Person-environment fit in the context of sports and ... - PsyArXiv

Person-environment fit in the context of sports and its association with

subjective well-being

Sophia Terwiela, Sarah Kritzlera, John F. Rauthmannb and Maike Luhmanna

aFaculty of Psychology, Ruhr University Bochum

bDepartment of Psychology, Universität Bielefeld

This is a preprint version of a manuscript submitted for publication. It might be

published in a different version in the future.

Version: June 2021

The analysis plan was preregistered on OSF: https://osf.io/27hkr

Data, analysis scripts, and online supplementary materials (OSM) are openly available online:

https://osf.io/arxhq/

Author Note

Sophia Terwiel* https://orcid.org/0000-0002-0278-4609

Sarah Kritzler https://orcid.org/0000-0002-9682-1502

John F. Rauthmann https://orcid.org/0000-0001-6115-3092

Maike Luhmann https://orcid.org/0000-0001-6211-9304

*Correspondence concerning this article should be addressed to Sophia Terwiel, Email: [email protected].

https://osf.io/27hkr

https://osf.io/arxhq/

mailto:[email protected]

Supplementary fit in the context of sports 2

Abstract

Objectives

Physical activity and sports participation are positively related to physical and mental health

as well as to subjective well-being. Various approaches have been used to explain these

associations. In our study, we propose that person-environment fit can partly explain the

association between sports and subjective well-being. We examined to what extent the fit

between an athlete’s individual personality trait levels and the typical personality trait levels

of athletes in their sports discipline (supplementary fit) is associated with different indicators

of subjective well-being.

Design

In two online surveys, we assessed typical and individual Big Five personality trait

levels using the BFI-2-S. In Sample 1, 4,927 athletes of 96 sports rated the typical Big Five

trait levels of either male or female athletes of their main sport. In Sample 2, 4,340 athletes of

94 sports rated their own Big Five trait levels and four indicators of subjective well-being: life

satisfaction, sports-life satisfaction, positive affect, and negative affect.

Method

First, we derived sport-specific typical Big Five trait levels for male and female

athletes of 96 sports. Second, we investigated how variable-oriented supplementary fit in the

context of sports is associated with four indicators of subjective well-being (life satisfaction,

sports-life satisfaction, positive affect, negative affect) using multilevel polynomial regression

analyses with subsequent response surface analyses. All analyses were preregistered.

Results

We found both similarities and differences in typical Big Five trait levels for male and

female athletes of different sports reflecting gender- and sport-specific characteristics of

athletes of different sports. Variable-oriented supplementary fit between typical and


individual Big Five trait levels was not significantly associated with any of the outcome

variables.

Conclusion

Variable-oriented supplementary fit between typical and individual Big Five trait

levels was not associated with subjective well-being in the broad context of the sports type

that athletes are performing.

Keywords: person-environment fit, Big Five traits, personality, personality, similarity,

response surface analysis (RSA)


Introduction

Physical activity and sports participation are positively related to physical and mental

health outcomes (e.g., Chekroud et al., 2018), such as reduced risk of cardiovascular disease

(Curtis et al., 2017), reduced overall mortality (Lee et al., 2012), or reduced symptoms of

depression and anxiety (Schuch et al., 2016; Wegner et al., 2014). In addition, active sports

participation has been related to increased subjective well-being (Buecker et al., 2020; Zhang

& Chen, 2019).

As defined by Diener (1984), subjective well-being is a multidimensional construct

encompassing a cognitive and an affective component. The cognitive component refers to

how people evaluate their life overall (life satisfaction) or specific life domains (e.g., sports-

life satisfaction). The affective component refers to the frequency of experiencing positive

and negative affect (Diener, 1984, 2012; Diener & Emmons, 1984). As pointed out by Zhang

and Chen (2019), the underlying mechanisms linking physical activity and subjective well-

being still need investigation. Various mechanisms are assumed to mediate this association,

ranging from biological explanations to social integration theories (e.g., Netz et al., 2005).

In the present study, we focus on person-environment fit as a possible explanation for

the positive association between physical activity and subjective well-being. Person-

environment fit refers to the match between characteristics of an individual (e.g., abilities) and

characteristics of the environment (e.g., job demands: Kristof-Brown & Guay, 2011; Kristof-

Brown, Zimmerman, & Johnson, 2005). A better fit has been linked to higher employee

affective commitment (Greguras & Diefendorff, 2009), higher income (Denissen et al., 2018),

and higher job satisfaction and subjective well-being (Hardin & Donaldson, 2014). Person-

level characteristics can refer to people’s abilities, skills, or personality traits. Personality

traits capture relatively stable patterns of how people feel, think, and behave (Roberts &

Mroczek, 2008). They are often organized in terms of the so-called Big Five (John, 2021):

openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. The


environment-level characteristics can refer to variables, such as job demands or characteristics

of surrounding people (for a summary, see Schneider, 2001).

In the context of sports, the focus of studies has mostly lied on either only person-level

characteristics (e.g., skills), only environmental-level characteristics (e.g., presence or absence

of an audience), or their interaction (e.g., interaction between personality and the presence or

absence of an audience), whereas person-environment fit has been investigated only rarely

(e.g., Zepp & Kleinert, 2015) In the context of sport the environment has been conceptualized

in terms of the athlete’s sports team (Zepp & Kleinert, 2015) or the dyadic coach-athlete

couple (Jackson et al., 2011). In addition to the narrow environment (i.e., dyadic coach-athlete

couple or sports team), the environment can also be defined more broadly, for example in

terms of the characteristics of the sports type an athlete is performing. However, an

investigation of person-environment fit considering such a broader sport environment is still

missing. Thus, in the present study, we used multilevel polynomial regression analyses with

subsequent response surface analyses to test to what extent the fit between the typical

personality trait levels of athletes within a specific sport and the athletes’ individual

personality trait levels is associated with higher subjective well-being.

Personality Traits in the Context of Sports

Personality traits are robustly associated with subjective well-being (Anglim et al.,

2020; Lucas & Diener, 2009; Strickhouser et al., 2017). For example, extraversion and

conscientiousness are positively related to positive affect, whereas neuroticism is positively

related to negative affect (Anglim et al., 2020). In the context of sports, several personality

traits have been linked to performance (Allen & Laborde, 2014) and subjective well-being

(Downward & Rasciute, 2011). Moreover, it has been shown that there is a bidirectional

relationship between sports participation and personality traits (Allen et al., 2017; Bakker,

1991; Rhodes & Smith, 2006). In a longitudinal study, Allen et al. (2017) found that not only

were openness and conscientiousness related to subsequent increases in physical activity, but


initial levels of physical activity were also related to subsequent increases in openness to

experience, and initial levels of agreeableness to subsequent decreases in physical activity.

Distinct personality trait levels have not only been found for non-athletes versus

athletes (Rhodes & Smith, 2006) but also for athletes of different achievement levels

(Kirkcaldy, 1982; Newcombe & Boyle, 1995; Schurr et al., 1984; Sheard & Golby, 2010) and

gender (Newcombe & Boyle, 1995; Nicholls et al., 2009). For the Big Five traits specifically,

distinct trait levels patterns for athletes of different types of sports have been observed

(Aidman & Schofield, 2004; Bojanić et al., 2019; Castanier et al., 2010; Nia & Besharat,

2010; Rhea & Martin, 2010; Tok, 2011). While findings on personality trait level differences

between single sports are inconsistent (see O'Sullivan et al., 1998; c.f. Trninić et al., 2016)

those between athletes of different types of sports, such as team and combat sports athletes

(Bojanić et al., 2019), high-risk sports athletes and other athletes (Castanier et al., 2010;

Evans et al., 2012; Rhea & Martin, 2010; Tok, 2011), or team and individual sports athletes

(Allen et al., 2011; Nia & Besharat, 2010) are more consistent. Team sports athletes, for

example, seem to be higher in extraversion and lower in conscientiousness than individual

sports athletes (Allen et al., 2011; Nia & Besharat, 2010). Further, personality trait level

differences between male and female athletes resemble gender differences in the general

population. Specifically, female athletes were found to be higher in neuroticism,

agreeableness, and conscientiousness (Allen et al., 2011).

To sum up, personality traits and especially the Big Five traits have been demonstrated

to differ among athletes of different types of sports and gender (Allen et al., 2011). Given

these findings, similar differences might exist regarding sport-specific typical personality trait

levels. For example, athletes in team sports should be – in general – more often confronted

with people with higher levels of extraversion than individual sports athletes. However,

typical Big Five traits have yet only been described for single sports (e.g., Cameron et al.,


2012). Thus, in the present study, we derive typical Big Five trait levels of athletes of

different sports.

Person-Environment Fit

Person-environment fit is defined as the match between characteristics of an individual

and characteristics of the environment (Kristof-Brown, Zimmerman, & Johnson, 2005). This

match can take on a complementary or supplementary form (Kristof, 1996; Muchinsky &

Monahan, 1987). In the case of complementary fit, persons and environments complement

each other by adding something that the other is lacking. For example, an employee could

complement their company by having language skills no one in the company already has, but

that are needed. In the case of supplementary fit, the person and environment supplement each

other such that both share more of the same (Kristof, 1996). For example, an employee could

supplement their company by sharing their company values or they could supplement the

members of the team by showing characteristics similar to those of other team members.

Supplementary fit may arise based on either a socialization or selection effect. Specifically, a

socialization effect means that people who share a common environment (e.g., sports context)

develop similar trait levels of characteristics due to adapting to it in normative ways. In

contrast, a selection effect means that people with similar trait levels seek certain

environments or choose to remain in those where other people also share their trait levels.

But why would people seek environments with like-minded people or adjust to others

in a given environment? This can be explained by the general human need to belong and the

desire for interpersonal attachment (Baumeister & Leary, 1995). Human beings have the

constant drive to establish and maintain positive relationships with others, and this can be

more easily satisfied when being around like-minded people (perhaps because of the self-

validating effects of the environment). A supplementary fit between one’s own personality

trait levels and averaged levels of surrounding people has been hypothesized to be associated

with positive outcomes such as self-esteem (Bleidorn et al., 2016). However, there have also


been studies that did not find any fit effects, especially when applying stricter methodological

conceptualizations of person-environment fit (e.g., Kritzler & Luhmann, 2021).

In the context of sports, supplementary fit can be defined as the fit between (levels of

some characteristics of) an individual athlete and the people surrounding them, for example,

the coach or other athletes. Investigations of dyadic interactions between coaches and athletes

(Jackson et al., 2011) showed increased commitment and a greater feeling of relatedness when

both athlete and coach were high in agreeableness and conscientiousness, and decreased

commitment and a weaker feeling of relation with higher dissimilarity in extraversion and

openness to experience. In addition, Zepp and Kleinert (2015) found that the perceived

supplementary fit of soccer players to their team (i.e., whether they perceived themselves to

be similar to their team members) was associated with both higher subjective well-being and

better individual performance.

Both studies investigated supplementary fit in narrowly defined environments, that is,

the sports teams and dyadic coach–athlete couples, respectively. Another attempt could be to

define the environment more broadly, specifically the environment could be reflected by

characteristics of the sports type an athlete is performing in. Such a broader environment

could include the personality trait levels of people who would typically surround the athlete

while performing their sport, in contrast to personality trait levels of only specific persons

such as the coach or one’s team members. These typical personality trait levels of other

persons can be conceptualized as either a mean-level value for a group of people or the

normative person in the given context (Furr, 2008). To our knowledge, the supplementary fit

between typical and individual athletes’ personality trait levels for different sports has not yet

been investigated. However, athletes spend a lot of their time in the sporting environment

surrounded by other athletes of their sport. Thus, the effects of variable-oriented

supplementary fit in the context of sports might be relevant to subjective well-being.


Polynomial Regression and Subsequent Response Surface Analyses

Person-environment fit has been operationalized differently in different literatures

(Rauthmann, 2021). Because we were interested in effects of person-environment fit, we

focused on variable-oriented fit for outcomes (i.e., a person and an environment variable

predict an outcome). Basically, the congruence between two variables, from the person and

the environment, is expected to be associated with (or predict) a certain outcome variable

(Edwards, 1994; Humberg et al., 2019; Muchinsky & Monahan, 1987; Spokane et al., 2000).

More precisely, we did not assess perceived person-environment fit (e.g., Zepp & Kleinert,

2015) but assessed fit in such a way that person-level and environment-level characteristics

are measured separately, and then a fit index is derived (Rauthmann, 2021). Thus, in this

study, we tested to what extent the level of congruence between two variables (here:

individual and typical personality trait levels) is related to a specific outcome variable (here:

different indicators of subjective well-being, e.g., sports-life satisfaction).

Congruence has been examined using difference scores (e.g., subtracting the

environment variable from the person variable), but this approach has important

disadvantages (Edwards, 1994; Humberg et al., 2019). For example, several measures are

combined into a single index, which complicates the interpretation of the results (Edwards,

1994). A better-suited method examining hypotheses about person-environment fit linked to

an outcome is to perform multilevel polynomial regression analyses with subsequent response

surface analyses (Barranti et al., 2017; Edwards, 1994; Humberg et al., 2019; Shanock et al.,

2010).

Polynomial regression analysis with subsequent response surface analysis allows

testing congruence hypotheses while additionally investigating the main effects of the two

variables on the outcome variable (Edwards, 1994; Humberg et al., 2019; Schönbrodt et al.,

2018; Shanock et al., 2010). It has recently been successfully applied to test congruence

hypotheses in several different contexts and with regard to diverse research questions (e.g.,


Bleidorn et al., 2016; Denissen et al., 2018; Du et al., 2019; Gilbreath et al., 2011; Luan &

Bleidorn, 2020; Smith & DeNunzio, 2020; Yang et al., 2008).

Polynomial regression analysis with subsequent response surface analysis is a two-step

procedure. First, a polynomial regression analysis is performed in which the outcome (e.g.,

sports-life satisfaction) is predicted by two variables (e.g., individual personality trait level

and typical personality trait level), their interaction term, and their squared terms (quadratic

effects). Second, the relation between these variables can be plotted as a surface in a three-

dimensional space. The surface parameters a1, a2, a3, a4, and a5 can be calculated based on the

regression coefficients and contain information about the shape of the three-dimensional

response surface. These parameters can be used to determine whether a fit effect is present

(for details, see Methods section).

The Present Study

The main goal of this preregistered study was to examine to what extent the sport-

specific supplementary fit between typical athletes’ and athletes’ individual Big Five trait

levels is associated with higher subjective well-being. As person-environment fit has rarely

been investigated in the sports context, particularly with respect to the Big Five, we had no

specific hypotheses for fit effects for the different combinations of Big Five trait levels (e.g.,

typical extraversion – individual extraversion). We used multilevel polynomial regression

analyses and subsequent response surface analyses to test the following hypotheses:

Congruence between typical athletes’ and individual athletes’ Big Five trait levels is

associated with higher levels of life satisfaction (Hypothesis 1a), sports-life satisfaction

(Hypothesis 1b), and positive affect (Hypothesis 1c), as well as with lower levels of negative

affect (Hypothesis 1d).

Additionally, we hypothesized main effects of typical and individual personality trait

levels on subjective well-being. Regarding individual trait levels, we wanted to replicate

findings of the main effects of Big Five traits on subjective well-being (Anglim et al., 2020;


Strickhouser et al., 2017). Regarding typical trait levels, we had no specific hypotheses for the

pattern of main effects due to the lack of research in this area. We tested the following

hypotheses: Higher levels of individual athletes’ openness to experience, conscientiousness,

extraversion, and agreeableness, as well as lower levels of neuroticism, are associated with

higher levels of life satisfaction (Hypothesis 2a), sports-life satisfaction (Hypothesis 2b), and

positive affect (Hypothesis 2c), as well as with lower levels of negative affect (Hypothesis

2d). As an additional aim of the present study, we derived values for the typical Big Five trait

levels of athletes of several different sports.


Methods

Data Collection and Samples

We performed two separate data collections to ensure the independence of typical

(Sample 1) and individual (Sample 2) personality traits. For both data collections, a German

and an English version of an online survey were provided using Qualtrics (Qualtrics, 2020).

Each survey took about 20 minutes to complete. Data collections were approved by the local

ethics committee of the Ruhr-University Bochum. We recruited participants in sport-specific

groups via Reddit, online forums, Facebook, and personal contact with sports teams. As

compensation, participants received either feedback on the sport-specific typical athlete’s

personality via email (Sample 1) or feedback on their individual Big Five (Sample 2).

Participants had to give informed consent and confirm that they were at least 18 years old and

regularly practiced sports to take part in the survey. The planned data analysis of this study

was preregistered here: https://osf.io/27hkr. Due to an error in the process of item recoding,

the preregistered and actual sample sizes differ slightly. We document deviations from the

preregistration in the supplementary material.

Sample 1

In Sample 1, 4,927 athletes of 96 sports were included in the final sample. They

randomly rated the typical Big Five personality trait levels of either a typical male athlete

(Nmale = 2,837) or a typical female athlete (Nfemale = 2,090) of their main sport. The ratings for

each sport were aggregated separately for males and females. Sample sizes ranged from 2 to

89 (M = 19.23) for typical female and from 2 to 106 (M = 23.39) for typical male athletes’

ratings for different sports (see supplementary material Table S1.1 and Table S1.2). To

determine the final sample size of Sample 1, we performed the following steps. First, we

excluded participants who did not complete all personality trait items (N = 5,471). Second, we

excluded participants who provided an incorrect response to an instructed response item (N =

96; Gummer et al., 2021). Third, we excluded participants (N = 1,253) who failed to provide a


correct response to a quality item (“To ensure data quality, we would like you to indicate for

which kind of athlete you had to answer the questions on the last page.”). Fourth, we excluded

participants for whom the main sport could not be manually coded (N = 320; see Measure

section – Sports). Lastly, we excluded sports (N = 807, kSports = 56) for which either the male

or female Big Five traits were rated by less than two raters or for which the inter-rater

agreement for the typical Big Five traits (ICC; Shrout & Fleiss, 1979) was below 0.6

(Cicchetti, 1994). ICCs ranged from 0.61 to 0.98 (M = 0.84) for typical female and from 0.63

to 0.99 (M = 0.88) for typical mal athletes’ ratings. A list of sample sizes and ICCs for all

sports included in as well as excluded from the data analysis can be found in the

supplementary material (Table S1.1 and Table S1.2).

Sample 2

In Study 2, 4,340 athletes of 94 sports (Nfemale = 1,194, Nmale = 3,146) were included in

the final sample. The sample sizes ranged from 2 to 286 (M = 46.17) for different sports. The

number of participants for each sport can be seen in the supplementary material Table S5.

Participants rated their own Big Five traits and answered questions regarding life satisfaction,

sports-life satisfaction, and positive and negative affect.

To determine the final sample size of Sample 2, we performed the following steps.

First, we excluded participants performing a sport that was not included in Sample 1 (i.e., for

which we had no information on the typical personality trait levels; N = 1,303 k = 57).

Second, we excluded participants who did not answer all BFI-2-S items (N = 1,325). Third,

we excluded participants who provided an incorrect response to an instructed response item

(N = 15; Gummer et al., 2021). Fourth, as we had only assessed typical trait levels for male

and female athletes, we excluded participants who indicated their gender as neither female nor

male (N = 41). Fifth, we excluded participants performing sports for which the number of

participants was below two (N = 1, k = 1). Sample sizes ranged from 0 to 132 (M = 12.70) for

female and from 0 to 194 (M = 33.47) for male athletes for different sports. For a summary of


the final number of participants for each sport and gender, see supplementary material Table

S2. Note that for single multilevel polynomial regressions and subsequent response surface

analyses, sample sizes differ slightly based on the exclusion of participants with missing items

for the scales of the outcomes (see Table 1).

Measures

Sample items, descriptive statistics, and internal consistency for all relevant variables

can be found in Table 1.

Typical Big Five Traits

The typical Big Five traits of male and female athletes of the different sports were

assessed in Sample 1 using an adapted version of the 30-item BFI-2-S (German version:

Danner et al., 2016; Soto & John, 2017). The instruction of the BFI-2-S was adapted to fit the

assessment of either a typical male or a typical female athlete of the participants’ main sport

(e.g., “To characterize a typical male athlete, imagine what kind of men you meet when going

to training or competitions and what you would state to be typical for, e.g., a male soccer

player.”). The questionnaire comprised six items per Big Five trait that were rated on a 5-

point Likert-type scale (1 = disagree strongly, 5 = agree strongly). Responses were averaged

for each trait to form an unweighted mean score where higher values reflected higher levels of

Big Five traits. The scores were aggregated within sports for the analyses. For the typical

male and female athletes, the internal consistency of the scales ranged from a Cronbach’s

value of .66 to .80 and was thus satisfactory considering the use of a short-form of the

questionnaire. Descriptive statistics for the typical male and female Big Five trait levels

divided by sports can be found in the supplementary material Table S4. For more details see

the preregistration of the data collection of Sample 1: https://osf.io/t5vwx

Individual Big Five Traits

The individual Big Five traits of athletes of the different sports were assessed in

Sample 2 using the 30-item BFI-2-S (German version: Danner et al., 2016; Soto & John,


2017). The items were rated on a 5-point Likert-type scale (1 = disagree strongly, 5 = agree

strongly). Responses were averaged for each trait to form an unweighted mean score where

higher values reflected higher levels of Big Five traits. The internal consistency of the scales

ranged from Cronbach’s alpha values of .70 to .82 and was thus good. Descriptive statistics

for the Big Five traits, broken down by sports, can be found in the supplementary material

Table S5.

Sports

In both samples, participants indicated which sport they were mainly practicing. They

chose the sport from a list of 167 sports which was created by combing online sources and

doing a scientific literature search. The sports were then manually recoded by two

independent raters, specifically the first-author and a trained research assistant, for each

sample based on further information given in an open-format question. The interrater

agreement was excellent in both Sample 1 (Cohen’s kappa = .99) and Sample 2 (Cohen’s

kappa = .89). In cases when the two raters did not agree, the rating of the first author was

favored. For a detailed overview of the manually coding process see Terwiel et al., 2020.

Life Satisfaction

Life satisfaction was measured in Sample 2 with the 5-item Satisfaction with Life

Scale (SWLS; Diener et al., 1985; German version: Glaesmer et al., 2011). Participants

indicated how satisfied they are with their lives on a 7-point Likert-type scale (1 = strongly

disagree, 7 = strongly agree). Responses were summed to form a summary score where higher

values reflected higher levels of life satisfaction.

Sports-life Satisfaction

The satisfaction with one’s sports life was measured with an adapted version (Baudin

et al., 2011) of the 5-item Satisfaction with Life Scale (SWLS; Diener et al., 1985; German

version: Glaesmer et al., 2011). The items of the SWLS were adapted such that they were

referring to a participant’s life domain of sport (instruction: “Indicate how satisfied you are


with your sport life”). The participants indicated their satisfaction with their sports life on a 7-

point Likert-type scale (1 = strongly disagree, 7 = strongly agree). Responses were summed to

form a summary score where higher values reflected higher levels of sports-life satisfaction.

Positive and Negative Affect

The affective component of subjective well-being was measured with the 12-item

Scale of Positive and Negative Experiences (SPANE; Diener & Emmons, 1984; German

version: Rahm et al., 2017). The scale assesses positive and negative experiences during the

past four weeks on a 5-point Likert-type scale (1 = very rarely or never, 5 = very often or

always). The correlation between the subscales of positive and negative affect was r = -.59

and thus below our predefined criterion of .70 (for details, see preregistration). We therefore

performed the statistical analyses separately for positive and negative affect. Responses for

positive and negative affect were summed separately to form summary scores where higher

values reflected higher levels of positive or negative affect, respectively.


Table 1: Mean values, standard deviations, sample size, internal consistency (α and ωh) and

sample items for all scales

Variables M SD N Α ωh Sample item

Typical Big

Five Traits

- female

O 3.40 0.58 2,090 .73 .53 Is complex, a deep thinker.

C 3.55 0.57 2,090 .71 .57 Tends to be disorganized.

E 3.57 0.57 2,090 .67 .51 Is full of energy.

A 3.63 0.65 2,090 .78 .64 Assumes the best about people.

N 2.56 0.71 2,090 .80 .70 Worries a lot.

Typical Big

Five Traits

- male





N 2.43 0.66 2,837 .75 .55 Worries a lot.

Individual

Big Five

Traits





N 2.64 0.90 4,340 .82 .67 Worries a lot.

Subjective

well-being

LS 23.25 6.65 4,281 .87 - I am satisfied with my life.

SLS 21.80 6.45 4,210 .85 - I am satisfied with my sport life.

PA 21.80 4.16 4,126 .89 - Positive

NA 14.33 4.13 4,127 .80 - Negative

Note: O = Openness to experience, C = Conscientiousness, E = Extraversion, A =

Agreeableness, N = Neuroticism, LS = Life satisfaction, SLS = Sports-life satisfaction, PA =

Positive affect, NA = Negative affect. Sample items were taken from the BFI-2S.

Analyses

All statistical analyses were performed in R Version 4.0.3 (R Core Team, 2021) using

the R packages lme4 (Bates et al., 2015), lmerTest (Kuznetsova et al., 2017), and RSA

(Schönbrodt & Humberg, 2021).We preregistered the analyses (see

https://osf.io/27hkr/?view_only=52eb3acc93ab404a8c5b1b54139e5630). All data, analysis

scripts, and further materials can be found online (see

https://osf.io/arxhq/?view_only=fd2686e24b8647d494a96e684bb73625).

https://osf.io/27hkr/?view_only=52eb3acc93ab404a8c5b1b54139e5630

https://osf.io/arxhq/?view_only=fd2686e24b8647d494a96e684bb73625


Centering

As predictors, the individual and typical Big Five trait levels (openness to experience,

conscientiousness, extraversion, agreeableness, and neuroticism) were centered on the grand

mean of both predictors. Specifically, the mean value was calculated across both individual

and the typical trait. We did so due to current recommendations to ensure that a value of zero

is common to both predictors, specifically typical and individual Big Five trait levels.

Multilevel Polynomial Regression and Subsequent Response Surface Analyses

To test the supplementary fit hypotheses, we performed multilevel polynomial

regression analyses with subsequent response surface analyses (see Humberg et al., 2019).

Considering the nested structure of the data (athletes nested within sports), the data were

analyzed using random-intercept multilevel polynomial regression analyses (Nestler et al.,

2019). In each model, we included the typical trait, individual trait, the interaction between

typical trait and individual trait, squared typical trait, and squared individual trait as

predictors. Additionally, we included gender (0 = male, 1 = female) as a covariate as

individual and typical personality trait levels were matched with respect to the athletes’

gender (i.e., female typical – individual values of female athlete). Multilevel polynomial

regressions and subsequent response surface analyses were conducted separately for each of

the Big Five traits and each outcome, resulting in 5 (Big Five trait: openness to experience,

conscientiousness, extraversion, agreeableness, and neuroticism) × 4 (outcomes: sports-life

satisfaction, life satisfaction, positive affect, and negative affect) = 20 different response

surface analyses.

Fit effects were investigated by testing whether the response surface parameters

followed a pre-defined pattern indicating a fit effect (Humberg et al., 2019). Response surface

parameters are calculated based on the regression coefficients and reflect characteristics of the

graphically depicted response surfaces. More precisely, they reflect the shape of the surface

above two lines, the line of congruence (LOC, x = y) and the line of incongruence (LIOC, x =


-y). We hypothesized a congruence effect between both predictor variables (typical and

individual personality trait) in the presence of main effects for both predictors. This would be

reflected by patterns indicating a broad congruence effect. Following the definition by

Humberg et al. (2019), a broad congruence effect is present when following conditions are

met: (1) there are significant main effects of both predictor variables, (2) the surface above the

LOIC is U-shaped with a maximum above the line of LOC, and (3) the first principal axis of

the fixed effects must not differ from the LOC (for more details see Humberg et al., 2019).

We report the marginal as well as the conditional R2 for each model following the

recommendation by Nakagawa and Schielzeth (2013). The proportion of variance that is

explained by fixed effects only is indicated by the marginal R2, whereas the proportion of

variance that is explained by both fixed and random effects is indicated by the conditional R2.


Results

Description of Typical Big Five Trait Levels

We descriptively investigated the patterns of typical Big Five trait levels separately for

all sampled sports and compared them to the typical trait levels across all sports. The sport-

specific typical trait levels often resembled the patterns of the typical Big Five trait levels

across all sports (e.g., Ultimate). However, for some sports, single traits substantially deviated

from this overall pattern (e.g., openness to experience, neuroticism, agreeableness, and

conscientiousness for female athletes performing Ballet; see Figure 1 and supplementary

material Table S4 and Figure S1). Moreover, for some sports, the patterns of typical male and

female athletes resembled each other (e.g., Soccer or Ultimate), whereas for others, the male

and female patterns were rather distinct (e.g., Ballet and Kung Fu).


Figure 1: Graphical presentation of the typical Big Five trait levels (O = Openness to

experience, C = Conscientiousness, E = Extraversion, A = Agreeableness, N = Neuroticism)

for male and female athletes of exemplary sports. The black lines reflect male and the grey

lines female trait levels. The sport-specific trait levels are depicted in solid lines, whereas the

trait levels across all sports are depicted in dotted lines. The error bars reflect the standard

errors.

The correlations between typical and individual values for the same trait across sports

(e.g., the correlation between typical extraversion and individual extraversion) were all

significant with coefficients close to .1. We found high correlations between typical

agreeableness and typical neuroticism (r = -.65) and between typical agreeableness and

typical openness to experience (r = .58; for all intercorrelations, see supplementary material

Table S3). Moreover, we investigated the overlap between the distributions of individual and


typical Big Five traits (aggregated within sports) for male and female athletes (see

supplementary material Table S5 for the individual values). Individual scores were indicated

as overlapping when they fell into the observed range (minimum and maximum) of the

gender- and sport-specific typical traits. If a high percentage of individual scores overlaps

with the typical values, this implies that most athletes can find a sports environment that fits

their individual personality trait levels. Exemplary graphs for female and male distributions of

extraversion are depicted in Figure 2 (for all other traits, see supplementary material Figure

S2). For extraversion, 52.0% of female athletes and 65.7 % of male athletes had an individual

score lying within the range of typical values, which means that they could find a sports type

that would match their level of extraversion. The corresponding percentages were: for

conscientiousness, 50.3% of female and 74.8% of male athletes; for agreeableness, 60.2% of

female and 83.63% of male athletes; for openness to experience, 58.9% of female and 74.0%

of male athletes; and for neuroticism, 58.9% of female and 68.6% of male athletes.

Figure 2: Graphical presentation of the density distributions and their overlap for the typical

and individual Big Five trait of extraversion. Individual and typical trait level were assessed

on a 5-point Likert-type scale (1 = Disagree strongly, 5 = Agree strongly). The distribution of

individual scores is depicted in grey, the distribution of typical scores is depicted in black, and

the overlapping area is depicted in darker grey.


Supplementary Fit

Our main research aim was to test to what extent supplementary fit between typical

and individual Big Five trait levels was associated with different indicators of subjective well-

being (see Hypotheses 1a – 1d and 2a – 2d).

Multilevel Polynomial Regression and Subsequent Response Surface Analyses

We only estimated random intercept models as most of the random slope models did

not converge. Generally, the models for most outcomes and most combinations of Big Five

traits accounted only for a small amount of the total variance (R2conditional: 0.03 – 0.10). For

single combinations of traits and outcomes (e.g., neuroticism and negative affect), the models

accounted for a larger amount of 14 to 40% of the total variance (for detailed results for all

models, see supplementary material Tables S8.1 to S8.4). The differences between marginal

and conditional R2 were rather small in all models, indicating that most variance was

explained by fixed effects rather than by random effects due to different types of sports.

Figure 3. Summarized results of multilevel polynomial regression analyses for all four

outcomes (LS = Life satisfaction, SLS = Sports-life satisfaction, PA = Positive affect, NA =

Negative affect) and combinations of Big Five traits (O = Openness to experience, C =

Conscientiousness, E = Extraversion, A = Agreeableness, N = Neuroticism). Each column

represents a polynomial regression model for a certain combination of Big Five trait and

outcome and each square represents a regression coefficient within this model. Negative

regression coefficients are depicted in blue, positive in red. Statistically significant effects (ps

< .05) are indicated by an asterisk in filled cells, non-significant effects by dashed cells. The

typical and individual trait levels were centered on the grand-mean of both predictors. The

quadratic and interaction terms were calculated based on the centered variables.


In line with previous research, higher individual levels of openness to experience,

conscientiousness, extraversion, and agreeableness were mostly associated with higher levels

of subjective well-being, and higher individual levels of neuroticism were associated with

lower levels of subjective well-being (see Figure 3 and supplementary material Tables S8.1 to

S8.4). In addition, we found several significant main effects of typical Big Five traits for the

indicators of subjective well-being. The effects pointed in such a way that in sports in which

the typical athlete is rated to be higher on a specific trait (e.g., extraversion), individual

athletes generally had lower levels of subjective well-being. Specifically, typical openness to

experience was negatively related to life satisfaction (b = -2.41, p = .050), typical extraversion

was negatively related to positive affect (b = -1.60, p = .024), and typical conscientiousness

was positively related to negative affect (b = 2.85, p = .043). Main effects of gender and

interaction effects with gender showed inconsistent patterns (see Figure 3).

In Hypotheses 1a to 1d, we hypothesized that congruence between typical athletes’

Big Five trait levels and individual athletes’ Big Five trait levels is associated with higher

levels of life satisfaction, sports-life satisfaction, and positive affect, and with lower levels of

negative affect. In addition, we hypothesized in Hypotheses 2a to 2d that these congruence

effects would be complemented by main effects of typical and individual Big Five traits. Such

a combination of a congruence and main effects for both predictors would be reflected by a

broad congruence effect in the response surface analyses (Humberg et al., 2019). To facilitate

the interpretation of the results of the inference test of the multilevel polynomial regression

analyses, all models were plotted (see Figure 4, for all other graphs see supplementary

material Figure S2). In each figure, the x-axis represents the typical and the y-axis the

individual Big Five trait levels. The corresponding predicted value for the outcome on the z-

axis for the given combination of predictor x and y is represented by the height of the surface.

The color of the surfaces also indicates the predicted levels of the outcome z (green = higher

levels, orange-red = lower levels). The LOC and LOIC are represented by the blue lines, and


the black bag represent a two-dimensional boxplot to indicate the range of observed values.

The response surface parameters are depicted above the corresponding graph. We did not find

a broad congruence effect in any of our 20 models (5 Big Five traits × 4 outcomes: sports-life

satisfaction, life satisfaction, positive affect, and negative affect; for examples, see Figure 4;

for all response surface parameters, see supplementary material Tables S9.1 to S9.4).


Figure 4: Response surface plots depicting the association between typical and individual Big

Five trait levels and Life Satisfaction. The parameters for the response surface a1, a2, a3, a4 and

a5 are depicted above the graph. The values of typical athletes’ trait levels are depicted on the

x-axis, the values of individual athletes’ trait levels on the y-axis, and the individual athletes’

life satisfaction on the z-axis. The variables on the x- and y-axis acted as the predictors of the

variable on the z-axis. The line of congruence (LOC) and the line of incongruence are both

depicted in blue.



Discussion

In the present study, we descriptively investigated the patterns of typical male and

female Big Five trait levels of athletes of different sports and examined to what extent sport-

specific variable-oriented supplementary fit of typical athletes’ and individual athletes’ Big

Five trait levels was associated with higher levels of subjective well-being.

The descriptions of typical Big Five traits revealed that sport-specific typical and the

typical Big Five trait patterns across all sports, with some exceptions (e.g., Ballet), often

resembled each other. Thus, there were only small differences in Big Five trait patterns

between different sports. However, there were differences between sport-specific typical and

the overall typical trait patterns for some sports. Some of these different patterns can be

explained with small sample sizes for single sports (e.g., Kung Fu). However, for other sports,

those patterns most likely reflect the perception of sport-specific and gender-specific

differences in personality traits (e.g., Horseback riding – higher levels of neuroticism and

agreeableness for female athletes). Further, the patterns for male and female athletes with

some exceptions (e.g., Ballet) often resembled each other. Overall, differences between

typical male and typical female athletes were only small. Typical female athletes were

described to be slightly higher in openness to experience (d = 0.15), conscientiousness (d =

0.26), agreeableness (d = 0.28), and neuroticism (d = 0.19), as well as lower in extraversion (d

= -0.14) than male athletes (see Table 1). The described typical Big Five trait levels for

athletes of different sports and gender, could be a first step towards norm values. Such norm

values could, for example, be used in future research on stereotypical perception of athletes’

personality traits, in investigations of socialization or selection effects, the investigation of

other types of person-environment fit, or personnel selection in the context of sports.

We found no broad congruence effects in the response surface analyses for any of the

indicators of subjective well-being (life satisfaction, sports-life satisfaction, positive affect,

negative affect). In conclusion, we did not find any evidence that the variable-oriented


supplementary fit between typical and individual sport-specific Big Five traits was associated

with higher levels of indicators of subjective well-being.

Nonetheless, we found main effects for typical athletes’ openness to experience,

extraversion, and conscientiousness on indicators of subjective well-being. For example, we

found that typical extraversion was negatively related to life satisfaction. A possible

explanation could be that a context in which the surrounding people are described to be high

in extraversion reflects one that is high in potential conflict. This in turn might negatively

influence the athlete’s individual subjective well-being. In addition, typical conscientiousness

was negatively related to positive affect of individual athletes. A context in which athletes are

typically perceived as highly conscientious might reflect a context that values productiveness

and responsibility which in turn be associated with lower levels of subjective well-being.

Robustness and Conceptualization of Person-Environment fit

Our results imply that variable-oriented supplementary fit between typical and

individual Big Five trait levels is not associated with subjective well-being in the broad

context of the sports type. This might be due to our broad conceptualization of the context of

sports type, the types of sports that we included, the nature of supplementary fit itself, or the

robustness of the operationalization and measurement of person-environment fit.

First, supplementary fit might not be relevant in the context of the broadly-defined

context of sports type, but instead it might be only relevant that athletes have a good

supplementary fit to the specific sports team or sports group (e.g., spinning class) they are

interacting with (Jackson et al., 2011; Zepp & Kleinert, 2015). Athletes might have only few

interactions with athletes of the same sport outside of their own groups and teams.

Additionally, as mentioned before, the need to belong could be a potential mechanism linking

supplementary fit to subjective well-being. In a context in which athletes are surrounded by

like-minded people, the need to belong might be much easier to be satisfied. However,

interactions with other athletes of the performed sport might often happen in the context of


competitions in which the need to belong might be even more pronounced for the members of

the own team and not the opponents’ team or the general sports type.

Second, supplementary fit might only be relevant in sports contexts that are high in

sociality (e.g., team sport, but compare Terwiel et al., 2020). In such a context, the need to

belong might be more relevant to athletes than in rather loosely connected sports groups

because they interact more and show higher levels of identification with the group. Indeed,

Zepp and Kleinert (2015) found that identification with a sports team is a relevant factor

linking supplementary fit to subjective well-being.

Third, instead of supplementary fit, complementary fit – a person complements their

environment by adding something to it – might be important. Thus, it might be more relevant

to have a more diverse context when performing sports in terms of levels of Big Five traits. A

sports context in which each athlete is high in extraversion might indeed be associated with

more conflicts. In fact, Kristof-Brown, Barrick, and Stevens (2005) found that complementary

person-team fit for extraversion was associated with higher team attraction. Additionally,

variation for some personality traits (e.g., extraversion) has been linked to more successful

group performance in a non-sports context (Kramer et al., 2014).

Fourth, it should be considered that the measurement and operationalization of person-

environment fit is not as robust as often assumed. As mentioned before, person-environment

fit has been operationalized differently in different literatures (Rauthmann, 2021). In our

study, we focused on variable-oriented fit for outcomes, in our case indicators of subjective

well-being. Thus, variable-oriented supplementary fit of typical and individual Big Five trait

levels might just not be associated with subjective well-being—but other types of fit might be.

Additionally, previous research investigating fit effects was often operationalized using

suboptimal methods, such as difference scores. As discussed above, these methods come with

important limitations (Edwards, 1994; Humberg et al., 2019). In this study, we therefore

applied a stricter conceptualization of person-environment fit using response surface analyses


(Humberg et al., 2019). This might more likely yield null results than traditional approaches.

Further, even though meta-analytical investigations of person-environment fit (e.g., in the

work context) using multilevel polynomial regression analyses with subsequent response

surface analyses imply that fit effects can be identified using this methodological approach,

these meta-analyses often failed to test for publication bias (e.g., Kristof-Brown, Zimmerman,

& Johnson, 2005). Concluding, it cannot be ruled out that the published literature

overestimates true fit effects identified using this method.

Limitations

The overlap between individual and typical Big Five trait levels (see Figure 2)

indicated that between 50.3% and 74.0% of individual scores fell into the range of the sport-

specific aggregated typical values. Thus, finding a fitting sports environment would be

possible for most athletes in our sample. However, the range of typical values was still limited

compared to the range of the individual values due to the aggregation of the typical values.

Further, we matched the individual athletes’ trait levels to the typical trait levels of

their corresponding gender (i.e., individual male – typical male). However, this might be

problematic for sports in which teams and sports groups are performing in mixed groups or

teams (e.g., Running or Ultimate). In those sports, the surrounding people might not be

reflected by typical male or female athletes. However, differences between typical male and

female trait levels were often negligible, especially for sports in which teams are often mixed

(e.g., Ultimate). Finally, although we used 96 different types of sports that included the most

popular sports in the Western world, the number of sports was still limited considering the

steadily increasing number of sports (Lipoński et al., 2003).

Conclusion

In summary, we could not confirm that variable-oriented supplementary fit between

typical and individual Big Five trait levels was associated with subjective well-being in the

broad context of the sports type. We discussed those null results considering study-specific


and general operationalization and measurement of person-environment fit. Future research

should investigate moderating effects (e.g., Sociality) on the supplementary and

complementary fit in different sports contexts (e.g., sports type or sports team), while taking

into account the problem of robustness, conceptualization of variables, and method choice.

Even though we were not able to support trait-level fit hypotheses for sports-related well-

being with our methodology, we encourage other researchers to build on this work and further

expand it.


References

Aidman, E., & Schofield, G. (2004). Personality and individual differences in sport. In T.

Morris & J. Summers (Eds.), Sport psychology: Theory, applications and issues (2nd

ed., pp. 22–47). John Wiley & Sons Australia.

Allen, M. S., Greenlees, I., & Jones, M. (2011). An investigation of the five-factor model of

personality and coping behaviour in sport. Journal of Sports Sciences, 29(8), 841–850.

https://doi.org/10.1080/02640414.2011.565064

Allen, M. S., & Laborde, S. (2014). The role of personality in sport and physical activity.

Current Directions in Psychological Science, 23(6), 460–465.

https://doi.org/10.1177/0963721414550705

Allen, M. S., Magee, C. A., Vella, S. A., & Laborde, S. (2017). Bidirectional associations

between personality and physical activity in adulthood. Health Psychology, 36(4),

332–336. https://doi.org/10.1037/hea0000371

Anglim, J., Horwood, S., Smillie, L. D., Marrero, R. J., & Wood, J. K. (2020). Predicting

psychological and subjective well-being from personality: A meta-analysis.

Psychological Bulletin, 146(4), 279–323. https://doi.org/10.1037/bul0000226

Bakker, F. C. (1991). Development of personality in dancers: A longitudinal study.

Personality and Individual Differences, 12(7), 671–681. https://doi.org/10.1016/0191-

8869(91)90222-W

Barranti, M., Carlson, E. N., & Côté, S. (2017). How to Test Questions About Similarity in

Personality and Social Psychology Research. Social Psychological and Personality

Science, 8(4), 465–475. https://doi.org/10.1177/1948550617698204

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects

Models Using lme4. Journal of Statistical Software, 67(1), 1–48.

https://doi.org/10.18637/jss.v067.i01


Baudin, N., Aluja, A., Rolland, J.‑P., & Blanch, A. (2011). The role of personality in

satisfaction with life and sport. Psicologia Conductual, 19(2), 333.

Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal

attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497–

529.

Bleidorn, W., Schönbrodt, F., Gebauer, J. E., Rentfrow, P. J., Potter, J., & Gosling, S. D.

(2016). To Live Among Like-Minded Others: Exploring the Links Between Person-

City Personality Fit and Self-Esteem. Psychological Science, 27(3), 419–427.

https://doi.org/10.1177/0956797615627133

Bojanić, Ž., Nedeljković, J., Šakan, D., Mitić, P. M., Milovanović, I., & Drid, P. (2019).

Personality Traits and Self-Esteem in Combat and Team Sports. Frontiers in

Psychology, 10, 2280. https://doi.org/10.3389/fpsyg.2019.02280

Buecker, S., Simacek, T., Ingwersen, B., Terwiel, S., & Simonsmeier, B. A. (2020). Physical

activity and subjective well-being in healthy individuals: A meta-analytic review.

Health Psychology Review, 1–19. https://doi.org/10.1080/17437199.2020.1760728

Cameron, J. E., Cameron, J. M., Dithurbide, L., & Lalonde, R. N. (2012). Personality traits

and stereotypes associated with ice hockey positions. Journal of Sport Behavior,

35(2), 109–124.

Castanier, C., Le Scanff, C., & Woodman, T. (2010). Who takes risks in high-risk sports? A

typological personality approach. Research Quarterly for Exercise and Sport, 81(4),

478–484.

Chekroud, S. R., Gueorguieva, R., Zheutlin, A. B., Paulus, M., Krumholz, H. M., Krystal, J.

H., & Chekroud, A. M. (2018). Association between physical exercise and mental

health in 1·2 million individuals in the USA between 2011 and 2015: a cross-sectional

study. The Lancet, 5(9), 739–746. https://doi.org/10.1016/S2215-0366(18)30227-X


Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and

standardized assessment instruments in psychology. Psychological Assessment, 6(4),

284.

Curtis, G. L., Chughtai, M., Khlopas, A., Newman, J. M., Khan, R., Shaffiy, S., Nadhim, A.,

Bhave, A., & Mont, M. A. (2017). Impact of physical activity in cardiovascular and

musculoskeletal health: Can motion be medicine? Journal of Clinical Medicine

Research, 9(5), 375–381. https://doi.org/10.14740/jocmr3001w

Denissen, J. J. A., Bleidorn, W., Hennecke, M., Luhmann, M., Orth, U., Specht, J., &

Zimmermann, J. (2018). Uncovering the power of personality to shape income.

Psychological Science, 29(1), 3–13. https://doi.org/10.1177/0956797617724435

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542.

Diener, E. (2012). New findings and future directions for subjective well-being research.

American Psychologist, 67(8), 590–597. https://doi.org/10.1037/a0029541

Diener, E., & Emmons, R. A (1984). The independence of positive and negative affect.

Journal of Personality and Social Psychology, 47(5), 1105–1117.

https://doi.org/10.1037/0022-3514.47.5.1105

Diener, E., Emmons, R. A, Larsen, R. J., & Griffin, S. (1985). The Satisfaction With Life

Scale. Journal of Personality Assessment, 49(1), 71–75.

https://doi.org/10.1207/s15327752jpa4901_13

Downward, P., & Rasciute, S. (2011). Does sport make you happy? An analysis of the well‐

being derived from sports participation. International Review of Applied Economics,

25(3), 331–348.

Du, H., Chen, A., Chi, P., & King, R. B. (2019). Person-culture fit boosts national pride: A

cross-cultural study among 78 societies. Journal of Research in Personality, 81, 108–

117. https://doi.org/10.1016/j.jrp.2019.05.008


Edwards, J. R. (1994). The Study of Congruence in Organizational Behavior Research:

Critique and a Proposed Alternative. Organizational Behavior and Human Decision

Processes, 58(1), 51–100. https://doi.org/10.1006/obhd.1994.1029

Evans, M. B., Eys, M. A., & Bruner, M. W. (2012). Seeing the “we” in “me” sports: The need

to consider individual sport team environments. Canadian Psychology, 53(4), 301–

308. https://doi.org/10.1037/a0030202

Furr, R. M. (2008). A framework for profile similarity: Integrating similarity, normativeness,

and distinctiveness. Journal of Personality, 76(5), 1267–1316.

https://doi.org/10.1111/j.1467-6494.2008.00521.x

Gilbreath, B., Kim, T.‑Y., & Nichols, B. (2011). Person-environment fit and its effects on

university students: A response surface methodology study. Research in Higher

Education, 52(1), 47–62. https://doi.org/10.1007/s11162-010-9182-3

Glaesmer, H., Grande, G., Braehler, E., & Roth, M. (2011). The German version of the

Satisfaction With Life Scale (SWLS). European Journal of Psychological Assessment.

27(2), 127-132.

Greguras, G. J., & Diefendorff, J. M. (2009). Different fits satisfy different needs: Linking

person-environment fit to employee commitment and performance using self-

determination theory. Journal of Applied Psychology, 94(2), 465–477.

https://doi.org/10.1037/a0014068

Gummer, T., Roßmann, J., & Silber, H. (2021). Using Instructed Response Items as Attention

Checks in Web Surveys: Properties and Implementation. Sociological Methods &

Research, 50(1), 238–264. https://doi.org/10.1177/0049124118769083

Hardin, E. E., & Donaldson, J. R. (2014). Predicting job satisfaction: A new perspective on

person-environment fit. Journal of Counseling Psychology, 61(4), 634–640.

https://doi.org/10.1037/cou0000039


Humberg, S., Nestler, S., & Back, M. D. (2019). Response Surface Analysis in Personality

and Social Psychology: Checklist and Clarifications for the Case of Congruence

Hypotheses. Social Psychological and Personality Science, 10(3), 409–419.

https://doi.org/10.1177/1948550618757600

Jackson, B., Dimmock, J. A., Gucciardi, D. F., & Grove, J. R. (2011). Personality traits and

relationship perceptions in coach–athlete dyads: Do opposites really attract?

Psychology of Sport and Exercise, 12(3), 222–230.

https://doi.org/10.1016/j.psychsport.2010.11.005

John, O. P. (2021). History, measurement, and conceptual elaboration of the Big Five trait

taxonomy: The paradigm matures. In O. P. John & R. W. Robins (Eds.), Handbook of

personality: Theory and research (pp. 35–82). The Guilford Press.

Kirkcaldy, B. D. (1982). Personality profiles at various levels of athletic participation.

Personality and Individual Differences, 3(3), 321–326.

Kramer, A., Bhave, D. P., & Johnson, T. D. (2014). Personality and group performance: The

importance of personality composition and work tasks. Personality and Individual

Differences, 58, 132–137. https://doi.org/10.1016/j.paid.2013.10.019

Kristof, A. L. (1996). Person‐organization fit: An integrative review of its conceptualizations,

measurement, and implications. Personnel Psychology, 49(1), 1–49.

https://doi.org/10.1111/j.1744-6570.1996.tb01790.x

Kristof-Brown, A., Barrick, M. R., & Stevens, C. K. (2005). When opposites attract: A multi-

sample demonstration of complementary person-team fit on extraversion. Journal of

Personality, 73(4), 935–957. https://doi.org/10.1111/j.1467-6494.2005.00334.x

Kristof-Brown, A., & Guay, R. P. (2011). Person–environment fit. In S. Zedeck (Ed.),

American Psychological Association handbook of industrial and organizational

psychology (3rd ed., pp. 3–50). American Psychological Assoc.

https://doi.org/10.1037/12171-001


Kristof-Brown, A., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of Individuals

fit at work: A meta‐analysis OF person–job, person–organization, person–group, and

person–supervisor fit. Personnel Psychology, 58(2), 281–342.

Kritzler, S., & Luhmann, M. (2021). Be Yourself and Behave Appropriately: Exploring

Associations Between Incongruent Personality States and Positive Affect, Tiredness,

and Cognitive Performance. https://doi.org/10.31234/osf.io/9utyj

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: Tests in

Linear Mixed Effects Models. Journal of Statistical Software, 82(13), 1–26.

https://doi.org/10.18637/jss.v082.i13

Lee, I.‑M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., & Katzmarzyk, P. T. (2012).

Effect of physical inactivity on major non-communicable diseases worldwide: an

analysis of burden of disease and life expectancy. The Lancet, 380(9838), 219–229.

https://doi.org/10.1016/S0140-6736(12)61031-9

Lipoński, W., Farmer, M., & Hagarty, K. (2003). World sports encyclopedia. Oficyna

Wydawnicza Atena.

Luan, Z., & Bleidorn, W. (2020). Self-other personality agreement and internalizing problems

in adolescence. Journal of Personality, 88(3), 568–583.

https://doi.org/10.1111/jopy.12511

Lucas, R. E., & Diener, E. (2009). Personality and subjective well-being. In E. Diener (Ed.),

The Science of Well-Being. Social Indicators Research Series (pp. 75–102). Springer.

Muchinsky, P. M., & Monahan, C. J. (1987). What is person-environment congruence?

Supplementary versus complementary models of fit. Journal of Vocational Behavior,

31(3), 268–277. https://doi.org/10.1016/0001-8791(87)90043-1

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from

generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2),

133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x


Nestler, S., Humberg, S., & Schönbrodt, F. D. (2019). Response surface analysis with

multilevel data: Illustration for the case of congruence hypotheses. Psychological

Methods, 24(3), 291–308. https://doi.org/10.1037/met0000199

Netz, Y., Wu, M.‑J., Becker, B. J., & Tenenbaum, G. (2005). Physical activity and

psychological well-being in advanced age: A meta-analysis of intervention studies.

Psychology and Aging, 20(2), 272–284. https://doi.org/10.1037/0882-7974.20.2.272

Newcombe, P. A., & Boyle, G. J. (1995). High school students' sport personalities: Variations

across participation level, gender, type of sport, and success. International Journal of

Sport Psychology, 26, 277–294.

Nia, M. E., & Besharat, M. A. (2010). Comparison of athletes’ personality characteristics in

individual and team sports. Procedia - Social and Behavioral Sciences, 5, 808–812.

https://doi.org/10.1016/j.sbspro.2010.07.189

Nicholls, A. R., Polman, R. C., Levy, A. R., & Backhouse, S. H. (2009). Mental toughness in

sport: Achievement level, gender, age, experience, and sport type differences.

Personality and Individual Differences, 47(1), 73–75.

https://doi.org/10.1016/j.paid.2009.02.006

O'Sullivan, D. M., Zuckerman, M., & Kraft, M. (1998). Personality characteristics of male

and female participants in team sports. Personality and Individual Differences, 25(1),

119–128.

Qualtrics. (2020) [Computer software]. Provo, Utah, USA. https://www.qualtrics.com

R Core Team (2021). R: A language and environment for statistical computing.

Rahm, T., Heise, E., & Schuldt, M. (2017). Measuring the frequency of emotions—validation

of the Scale of Positive and Negative Experience (SPANE) in Germany. PLoS ONE,

12(2), e0171288.


Rauthmann, J. F. (2021). Capturing Interactions, Correlations, Fits, and Transactions: A

Person-Environment Relations Model. In J. F. Rauthmann (Ed.), The Handbook of

Personality Dynamics and Processes. Elsevier.

Rhea, D. J., & Martin, S. (2010). Personality trait differences of traditional sport athletes,

bullriders, and other alternative sport athletes. International Journal of Sports Science

& Coaching, 5(1), 75–85.

Rhodes, R. E., & Smith, N. E. I. (2006). Personality correlates of physical activity: A review

and meta-analysis. British Journal of Sports Medicine, 40(12), 958–965.

https://doi.org/10.1136/bjsm.2006.028860

Roberts, B. W., & Mroczek, D. (2008). Personality Trait Change in Adulthood. Current

Directions in Psychological Science, 17(1), 31–35. https://doi.org/10.1111/j.1467-

8721.2008.00543.x

Schneider, B. (2001). Fits About Fit. Applied Psychology, 50(1), 141–152.

https://doi.org/10.1111/1464-0597.00051

Schönbrodt, F. D., & Humberg, S. (2021). RSA: An R package for response surface analysis

(version 0.10.4). https://cran.r-project.org/package=RSA

Schönbrodt, F. D., Humberg, S., & Nestler, S. (2018). Testing Similarity Effects with Dyadic

Response Surface Analysis. European Journal of Personality, 32(6), 627–641.

https://doi.org/10.1002/per.2169

Schuch, F. B., Vancampfort, D., Richards, J., Rosenbaum, S., Ward, P. B., & Stubbs, B.

(2016). Exercise as a treatment for depression: A meta-analysis adjusting for

publication bias. Journal of Psychiatric Research, 77, 42–51.

https://doi.org/10.1016/j.jpsychires.2016.02.023

Schurr, K. T., Nisbet, J., & Wallace, D. (1984). Myers-Briggs Type Inventory characteristics

of more and less successful players on an American football team. Journal of Sport

Behavior, 7(2), 47.


Shanock, L. R., Baran, B. E., Gentry, W. A., Pattison, S. C., & Heggestad, E. D. (2010).

Polynomial Regression with Response Surface Analysis: A Powerful Approach for

Examining Moderation and Overcoming Limitations of Difference Scores. Journal of

Business and Psychology, 25(4), 543–554. https://doi.org/10.1007/s10869-010-9183-4

Sheard, M., & Golby, J. (2010). Personality hardiness differentiates elite‐level sport

performers. International Journal of Sport and Exercise Psychology, 8(2), 160–169.

https://doi.org/10.1080/1612197X.2010.9671940

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability.

Psychological Bulletin, 86(2), 420.

Smith, R. W., & DeNunzio, M. M. (2020). Examining personality—Job characteristic

interactions in explaining work outcomes. Journal of Research in Personality, 84,

103884. https://doi.org/10.1016/j.jrp.2019.103884

Spokane, A. R., Meir, E. I., & Catalano, M. (2000). Person–environment congruence and

Holland's Theory: A review and reconsideration. Journal of Vocational Behavior,

57(2), 137–187. https://doi.org/10.1006/jvbe.2000.1771

Strickhouser, J. E., Zell, E., & Krizan, Z. (2017). Does personality predict health and well-

being? A metasynthesis. Health Psychology, 36(8), 797–810.

https://doi.org/10.1037/hea0000475

Terwiel, S., Rauthmann, J. F., & Luhmann, M. (2020). Using the situational characteristics of

the DIAMONDS taxonomy to distinguish sports to more precisely investigate their

relation with psychologically relevant variables. PLoS ONE, 15(10), e0241013.

https://doi.org/10.1371/journal.pone.0241013

Tok, S. (2011). The Big Five personality traits and risky sport participation. Social Behavior

and Personality, 39(8), 1105–1111. https://doi.org/10.2224/sbp.2011.39.8.1105

Trninić, V., Trninić, M., & Penezić, Z. (2016). Personality differences between the players

regarding the type of sport and age. Acta Kinesiologica, 10(2), 69–74.


Wegner, M., Helmich, I., Machado, S., E Nardi, A., Arias-Carrión, O., & Budde, H. (2014).

Effects of exercise on anxiety and depression disorders: Review of meta-analyses and

neurobiological mechanisms. CNS & Neurological Disorders Drug Targets, 13(6),

1002–1014.

Yang, L.‑Q., Levine, E. L., Smith, M. A., Ispas, D., & Rossi, M. E. (2008). Person–

environment fit or person plus environment: A meta-analysis of studies using

polynomial regression analysis. Human Resource Management Review, 18(4), 311–

321. https://doi.org/10.1016/j.hrmr.2008.07.014

Zepp, C., & Kleinert, J. (2015). Symmetric and complementary fit based on prototypical

attributes of soccer teams. Group Processes & Intergroup Relations, 18(4), 557–572.

https://doi.org/10.1177/1368430214556701

Zhang, Z., & Chen, W. (2019). A systematic review of the relationship between physical

activity and happiness. Journal of Happiness Studies, 20(4), 1305–1322.

https://doi.org/10.1007/s10902-018-9976-0

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