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Myers-Briggs Type Indicator (MBTI): 16 Personality Types

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The Myers-Briggs Type Indicator (MBTI) is an introspective, self-report evaluation that identifies a person’s personality type and psychological preferences.

The MBTI propose that four different cognitive functions determine one’s personality: extraversion vs. introversion, sensing vs. intuition, thinking vs. feeling, and judging vs. perceiving.

MBTI Meaning

MBTI, short for Myers-Briggs Type Indicator, is a widely used personality assessment tool based on Carl Jung’s theories.

It categorizes individuals into one of 16 personality types, providing insights into their preferences in four dimensions: extraversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving. MBTI is commonly used for personal development, career counseling, and team building.

According to the MBTI theory, you combine your preferences to determine your personality type. The 16 types are referred to by an abbreviation of the initial letters of each of the four type preferences of each cognitive function.

For example, “ISTP” would denote introversion, sensing, thinking, and perceiving. No combination is considered “better” or “worse” than another– all types are considered equal.

The MBTI emphasizes that each individual has specific preferences in the way they view the world, and this assessment provides insight into the differences and similarities in people’s experiences of life.

The Development of the Myers-Briggs Test

The MBTI tool was developed by Isabel Briggs Myers and her mother Katharine Cook Briggs in 1942 and is based on psychological conceptual theories proposed by Swiss psychiatrist Carl Jung in his work, Psychological Types.

Jung’s theory of psychological types was based on the existence of four essential psychological functions – judging functions (thinking and feeling) and perceiving functions (sensation and intuition ).

He believed that one combination of the functions is dominant for a person most of the time.

Jung’s theory holds that human beings are either introverts or extroverts , so the combinations are expressed in either an introverted or extroverted form (This is why E or I is the first letter of the series). The remaining three functions operate in the opposite orientation.

The Four Dichotomies:

This assessment aims to assign individuals into one of four categories based on how they perceive the world and make decisions, enabling respondents to further explore and understand their own personalities.

The four categories are: introversion or extraversion, sensing or intuition, thinking or feeling, and judging or perceiving. Each person is said to have one preferred quality from each category, producing 16 unique personality types.

Extraversion (E) vs. Introversion (I)

• These are opposite ways to direct and receive energy. Do you prefer to focus on the outer world or your inner world?
• This dichotomy describes how people respond and interact with others and orient themselves within the world around them.

Extraverts tend to be action-oriented – focusing on other people and things, feeling energized by the presence of others, and emitting energy outwards.

Introverts are more thought-oriented. They enjoy deep and meaningful social interactions and feel recharged after spending time alone.

Sensing (S) vs. Intuition (N)

• Do you prefer to focus on the basic information you take in, or do you prefer to interpret and add meaning?
• This dichotomy describes how people gather and perceive information.
• Sensing-dominant people tend to prefer to focus on facts and details and perceive the world around them through their five senses.
• Intuition-dominant types are more abstract in their thinking, focusing on patterns, impressions, and future possibilities.

Thinking (T) vs. Feeling (F)

• When making decisions, do you prefer to first look at logic and consistency or first look at the people and special circumstances?

This dichotomy describes how people make decisions and use judgments.

Thinking types use logic and facts to judge the world, while feeling types tend to consider emotions.

Judging (J) vs. Perceiving (P)

• In dealing with the outside world, do you prefer to get things decided, or do you prefer to stay open to new information and options?

This dichotomy describes how people tend to operate in the outside world and reveals the specific attitudes of the functions.

Those judging dominant tend to be more methodical and results-oriented and prefer structure and decision-making.

Perceiving dominant individuals are more adaptable and flexible and tend to be good at multitasking.

The dominant function is the primary aspect of personality, while the auxiliary and tertiary functions play supportive roles.

The 16 Personality Types

Istj – the logistician.

These individuals tend to be serious, matter-of-fact, and reserved. They appreciate order and organization and pay a great deal of attention to detail.

They like to plan things out in advance and place an emphasis on tradition and law. They are responsible and realistic and can be described as dependable and trustworthy.

ISFJ – The Defender

These individuals are friendly, responsible, and reserved. They are service and work-oriented, committing to meeting their obligations and duties.

They are loyal, considerate, and place a lot of focus on the care of others. They are non-confrontational and value an orderly and harmonious environment.

INFJ – The Advocate

People with this personality type are serious, logical and hardworking. They are also compassionate, conscientious, and reserved.

They value close, deep connections and are sensitive to the needs of others, but also need time and space alone to recharge.

INTJ  The Architect

These people are highly independent, self-confident and prefer to work alone. They are analytical, creative, logical, and driven.

They place an emphasis on logic and fact rather than emotion and can be viewed as perfectionist.

They tend to have high expectations of competence and performance for themselves and others.

ISTP – The Crafter

People with this personality type are fearless and independent. They love adventure, new experiences, and risk-taking.

They tend to be quiet observers and are not well attuned to the emotional states of others, sometimes coming across as insensitive or stoic.

They are results- oriented, acting quickly to find workable solutions and understand the underlying cause of practical problems.

ISFP – The Artist

These individuals are quiet, friendly, easy going, and sensitive. They have a strong need for personal space and time alone to recharge.

They value deep connection and prefer to spend time with smaller groups of close friends and family.

They are highly considerate and accepting, avoiding confrontation and committed to their values and to people who are important to them.

INFP – The Mediator

These people are creative, idealistic, caring, and loyal. They have high values and morals, and are constantly seeking out ways to understand people and to best serve humanity.

They are family and home-oriented and prefer to interact with a select group of close friends.

INTP – The Thinker

People with this personality type are described as quiet, contained, and analytical. They are highly focused on how things work and on solving problems, and tend to be good at logic and math.

They are more interested in ideas and theoretical concepts than in social interaction. They are loyal and affectionate to their closest friends and family, but tend to be difficult to get to know.

ESTP – The Entrepreneur

These individuals are action-oriented, taking pragmatic approaches to obtain results and solve problems quickly. They are often sophisticated, charming, and spontaneous.

They are outgoing and energetic, and enjoy spending time with a wide circle of friends and acquaintances. They focus on the here and now and prefer the practical over the abstract.

ESFP – The Entertainer

These people tend to be outgoing, friendly, and impulsive, seizing energy from other people. They love to be the center of attention and enjoy working with others in new environments.

They can be described as easy going, fun, and optimistic. They are spontaneous and focused on the present moment, and enjoy learning through hands-on experiences with other people.

ENFP – The Champion

These individuals are enthusiastic, creative, energetic, and highly imaginative. They have excellent people and communication skills and are good at giving others appreciation and support.

They do, however, seek approval from others. They value emotions and expression. They dislike routine and might struggle with disorganization and procrastination.

ENTP – The Debater

People with this personality type can be described as innovative, outspoken, and lively. They are idea-oriented and are more focused on the future rather than on the present moment.

They enjoy interacting with a wide variety of people and love to engage with others in debates. They tend to be easy to get along with, but also can be argumentative at times. They are great conversationalists and make good entrepreneurs.

ESTJ – The Director

These people are responsible, practical, and organized. They are assertive and like to take charge, focused on getting results in the most efficient way possible. They have clear standards and place a high value on tradition and rules.

They can be seen as rigid, stubborn, or bossy as they are forceful in implementing their plans. However, they tend to excel at putting plans into action because they are hardworking, self-confident, and dependable.

ESFJ – The Caregiver

These individuals are warmhearted, conscientious, and harmonious. They wear their hearts on their sleeves and tend to see the best in others.

They enjoy helping others and providing the care that people need, but want to be appreciated and noticed for their contributions. They are careful observers of others and excel in situations involving personal contact and community.

ENFJ – Protagonist

These people are responsible, warm, and loyal. They are highly attuned to the emotions of others and capable of forging friendships with essentially anybody.

They have a desire to help others fulfill their potential, and they derive personal satisfaction from helping others. They tend to make good leaders as they are highly capable of facilitating agreement among diverse groups of people.

ENTJ – The Commander

These individuals like to take charge. They value organization and structure and appreciate long-term planning and goal setting.

They have strong people skills and enjoy interacting with others, but they are not necessarily attuned to their own emotions or the emotions of others.

They have strong leadership skills and tend to make good executives, captains, and administrators.

Benefits of MBTI

• Companies can learn how to support employees better, assess management skills, and facilitate teamwork
• Coaches can utilize the information to help understand their preferred coaching approach
• Teachers can assess student learning style
• Teens and young adults can better understand their learning, communication, and social interaction styles
• Teens can determine what occupational field they might be best suited for
• Individuals can gain insight into their behavior
• Partners can better understand themselves and their spouses, allowing for more cohesive teamwork and greater productivity

Criticisms of MBTI

The MBTI has been criticized as a pseudoscience and does not tend to be widely endorsed by psychologists or other researchers in the field. Some of these critiques include:

• There is little scientific evidence for the dichotomies as psychometric assessment research fails to support the concept of a type, but rather shows that most people lie near the middle of a continuous curve.
• The scales show relatively weak validity as the psychological types created by Carl Jung were not based on any controlled studies and many of the studies that endorse MBTI are methodologically weak or unscientific.
• There is a high likelihood of bias as individuals might be motivated to fake their responses to attain a socially desirable personality type.
• Test-retest reliability is low (ie: test takers who retake the test often test as a different type)
• The terminology of the MBTI is incomprehensive and vague, allowing any kind of behavior to fit any personality type.

Take the MBTI (Paper Version)

Myers, I. B. (1962). The Myers-Briggs Type Indicator: Manual (1962).

Myers, Isabel B.; Myers, Peter B. (1995) [1980]. Gifts Differing: Understanding Personality Type. Mountain View, CA: Davies-Black Publishing. ISBN 978-0-89106-074-1.

Pittenger, D. J. (2005). Cautionary Comments Regarding the Myers-Briggs Type Indicator . Consulting Psychology Journal: Practice and Research, 57(3), 210-221.

The purpose of the Myers-Briggs Type Indicator®. The Myers & Briggs Foundation: MBTI Basics. (n.d.). Retrieved from https://www.myersbriggs.org/my-mbti-personality-type/mbti-basics/

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• Published: 17 September 2018

A robust data-driven approach identifies four personality types across four large data sets

• Martin Gerlach 1 ,
• Beatrice Farb 1 ,
• William Revelle 2 &
• Luís A. Nunes Amaral   ORCID: orcid.org/0000-0002-3762-789X 1 , 3 , 4 , 5  

Nature Human Behaviour volume  2 ,  pages 735–742 ( 2018 ) Cite this article

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Matters Arising to this article was published on 16 September 2019

Understanding human personality has been a focus for philosophers and scientists for millennia 1 . It is now widely accepted that there are about five major personality domains that describe the personality profile of an individual 2 , 3 . In contrast to personality traits, the existence of personality types remains extremely controversial 4 . Despite the various purported personality types described in the literature, small sample sizes and the lack of reproducibility across data sets and methods have led to inconclusive results about personality types 5 , 6 . Here we develop an alternative approach to the identification of personality types, which we apply to four large data sets comprising more than 1.5 million participants. We find robust evidence for at least four distinct personality types, extending and refining previously suggested typologies. We show that these types appear as a small subset of a much more numerous set of spurious solutions in typical clustering approaches, highlighting principal limitations in the blind application of unsupervised machine learning methods to the analysis of big data.

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Acknowledgements

L.A.N.A. thanks the John and Leslie McQuown Gift and support from the Department of Defense Army Research Office under grant number W911NF-14-1-0259. W.R.’s work was partially supported by a grant from the National Science Foundation: SMA-1419324. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank J. Johnson for making the Johnson-300 and the Johnson-120 data sets publicly available; D. Stillwell, M. Kosinski and the myPersonality project for sharing the myPersonality-100 data; and the BBC LabUK for making the BBC-44 data set publicly available.

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M.G., B.F., W.R. and L.A.N.A. designed the research. M.G., B.F., W.R. and L.A.N.A. performed the research. M.G. and B.F. analysed the data. M.G., W.R. and L.A.N.A. wrote the paper.

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Gerlach, M., Farb, B., Revelle, W. et al. A robust data-driven approach identifies four personality types across four large data sets. Nat Hum Behav 2 , 735–742 (2018). https://doi.org/10.1038/s41562-018-0419-z

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Research Article

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

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

* E-mail: [email protected]

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

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

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

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

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

• Published: January 7, 2021
• https://doi.org/10.1371/journal.pone.0244849
• Peer Review
• Reader Comments

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

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

Editor: Stephan Doering, Medical University of Vienna, AUSTRIA

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

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

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

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

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

Introduction

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

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

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

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

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

The empirical ground of personality types

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

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

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

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

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

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

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

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

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

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

Research gap and novelty of this study

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

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

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

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

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

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

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

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

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

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

Materials and methods

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

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

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

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

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

Risk taking.

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

Impulsivity/Spontaneity.

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

Affective and cognitive wellbeing.

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

Locus of control.

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

Self-esteem.

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

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

Derivation of the prototypes

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

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

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

Determining the number of clusters

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

External validity criteria.

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

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

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

Internal validity criteria.

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

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

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

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

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

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

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

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

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

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

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

Internal and external cluster fit indices

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

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

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

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

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

Description of the prototypes

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

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

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

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

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

Stability and consistency

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

Predictive validity

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

https://doi.org/10.1371/journal.pone.0244849.t004

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

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

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

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

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

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

https://doi.org/10.1371/journal.pone.0244849.t005

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

https://doi.org/10.1371/journal.pone.0244849.g003

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

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

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

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

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

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

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

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

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

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

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

Limitations

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

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

Acknowledgments

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

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Journal of Management History

ISSN : 1751-1348

Article publication date: 19 August 2022

Issue publication date: 6 April 2023

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

Design/methodology/approach

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

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

Originality/value

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

• Personality
• Systematic literature review

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

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Personality

Personality refers to the enduring characteristics and behavior that comprise a person’s unique adjustment to life, including major traits, interests, drives, values, self-concept, abilities, and emotional patterns. Various theories explain the structure and development of personality in different ways, but all agree that personality helps determine behavior.

The field of personality psychology studies the nature and definition of personality as well as its development, structure and trait constructs, dynamic processes, variations (with emphasis on enduring and stable individual differences), and maladaptive forms.

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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.

Peer Review reports

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.

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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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Personality Characteristics

How personality develops, impact of personality, personality disorders.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Personality Tests

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

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

Personality Traits

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

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

Try Our Free Personality Test

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

Psychodynamic Theories

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

Behavioral Theories

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Myers & Briggs Foundation.  MBTI basics .

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

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

Mayo Clinic. Personality disorders .

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

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

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

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• J Res Med Sci
• v.20(4); 2015 Apr

The association of personality traits and coping styles according to stress level

Hamid afshar.

Department of Psychiatry, Psychosomatic Research Center, Isfahan, Iran

Hamid Reza Roohafza

1 Isfahan Cardiovascular Research Institute, Cardiac Rehabilitation Research Center, Isfahan, Iran

Ammar Hassanzadeh Keshteli

2 Department of Gasteroenterology, Integrative Functional Gastroenterology Research Center, Isfahan, Iran

Mina Mazaheri

3 Department of Psychiatry, Psychosomatic Research Center, Isfahan, Iran

4 Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

Peyman Adibi

5 Department of Gasteroenterology, Integrative Functional Gastroenterology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Background:

Some personality traits and coping styles could be as risk factors in stressful situations. This study aimed to investigate the association of personality traits and coping styles according to the stress level.

Meterials and Methods:

This cross-sectional study was performed in 2011. A total of 4628 individuals over 20 years were selected by random sampling from nonacademic employees that working in 50 different centers across Isfahan province. Data were collected using 12-item General Health Questionnaire (GHQ-12), Big Five Personality Inventory Short Form and coping strategies scale, and individuals were divided into high and low-stress groups in term of GHQ-12. To analyze the data, a binary logistic regression analysis was conducted.

Mean age of participants was 36.3 ± 7.91 years and 56.26% (2604) of them were female. Neuroticism with adjusting covariates of demographic characteristics and the rest of personality traits was a risk factor for stress level with odds ratios (OR) OR:1.24; but other personality traits were protective. Also, active coping styles were protective factors for OR of stress level with adjusting covariates of demographic characteristics and the rest of coping styles, and positive reinterpretation and growth was the most effective of coping style with OR:0.84.

Conclusion:

Some personality traits are associated with passive copings and cause high-stress level. So, it could be concluded that improve and strengthen effective coping strategies in individual with maladaptive traits should be considered as a crucial component of prevention and control programs of stress.

INTRODUCTION

Nowadays, everyone in their daily lives will experience some form of stress and inevitably tries to utilize a unique way to response.[ 1 ] Stress represents a normal, necessary and unavoidable life phenomenon that can generate temporary discomfort, as well as long-term consequences. Scientific information confirms the idea that personality traits are an important factor in identifying, responding and approaching stress events.[ 2 ] Personality traits are as prepreparation for thinking or acting in a similar style in response to a variety of different stimuli or situations.[ 3 ] Studies have shown that some personality traits can predict stress level.[ 4 , 5 , 6 ] Maladaptive personality traits (e.g., neuroticism) is related with increased exposure to stressful life events and likely to make individuals susceptible in experiencing negative emotion and frustration,[ 4 ] While, adaptive personality traits (e.g., high extraversion and conscientiousness) were less affected by daily stresses.[ 7 ]

Also, personality traits could predict coping styles[ 8 ] and influence the coping style we choose.[ 9 ] Coping is a regulatory process that can reduce the negative feelings resulting from stressful events.[ 10 ] Coping is like the changing of thoughts and actions to manage the external and/or internal demands for a stressful event.[ 11 , 12 ] Indeed, coping is a dynamic process that fluctuates over time in response to changing demands and appraisals of the situation.[ 13 ] Three main coping styles are problem-focused coping, emotion-focused coping, and avoidant coping. Problem-focused coping (e.g., problem engagement and positive re-interpretation and growth) involves altering or managing the problem that causes the stress and is highly action-focused.[ 14 ] Emotion-focused coping styles are quite varied, but they all diminish the negative emotions associated with stressor, thus those coping are action-orientated.[ 15 , 16 ] Adaptive forms of emotion-focused coping are seeking support and accepting responsibility.[ 17 , 18 ] The third main coping style is avoidant. Avoidant coping can be described as cognitive, and behavioral efforts directed toward minimizing, denying or ignoring dealing with a stressful situation.[ 19 ] Although some researchers are placed avoidant coping and emotion-focused coping in a group, the styles are conceptually distinct. Avoidant coping is focused on ignoring a stressor and is, therefore, passive.[ 15 , 19 ]

The relationship of personality traits and coping processes has been considered in many studies.[ 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] Some studies have shown that adaptive personality traits are significantly positively associated with active coping styles,[ 20 , 21 , 26 ] While maladaptive personality traits (neuroticism) are positively associated with avoidance coping.[ 21 , 18 ] The association between personality and coping styles suggest that individuals with maladaptive personalities are at a greater risk for experiencing psychological distress as they probably use a maladaptive coping style such as avoidant coping.[ 9 ] However, not all the findings regarding the relationship between personality and coping have been consistent. Some researcher have failed to find a significant relationship between some personality traits (agreeableness, conscientiousness, and openness) and coping.[ 17 , 27 ] For example, the significant relationship has not found between extraversion and either problem-focused coping[ 18 , 27 ] or generally adaptive forms of emotion-focused coping such as seeking support and accepting responsibility.[ 17 , 18 ] Accordingly, the main goal of this study is more comprehensive examining the association of personality traits and coping styles according to the stress level in a large sample.

MATERIALS AND METHODS

The current study was conducted within part of the Study on the Epidemiology of Psychological-Alimentary Health and Nutrition (SEPAHAN) project. This project was a community-based program designed to study the epidemiology of functional gastrointestinal disorders (FGIDs) in Iran in 2011. Furthermore, the role of different lifestyle, nutritional, and psychological factors in FGIDs symptoms and their severity was investigated. Details of this project have been published recently.[ 28 ]

Study population

The current study is a part of the SEPAHAN (ref). In this cross-sectional study, the studied sample was selected using multistage cluster sampling and convenience sampling in last stage among 4 million people in 20 cities across Isfahan province. In SEPAHAN study, data were collected in two separate phases to increase the accuracy, as well as the response rate. In the first phase, all participants were asked to complete a self-administered questionnaire about demographic and lifestyle factors including nutritional habits and dietary intakes. In the second phase, further information on gastrointestinal functions and different aspects of psychological variables were collected using another bunch of self-administered questionnaires (response rate: 86.16%). In the current analysis, we used data from 4,763 adults who had completed data on demographic data, personality traits, life event, coping with stress, social support, and psychological outcome such as depression and anxiety. The protocol of the study was approved by the ethics committee of IUMS and was clarified for all the participants, and a written informed consent was obtained from all participants.

The protocol of study was approved by the Medical Research Ethics Committee of IUMS (#189069, #189082, and #189086), and it was clarified for all the participants and a written informed consent was obtained from all them.

Measurements

After assuring to individuals about the confidentiality of the information, data on demographic characteristics, personality traits and coping styles were collected by standardized self-administered questionnaires.

Demographic factors

Demographic factors applied in this study were age, sex as male and female, marital status as unmarried (single, widow and divorce) and married, educational level as 0-12 years (undergraduate), and >12 years (graduate).

12-item general health questionnaire

The stress level was measured by the Iranian validated version of GHQ-12. GHQ-12 is a consistent and reliable instrument for using in general population studies. Each item is rated on a four-point scale (less than usual, no more than usual, fairly more than usual and much more than usual). The system used to score the GHQ-12 questionnaires was the 0-0-1-1 method. Using this method, a participant could have been scored between 0 and 12 points; a score of 4 or more was used to identify a participant with high-stress level. Validity of the GHQ-12 is good and it has the satisfactory internal consistency ( a = 0.87).[ 29 ]

Big five personality inventory short form

This scale was developed by Costa and McCrae (1992). It consisted of 60 items grouped into five subscales: Extraversion, neuroticism, agreeableness, openness to experience and conscientiousness. Each of the five personality traits is assessed using 12-items. Respondents rate each item on a one (strongly disagree) to five (strongly agree) scale. Certain items are reverse scored. Higher scores indicate higher levels of that particular personality trait.[ 30 ] The reliability for the entire scale ( a =.70) and subscales (as >68) were adequate.[ 31 ] In Iranian sample, the internal consistency of the subscales was 0.83-0.39.[ 32 ]

Coping strategies scale

A multicomponent self-administered coping strategies questionnaire was used to assess the cope with stressful life event. It consisted of the 23 items grouped into five subscales: Positive reinterpretation and growth, Problem engagement, Acceptance, seeking support and Avoidance. The reliability of the questionnaire was determined using Cronbach's alpha coefficient ( a = 0.84). Each item was scored on a 3-point scale (never = 0, sometimes = 1, and often = 2). For each scales, separate scores were reported.[ 33 ] Furthermore, Iranian form of cope scale had a good validity and reliability.[ 34 ]

Statistical analysis

Descriptive analysis of the study population was performed (i.e., mean ± standard deviation for continuous variables and percentages for discrete variables), and differences between groups were analyzed with t -test and Chi-square test. Pearson correlation coefficient was used to evaluate the correlation of personality traits with coping styles. Moreover, for evaluating of the normality of data, kolmograph-smirnov test was used.

A binary logistic regression analysis was performed to separately find the association between personality traits and coping styles with stress level. The dependent variable was stress level (low/high) and the independent variables were personality traits and coping styles. ORs were reported with the corresponding 95% confidence intervals.

The Statistical Package for the Social Sciences version 15.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analyses. A P < 0.05 was considered statistically significant for all analyses.

In this study, 4628 individuals with mean age 36.3 ± 7.91; 2604 (56.26%) female; 2585 (55.8%) graduate; 3658 (79.1%) married were examined. The scores on stress level were recorded into two categories, namely, low stress and high stress. Individuals with high stress (1097, 23.1%) significantly were younger, female, undergraduate and married. The descriptive results are presented in Table 1 .

Descriptive statistics, means and SD of demographic characteristics, personality traits and coping style according to stress level ( n = 4628)

Correlations between personality traits and coping styles were computed. As shown in Table 2 , extraversion, openness, agreeableness and conscientiousness were positively correlated with problem engagement, seeking support, positive reinterpretation and growth and acceptance, and negatively with avoidance; while neuroticism was negatively correlated with problem engagement, seeking support, positive reinterpretation and growth and acceptance, and positively with avoidance.

Pearson correlations between personality traits and coping styles

To examine the association of personality traits and coping styles according to stress level, a binary logistic regression was conducted with stress level serving as the dependent variable. The results are shown in Table 3 . In crude analysis, neuroticism was a risk factor for stress level with OR, 95% confidence intervals: 1.24 (1.22, 1.26); but other personality traits were protective factors. The most protective factor was extraversion with 0.83 (0.82, 0.85). Also, active coping styles were protective factors for stress level, and positive reinterpretation and growth was the most effective of coping style with 0.64 (0.60,0.69). In model 1, with adjusting covariates of demographics characteristics (age, sex, marital status and educational level) didn’t show sensible changing in OR stress. Similarly, in model 2, with adjusting covariates of demographic characteristics, and the rest of personality traits didn’t show sensible changing in OR stress. Also, in model 3, with adjusting covariates of demographic characteristics, and the rest of coping styles didn’t show sensible changing in OR stress.

Binary logistic regression analysis for variables predicting stress

In this study, the association of five personality traits and coping styles was examined. As excepted, results showed adaptive personality traits were positively associated with active coping styles, and negatively with avoidance coping; and maladaptive personality trait (neuroticism) was negatively associated with active coping styles, and positively with avoidance coping. Openness and conscientiousness had the most significantly positive correlation with problem engagement, and extraversion and agreeableness had the most significantly positive correlation with positive reinterpretation and growth.

Studies have shown individuals with neuroticism use passive coping strategies but extravert individuals utilize active copings.[ 7 , 21 , 22 , 23 , 35 ] Costa et al ., reported that neuroticism is negatively related to the use of some effective coping styles such as problem-focused and active coping,[ 24 ] and positively associated with avoidance coping.[ 18 , 21 ] Furthermore, most research shows that extraversion is positively related to active coping styles like problem-focused coping styles and looking for social support,[ 21 , 25 , 36 ] and it predicts avoidance negatively.[ 21 ] Conscientious is significantly positively associated with problem-focused coping and its various components like planning, restraint coping and acceptance of responsibility.[ 20 , 26 ] Agreeableness is positively associated with social support seeking,[ 20 , 21 , 35 ] active coping, planning and positive reappraisal, and negatively associated with self-blame, avoidance and wishful thinking.[ 20 , 21 ] Also, research findings show positive relationships between openness and active coping and positive reinterpretation, and negative correlations with avoidance coping.[ 20 ]

Considering the research, it seems that only individuals with neuroticism have difficulty to cope adaptively. They usually use ineffective coping strategies that have poor results. In explaining this finding, it can be elucidated that neuroticism has been associated with more subjective reports of stress symptoms and the occurrence of stressful life events.[ 6 , 37 ] Individuals with high neuroticism are susceptible to psychological helplessness and irrational thoughts and have less ability to control their impulses.[ 38 ] They have a tendency to experiencing negative emotions[ 39 ] and, therefore, may be to direct their coping efforts toward managing those painful emotions.[ 22 ] So, it is more possible that these individuals get involved in passive and maladaptive coping styles.[ 7 ]

Past efforts have indicated that certain heritable personality attributes make individuals naturally more resistant or susceptible to eustress or distress and its benefits or disadvantages. Specifically, elements of neuroticism and more protective traits like conscientiousness have been linked to differential interpretations of stimuli, eustressful or distressful, challenging or threatening. It is believed that conscientiousness results in challenge appraisal or eustressful condition because of sharing in rational solution formation while neuroticism leads to threat appraisal or distressful experience because it is associated with negative reactions.[ 40 ]

Some authors assume that coping styles can directly be derived from personality traits,[ 40 ] indeed, coping is personality in action.[ 41 ] So, it is supposed that personality traits may influence the effectiveness of coping styles. It means the styles that are useful for some individuals may be less effective or even harmful for individuals that have different personality traits.[ 42 , 43 ] Effectiveness of coping refers to the usefulness degree of coping styles in reducing distress. Thus, there is a possibility that high-neuroticism individuals are emotionally more reactive because they choose maladaptive coping styles, or that they choose similar styles to those chosen by low-neuroticism individuals (problem-focused coping) that they are ineffective at alleviating their distress.[ 42 , 44 ] However, it is believed that deeper understanding of the role of personality in the coping process requires an assessment of personality traits and specific coping strategies, and use of laboratory and daily report studies.[ 26 ]

The strengths of this study are the large sample of respondents and the application of validated instruments. Limitations are the cross-sectional design, self-report questionnaires and non-controlling other factors that may affect stress level.

The current research provided a more complete picture of the relationship of personality traits with coping ways in stressful situations. It showed that adaptive traits with active copings and maladaptive traits with passive copings were associated, and traits associated with passive copings cause high-stress level. So, it could be concluded that improve and strengthen effective coping strategies in individual with maladaptive traits should be considered as a crucial component of prevention and control programs of stress. Also, the findings could be used for determining specific training programs for managing psychological distresses. But, it seems that the active and effective copings require a systematic work considering the role of personality traits in them, especially in “at risk” traits.

AUTHOR'S CONTRIBUTION

All authors contributed to the study design. PA was Leader of the research. AF and HRR conducting the statistical analysis and MM prepared the Manuscript. HRR and HA read and editing the manuscript. All authors read and approved the final version of the manuscript.

ACKNOWLEDGMENTS

We wish to thank all staff of Isfahan University of Medical Sciences (MUI) who participated in our study.

Source of Support: Nil.

Conflict of Interest: None declared.

ORIGINAL RESEARCH article

Exploring the relationships between personality and color preferences.

• 1 Department of Art Therapy, Hanyang Cyber University, Seoul, Republic of Korea
• 2 Graduate School of Counseling Psychology, Hanyang University, Seoul, Republic of Korea

Introduction: This study set out to quantitatively examine the relationship between personality and color, focusing on connotations and preference.

Method: A total of 854 Koreans, aged from 20 to 60, participated in the study. They indicated which colors they associated with various personality words, completed the Ten Item Personality Inventory, and ranked their color preferences. We analyzed the data using frequency analysis, correlation analysis, t -tests, regression analysis, and cluster analysis.

Results: The analyses revealed that all five personality types have characteristic color associations. Through regression analysis, we found that color preference can significantly predict personality. The comparison among personality groups produced by cluster analysis confirmed that people with strong specific personalities prefer the colors that symbolize their personalities.

Discussion: This study’s findings highlight the relationship between personality and color preference. The limitations and suggestions for future studies are also presented.

Introduction

Inter-individual differences in color preference have been noted by a number of studies ( Hurbert and Ling, 2007 ; Lee et al., 2009 ; Al-Rasheed, 2015 ; Fetterman et al., 2015 ; Tao et al., 2015 ). Previous research found how much people prefer different colors according to their culture, gender, and age group ( Palmer and Schloss, 2015 ). For instance, preference for red especially vary by culture ( Hurbert and Ling, 2007 ; Al-Rasheed, 2015 ). The Chinese tend to prefer red ( Zhang et al., 2019 ), showing a much higher preference for it than the British ( Hurbert and Ling, 2007 ). In addition, a study comparing the color preferences of the British and Arabs showed that the British prefer blue and Arabs prefer red ( Al-Rasheed, 2015 ). Meanwhile, a study of gender differences in color preference found that females have a strong preference for reddish colors, while males prefer green–blue ( Hurbert and Ling, 2007 ).

Recently, color researchers have devoted more attention to the relationship between color and personality ( Kim, 2005 ; Kim and Park, 2008 ; Cha et al., 2009 ; Je et al., 2011 ; Kim and Kim, 2013 ; Fetterman et al., 2015 ; Tao et al., 2015 ; Cha and Jung, 2018 ; Lee and Lee, 2021 ). Most of the studies have been successful in finding associations between personality and color preference, which can be explained in terms of color-evoked emotions. As colors have three dimensions (active–passive, light–heavy, and cool–warm), they could evoke certain emotions accordingly ( Ou et al., 2004 ). However, it is still challenging, as there have been exceptions ( Kim and Park, 2008 ; Kim and Kim, 2013 ; Cha and Jung, 2018 ). For example, since red is a vibrant, energetic, warm, and vigorous color, it has been widely believed that people who prefer it are similarly energetic and enthusiastic ( Verner-Bonds, 2000 ). However, recent empirical studies have shown that thinking-type people who prioritize logic and objectivity prefer red color to blue, while blue is widely perceived as symbolizing logic ( Gage, 2000 ; Kim and Kim, 2013 ; Jue, 2017 ; Cha and Jung, 2018 ). Similarly, Kim and Park (2008) , who used the introvert-extrovert personality classification, found that introverts prefer red much more than extroverts. Introverts tend to direct their attention inward, which runs contrary to the widely known characteristics of red–action, power, and dominance ( Andrews, 2005 ; McLeod, 2006 ). These results raise the need to examine the relationship between color impressions and the personalities.

This study set out to investigate color impressions in the general public and to elucidate the relationship between personality type and color preference. Previous studies of the relationship between personality and color preferences have used personality classifications such as Myers–Briggs Type Indicator (MBTI; Kim, 2006 ) and Ennergram ( Lee and Lee, 2021 ) to examine personality types, or psychological states including depression and/or anxiety ( Cho and Oh, 2016 ). In this study, we classified personality types based on the Five Factor Model, which is commonly used in personality research ( Grice, 2019 ). Using words that describe the Big Five Personalities to examine color associations enabled us to approach the relationship between color impressions and personality from various angles.

Materials and methods

Participants.

A total of 854 Korean adults responded to the survey. Participants read research participation brochures online and voluntarily participated in the study. The surveys were conducted online only. The gender ratio of respondents was 474 women (55.5%), 355 men (41.6%), and 25 (2.9%) others. Their average age was 32.0 years (S.D. 7.7 years), ranging from a minimum age of 20 to a maximum age of 60.

Ten item personality inventory

We used the Ten Item Personality Inventory (TIPI) developed by Gosling et al. (2003) . The TIPI is the most brief measure of the Big Five Personality domain. It consists of a total of 10 items, and each item has two adjectives. It is rated on a seven-point Likert scale. Two items per personality type consist of adjectives describing the extremes of said personality; one item shows the core of that personality, and the other shows the opposite trait of that personality. Thus, the opposite items are reverse-scored. The test–retest correlation reported by Gosling et al. was r  = 0.72, and the convergent correlation was r  = 0.77.

Color stimuli and preference calculation

We used the 10 primary colors described by Zhang et al. (2019) and Kim (2006) , including red, orange, yellow, green, light blue, dark blue, purple, white, black, and gray. We chose these colors for the following reasons. First, although colors can be presented in many different ways depending on hue, chroma, or brightness, research regarding color preference has mainly focused on hue manipulation and shown that people prefer high-chroma colors when presented with colors without objects ( Ou et al., 2004 ; Pazda and Thorstenson, 2018 ). In addition, considering that previous studies investigating color preference have found that varying brightness and saturation does not affect hue preference ( Hurbert and Ling, 2007 ), we did not manipulate brightness or saturation levels. Instead, we presented typical hue category colors with high chroma, rainbow colors. The advantage of using the rainbow color array is that it is the most familiar color composition for lay people ( Kim, 2006 ).

Ten square color pieces and their color names were presented together, and respondents were asked to choose the color that most closely matched their preference. For example, if they wanted to choose reddish purple, they would choose purple. Colors were ranked from 1st to 10th, and we revers-scored the color rankings on a scale of 1–10. In other words, first ranked colors received 10 points, second ranked colors received nine points, etc. Therefore, the higher the score, the stronger the color preference.

This study passed the IRB review of the researcher’s institution. We post flyers online, soliciting participation. When participant candidates clicked the survey link in the flyer, they saw an overview of the study, a list of their rights as participants, and a survey manual. After they provided their consent to participate in the study, the survey started. In this study, we did not collect personal information that could identify the participants, except their ages and genders.

A total of 10 colors were presented as color pieces and words. People ranked their favorites among these colors in order from 1st to 10th. If the exact colors that the participants wanted were not available, they were asked to choose the closest match. Next, the TIPI test was performed. Participants read sentences describing personality types and indicated on a seven-point scale how similar or dissimilar the types described in the sentences were to their personalities.

After the TIPI test, 20 personality adjectives from the TIPI test were presented one by one, and participants were asked to choose the colors that best matched each word. Participants could choose a minimum of one color and a maximum of three colors per word. For example, when “extraverted” was presented, a participant could respond with between one and three colors, such as red, yellow, and orange. We provided participants no information regarding the character of each word.

Analysis method

We analyzed the data using SPSS 26.0, conducting frequency analysis and calculating the percentages and descriptive statistics for each color preference and participant description. We also performed correlation analysis between personality types and colors. Next, we divided personality types into upper and lower groups (e.g., lower < M–1SD, and M + 1SD < upper) and conducted a t -test to examine the differences in color preferences between the two groups.

To obtain a more holistic picture of the interrelationships, we converted the original scale scores to standardized scores and performed a multiple regression analysis. In the regression analysis, color preference was set as the independent variable and the traits were the dependent variables.

Finally, using standardized scores, we performed cluster analysis on trait scores, and then compared them in terms of color preference. Cluster analysis is a multivariate analysis technique in which similar objects are grouped together, and each group formed at this time is called a cluster. There are hierarchical and non-hierarchical methods for cluster extraction. The former is performed first, and then the latter is performed to determine the cluster to which each individual belongs. In the hierarchical cluster analysis, the distance between clusters was calculated using Ward’s method, and in the non-hierarchical cluster analysis, the K-means method was implemented. After determining the optimal number of clusters through a dendrogram, an (ANOVA) and a pos hoc comparison were performed to verify the difference between preferred colors according to each cluster.

Colors associated with personality words

Table 1 presents the results of our examination of the associations between colors and the personality adjectives. The frequency analysis revealed that participants chose the same or similar colors for two words indicating one personality type, even when the personality words were presented separately. For example, the highest proportion of participants chose red as the color associated with the two words describing extraversion—extraverted and enthusiastic. Likewise, for agreeableness (sympathetic and warm), the highest proportion of participants selected yellow, while for emotional stability and sincerity, they ranked green as color most strongly associated with the personality words.

Table 1 . Colors associated with personality words.

Correlation analysis results

We conducted a correlation analysis to examine the relationship between personality types and color preferences (see Table 2 ). While the analysis showed no significant correlation between extroversion and color preference, the remaining four personality types showed significant correlations with specific colors. First, it revealed a positive correlation between agreeableness and preference for yellow, light blue, and white, and a negative correlation between agreeableness and preference for red. Second, the analysis also showed a positive correlation between conscientiousness and preference for light blue and dark blue, and a negative correlation between conscientiousness and preference for red. Meanwhile, emotional stability was positively correlated with preference for light blue, dark blue, and white, and negatively correlated with red and yellow. Finally, open to new experiences was positively correlated with preference for bright blue and white, and negatively correlated with preference for orange.

Table 2 . Correlation coefficients and descriptive statistics for measurement variables.

Comparison between the two personality type groups

Next, we explored the relationship between the respondents’ personalities and color preferences by comparing the upper and lower personality groups (see Table 3 ). The group with strong extroversion had a stronger preference for green than the group with low extroversion. Those with high agreeableness preferred yellow, and those with high conscientiousness preferred light blue and dark blue. When the emotional stability was high, the preference for red was lower, and the preference for bright blue was higher. Finally, those with strong openness to new experiences preferred green.

Table 3 . Comparison between the two personality type groups.

Regression analysis results

To verify whether color preference significantly predicts personality traits, we conducted multiple regression analysis with color preference as the independent variable and personality traits as the dependent variables. All data used were standardized scores, and the enter method was used for the analysis. The results of multiple regression analysis are presented in Table 4 .

Table 4 . The effect of color preference on big five personality trait.

We found that color preferences significantly predicted all personality traits except extraversion. Agreeableness was significantly predicted by yellow, light blue, and white preferences. It was found that the preference of light blue significantly predicted conscientiousness. Emotional stability was significantly predicted by red and light blue preferences. Openness to new experiences was predicted by green, purple, and white preferences.

Cluster analysis and ANOVA results

After standardizing the TIPI personality test results, the hierarchical cluster analysis was performed using Ward’s method. As a result of checking the dendrogram, we decided to set the number of clusters to five. Based on the five clusters, we conducted the K-means method, which is widely used among nonhierarchical clustering methods. The results of the final cluster classification are presented in Table 5 . The five cluster characteristics were named as follows: anti-conscientiousness, emotional stability, extraversion, openness to new experiences, and agreeableness.

Table 5 . Cluster analysis results.

Next, ANOVA was performed to determine if there was a difference in color preference for these five clusters. The results are presented in Table 6 . The differences between groups were significant for the following colors: red, orange, yellow, green, light blue, dark blue, and white. Then, we conducted a post hoc comparison to check which groups differed from each other. We used the Tukey HSD test for the post hoc comparison, and the results are also presented in Table 6 .

Table 6 . Comparison between five clusters’ color preference.

Using quantitative data and statistical analysis, this study examined the relationship between personality and color preference. The main results confirmed that people have certain tendencies for the color connotations and associations. For instance, when participants heard the words “extraverted” or “enthusiastic,” they chose red most frequently, followed by warm colors such as orange and yellow. This result aligns with the contention that red represents energy, passion, strength, vigor, and physical activity ( Verner-Bonds, 2000 ; Park and Song, 2014 ; Jue, 2017 ).

Red and yellow are long-wavelength colors, and green and blue are short-wavelength colors. Our analysis showed associations between long-wavelength warm colors and the two words representing extraversion and between short-wavelength or achromatic colors and the two words opposite to extraversion. These results imply that a more or less consistent correlation between color impressions and personality types.

While sympathy and warmth, which constitute agreeableness, were associated with yellow, the anti-agreeable qualities, critical and quarrelsome, were associated with intense colors that contrast with yellow. In particular, participants ranked red and black as the colors first- and second-most strongly associated with quarrelsomeness. Red-black is an especially sinister color combination known as rouge noir ( Jue, 2017 ); it looks powerful and intense, represents antagonism, and evokes quarrelsomeness.

Indeed, red makes a strong impression. For that reason, participants selected it as the color that most strongly connotes anti-conscientiousness and emotional volatility. Such intensity, vitality, and attributes reminiscent of blood also manifested in the relationship between age and color preference. For instance, 33.3% of elderly females chose red as their most preferred color ( Suh, 2011 ).

Meanwhile, participants chose green (red’s complementary color) as the color most strongly associated with conscientiousness and emotional stability. Green is usually found in nature and its connotations include fertility, growth, peace, and safety. A previous study identified “pure (28.6%)” and “natural (21.4%)” as adjectives that are frequently used to describe green ( Park and Song, 2014 ). This offers a plausible explanation for the associations between green and dependable, self-disciplined, calm, emotionally stable, and openness to new experiences found in this study.

Blue was not the first color associated with personality adjectives, but it was chosen as the color second- or third-most strongly associated with reserved, critical, dependable, self-disciplined, calm, and emotionally stable. Sharpe (1974) pointed out that warm colors are exciting and stimulating, while cold colors are stable. Also, people who direct attention inward rather than outward are attracted to short-wavelength blue with the property of contraction. Likewise, we found that short-wavelength blue is associated with emotion-suppressing characteristics, while the long-wavelength red, located on the opposite spectrum, is associated with emotion-expressing characteristics ( Holtzschue, 2011 ). Furthermore, through regression and cluster analyses, blue was found to be a valid predictive variable that distinguishes different personalities.

Many participants identified black as a color associated with the words conventional and uncreative. Black absorbs all light and does not reflect it, leaving a heavy and dark impression. One previous study found that in the context of object and clothing design, black gives a modern, noble, and refined impression ( Choi, 2012 ; Park and Song, 2014 ), but when we presented it separately from objects, we found that it left a negative impression. Participants also selected gray and white as colors associated with the words conventional and uncreative. Gray is the color of ashes—the opposite of vitality. Nevertheless, we found that, like all colors, gray carried a mix of positive and negative associations, ranging from tranquility, wisdom, and intellect to disorganized and critical.

Through the correlation analysis and the regression analysis, we were able to examine the relationship between personality type and color preference. The results of both analyses were similar, except for a few colors. For instance, preferred colors that significantly predicted conscientiousness included light blue only, and excluded red and dark blue, which were found to be significant in the correlation analysis results. The difference between the correlation analysis results and the regression analysis results might be due to the standardization of scores; the latter used the standardized scores while the former used the original scores.

The regression analysis results showed that red and light blue preferences significantly predicted emotional stability. That is, as the strength of the red preference increased, conscientiousness decreased, and as the strength of the blue preference increased, the conscientiousness increased. This aligns with our personality–color association results and echoes findings of Cho and Oh (2016) regarding the relationship between psychological state and color preference. That is, the high-anxiety group chose “warm colors” such as red, orange, and yellow as their preferred colors, while the low-anxiety group preferred “cold colors” such as blue. Both the high- and low-anxiety groups reported that their color preferences and aversions stemmed from the feelings evoked by the colors.

We tested how color preferences change according to personality types in the following two ways. First, we divided participants into two groups based on the strength or weakness of their personality types, and then compared the groups’ color preferences. Second, we also divided the entire participants into five clusters through cluster analysis and compared their color preferences. There were significant differences in color preference according to personality clusters. In general, we found that those with stronger personalities tended to prefer the colors associated with their personalities. For example, those with strongly agreeable personalities preferred yellow, which is associated with that characteristic. A previous study examining the relationship between MBTI personality and color preference found that the preference for yellow was lower among thinking-type people than among emotional-type people ( Cha and Jung, 2018 ). Since that agreeableness is a characteristic of emotional-type people, our results align with these past findings.

People with strong “open to new experiences” personalities had a more robust preference for green than those without this attribute. Our investigation of word-color associations showed the strongest association ratios of green and purple for the corresponding personality, and these results were also obtained in the regression analysis performed after standardizing the scores. Notably, the results show the relationship between a specific personality and its associated color, as do our findings regarding the relationships between blue and both conscientiousness and emotional stability. High conscientiousness participants preferred blue to a greater extent than low conscientiousness participants. Likewise, emotionally stable participants preferred light blue to a greater extent than unstable participants. Previously, participants chose blue as the color most closely associated with conscientiousness and emotional stability. In particular, they indicated that light blue symbolizes dependability and emotional stability, while dark blue symbolizes dependability, self-discipline, and calm. Similarly, in a previous study of color preference and anxiety levels, the low anxiety group preferred cold colors such as blue, while the high anxiety group preferred warm colors ( Cho and Oh, 2016 ). Meanwhile, we found that emotionally stable participants disliked red the most. In a broad sense, this finding is consistent with results linking red and emotional volatility.

Park and Song (2014) explored why people prefer specific colors, and 76.3% of their participants answered that they liked the feeling they received from their favorite color. Obtained from college students in their 20s, these results echo those of a study focused on color experts ( Kim and Park, 2022 ). For example, participants associated the word “modern” with the whole range of achromatic colors as well as blue; meanwhile, they associated “natural” with green and yellow. In this study, we investigated the colors 876 people between the ages of 20 and 60 years associated with personality words, and our results align with the findings of previous associative color studies.

Unexpectedly, we also found that extroverts prefer green over red. This contrasts with the tendency of other personality types to like the colors associated with their personalities. It also runs counter to the findings of previous studies showing that extraverts have high tolerance for light ( Ludvigh and Happ, 1974 ), like high chroma colors ( Pazda and Thorstenson, 2018 ), and generally prefer high-intensity stimuli. Indeed, although people generally associate extraversion with red, we found that extraverts prefer green—red’s complementary color of red. Do these results reflect the cultural characteristics of the East, which tend to be unfavorable for extroverts? For example, have Korean society’s efforts to teach extroverts temperance and emotional serenity affected them individually? Do extroverts need a color that adds stability and vitality to their passionate and extroverted sides? Or does red have any properties that make it more unique than other colors? For instance, people love red, but they might sometimes get tired of its intensity. Our findings provide only limited insight into extroverts’ color preferences, and we cannot exclude the possibility of cultural influence; future studies should endeavor to consider red’s multiple meanings and complex symbolism together.

This study’s limitations and suggestions for future studies are as follows. First, significant correlations were found between personality and color preference, but the correlation coefficients were fairly small, and thus the correlations were not very strong, only showing rough trends. Second, we measured the participants’ traits though a simple TIPI tool. Although TIPI has been reported to discriminate the big five personality traits well, it may be coarse compared to the detailed questionnaire. Third, because this study investigated the relationship between personality and color preference among Korean adults, the generalizability of its findings is limited. The fact that color preferences and senses of color often differ across ages, genders, and cultures highlights the need for explorations of the relationship between color preference and personality with people from diverse countries and regions. Fourth, we did not ask about the participants’ sexual orientation. Therefore, we may have omitted variables that could have influenced the analysis of color preference results.

Data availability statement

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

Ethics statement

This study was conducted at Hanyang Cyber University, Seoul, Republic of Korea, between August 2022 and September 2022. This study was approved by Hanyang Cyber University’s Institutional Review Board (Reference HYCU-IRB-2022-005). The patients/participants provided their written informed consent to participate in this study.

Author contributions

JH: data curation, investigation, and project administration. JJ and JH: formal analysis. JJ: methodology, visualization, and writing—original draft. All authors contributed to the article and approved the submitted version.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1/1A4053429).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Keywords: personality, big five personality, color, color preference, color connotation

Citation: Jue J and Ha JH (2022) Exploring the relationships between personality and color preferences. Front. Psychol . 13:1065372. doi: 10.3389/fpsyg.2022.1065372

Received: 09 October 2022; Accepted: 17 November 2022; Published: 19 December 2022.

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Copyright © 2022 Jue and Ha. 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) and the copyright owner(s) 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: Juliet Jue, [email protected] ; Jung Hee Ha, [email protected]

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.