out these preferences are adaptive. The personality traits of agreeableness and emotional stability, as well as conscientiousness predict a significantly lower probability of experiencing divorce ( Roberts et al., 2007 ).

Given these findings alone, we believe that HRS and other panel studies should invest more in assessing personality with a particular focus on personality traits. Nonetheless, some of the evidence cited above is not directly relevant to particular panel studies and may be construed as less than definitive for methodological reasons (e.g., it is predominantly cross-sectional). Being sensitive to the particular set of issues that confront research on populations, such as retirees in the HRS, we have compiled a more focused review of the literature on three topics: The genetics of personality traits, personality traits and wealth, and personality traits and health.

We have refined our review with the following ideas in mind. First, there is often an implicit if not explicit circularity in personality research (if not psychological research in general). For example, researchers often ask people whether they work hard in general and then use answers to these questions to predict whether they work hard in a particular setting (e.g., their job). Economists describe this as the endogeneity problem. To this end, we have tried to review research that reduces the endogenity problem, typically through employing more rigorous methodological designs. For example, we have emphasized prospective longitudinal studies, and where possible, those studies that bridge key transitions, such as using personality assessed before people enter the labor market to predict labor market outcomes. We also focus on studies that employ multiple methods, such as observer reports of personality, which significantly diminish the circularity problem. We emphasize research that uses objective criteria that are not gathered using self-report techniques. Finally, we focus on research that is most relevant to the age period of the HRS and other ongoing panel studies.

Personality and Molecular Genetics

From our perspective, personality traits provide a clear phenotypic conduit through which genetic variation will affect important life outcomes, such as health and wealth ( Roberts & Jackson, 2008 ). Behavior genetics studies have shown repeatedly that personality traits are genetically influenced, with estimates of heritability ranging from 40 to 60% ( Krueger & Johnson, 2008 ). Of course, heritability estimates are fraught with indeterminacy, so the argument that personality traits are ideal phenotypic conduits of genetic factors would be bolstered by research showing that genetic polymorphisms reliably predict individual differences in personality traits.

Personality traits were first associated with specific genetic polymorphisms almost fifteen years ago ( Benjamin et al., 1996 ; Ebstein et al., 1996 ). Since then, a large number of genetic polymorphisms have been associated with personality traits. In this section we review the accumulated evidence linking genetic polymorphisms to personality traits, and offer future directions in the search for the genetic architecture of personality. To guide our review we will focus on genetic polymorphisms associated with the Big Five—extraversion, agreeableness, conscientiousness, neuroticism, and openness ( Goldberg, 1993 ). Most studies in the personality genetics literature rely either on single dimension scales (e.g., the sensation seeking scale; Zuckerman et al., 1978 ) or on two omnibus personality measures: The Temperament and Character Inventory (TCI; Cloninger et al., 1993 ) and the NEO (either the shorter FFI version or the longer NEO-PI-R; Costa & McCrae, 1992 ). The TCI does not neatly overlap with the more accepted Big Five model ( Farmer & Goldberg, 2008 ) and thus interpreting the results of the TCI within the framework of the Big Five is difficult (for a review of the findings of TCI/TPQ, see Ebstein, 2006 ; Munafo et al., 2003 ; Noblett & Coccoro, 2005 ; Reif & Lesch, 2003 ).

Extraversion

The personality trait of extraversion refers to an energetic approach toward the social and material world and includes lower order traits such as sociability, activity, assertiveness, and positive emotionality ( John & Srivastava, 1999 ). Theoretical models of the neurobiology of extraversion posit that the dopamine and opioid systems likely govern sensitivity to reward and positive emotions ( Depue & Collins, 1999 ; Depue & Morrone-Strupinsky, 2005 ). Correspondingly, most studies focus on polymorphisms associated with dopamine transport and reuptake. For example, the exon III repeat polymorphism in the DRD4 gene is linked to both sensation seeking and extraversion more generally ( Benjamin et al., 1996 ; Bookman, Taylor, Adams-Campbell, & Kittles, 2002 ; Eichhammer et al., 2005 ; Golimbet et al., 2007 ; Ozkaragoz & Noble, 2000 ; Tochigi et al., 2006 ). Other dopamine receptors (e.g. DRD2 & DRD3) have also been associated with extraversion ( Ratsma et al., 2001 ).

Additionally, the Val/Val repeat polymorphism in the COMT gene—which catabolizes dopamine at a faster rate than the met/met counterpart and results in a significant reduction of synaptic dopamine—is associated with greater levels of extraversion ( Golimbet et al., 2007 ; Reuter & Hennig, 2005 ; Wacker et al., 2010 ). Moreover, a recent study using a haplotype approach (combing many unlinked Single Nucleotide Polymorphisms or SNPs) examined 36 markers within the COMT gene and found multiple markers associated with extraversion (Stein et al., 2005). A recent application of the multiple marker approach associated polymorphisms in multiple dopamine genes (DDC, DAT1, DBH) with sensation seeking (Derringer et al., 2010). While less work has been done with opiod related genes, preliminary evidence suggests that the opiod receptor gene OPRM1 is associated with extraversion ( Luo et al., 2008 ).

Agreeableness

The personality trait of agreeableness contrasts a prosocial orientation towards others with antagonism and includes lower order traits such as altruism, compliance, trust, and modesty ( John & Srivastava, 1999 ). Most work with the personality trait of agreeableness focuses on antisocial or aggressive behavior on the negative end and trust on the positive. Less evidence exists regarding the biological substrate of agreeableness so no neurobiological systems have been studied extensively. That said, there is evidence that agreeableness is associated with multiple biological systems. Similar to extraversion, Val/Val repeat polymorphism in the COMT gene is associated with aggression and antisocial behavior ( Volavka et al., 2004 ; Tiihonen et al., 1999 ). Additionally, the serotonin system is associated with agreeableness ( Carver & Miller, 2006 ). Variation in the promoter region of the serotonin transporter gene has also been associated with agreeableness ( Canli & Lesch, 2007 ; Jang et al., 2001 ; Wand et al., 2002 ).

Given that agreeableness is related to substance abuse ( Martin & Sher, 1994 ), it is not surprising that genes directly related to substance abuse are associated with agreeableness. Variation in the M2 cholinergic receptor (CHRM2), which is responsible for the Acetylcholine release, is associated agreeableness ( Luo et al., 2007a ). A polymorphism in the receptor region of the ADH4 gene, which influences the expression of the enzyme that metabolizes acetaldehyde, is also associated with agreeableness ( Luo et al., 2007b ). The Cannabinoid receptor 1 gene (CNR1) gene has been related to levels of agreeableness ( Juhasz et al., 2009 ). Additionally, a recent genome wide association found the Clock gene was associated with agreeableness ( Terracciano et al., 2010 ). In sum, there are numerous examples that agreeableness is associated with genetic polymorphisms. However, the specific neurobiological systems involved are not entirely clear. Future investigations of agreeableness would profit to focus on genes also implicated in substance abuse.

Conscientiousness

Conscientiousness is the tendency to be controlled, task- and goal-directed, norm following, responsible, planful, and organized ( John & Srivastava, 1999 ). Similar to agreeableness, there is less theory about the underlying biological mechanisms involved in the personality trait of conscientiousness. However, also similar to agreeableness, conscientiousness is associated with substance abuse and other psychiatric disorders that are characterized by low levels of impulse control, such as substance abuse and ADHD (Nigg et al., 2003; Widiger, 2008 ). Thus, many of the associations with conscientiousness involve polymorphisms already associated with substance dependence or psychiatric disorders.

Consistent with this the DRD4, COMT, GABRA1, GABRA6, TPH1, CHRM2 and MAOA polymorphisms have all been associated with impulsivity and conscientiousness more broadly ( Kreek et al., 2005 ; Dragan &Oniszczenko, 2007 ; Rosenberg et al., 2006 ; Tochigi et al., 2006 ; Luo et al., 2007 ). These are the same polymorphism linked to ADHD and other disorders marked by impulsivity (e.g., Dick et al., 2006 ; 2008 ; Swanson et al., 2000 ).

A recent investigation found that the Dopamine-β-hydroxylase (DβH) gene was associated with levels of conscientiousness ( Hess et al., 2009 ). Links between conscientiousness and the serotonin system also exist ( Carver & Miller, 2006 ). For example, variations in the serotonin receptor 2A gene (5-HT2A) and serotonin transporter gene (5-HTT) are related to conscientiousness ( Heck et al., 2009 ; Tochigi et al., 2006 ). All in all, the genes related to conscientiousness appear to be genes that are also related to disorders that index a lack of control—or conversely, that are marked by impulsivity. Thus, investigating the neurophysiology of impulsivity should also inform the neurophysiology of conscientiousness.

Neuroticism

The personality trait of neuroticism contrasts emotional stability and even-temperedness with negative emotionality, such as feeling anxious, nervous, sad, and tense ( John & Srivastava, 1999 ). Many studies examine the neurobiology of neuroticism because of the overlap with anxiety and many psychiatric disorders. Perhaps the most commonly studied gene associated with neuroticism is a repeat in the promoter region of the serotonin transporter gene (5-HTTLPR) first reported by Lesch et al., (1996) . A number of meta-analyses of the association between this gene and neuroticism exist, though the results are equivocal. One meta-analysis found no effect; another found an effect and a third found that the effect depended on the measure used (Schinka et al., 2003; Sen et al., 2004a ; Munafo et al., 2003 ). Regardless of the specific meta-analysis, it appears that the higher bound effect of the serotonin transporter gene on neuroticism does not exceed 3% (Munafo et al., 2007).

A number of other genes have been associated with Neuroticism, many of which are involved with the serotonin system. For example, genes associated with the transport, reuptake, catabolization or reception of serotonin such as COMT, 5-HT1A, 5-HT2A, and TCAT have been associated with neuroticism ( Stein et al., 2004 ; Strobel et al., 2003 ). Additionally, BDNF, DRD2, the tyrosine hydroxylase repeat polymorphism (TCAT), and GABA6 were all associated with neuroticism ( Ebstein, 2006 ; Perrson et al., 2000 ; Sen et al., 2004b ). In contrast to extraversion, which is associated primarily with the dopaminergic genes, neuroticism appears to be primarily associated with serotogernic genes.

Openness to Experience is defined as preferring depth, originality, and complexity in an individual’s mental and experiential life ( John & Srivastava, 1999 ). Even though the heritability of openness is the same as the other Big Five traits, fewer studies have investigated the neurobiology of openness. However, given that openness is reliably associated with intelligence, the neurobiology of intelligence may offer insights on which neurobiological system is involved in the trait of openness (DeYoung et al., 2009). The COMT gene, which has previously been associated with memory, is associated with the personality trait of openness ( Harris et al., 2005 ). Additional studies also find that the serotonin transporter ( Harro et al., 2009 ) and the corticotropin-releasing hormone receptors 2 (CRHR2) genes are associated with openness ( Tochigi et al., 2006 ).

While the findings from research linking specific polymorphisms to personality traits are promising, a number of challenges exist in the field of personality genetics. First, many of these genes are associated with multiple personality traits as well as other psychological variables like IQ (e.g. COMT), implying that polymorphisms associated with psychological variables are pleiotropic. Second, the replication rate is poor. The small number of meta-analyses conducted to date find that polymorphisms explain very little variance in personality traits, typically just a few percent (e.g., Munafo et al., 2003 ). A number of alternative approaches to examining specific polymorphisms have been proposed, including genome-wide association studies, combining multiple methods of assessment to estimate phenotypes, and gene-by-environment interactions, which we review below.

Genome wide association studies

The studies reviewed above rely on a priori hypothesized associations with personality traits. In contrast, a genome wide association study (GWAS) takes a less hypothesis driven approach by analyzing numerous SNPs across the entire genome (usually around 500,000 markers). Large-scale GWAS studies are touted as optimal research strategies to uncover the genetic basis for complex phenotypes. Currently, there are a small number of genome wide association studies for personality traits ( Terraciano et al., 2010 , 2010; Shifman et al., 2008 ; Krueger et al., 2010 ; Gillespie et al., 2008 ). Unfortunately, the results of GWAS studies fail to replicate the same polymorphisms reviewed above and also mostly fail to replicate each other in a recent meta-analysis of GWAS studies ( DeMoor et al., in press ). Moreover, the SNPs that are significantly associated with personality traits in GWAS studies rarely account for more than 1% of the variance. If all of the significant SNPS are aggregated together the amount of explained variance is around 5%. However, the inability to identify replicable genetic polymorphisms that are strongly associated with personality traits is not unique to personality traits and extends to any polygenic trait. For example, the replication rate and the amount of variance explained are on par with GWASs of heart disease and even height, both of which are highly heritable ( Krueger et al., 2010 ).

A future direction for GWAS data sets is to combine many genetic markers together at the same time in a theory driven approach. The relative number of SNPs assessed in GWAS studies allows one to aggregate polymorphisms thought to influence the same neurophysiological system instead of focusing on one polymorphism at a time ( Plomin, Haworth, & Davis, 2009 ). If the initial GWAS studies and meta-analyses are correct, then a single SNP will not be able to explain more than a one or two percent of the variance in any outcome. Combining multiple SNPs together into what is known as a SNP set allows the investigation of many numerous genes at the same time. Recently, this approach was adopted in an examination of dopamine system and its relation to sensation seeking. Roughly three hundred SNPS from twelve genes were identified as involved in the dopamine system and possible related to sensation seeking. Together the SNP set comprising 12 SNPS correlated .20 with sensation seeking (Derringer et al., 2010; McCrae et al., 2010).

Multiple methods

Another potential way to enhance the personality signal that is being detected by genetic approaches is to use additional methods other than self-reports. For example, to our knowledge no study of genetic polymorphisms has used observer reports of personality. Additionally, an approach that focuses on more basic biological markers that mediate the pathways from gene to behavior may find stronger links to genetic polymorphisms. This endophenotype approach has been successfully used in the substance abuse literature ( Iacono et al., 2008 ) and may prove useful for personality traits. These endophenotypes are usually assessed by endocrinological or neurophysiological methods, such as EEG. Interestingly, a recent GWAS study found SNPs that explained almost 9% of the variance in resting EEG beta waves ( Hodgskinson et al., 2010 ). Nueroimaging (fMRI) also offers a way to examine the mediating pathways of gene effects. For example, the serotonin transporter gene is associated with less grey matter in ACC and amygdala for short allele carriers compared to long allele carriers ( Pezawas et al., 2005 ).

Environments

Thus far the majority of studies are predicated on the assumption of additive genetic effects, which assume a one-to-one association between genotype and phenotype. The additive model of genetic effects may lack efficacy or generalizability because increasing evidence points to the conditional nature of the genome ( Robinson, Fernald & Clayton, 2008 ). That is, it may be more common that the genome interacts multiplicatively with environmental factors, in a process in which specific genes are turned on at various times in the life course, and possibly turned off thereafter ( Rutter, Moffitt, & Caspi, 2006 ). Thus combining an informed assessment of relevant environmental experiences with genetic assessments is likely to enhance our understanding of how genes influence personality and consequentially important life outcomes.

Integrating environments with genetic testing may be accomplished many ways but the two most useful approaches are to investigate how genes lead a person to experience different environments (gene-environment correlations) and how people respond differently to an environment (gene-environment interaction). Many relevant environments and environmental experiences are already assessed in the HRS and other panel studies, such as poor health or low SES. These experiences could be utilized in gene-environment interaction research where some genetic diathesis interacts with environmental experiences to predict personality traits and other outcomes.

Moreover, the gene-environment interplay approach is consistent with the previous recommendations for how to successfully incorporate genetic information into ongoing longitudinal research. For example, environments have been successful incorporated into self-report association studies ( Caspi et al., 2002 ); neuro-imaging ( Canli, 2008 ) and even with GWAS studies. For example, a recent study using a GWAS found that the effect of each SNP depended on the environment one was raised in ( Dick et al., 2010 ). If this is common—and evidence suggests that it is (e.g., Moffitt, Caspi & Rutter, 2005 )—then incorporating environments in GWAS studies could greatly increase the overall variance explained in phenotypes of interest, such as personality.

Despite the promise of these alternative approaches, a number of difficulties remain for the field of personality genetics. The uniformly large estimates of the heritability of personality combined with the unimpressive results of candidate gene and GWAS approaches to detecting the underlying genes calls into question some of the initial assumptions behind broad-based genetic testing. For example, the first generation of research focusing on specific candidate genes and GWAS was based on the assumption that relatively common genetic variants will have strong and direct causal links to personality traits or other complex phenotypes. Clearly, this assumption has proven overly optimistic at best as was anticipated by geneticists a decade ago ( Terwilleger, 2001 ). The most consistent finding across all efforts to detect and replicate specific genetic effects on personality (or other phenotypes) is that the effect of any given polymorphism, whether direct, or in interaction with an environment, is remarkably small. As such, alternative models of genetic effects must further be explored. Many rare variants of genes may be responsible for complex phenotypes, or other forms of genetic variation, such as copy number variants, may play a more important role than SNPs. Future efforts to link genetic variation to complex phenotypes, such as personality traits, would be wise to keep the remarkably small effect sizes of these initial efforts in mind. Detecting and replicating gene-personality association, or even gene-environment interplay will be hampered by low power, even in studies as large as the HRS.

Personality and Economic Outcomes

The notion that dispositions to act, think, and feel in certain ways likely influence earnings and wealth seems intuitive. One need only consider acquaintances who do better or worse, in economic terms, to generate hypotheses about the traits that might reliably aid or impair economic performance. Nevertheless, labor economists have traditionally treated human capital as isometric with cognitive ability and knowledge. That is, the idea that personality traits might also influence productivity and success in the labor market is relatively new to most economists ( Borghans et al., 2008 ).

To date, interest has outpaced actual research studies on the role of personality traits in determining economic outcomes. For example, we were able to identify only one published article relating Big Five personality traits to lifetime saving and borrowing behavior. Nyhus and Webley (2001) used a survey of 734 Dutch households which included questions about household assets and debt as well as personality. More emotionally stable and introverted individuals saved more and borrowed less. More agreeable individuals, in contrast, saved less and borrowed more. Contrary to the authors’ hypotheses, conscientiousness was not related to either saving or borrowing. Notably, the personality traits of both household heads and partners explained variance in outcomes, though the effects were typically not symmetric.

More research has examined associations between personality traits and earnings. A meta-analysis by Ng et al. (2005) identified 7 studies reporting associations between Big Five traits and salary prior to 2003. We identified an additional 12 studies, representing a total sample of k = 19, N = 22,652. Table 1 summarizes correlations from this updated meta-analysis. Notably, all estimated effect sizes were relatively small in magnitude but, given the sample size, statistically significant. 1

Meta-analytic Associations between Big Five Personality Traits and Salary

There were 19 samples with an aggregate sample of N = 22,652. CI = confidence interval; r = correlation; ρ = correlation corrected for scale reliability; Q = Cochran’s measure of homogeneity; I 2 = Higgins and Thompson’s (2002) measure of heterogeneity.

As shown in Table 1 , Q -statistics for heterogeneity in effect size were significant, and I 2 estimates for proportion of this variance that is systematic were high for all personality trait-salary associations. We therefore undertook two moderator analyses to explain the systematic variance in effect size estimates. First, given the wage gap separating women from men ( Weinberg, 2007 ) and gender differences in personality ( Costa, Terracciano, & McCrae, 2001 ), as well as the fact that women represented only 36% of our meta-analytic sample, we tested gender as a continuous moderator for each trait-earnings correlation. Second, given considerations of reverse causality and third-variable confounds in cross-sectional studies, we tested study design (k =11 cross-sectional studies vs. k = 8 longitudinal studies) as a categorical moderator for each trait-earnings correlation. Three out of ten moderator analyses reached statistical significance, and we discuss these findings in the context of the main analyses below.

As shown in Table 1 , emotional stability was the strongest Big Five correlate of earnings, r = .13, corrected ρ = .14. This finding is consistent with Hogan and Holland (2003) , who found adjustment, a measure of emotional stability, to be the most potent predictor of occupational performance. The positive association between emotional stability and earnings is also congruent with research on core self-evaluations, a trait shown theoretically and empirically to overlap with emotional stability ( Judge, Erez, Bono, & Thoresen, 2002 ) and defined as one’s subjective assessment of one’s capabilities and control over the environment. Judge and Hurst (2007) have shown that core self-evaluations potentiate the effect of early life advantages on mid-life income. Specifically, youth with more positive core self-evaluations were more likely to capitalize on early advantages such as academic achievement and family socioeconomic status. Positive core self-evaluations also predict better job performance, higher job satisfaction, lower levels of stress and conflict, and better coping with setbacks ( Judge, 2009 ). A disposition toward sadness, anxiety, hostility, and other negative emotions would be expected to undermine performance at work. Indeed, emotional stability reliably predicted job performance (corrected ρ = .13, 90% CI [.01, .22]) in a quantitative summary of 5 independent meta-analyses examining associations between Big Five personality traits and job performance (Barrick et al., 2001). We speculate that, in addition, more emotionally stable individuals may earn higher salaries because they are more likely to pursue lucrative but stressful professions.

It is also possible, of course, that higher earnings contribute to higher levels of emotional stability. Indeed, in a longitudinal study of Baltimore residents, income measured in middle adulthood prospectively predicted increases in emotional stability a decade later ( Sutin, Costa, Miech, & Eaton, 2009 ). Finally, the observed association between emotional stability and earnings could reflect unmeasured third variables associated with both variables. For example, certain vocational interests are related to particular Big Five personality traits ( Larson, Rottinghaus, & Borgen, 2002 ) and certain vocations (e.g., investment banker) tend to have higher salaries than others (e.g., painter). However, emotionally stability is not robustly related to any of the six vocational interests in the RIASEC 2 taxonomy ( Larson et al., 2002 ).

Table 1 shows that extraverts earned higher salaries, r = .10, corrected ρ = 11. Extraverts do not, however, reliably receive higher job proficiency ratings (corrected ρ = .15, 90% CI [−.03, .27]) (Barrick et al., 2001). Moreover, moderator analyses indicated that the effect of extraversion is inversely proportional to the percentage of women in the sample, r = −.19, p < .001. Given that our meta-analytic sample was disproportionately male, it seems reasonable to assume that in a balanced sample of men and women, the association between extraversion and earnings would be substantially diminished. In our view, therefore, the evidence that more sociable, gregarious, assertive, and lively individuals generally perform better than their more introverted colleagues is equivocal. Consistent with the comparatively high Q-statistic for extraversion-earnings correlations, it is possible that dominance and sociability facets of extraversion are differentially related to both job performance and earnings, and we recommend future studies using facet-level personality measures test this hypothesis. Future studies might also test the possibility that extraverts select into more highly paid professions (e.g., sales; management positions) than do introverts, possibly because extraverts are more likely to hold enterprising vocational interests ( r = .41; Larson et al., 2002 ).

Conscientiousness was positively related to salary ( r = .06, corrected ρ = .07) but more modestly than expected in overall analyses. Moderator analyses indicated that only conscientiousness was a significantly better predictor of earnings in longitudinal studies ( r = .14, corrected ρ = .16) than in cross-sectional studies ( r = .03, corrected ρ = .04). The stronger estimates in longitudinal studies are consistent with the finding that conscientiousness predicts job performance (corrected ρ = .27, 90% CI = [.10, .35]) better than any other Big Five trait (Barrick et al., 2001). We can only speculate as to why longitudinal findings differ from cross-sectional findings for conscientiousness but not for other Big Five traits. One possibility is that conscientious individuals tend to hold themselves to higher standards, and, thus, rate themselves lower than they should on self-report questionnaires. Such a bias should attenuate associations with any outcome, including earnings. Consistent with this speculation, individuals in East Asian cultures, including Korea, China, and Japan, rate themselves lower in conscientiousness than individuals in North America, South America, Europe, and Africa ( Schmitt, Allik, McCrae, & Benet-Martínez, 2007 ).

What else might explain the relatively modest association between conscientiousness and earnings in cross-sectional studies? Conscientiousness is not robustly associated with any dimension of vocational interest in the RIASEC taxonomy; but it is nevertheless possible that more conscientious individuals select themselves into less lucrative professions. Another possibility is that conscientious individuals perform better on the job yet fail to realize income commensurate with their superior performance, at least earlier in their careers. Finally, distinct facets of conscientiousness could have opposing effects on salary – for instance, a positive association between salary and industriousness might negate a negative association between salary and traditionalism? Published research cannot resolve these important questions, suggesting future studies are needed to unravel the potentially complex causal pathways linking conscientiousness and its facets to earnings in labor market.

Openness to experience was also associated with higher salaries, r = .06, corrected ρ = .06. Gender significantly moderated this relation: the effect of openness was positively associated with the proportion of women in the sample, r = .14, p < .001. Given that the overall meta-analytic sample was disproportionately male, the openness-earnings association might be larger in the population. However, the openness-earnings association could also reflect, at least in part, the third-variable confound of intelligence. General intelligence is both associated with openness to experience ( r = .33, Ackerman & Heggestad, 1997 ) and earnings ( Heckman, Stixrud, & Urzua, 2006 ). Unfortunately, most studies of personality and earnings did not report openness-earnings associations that controlled for intelligence. A notable exception is a longitudinal study by Judge et al. (1999) using data from three studies that followed participants from early childhood to retirement. Childhood openness to experience predicted higher earnings in late adulthood (β = .26, p < .05), but this effect diminished to non-significance (β = −.02, ns ) when childhood intelligence was entered as a covariate. Similarly, among 5,000 adults who graduated from Wisconsin high schools 35 years prior, the cross-sectional association between openness to experience and earnings (β = .10, p < .001 for men; β = .12, p < .001 for women) diminished when controlling for intelligence measured in high school (β = .06, p < .001 for men, β = .09, p < .001 for women) ( Mueller & Plug, 2006 ). Since individuals more open to experience tend to have artistic ( r = .48) and investigative ( r = .28) interests ( Larson et al., 2002 ), one might expect them, in fact, to select into less lucrative professions (e.g., musician, professor) than individuals of comparable intelligence and opportunity.

Should future studies confirm that openness is correlated with higher earnings, even when controlling for IQ, what might explain this association? Meta-analytic estimates of the effect of openness to experience on job performance are small and non-significant, corrected ρ = .07, 90% CI = [−.09, .19] (Barrick et al., 2001). In contrast, openness to experience is more strongly associated with years of education ( r = .31, p < .001) than any other Big Five trait ( Goldberg, Sweeney, Merenda, & Hughes, 1998 ). Thus, to the extent that individuals who are more creative, curious, original, and intellectual earn more than their counterparts, it may be because they enter the labor market with better academic credentials rather than because of superior performance on the job.

Finally, Table 1 shows that more agreeable individuals earn lower salaries ( r = −.04, corrected ρ = −.04), a somewhat surprising finding given that the ability to get along with others would seem advantageous in most occupations. Agreeable individuals do not reliably earn better job performance ratings, corrected ρ = .13, 90% CI [−.01, .22] (Barrick et al., 2001). It is possible that agreeable individuals may “get along” better than they “get ahead” ( Hogan & Holland, 2003 ). For instance, kind, trusting, empathic individuals may not be sufficiently aggressive when negotiating salary contracts and otherwise optimizing their own welfare when in conflict with others. Likewise, agreeable individuals may not assert themselves as effect leaders when their decisions conflict with the opinions of colleagues. Finally, agreeable individuals, who have more social vocational interests ( r = .19, Larson et al., 2002 ), may also select into helping professions (e.g., social work) with lower salaries.

Personality and Health

The research linking personality to health is less nascent than that between personality and wealth. It is now commonly accepted that personality, and personality traits in particular, play a significant role in the health process (see Hampson, 2008 for a brief review). The following review highlights studies that rely on study features described above that diminish the endogeneity problem.

Several studies have examined the prospective relation between childhood personality and adult health outcomes. The first study is the Hawaii Personality and Health cohort, a longitudinal study of 1,054 individuals who were attending elementary school in Hawaii between the years 1959 and 1967 ( Hampson, Goldberg, & Dubanoski, 2007 ). These same individuals were re-contacted in 1999 and 2000 at which time they provided information on their education, health behaviors, and health. A measure of the childhood Big Five traits was used to predict intervening variables of educational attainment and health behaviors, which were in turn used to predict overall health. Four of the Big Five had direct or indirect relations to health status assessed 40 years later in midlife. Conscientiousness was positively related to educational attainment as well as health status, which indicated that conscientiousness had a direct effect on health above and beyond education and health behaviors. Extraversion had a complex relationship to health. It was positively related to physical activity, which was in turn positively related to health status. On the other hand, it was also positively related to smoking behavior, which was negatively related to health status. Agreeableness was negatively related to smoking behavior and positively related to educational achievement, and thus had a positive effect on health in midlife. Openness to experience was only related to higher educational attainment.

In a recent paper (Moffitt et al., 2011), childhood self-control was used to predict adult (age 32) health, wealth, and crime in the Dunedin Multidisciplinary Health and Development study (N = 1037). Self-control was assessed with a composite of ratings done by the participants, their parents, teachers, and trained clinicians. Physical health was assessed through a amalgam of objective indices of cardiovascular functioning, respiratory health, dental quality, sexual health, and inflammatory factors. In all cases, the effect of childhood self-control on adult health was conditioned against and compared to childhood SES and IQ. Low childhood self-control predicted lower physical health. The effect of low self-control was consistently independent of childhood SES and IQ. Moreover, across all outcomes, the effect sizes for childhood low self-control, SES, and IQ were comparable. The effect of low self-control on adult health was explained, in part, by adolescent “snares”, such as dropping out of school and smoking tobacco. These intervening factors highlight the fact that personality traits do not necessarily affect outcomes such as health and wealth directly, but more likely through problematic or protective behaviors taken up on the way to poor or good health.

A number of studies have examined personality and health in middle and older aged samples similar to the HRS. Several studies have used the MIDUS sample for example. In the first study, personality traits were used to predict Body Mass Index independent of parental levels of occupational status and adult levels of occupational status, education, and income ( Chapman, Fiscell, Duberstein, Coletta, Kawachi, 2009 ). Parental occupational status was a protective factor, but its effect was fully explained by the effect of adult socioeconomic indicators. Unfortunately, there were no consistent SES predictors of obesity, such as adult levels of education or income. In contrast, the personality trait of conscientiousness predicted lower BMI scores even when taking into account childhood and adult measures of socioeconomic status.

Several studies have linked conscientiousness to physical health using more objective indices of either conscientiousness or physical health. For example, observer ratings of conscientiousness compiled from friends, family members, and interviewers predicted self-report ratings of physical health ( Lodi-Smith et al., 2010 ). In fact, observer rated conscientiousness predicted above and beyond self-reported ratings of conscientiousness. Alternatively, self-reported ratings of conscientiousness predicted lower medical illness burden as rated by doctors in a clinical setting in a sample of patients between the ages of 65 and 97 ( Chapman, Lyness, & Duberstein, 2007 ). Finally, in a recent paper we used personality ratings gathered from the subsample of HRS couples assessed in 2008 to predict physical health ( Roberts, Smith, Jackson, & Edmonds, 2009 ). Like many studies we found that self-reported conscientiousness and neuroticism predicted self-reported physical health. In addition, we found that if a person was married to a spouse who was more conscientious, then his or her own ratings of health were higher, which we dubbed the compensatory conscientiousness effect.

Finally, personality traits have been linked to mortality with increasing regularity ( Roberts et al., 2007 ). One excellent example of a long-term prospective study is the Scottish Mental Survey (SMS; Deary, Batty, Pattie, & Gale, 2008 ). The SMS has been tracked since 1947 at which time measures of IQ were acquired. Three years after, in 1950, personality data were collected from teachers. Despite relatively impoverished measures of personality (3-item, post-hoc scales), ratings of dependability (a facsimile of conscientiousness), predicted mortality over the subsequent 55 years. Moreover, the effect of dependability (hazard ratio = .78) was similar in magnitude to the effect of IQ (hazard ratio = .70), and independent of the effect of family background, BMI, education, and childhood illness. A more recent study of the Edinburgh Artery Study (EAS), which is similar in age and composition to the HRS sample (older, diverse in terms of SES) showed similar effects for conscientiousness and extended the analyses to the remaining Big Five ( Michelle et al., 2009 ). In this study, both conscientiousness and openness emerged as protective factors, in particular for men. When examined in a structural equation-modeling framework, conscientiousness predicted all-cause mortality above and beyond age, social class, smoking, and blood pressure at a similar magnitude (β = −.14) as social class (β = .14).

As can be seen by these studies, personality traits play an important role in shaping the health experiences people have and in turn the length and quality of their lifespan. We have highlighted studies that diminish the endogeniety problem, such that we find personality assessed well-before health problems arise can predict important outcomes, as well as the fact that it can predict objective health outcomes.

General Recommendations for the Use and Enhancement of Personality Assessment in Ongoing Panel Studies

Personality traits have had a somewhat quixotic existence in large panel studies. For example, in the HRS, there was an initial effort to assess personality in 1996 deemed prematurely to be unsuccessful (see below), and then several different variants incorporated more recently. Specifically, the trait adjectives used in the MIDUS study were assessed in the 2006 HRS assessment along with several additional items intended to expand the assessment of conscientiousness and neuroticism drawn from Lew Goldberg’s IPIP system. Subsequently, a newer measure of a more elaborated model of conscientiousness was incorporated in the 2008 assessment ( Hill et al, under review ). Going forward, we would propose that personality traits be systematically assessed in panel studies such as the HRS. Because of the demands of assessment in these large studies (e.g., the cost of increasing the survey length), we make this recommendation with three overarching constraints. First, the assessment needs to be short. Second, specific traits that are more predictive of relevant outcomes should be assessed more thoroughly. And third, the personality measure should be “harmonized” with other studies, which entails using the same or overlapping measures of personality.

In terms of brevity, there are thankfully many good options available. For example, the HRS borrowed the adjectival system from the MIDUS study, and this adjective set works quite well and is short. There are other options, such as the Big Five Inventory (BFI; John & Srivastava, 1999 ), NEO-FFI ( Costa & McCrae, 1992 ), and the Ten-Item Personality Inventory (TIPI; Gosling, Rentfrow, & Swann, 2003 ). In our opinion, any of these four assessment tools would work well, but we would recommend using the BFI because it has been used more widely in large panel studies. For example, some form of the BFI is now being used in the Wisconsin Longitudinal Study, GSEOP, and British Household Panel Study (BHPS). If the goal is to create a body of data that can be translated across cultures and samples, employing the BFI would be a better option than the MIDUS adjectives.

The third constraint on investing more in personality assessment is the option to drill deeper and assess specific traits in more detail. Enhancing the assessment of specific personality traits needs to be weighed against both the costs of asking more questions as well as the incremental utility of the specificity of measures beyond the Big Five level of assessment. For example, a number of researchers associated with large panel studies utilized by economists have expressed the desire to assess “self-control” more directly and in a more expansive way because of the assumption that it should predict economic behavior, such as savings, income, or some form of delay discounting. Whether or not this is a good idea depends on 1) whether self-control is the right construct, and 2) whether it provides anything beyond what one already gets with a generic Big Five measure. Both of these issues come down to a question of validity. Is the outcome one has in mind, such as savings, going to be predicted better by a measure of self-control than some other measure and specifically a measure of conscientiousness? The above review of the links between the Big Five and income would argue for digging deeper in traits other than conscientiousness.

A straightforward way to answer the questions that arise from considering the inclusion of more focused personality assessments is to run pilot research before including new measures of personality. Far too often the decision to include a new measure of personality rests on intuitions and face validity rather than good empirical work. For example, it is surprisingly common to include personality measures in large panel studies without running pilot research. Take for instance the idea of linking self-control to savings data or delay discounting. According to recent research ( Hirsh et al, in press ), self-control as typically assessed has little or no relation to delay discounting. However, positive affect—a component of extraversion—does predict delay discounting quite consistently. Alternatively, what one calls self-control may be indistinguishable from a more simple conscientiousness scale. For example, in our ongoing research we have found very little evidence that facets of conscientiousness, including self-control, predict outcomes such as health behaviors or emotions above and beyond the latent trait of conscientiousness ( Fayard et al, in press ). Studies like the HRS are too large and costly to be haphazard about selecting variables.

With that strongly worded caveat aside, what more specific measures would we recommend? In terms of dimensions related to health and economic outcomes, we would recommend assessing self-control and specific components of conscientiousness in more detail. Candidate systems and measures would be Donald Lynam’s UPPS system ( Whiteside & Lynam, 2001 ) for assessing impulsivity, which breaks self-control down into four facets. Adding some form of Duckworth’s (2009) Grit scale would also be sensible to predict financial outcomes. Joireman’s consideration of future consequences ( Joireman et al, 2005 ) is also a possibility, as it is akin to a general tendency to delay gratification.

Moving beyond the general recommendation to invest more in the assessment of personality, we also recommend that alternative methods of assessment be employed. As many large panel studies inevitably assess several members of a family, the simplest, and most accessible option given is to use spouse ratings or family ratings of the Big Five as observer ratings of personality. These additional ratings can be used to form a composite with self-reports or as a separate variable altogether. It is not uncommon, as we reported above, to find that observer ratings provide incremental validity above and beyond self-reported personality ratings. Certain forms of more “objective” indices of personality could also be considered. For example, there are a variety of experimentally induced inhibitory control measures that could be employed, such as go-no go tasks, flanker tasks, and risk tasks, in addition to self-reports. Like observer ratings of personality, these tasks typically complement self-reports (Edmonds, et al., 2008). As noted above, when combined with self-reports, these additional dimensions could be linked not only to outcomes of interest, but also to GWAS analyses.

We also recommend reassessing personality traits using identical measures in future waves of research and thus use them as both independent and dependent variables of interest. One reason to consider personality traits as dependent variables is that personality-trait change may be quite consequential for people. Mroczek and Spiro (2007) demonstrated that long-term increases in neuroticism were predictive of mortality in an 18-year survival analysis. Those who started high on neuroticism (above the sample median) and increased over 10 years had higher mortality, controlling for age, depression, and physical health. Similarly, we have recently conducted analyses showing that changes in conscientiousness predict changes in health behaviors and physical health over and above original standing on conscientiousness across two longitudinal studies ( Takahashi, et al, in preparation ).

Finally, we highly recommend incorporating some system to catch aberrant responding to the survey. In this case, aberrant responding refers to either responding randomly to the survey or, more typically, to employ what we call the “flush-right strategy.” The latter refers to a propensity for fatigued or demotivated participants to simply start circling or bubbling in responses down the extreme right hand column of the survey. We sometimes find this type of responding when we ask undergraduates to complete voluminous surveys for very little in return for their effort. Ironically, we have also found evidence of these forms of malingering in populations that are being well reimbursed (e.g., they are going through the motions just to get the money).

We make this recommendation based on our preliminary work on the 1996 and 2008 HRS personality data. In both cases, the psychometric qualities of the personality scales were puzzling. For example, the negatively keyed items (e.g., shy on an extraversion scale) were uncorrelated with the positive items (e.g., talkative). This pattern of results is common when there is aberrant responding to the personality items. When examined more closely, we found evidence for the flush-right strategy, in which a significant minority of persons were rating all items highly even if they were directly contradictory. When we controlled for this form of aberrancy, the psychometric properties of the personality measures improved markedly. Furthermore, when we examined the correlates of this type of responding we found that it was more often employed by older, less educated, less healthy women, who also happened to be lower on conscientiousness. It appears that a significant minority of the HRS sample became too fatigued to respond validly to the survey questions. Moreover, when we controlled for the aberrant responding the validity of the scales also emerged in ways that one would expect. Specifically, we now show that the measure of conscientiousness assessed in 1996 predicts mortality over the subsequent 10-year period and that this effect is independent of SES and IQ ( Hill et al, in press ). We recommend incorporating a systematic assessment of aberrant responding, which would not necessitate adding many more items to the survey. We feel this is of critical importance because most of the economic measures in the survey do not have balance of positively and negatively keyed items. Therefore, it is possible that people are completing the survey with a flush-right strategy and this pattern is going undetected as their data is not missing, but clearly invalid.

In conclusion, we recommend that the initial efforts to incorporate personality measures into the HRS and other panel studies be continued and expanded judiciously. Ideally, new constructs would be pilot tested first. We also recommend employing more than self-report methods to assess personality traits, with the least intrusive option being to use observer ratings. Adding validity scales would also be ideal. In terms of the genetic analyses, the most important recommendation would be to think clearly about the “environmental experience” measures. Does any given study really have adequate coverage of the type of environmental insults or palliative experiences that might interact with genetic polymorphisms? The answer to this question rests in a clearly informed theoretical model of adult development in old age, which should be a tractable challenge.

1 A description of each of these studies and notes on our analytic approach are available on request.

2 The six domains of vocational interests in the RIASEC model are Realistic, Investigative, Artistic, Social, Enterprising, and Conventional

References marked with an asterisk indicate that the studies were included in the meta-analysis.

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In Tight Presidential Race, Voters Are Broadly Critical of Both Biden and Trump

About half of voters say that, if given the chance, they would replace both candidates on the ballot, table of contents.

• The state of the 2024 presidential race
• Other findings: Biden’s job approval ticks up, Trump’s election-related criminal charges
• Educational differences in candidate support
• What are 2020 voters’ preferences today?
• How Biden’s supporters view his personal traits
• How Trump’s supporters view his personal traits
• Views of Biden’s presidency and retrospective evaluations of Trump’s time in office
• Attention to the candidates
• Does it matter who wins?
• What if voters could change the presidential ballot?
• How important is it for the losing candidate to publicly acknowledge the winner?
• 4. Joe Biden’s approval ratings
• Acknowledgments
• The American Trends Panel survey methodology
• Validated voters

Pew Research Center conducted this study to understand voters’ views on the 2024 presidential election, as well how the public views President Joe Biden. For this analysis, we surveyed 8,709 adults – including 7,166 registered voters – from April 8 to April 14, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this report , along with responses, and the survey methodology .

As the 2024 presidential race heats up, American voters face a similar set of choices as they did four years ago – and many are not happy about it.

With the election still more than six months away, a new Pew Research Center survey finds that the presidential race is virtually tied : 49% of registered voters favor Donald Trump or lean toward voting for him, while 48% support or lean toward Joe Biden.

A defining characteristic of the contest is that voters overall have little confidence in either candidate across a range of key traits, including fitness for office, personal ethics and respect for democratic values.

Where Trump has the advantage: More than a third of voters say they are extremely or very confident that Trump has the physical fitness (36%) and mental fitness (38%) needed to do the job of president.

Far fewer say the same of Biden (15% are at least very confident in his physical fitness; 21% are extremely or very confident in his mental fitness). Majorities say they are not too or not at all confident in Biden’s physical and mental fitness.

Where Biden has the advantage: More voters are extremely or very confident in Biden (34%) than in Trump (26%) to act ethically in office. And while 38% say they are at least very confident in Biden to respect the country’s democratic values, fewer (34%) express that level of confidence in Trump. The survey was conducted before the start of Trump’s “hush money” trial in New York City .

( Read more about voters’ views of Biden and Trump in Chapter 2. )

The new Center survey of 8,709 adults – including 7,166 registered voters – conducted April 8-14, 2024, finds large divides in voters’ candidate preference by age, education, and race and ethnicity. As was the case in 2020, younger voters and those with a four-year college degree are more likely to favor Biden than Trump.

Older voters and those with no college degree favor Trump by large margins.

Among racial and ethnic groups:

• White voters favor Trump (56%) over Biden (42%) by a wide margin.
• Roughly three-quarters of Black voters (77%) support Biden, while 18% back Trump.
• Hispanic voters are more evenly divided – 52% favor Biden, while 44% back Trump.
• Asian voters favor Biden (59%) over Trump (36%).

( Read more about voters’ candidate preferences in Chapter 1. )

Most voters who turned out in 2020 favor the same candidate in 2024. Among validated 2020 voters, overwhelming majorities of those who cast ballots for Biden (91%) and Trump (94%) support the same candidate this year. Registered voters who did not vote in 2020 are about evenly divided: 48% back Trump, while 46% support Biden.

A majority of voters say “it really matters who wins” the 2024 race. Today, 69% of voters say it really matters which candidate wins the presidential contest this November. This is somewhat smaller than the share who said this in April 2020 about that year’s election (74%). Nearly identical shares of Biden’s and Trump’s supporters say the outcome of the presidential race really matters.

About half of voters would replace both Biden and Trump on the 2024 ballot

Reflecting their dissatisfaction with the Biden-Trump matchup, nearly half of registered voters (49%) say that, if they had the ability to decide the major party candidates for the 2024 election, they would replace both Biden and Trump on the ballot .

Biden’s supporters are especially likely to say they would replace both candidates if they had the chance. Roughly six-in-ten (62%) express this view, compared with 35% of Trump supporters.

There also are stark age differences in these views: 66% of voters under 30 say they would replace both candidates if they had the chance, compared with 54% of those ages 30 to 49 and fewer than half (43%) of those 50 and older.

( Read more about voters’ feelings toward the upcoming election in Chapter 3. )

Evaluations of the Biden and Trump presidencies

• 42% of voters overall say Trump was a good or great president, while 11% say he was average. This is a modest improvement since March 2021, two months after he left office.
• 28% of voters say Biden is a good or great president, while 21% say he is average. These views are mostly on par with June 2020 assessments of the kind of president Biden would be – but today, a smaller share of voters say he is average.

( Read more about ratings of Biden’s and Trump’s presidencies in Chapter 1. )

• Biden’s approval among the general public: Today, Biden’s approval rating sits at 35% – roughly on par with his rating in January (33%). His job rating has climbed slightly among Democrats over that period, however. Today, 65% of Democrats approve of him – up 4 percentage points since January. ( Read more about Biden’s approval rating in Chapter 4. )
• Conceding the presidential election: A majority of voters say it is important that the losing candidate in November publicly acknowledge the winner as the legitimate president. But Trump’s supporters are far less likely than Biden’s to say it is very important (44% vs. 77%).  ( Read more about voters’ views on election concession in Chapter 3. )

Trump’s criminal charges related to the 2020 election

As Trump faces charges that he sought to overturn the outcome of the 2020 election, 45% of Americans say they think Trump’s actions broke the law. This compares with 38% who say his actions did not break the law – including 15% who say his actions were wrong but not illegal, and 23% who say he did nothing wrong. Nearly two-in-ten are not sure.

Democrats mostly say Trump broke the law; Republicans are more divided. An overwhelming majority of Democrats and Democratic-leaning independents (78%) say Trump’s actions in seeking to change the outcome of the 2020 election broke the law. 

Among Republicans and Republican leaners:

• 49% say Trump did nothing wrong.
• 21% say he did something wrong but did not break the law.
• 9% say Trump broke the law.
• 20% are not sure.

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