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Do You Feel Better When You Behave More Extraverted Than You Are? The Relationship Between Cumulative Counterdispositional Extraversion and Positive Feelings

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Abstract

The idea that increased levels of extraversion are beneficial to well-being is widespread. Drawing on the idea that behaving discordant to one’s trait level is demanding and effortful to maintain, and that repeated taxations of one’s self-regulatory resources are unpleasant, we examined the relationship between cumulative counterdispositional extraversion and positive feelings. In two experience-sampling (ESM) studies, participants repeatedly rated their level of state extraversion and positive feelings. Results revealed that cumulative positive deviations from one’s trait extraversion level were positively associated with positive feelings, whereas cumulative negative deviations were negatively associated with positive feelings. This confirms the idea that, also when looking at cumulative instances of extraversion-related behaviors, higher levels of extraversion go hand in hand with higher levels of positive feelings.
Do you feel better when you behave more extraverted than you are?
The relationship between cumulative counterdispositional extraversion and positive feelings
E. Kuijpers1, J. Pickett1, B. Wille2, J. Hofmans1
¹Vrije Universiteit Brussel
2Ghent University
Author Note
Evy Kuijpers, Department of Work and Organizational Psychology, Vrije Universiteit
Brussel. Jennifer Pickett, Department of Work and Organizational Psychology, Vrije Universiteit
Brussel. Bart Wille, Department of Personnel Management, Work and Organizational
Psychology, Ghent University, Belgium. Joeri Hofmans, Department of Work and
Organizational Psychology, Vrije Universiteit Brussel.
This work was supported by the Fonds Wetenschappelijk Onderzoek (FWO;
ResearchFoundation - Flanders) research fund [grant number G024618N].
Correspondence concerning this article should be addressed to Evy Kuijpers, Department
of Work and Organizational Psychology, Vrije Universiteit Brussel, Pleilaan 2, 1050 Brussels,
Belgium, email: Evy.Kuijpers@vub.be
This article is accepted for publication in Personality and Social Psychology Bulletin.
https://doi.org/10.1177%2F01461672211015062
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Abstract
The idea that increased levels of extraversion are beneficial to wellbeing is widespread. Drawing
on the idea that behaving discordant to one’s trait level is demanding and effortful to maintain,
and that repeated taxations of one’s self-regulatory resources are unpleasant, we examined the
relationship between cumulative counterdispositional extraversion and positive feelings. In two
experience-sampling studies, participants repeatedly rated their level of state extraversion and
positive feelings. Results revealed that cumulative positive deviations from one’s trait
extraversion level were positively associated to positive feelings, while cumulative negative
deviations were negatively associated to positive feelings. This confirms the idea that, also when
looking at cumulative instances of extraversion-related behaviors, higher levels of extraversion
go hand in hand with higher levels of positive feelings.
Keywords: Counterdispositional behaviors, personality dynamics, extraversion, positive
feelings, positive affect.
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Do you feel better when you behave more extraverted than you are?
The relationship between cumulative counterdispositional extraversion and positive
feelings
Personality appears to be a solid predictor of wellbeing (Anglim et al., 2020;, 1998; Lucas, 2018;
Steel, Schmidt, & Shultz, 2008; Sun, Kaufman, & Smillie, 2017). More specifically, research
shows that, among the Big Five personality dimensions, extraversion is the most relevant
dimension, leading to the widely accepted idea that increased levels of extraversion are
beneficial for wellbeing (Barrick & Mount, 2000; Salgado, 1997; Smillie, Cooper, Wilt, &
Revelle, 2012).
Because of the positive association between extraversion and wellbeing, particularly
when it is operationalized as experiencing positive emotions (Diener, Sandvik, Pavot, & Fujita,
1992; Lucas & Fujita, 2000), one might wonder whether it would be advisable for everyone to
act more often in an extraverted way. Supporting such a more is better” idea, a handful of
studies have demonstrated that people’s wellbeing increases when they show extraverted
behaviors, even when such extraverted behaviors are counter to one’s (introverted) dispositions
(Fleeson, Malanos, & Achille, 2002; Smillie et al., 2015; Wilt, Noftle, Fleeson, & Spain, 2012).
At the same time, however, research on counterdispositional behavior or contra-trait effort shows
that going against one’s natural tendencies is exhausting and therefore might have detrimental
wellbeing-related consequences (e.g., Zelenski, Santoro, & Whelan, 2012; Jacques-Hamilton et
al., 2019). Given these two conflicting perspectives, the question becomes how the positive
effects of behaving in a more extraverted way combines with the potential negative effects of
acting out of character. Answering this question is of major importance because it would not
only contribute to our understanding of the triggers of positive feelings, but it might also inform
us on ways to improve people’s positive affective experiences.
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The goal of the present study is to examine this issue. Importantly, such investigation
necessitates a shift in how we study the extraversion-wellbeing relationship. First, it implies
shifting the focus from an isolated focus on average tendencies or a focus on momentary states to
an integrative approach to personality (Pickett, Hofmans, & De Fruyt, 2020a). In such integrative
approach, one simultaneously takes into account one’s average tendency as well as deviations
from this average tendency. This is crucial because whether one benefits from extraversion-
related behaviors might not only depend on how extraverted one is on average, or the intensity
with which one engages in a variety of extraversion-related behaviors, but also on the interaction
between both. Second, to be able to answer the question whether more extraversion is better, it is
essential to look beyond momentary effects. This is crucial because the short-term positive
effects of behaving in an extraverted way might be overruled or even turn negative in the long
run if such behaviors are truly depleting. Addressing both issues, the goal of the present study is
to examine if repeatedly behaving out of one’s extraversion-related comfort zone (i.e., deviating
from one’s average state extraversion level) is associated to lower levels of positive feelings over
time.
Extraversion and Positive Feelings
There has been much interest in what makes people happy, with several studies
supporting the assumption that people scoring higher on extraversionor the tendency to be
bold, assertive, outgoing, talkative, gregarious, and enthusiastic (McCrae & Costa, 1999)are in
general happier than their more introverted counterparts (Smillie et al., 2015; Fleeson, Malanos,
& Achille, 2002) . There are three types of explanations for the positive relationship between
extraversion and happiness. One line of research focuses on the social nature of extraversion.
This is for example illustrated in the person-by-situation model stating that extraverts enjoy
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social interactions more than introverts do (Oerlemans & Bakker, 2014). However, empirical
research on this explanation has received mixed support (Lucas & Dyrenforth, 2008; Pavot,
Diener, & Fujita, 1990; Oerlemans & Bakker, 2014).
Another line of research focuses on the temperamental nature of extraverts, suggesting
that extraverted people have a higher baseline of positive affect compared to their more
introverted counterparts (Gross, Sutton, & Ketelaar, 1998; Headey & Wearing, 1989; Lykken &
Tellegen, 1996). According to this perspective, happiness levels would thus differ between
individuals because of structural differences within these individuals. When combined with the
previous account, extraverts would win twice; not only are they more likely to engage in
behavior that boosts their level of happiness, they are also naturally more predisposed to
happiness.
A final class of explanations suggests that extraverts are more reactive or sensitive than
introverts not only to social situations, but to positive stimuli and events in general. This is
reflected in the affective-reactivity hypothesis (Gross et al., 1998; Smillie, Cooper, Wilt, &
Revelle, 2012) which states that extraverts respond more positively to rewarding situations as
compared to introverts. If this is the case, a relatively fixed structural difference in reactivity
would be responsible for the difference in wellbeing between extraverts and introverts (Carver,
Sutton, & Scheier, 2000; Depue & Collins, 1999; Gable, Reis, & Elliot, 2000).
An integrative approach to personality: The density distribution approach
Although there are several theoretical accounts explaining why extraverts might be
happier than introverts, most studies on the extraversion-wellbeing link have focused on the
predictive role of personality traits, or individual differences in one’s habitual patterns of
behavior, thought, and emotion. To truly understand how extraversion relates to wellbeing,
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however, one not only needs to study how one’s habitual level of extraversion predicts
wellbeing, but also how momentary deviations from this habitual level relate to within-person
fluctuations in wellbeing.
Following the density distribution approach (Fleeson, 2001), it is clear that, although
someone’s average state level captures the central tendency of one’s corresponding states,
additional information can be derived from repeatedly observing one’s personality state
expressions. For example, when a person has a narrow distribution of personality states, this
implies that this person not often behaves away from the average state level. In contrast, a person
who has a state density distribution that is wider engages more frequently in counterdispositional
behaviors, or behaviors away from the average state level (see Figure 1).
Important for our exposition is that people constantly engage in behaviors in which they
deviate from their average state level, and because in all of those cases there is a discrepancy
between the average state and state level, all of those behaviors are counterdispositional to some
extent. Thus, according to this conceptualization, counterdispositional extraversion is not limited
to extraverts engaging in introverted behavior or introverts engaging in extraverted behavior.
Rather, the extent to which one behaves counterdispositionally at a given time is proportional to
the momentary discrepancy between the momentary and average state level. In case the
discrepancy is low, there is little counterdispositional behavior. In case the discrepancy is large,
one engages in serious counterdispositional behavior. Such a conceptualization of
counterdispositional behavior is in line with the idea that it takes increasingly more effort to
engage in behaviors that are further away from one’s usual way of behaving (i.e., the idea of
contra-trait effort; Gallagher, 2010).
The Cost of Counterdispositional Extraversion
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Counterdispositional behavior refers to the enactment of behavior in which one deviates
from ones dispositions (Little, 2008). One possible explanation that tries to answer why people
do not act extraverted more often despite the associated increase in positive affect is that
counterdispositional behavior might be costly for the individual. However, and despite the fact
that some have argued that pushing introverts to act more extraverted could be harmful (e.g.,
Cain, 2012; Little, 2008), little is known about the potential negative consequences of sustained
increases in everyday extraverted behavior (Jacques-Hamilton, Sun, & Smillie, 2019).
One line of research that tries to explain why counterdispositional behavior might be
associated with affective costs points in the direction of inauthenticity. Kernis and Goldman
(2005) defined authenticity as behaving in a way that is in agreement with one's values and
preferences. Research shows that self-reported authenticity is linked with positive life outcomes,
including high positive affect (van Allen & Zelenski, 2018), high self-esteem (Schlegel, Hicks,
Arndt, & King, 2009), high life satisfaction (Boyraz, Waits, & Felix, 2014), and low negative
affect (Kernis & Goldman, 2005). According to the trait-consistency hypothesis (Fleeson & Wilt,
2010) people feel the most authentic when behaving consistent with their personality traits, while
any deviation from this preferred level (i.e., trait level) would result in a decline in authenticity.
This idea is supported by the findings of Jacques-Hamilton et al. (2019), who showed that
acting counterdispositionally (i.e., introverts who acted extraverted) led to decreased feelings of
authenticity. On the contrary, Fleeson and Wilt (2010) failed to find support for this notion. In
addition, Cooper, Sherman, Rauthmann, Serfass, and Brown (2018) found that behaving
congruently with one’s traits does not predict experienced authenticity. Apparently, flexibility in
behavior is typically genuine, and does not hamper feelings of authenticity. Indeed, both
introverts and extraverts reported greater subjective authenticity when behaving in an extraverted
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way (Fleeson & Wilt, 2010). However, since causality was not established, feeling authentic may
also lead to behaving extraverted, rather than the other way around.
Another possible explanation why counterdispositional behavior might be associated with
affective cost follows from the Behavioral Concordance Model (BCM, Moskowitz, & Coté,
1995). The Behavioral Concordance Model states that behaving discordant to one’s trait level is
demanding and effortful to maintain and should therefore cause impaired levels of wellbeing. In
other words, the BCM posits that individuals experience more positively valanced affect when
engaging in behavior concordant with their trait because acting counterdispositionally is
associated with greater effort, which subsequently leads to the depletion of self-regulatory
resources. Monitoring and modifying behavior that does not feel natural requires effort and can
therefore deplete mental resources, resulting in cognitive fatigue (Whelan, 2014). According to
the BCM, any discrepancy between the trait level and the momentary state level should be
detrimental to the individual as it drains self-regulatory resources. The greater the deviation
between the state and the trait level, the more the individual’s self-regulatory resources are
depleted, and the more one’s level of wellbeing is affected (Moskowitz & Coté, 1995).
Despite the elegance of the BCM, empirical evidence is mixed. For instance, Gallagher,
Fleeson, and Hoyle (2011) found that extraverts who acted introverted reported greater effort
than introverts who acted extraverted. In the same vein, Zelenski, Santoro, and Whelan (2012)
found that counterdispositional extraversion leads to poorer Stroop performance (suggesting
depletion of one’s cognitive self-regulatory resources), but again this effect was limited to
dispositional extraverts who acted introverted. In addition, Jacques-Hamilton et al. (2019) found
that introverts who acted extraverted experienced increases in tiredness, however, delayed effects
of extraverted behavior on later feelings of tiredness were not found. Moreover, Zelenski et al.
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(2012) showed that positive affect increased when people behaved in a more extraverted way,
which is in line with the finding by Fleeson et al. (2002), showing that higher levels of positive
affect are reported when acting extraverted. In sum, although several studies have provided
evidence for the notion that acting out of character comes with a cognitive cost, these effects are
also largely restricted to extraverts acting introverted and not so much the other way around
(Whelan, 2014).
Studying cumulative effects over time
Several theoretical perspectives suggest that counterdispositional extraversion should
have negative wellbeing related effects, especially in terms of lower perceived authenticity and
stronger feelings of depletion. Despite the apparent plausibility of these theoretical arguments,
studies to date have provided little empirical evidence for the affective costs of
counterdispositional extraversion (Jacques-Hamilton, Sun, & Smillie, 2019).
One explanation for this lack of evidence might be found in the fact that most studies
have looked at the effects of acting out of character immediately after the counterdispositional
behavior took place. Immediately after one behaved in a counterdispositionally extraverted
manner, the potential depleting effects might still be concealed by the momentary boost in
happiness that extraverted behavior typically provides (Leikas & Ilmarinen, 2017; Pickett,
Hofmans, Feldt, & De Fruyt, 2020b). In other words, the relationship between
counterdispositional extraversion and wellbeing might be characterized by two contradictory
mechanisms. The counterdispositional element might deplete one’s wellbeing, while the positive
affective and energizing element of extraversion might boost it. In such situation, the depleting
effect might thus be compensated for or even be overpowered by the activating and positive
affective nature of extraversion. If this is true, then the negative consequences might not show
immediately, but only after the initial affective boost has faded, and the depletion starts to take
the upper hand. To the best of our knowledge, there are only two studies that looked at such
delayed effects. In a first study, Leikas and Ilmarinen (2017) showed that extraverted behavior
indeed predicted mental depletion after a three-hour delay. In the second study, Pickett et al.
(2020b) found that extraversion was concurrently positively associated with vitality, but in case
the extraverted behaviors were counterdispositional (i.e., someone high in introversion engaging
in extraverted behaviors), those extraverted behaviors lead to lower levels of vitality one hour
later.
If depleting effects of counterdispositional extraversion are indeed present, but concealed
by the initial increase in positive affect, these effects should show when studying repeated
instances of counterdispositional extraversion over a longer period of time. In line with this idea,
the goal of the present paper is to study the cumulative effects of counterdispositional
extraversion on positive feelings. The idea is that, when people have to engage in
counterdispositional behavior on a regular basis (i.e., when they regularly deviate from their
average state level), self-regulatory resources are repeatedly invoked, and those cumulated
events might cause repeated depletion and therefore lower levels of wellbeing in the long run. In
other words, drawing on the idea that repeated taxations of one’s self-regulatory resources are
unpleasant, we hypothesize that when individuals behave out their extraversion-related comfort
zone more often, they will experience less positive feelings. This idea of cumulative effects is
already acknowledged in other fields, for example in research on chronic stress and allostatic
load (Juster, McEwen, & Lupien, 2010). In particular, Godin, Kittel, Coppieters, and Siegrist
(2005) found a clear graded association of cumulative job stress with several mental health
indicators. Moreover, cumulative effects assessment (CEA) has often been used to systematically
analyze and assess cumulative change and may not only be relevant to stress research but also for
other study fields (Setiz, Westbrook, & Noble, 2011).
The present study
By adopting an integrative approach to personality, this study tests whether discrepancies
between one’s momentary and average level of extraversion are associated with positive
feelings. To do so, our study goes beyond momentary effects and examines potential cumulative
effects. Studying cumulative depleting effects over time is important because such cumulative
effects might overrule the temporarily short-term gains in positive affect, adhering to the idea
that combined results of the past, current, and future can impact affect to a different extent than
right after the behavior (Luhmann, Orth, Specht, Kandler, & Lucas, 2014).
As a test of this idea, we examine whether within-person variation in cumulative
counterdispositional extraversion relates to within-person variation in positive feelings in two
experience-sampling datasets. In the first study, we test whether individuals experience less
positive feelings in weeks in which they engaged more in counterdispositional extraversion.
Next, Study 2 serves as a replication study in which the within-person association between
cumulative counterdispositional extraversion and positive affect is tested using a two-day
window, rather than a weekly window. The within-person perspective on cumulative
counterdispositional extraversion that is adopted in both studies allows for a strong test of the
idea that cumulative counterdispositional extraversion and positive feelings are related, because
it looks at their association within the individual rather than between individuals. Drawing on the
idea that repeated instances of counterdispositional extraversion are depleting, we formulated the
following hypothesis:
Hypothesis 1. Within-person variation in cumulative counterdispositional extraversion is
negatively related to within-person variation in positive feelings.”
Moreover, several studies show that the effects of counterdispositional extraversion
might differ for people high and low in trait extraversion (Zelenski et al., 2012), while others
have failed to find such a pattern in their data (Margolis & Lyubermirsky, 2020; Pickett,
Hofmans, & De Fruyt, 2019). We will therefore also test whether one’s average level of state
extraversion moderates the relationship between cumulative counterdispositional extraversion
and positive feelings. Because of mixed previous findings, we have no explicit expectation about
such differential effects.
Finally, we will test how positive cumulative deviations from one’s average state level
relate to positive feelings (i.e., acting more extraverted than one typically does), as opposed to
negative cumulative deviations (i.e., acting less extraverted than one typically does). Such
distinction between positive and negative cumulative deviations is critical, as previous research
has shown that the effect of counterdispositional extraversion are most likely not symmetric (e.g.
Pickett et al., 2020b; Zelenski et al., 2012). To this end, we will decompose our index of
cumulative counterdispositional extraversion into a positive index (i.e., the cumulation of
instances in which one goes above the average state level) and a negative index (i.e., the
cumulation of in which one goes below the average state level). Based on the idea that any type
of counterdispositional behavior should be energy draining (Gallagher et al., 2010), we expect
the general undifferentiated counterdispositional extraversion index, but also both the positive
and negative index (i.e., positive and negative deviations from one’s average state level) to be
negatively related to positive feelings.
Study 1
Method
Participants. The total sample consisted of 58 Belgian and 32 German participants, with
one participant from the Netherlands and another one from Brazil (total sample size = 92
participants). 62% of the participants were female. Most of the participants completed higher
education (82%). The average age of the participants was 30 years (SD = 11.84). The
occupations that were held by the participants were diverse, ranging from teachers to financial
advisors. Participation was voluntary and each participant was personally informed about the
content and confidentiality of the study.
Procedure. Data were collected over a period of five months, from September 2017 to
February 2018. Participants were recruited by three research associates via their personal
networks. At the start of the study, participants were informed about the aim of the study and
they were provided with the opportunity to raise potential concerns to the researchers. An
informed consent form was attached to the first questionnaire and had to be signed online before
the participants could partake in the study. In this first questionnaire, trait extraversion (NEO-
FFI; Hoekstra & De Fruyt, 2014) and demographic information was assessed (i.e., nationality,
gender, age, and current profession). In the weeks following the first questionnaire, participants
took part in an experience sampling (ESM) study.
For the ESM study, participants needed to install the app Paco (GitHub, 2016) on their
smartphone. Using Paco, participants were asked to respond to the survey questions five times a
day (9 am, 10:30 am, 12 am, 1:30 pm, 3 pm) every workday over a consecutive four-week
period
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. Because the questionnaires remained available after the four-week period, certain
1
Data collection was limited to workdays since, in addition to positive feelings, we also collected a self-report
measure of work performance. Although the association with performance is also statistically significant, the self-
rated nature of the performance measure makes it highly susceptible for leniency errors (Meyer, 1980). Because of
respondents reported for more than four weeks (e.g., two participants responded for six weeks,
one participant for 10 weeks, all data were included in the analysis). To avoid memory
disturbance, each round of questions needed to be answered within 30 minutes after receiving the
notification. After the first two weeks of the study, participants were contacted via email and
kindly asked to continue answering the questions. After four weeks (i.e., the end of the ESM
study), the participants were thanked for their efforts and debriefed. If requested, they were
provided with their (trait) extraversion score. During this debriefing, participants with a response
rate of 70% or more were rewarded with a cinema ticket or with Amazon-Vouchers.
We obtained N = 4,828 repeated observations from 92 participants. In terms of data
preprocessing, we excluded observations that came in less than 10 minutes apart (one participant
and 75 observations were removed using this criterium) and we removed participants with data
for one week only (nine respondents and 36 observations were removed). As a result, our final
sample included N = 4,717 repeated observations from 83 participants (M = 67 observations per
respondent, ranging from seven to 155).
The general guidelines of our institution at the time of data collection were applied, and
according to these guidelines permission from an ethical committee was not required.
Nevertheless, we followed the American Psychological Association Codes of Ethics regarding
the use of an informed consent. In particular, we informed participants about the purpose and
expected duration of the study, their right to withdraw from the study at any point without any
consequences, and that their answers would be anonymized and kept confidential. Moreover,
participants were encouraged to contact us should any issues arise. We also declare that we
reported all measures, conditions, and data exclusions.
this reason, and because our measurement of positive feelings allows tapping into the extraversion-wellbeing
relationship (which is not the case for performance), we do not report the findings for work performance.
Measures.
State Extraversion was measured with the Dutch and German version of Saucier’s
(1994) Mini-Markers scale. The scale consists of eight items, of which four are reverse scored in
the Dutch version, and five in the German version. Example items are ‘talkative’ or ‘energetic’.
These behavioral markers have shown to reliably assess personality states, and this list has been
successfully used in various tests of the density distributions models (Fleeson, 2001; Fleeson et
al., 2002; Fleeson & Gallagher, 2009). Respondents were asked to rate to what extent the
adjectives described them at that particular moment using a seven-point Likert scale ranging
from: 1 = extremely inaccurate to 7 = extremely accurate. To test the reliability of our state
extraversion measures, we relied on the multilevel confirmatory factor analysis approach of
Geldhof et al. (2014), which we implemented in Mplus 8.4 (Muthén & Muthén, 2010). In this
approach, an omega reliability coefficient is calculated at the within-person level and at the
between-person level separately. On the within-person level, the omega coefficients were
= .86 for the Dutch version and = .86 for the German version, while the between-person
omega coefficients were = .80 and = .89 for the Dutch and German version, respectively.
Trait Extraversion was measured with the extraversion subscale of the NEO Five-Factor
Inventory (NEO-FFI; Hoekstra & Fruyt, 2014). For the German participants we used the German
version of the NEO-FFI (Borkenau & Ostendorf, 1993). This subscale has 12 items with
response categories ranging from 1 = never to 5 = always. An example question is “I really enjoy
talking to people”. The trait scores were solely used to test for state-trait isomorphism (i.e.,
whether the average state extraversion score correlated with the trait extraversion score).
Cronbach’s α for this scale was .78.
Positive feelings were measured using a single item. Abdel-Khalek (2006) examined the
accuracy of measuring happiness by a single item and concluded that it is reliable, valid, and
viable in community surveys as well as in cross-cultural comparison. Thus, for the sake of
clarity, efficiency, and to not burden our participants we simply asked: ‘How are you feeling
right now’. Response categories were ranging from 1 = not good at all to 7 = very good.
Analyses. To test whether within-person variation in weekly cumulative
counterdispositional extraversion relates to weekly within-person variation in positive feelings,
we first calculated for each participant the mean state extraversion score across the four-week
study period. Note that this aligns with the idea that one’s average state level is a sensible
descriptor of one’s trait personality (Fleeson, 2001). This idea was also supported by our data in
the sense that the average state extraversion scores (as measured by the mini-markers) were
moderately positively correlated with trait extraversion scores as measured by the NEO-FFI (r
= .21, p < .001). This finding is fully in line with Rauthmann, Horstmann, and Sherman (2018),
who demonstrated that convergent state-trait correlations are generally rather modest. Moreover,
they showed that, among the Big Five dimensions, extraversion is highly nomologically
homomorphous, which implies that for extraversion, trait and states seem to measure the same
thing.
Next, we calculated an index of weekly counterdispositional extraversion by computing
per observation within that week the squared difference between each state extraversion score
and the person’s mean state extraversion score (across the entire time period). Subsequently, we
averaged these squared differences across all measurements of that particular week. Being the
weekly average squared deviation from the average state level, this index captures how far the
individual on average deviated from his/her average state extraversion score during that
particular week. Note that this index is high when people often deviated from their average state
level (i.e., frequency) and when they deviated more strongly from the average state level (i.e.,
deviation), thereby providing a sensible and continuous index of weekly cumulative
counterdispositional extraversion.
It is important to clarify that our index of weekly cumulative counterdispositional
extraversion is similar, but not the same as the variance of one’s weekly state extraversion
scores. When computing a weekly variance, one compares the weekly state extraversion scores
to the average state extraversion score of that individual within that week. Drawing on the idea
that the average state level taps into one’s trait personality, we instead computed the global
average state score across all observations, while the weekly counterdispositional extraversion
scores were calculated by comparing the weekly state extraversion scores to this global average
trait score. The subtle difference between both measures can be seen from the strong yet
imperfect association between both measures (r = .84, p < .001).
Next, and provided that our hypothesis pertains to a within-person relation, we removed
all between-person variation from the weekly cumulative counterdispositional extraversion
scores by group-mean centering (or person-centering) them. By group-mean centering the
weekly cumulative counterdispositional extraversion scores, we test whether deviations from
one’s average weekly level of cumulative counterdispositional extraversion relate to one’s
weekly level of positive feelings. Because our data have a hierarchical data structure with weekly
observations nested within individuals, positive feelings were regressed on the person-centered
counterdispositional behavior scores using multilevel regression. All multilevel analyses were
performed using the lme4 package in R (Bates, Maechler, Solker, & Walker, 2014).
Power considerations. Because we aggregated our data to the weekly level, the effective
sample sizes for our analyses are 83 individuals and 347 weekly observations. Based on the
power calculations by Arend and Schäfer (2019), such sample sizes allow detecting small to
medium effects for our level-1 relationships (i.e., a minimum detectable effect size of .19 for an
ICC .50 and a target level of power .80). For the cross-level interactions, statistical power is
a bit lower with the minimum detectable effect size associated with a target level of power .80
being somewhere between .43 (in case the random slope variance is large) and .59 (in case the
random slope variance is medium). In sum, the present sample sizes allow detecting small to
medium level-1 effects and medium to large cross-level effects.
Results
Descriptives and correlations. As a first step, we calculated the percentage of within-
and between-person variance in our study variables using a series of random intercept models.
First, we calculated the intra-class correlation coefficients (ICCs) for positive feelings and state
extraversion on the full dataset (N = 4,717). These ICCs are .49 and .37, respectively, indicating
that 51% of the variation in positive feelings and 63% of the variation in state extraversion was
due to within-person variation in those constructs. Next, we calculated the ICC for weekly
cumulative counterdispositional extraversion (N = 347). This ICC equaled .39, implying that
61% of the variation in weekly cumulative counterdispositional extraversion lies within the
individual. Means, standard deviations, ICCs and correlations between extraversion and positive
feelings are shown in Table 1.
Hypotheses tests. Next, we tested the within-person relationship between weekly
counterdispositional extraversion and positive feelings (i.e., Hypothesis 1). To this end, we
regressed the weekly level of positive feelings on the person-centered weekly
counterdispositional extraversion index. Two models were tested: One in which the relationship
between weekly counterdispositional extraversion and positive feelings was constrained to be the
same for each participant (i.e., a fixed slope model) and one in which the relationship between
weekly counterdispositional extraversion and positive feelings was allowed to differ across
participants (i.e., a random slope model). When comparing both models using a Likelihood Ratio
test, the random slope model turned out to fit the data significantly better than the fixed slope
model (2 (2) = 10.45, p = .005). In this random slope model, weekly counterdispositional
extraversion was negatively related to positive feelings during that week (B = -.38, p = .006,
95% CI [-.640, -.130]), which supports Hypothesis 1 (see Model 1 in Table 2 for the full results).
A comparison of the residual variances of the null-model and the random slope model revealed
that 15.90% of the within-person variance in weekly positive feelings was predicted by within-
person variance in weekly counterdispositional extraversion
2
.
To further inspect individual differences in the relation between weekly
counterdispositional extraversion and positive feelings, we plotted the distribution of the slopes
in Figure 2. As can be seen, the large majority of those slopes are negative, which means that in
weeks one deviates more from his/her trait level, one experiences less positive feelings.
Subsequently, we tested whether the relationship between weekly counterdispositional
extraversion and the weekly level of positive feelings was different for people with different
levels of average state extraversion. This was done by adding a cross-level interaction between
2
Because the extent to which an individual can vary around ones average state score depends on how high/low the
average state score is, indices of within-person variation tend to be confounded by the person’s average state score
(Mestdagh, Pe, Pestman, Verdonck, Kuppens, & Tuerlinckx, 2018). Recently, Mestdagh and colleagues (2018)
proposed a solution to this issue by correcting the variability for the maximum possible variance given the person’s
average score. To test whether the confound between variability and mean affected our findings, we reran the
analysis using an index of weekly counterdispositional extraversion that is corrected using the method proposed by
Mestdagh et al. (2018). This analysis revealed that, in line with our original analysis, the corrected index of weekly
counterdispositional extraversion related negatively to positive feelings (B = -1.50, p = .005, 95% CI [-2.531, -
.471]).
the person’s average state extraversion score and weekly counterdispositional extraversion to the
model (on top of the main effects of the counterdispositional extraversion index and the person’s
average state extraversion score). The absence of a statistically significant interaction effect (B =
-.06, p = .756, 95% CI [-.444, .322]) reveals that individual differences in average state
extraversion do not explain the differential reactions to weekly counterdispositional extraversion
(See Table 2 model 3 for the full results)
3
. In other words, we found no support for the idea that
the relationship between counterdispositional extraversion and positive feelings was different for
people with different levels of average state extraversion.
Next, we tested whether cumulative positive deviations from one’s average state
extraversion level related differently to positive feelings than cumulative negative deviations
from the average state extraversion level. This allows testing whether the ‘direction’ of
counterdispositional behavior matters in the prediction of positive feelings. To this end, we
calculated separate indices of positive and negative counterdispositional extraversion. The
positive index (i.e., acting more extraverted than one on average does) was calculated by
computing per observation for which the state level exceeded the average state level the squared
difference between the extraversion state score and the person’s mean extraversion score. Next,
we averaged these squared differences across all measurements of that week. Likewise, the
negative index (i.e., acting less extraverted) was calculated in the same way for those instances
where the state level was below the average state level. By calculating positive and negative
cumulative counterdispositional extraversion in this way, the sum of the positive and negative
3
The analysis with the corrected index of weekly counterdispositional extraversion confirms this finding, showing
that individual differences in average state extraversion do not moderate the effect of weekly counterdispositional
extraversion on positive feelings (B = .01, p = .991, 95% CI [-2.178, 2.204]).
counterdispositional extraversion indices equaled the overall counterdispositional extraversion
index created earlier.
Regarding cumulative negative deviations from one’s average state level, we found a
negative relation with weekly levels of positive feelings (B = -.90, p < .001, 95% CI [-1.152,
-.640]), which was in line with our expectations. As the random slope model fitted the data better
than the fixed slope model (2 (2) = 51.92, p < .001), this relation appeared to be subject to
between-person differences. The left panel of Figure 3 provides a summary of those random
slopes, showing that for virtually all participants cumulative negative deviations were negatively
related to weekly level of positive feelings. Finally, in terms of effect size, a comparison of the
residual variances of the null-model and the random slope model showed that 43.71% of the
variance in positive feelings was predicted by weekly negative counterdispositional extraversion.
For the cumulative positive deviations from one’s trait extraversion level, we found that,
when comparing the fixed slope model to the random slope model, the random slope model fitted
the data best (2 (2) = 20.72, p < .001). Surprisingly, in this random slope model, the cumulative
positive counterdispositional behavior index related positively to weekly positive feelings (B =
1.17, p < .001, 95% CI [.762, 1.575]), with this predictor accounting for 34.54% of the variance
in weekly level of positive feelings. To further inspect individual differences in the relation
between cumulative positive counterdispositional behavior and positive feelings, we plotted the
distribution of the random slopes, showing that for almost all participants the relation is positive
(see the right panel of Figure 3). When including both predictors simultaneously in the model,
positive (B = .69, p = .002, 95% CI [.312, 1.061]) and negative (B = - .70, p < .001, 95% CI
[-.976, -.417]) counterdispositional extraversion remained significant predictors of positive
feelings, together accounting for 55.25% of the within-person variance in positive feelings. See
Table 2 (model 2) for full results.
As a final analysis, we tested whether the relationship between weekly cumulative
negative and positive counterdispositional extraversion and the weekly level of positive feelings
was different for people with different average state extraversion scores. To this end, we added a
cross-level interaction between average state extraversion and positive counterdispositional
extraversion and one between average state extraversion and negative counterdispositional
extraversion to the model. This analysis revealed a statistically significant interaction between
positive counterdispositional extraversion and average state extraversion (B = .50, p = .039, 95%
CI [.062, .946]), while the interaction between average state extraversion and negative
counterdispositional extraversion approached conventional levels of significance (B = .42, p
= .060, 95% CI [.009, .853]) (See Table 2 model 4 for the full results). Probing these cross-level
interactions (see Preacher, Curran, & Bauer, 2006 for probing cross-level interactions), shows
that for people with a low (-1 SD) average state extraversion level, the weekly level of positive
feelings was more strongly negatively related to weekly negative counterdispositional
extraversion (B = -1.11, p < .001) than for people with a high (+1 SD) average state extraversion
level (B = -.48, p = .011). Regarding weekly positive counterdispositional extraversion, we
found that for people with a low (-1 SD) average state extraversion level, the weekly level of
positive feelings was unrelated to weekly negative counterdispositional extraversion (B = .31, p
= .150), while for people with a high (+1 SD) average state extraversion level, the relation is
positive and statistically significant (B = 1.05, p < .001). These findings, which are shown in
Figure 4, reveal that people high on average state extraversion suffer less from negative
cumulative counterdispositional extraversion and benefit more from positive cumulative
counterdispositional extraversion than people low on average state extraversion.
Sensitivity analyses
Inclusion criteria. To evaluate the robustness of our findings, we tested whether
adopting different inclusion criteria alters our findings. In particular, we tested the impact of
varying the minimum number of observations per week, using a minimum of three (deleting 3
participants and 45 observations), four (deleting 7 participants and 97 observations), or five
(deleting 16 participants and 148 observations) observations per week. Results showed that the
effects of overall counterdispositional extraversion, but also those of weekly negative and
weekly positive counterdispositional extraversion remained statistically significant, regardless of
the inclusion criterium. The cross-level interaction between average state extraversion and
negative counterdispositional extraversion, however, was no longer marginally significant with
three (B = .26, p = .177, 95% CI [-.110, .621]) and five (B = .37, p = .110, 95% CI
[-.073, .810])) observations per week, while the cross-level interaction with positive
counterdispositional extraversion became marginally statistically significant when using a
minimum of three observations a week (B = .36, p = .090, 95% CI [-.039, .765]). This suggests
that the moderating effects of average state extraversion are more sensitive to the inclusion
criteria used. Full results of the sensitivity analyses are shown in Tables S1 (three observations a
week), S2 (four observations a week), and S3 (five observations a week).
Alternative time windows. Another way to evaluate the robustness of our findings is to
test whether our findings hold when using an index of cumulative counterdispositional
extraversion based on a different time frame. Therefore, instead of focusing on weekly effects,
we computed a counterdispositional extraversion index aggregated over two days (i.e., Monday-
Tuesday and Wednesday-Thursday of each week)
4
. In this way, we computed two indices of
counterdispositional extraversion per participant per week.
We again excluded observations that came in less than 10 minutes apart (leading to the
deletion of 75 observations). Secondly, since we only aggregated days that were consecutive,
some days were not used in the analysis and therefore deleted (eight participants and 740
observations were deleted). Additionally, we set a minimum of four observations per two-day
index (two participants and 278 observations were deleted). Lastly, participants had to have at
least two two-day indices of counterdispositional extraversion (eight participants were deleted).
This resulted in a final sample of 75 participants with N = 3,735 repeated observations.
The computation of cumulative counterdispositional extraversion was done in the same
way as described previously (see the Analyses section, p. 15-16), however, the only difference is
that we now averaged the squared differences between the average state level and the state level
across two days (instead of one week). This yielded 546 observations from 75 participants. Being
the average squared deviation from the average state level, our index captures how far the
individual on average deviated from his/her average state extraversion score during those two
days. Subsequently, positive feelings were regressed on the cumulative counterdispositional
extraversion scores using multilevel regression.
Paralleling our previous sensitivity analyses, the effects of overall counterdispositional
extraversion, negative and positive counterdispositional extraversion remained statistically
significant, while the moderating effect of average state extraversion with negative
counterdispositional extraversion became nonsignificant (See Table S4 for full results). This
4
Not every participant started on a Monday, therefore also other two-day combinations are possible (e.g., Tuesday-
Wednesday and Thursday -Friday). Only consecutive days that belong to the same week were aggregated.
again suggests that the moderation effects are fairly sensitive to a number of important design
choices.
Discussion
The findings of our first study revealed that there is a negative association between
counterdispositional extraversion and positive feelings. When taking a closer look at this
association, however, negative counterdispositional behavior and positive counterdispositional
behavior turned out to be differentially related to positive feelings. That is, in periods during
which participants behaved more often in a more introverted way than they typically do, they
reported lower levels of positive feelings, while in periods they more often behaved in a more
extraverted way than usual, they experienced higher levels of positive feelings. Although we also
found that people high on average state extraversion suffer less from negative cumulative
counterdispositional extraversion and benefit more from positive cumulative counterdispositional
extraversion than people low on average state extraversion, this finding turned out to be highly
dependent on different types of design choices made. Hence, evidence for a moderating role of
average state extraversion is very limited at best.
Study 2
Although the findings of our first study revealed that cumulative deviations from one’s
average state extraversion relate to fluctuations in positive feelings, two critical remarks need to
be made. A first remark pertains to the measurement of positive feelings. For practical reasons
and because of simplicity, we asked participants how they were feeling at the moment of
measurement using a single-item question. Whereas this single-item question is believed to result
in reliable, valid, and viable responses (Abdel-Khalek, 2006), more elaborate questionnaires
might be better suited for measuring positive feelings. Second, and despite the fact that we
performed sensitivity analyses to test the robustness of our findings, a true robustness check in
terms of a replication study might further strengthen our conclusions. To address both issues, we
analyzed a second experience-sampling dataset.
Method
Participants and procedure. The total sample consisted of 80 Belgian respondents with
N= 1,793 repeated observations. 57% of the participants were female and the average age of the
participants was 32 years (SD = 12.5). Data were collected over a period of 10 consecutive days,
from December 2 to December 11, 2019. Participants were recruited by a research associate via
the personal network. At the start of the study, participants were informed about the aim of the
study and an informed consent form had to be signed online before participants could partake in
the study. Next, they were asked via email to respond to a survey three times a day (in the
morning around 9 am, in the afternoon around 2 pm, and in the evening around 7 pm) using
Qualtrics XM
5
. In addition, a text was sent to their cellphones each time a new questionnaire
became available. Participants had two hours to complete the survey. After the study ended,
participants were thanked for their efforts.
Similar to Study 1, permission from an ethical committee was not required, however, we
followed the American Psychological Association Codes of Ethics regarding the use of an
informed consent. We also declare that we reported all measures, conditions, and data
exclusions.
Measures.
State Extraversion was measured with the Dutch version of the Ten Item Personality
Inventory (TIPI; Hofmans, Kuppens, & Allik, 2008). The scale consists of ten items in total, with
5
In addition to state extraversion and positive affect, we also collected self-report measures of negative affect,
depletion, and subjective authenticity.
two items measuring extraversion. An example item is ‘At this moment I am extraverted and/or
enthusiastic’. Respondents were asked to rate to what extent the adjectives described them at that
particular moment using a seven-point Likert scale ranging from: 1 = does not describe me at all
to 7 = describes me very well. The correlation between both items was r = .21 at the within-
person level, while the between-person correlation coefficient was r = .35. Although such
moderate-sized correlations might seem problematic at first sight, it is important to note that this
a recurring issue with the TIPI and which is due to a focus on content validity (i.e., covering the
different facets) rather than internal reliability (i.e., having similar, parallel items) (see Gosling,
Rentfrow, & Swann, 2003).
Positive Affect (PA) was measured using a Dutch version of the International Positive
and Negative Affect Schedule Short Form (I-PANAS-SF; Thompson, 2007). Participants had to
indicate to what extent they experienced the described feeling at the moment they completed the
questionnaire. An example item is ‘At this moment, I feel inspired’. Answer categories ranged
from 1 = totally not to 5 = completely. To test the reliability of our PA measure, we again relied
on the multilevel confirmatory factor analysis approach of Geldhof et al. (2014). On the within-
person level, the omega coefficient was = .67, while the between-person omega coefficient
was = .97.
Analyses. Both the computation of the cumulative counterdispositional extraversion
index as well as the subsequent analyses paralleled those of Study 1. The only difference is that,
when assessing cumulative effects, we looked at a three-day window rather than a five-day
window. The reason is that in Study 2, data were collected over 10 consecutive days, while
Study 1 only included workdays (i.e., Monday to Friday). Study 2 therefore also includes
weekend days, which makes week one (Monday to Friday) and week two (Saturday to
Wednesday) difficult to compare. For example, in weekends, positive affect might be higher
because people do not have to work, and during the weekend there may also be more (or for
some people less) opportunities to act in an extraverted way, depending on the work situation
and the personal situation. Hence, we decided to focus on Monday until Wednesday in both
weeks to make the two time periods as equivalent as possible. We also decided to not focus on a
two-day window (see our sensitivity analyses in Study 1) because participants were only
measured three times a day. Hence, a two-day window would result in a maximum of six
observations per time period, and this lower number of observations would complicate the
computation of a reliable index of cumulative counterdispositional extraversion.
Due to the focus on the three-day time window, two days per week (Thursday-Friday of
week one and Saturday-Sunday of week two) were not used in the analysis (resulting in the
removal of five participants and 658 observations). In addition, 10 observations were deleted
because they were incomplete and did not contain state extraversion and/or PA scores.
Moreover, we required a minimum of four observations per three-day index, but this constraint
did not lead to the deletion of participants or observations. Finally, we removed participants with
data for one three-day period only (six respondents and 77 observations were deleted). This
resulted in a final sample of 69 participants with N = 1,048 repeated observations (M = 15
observations per respondent, ranging from 10 to 17).
Results
Descriptives and correlations. As a first step, we calculated the percentage of within-
and between-person variation in our study variables. To this end, we calculated the intra-class
correlation coefficients (ICCs) for positive affect and state extraversion on the dataset with
observations from Monday to Wednesday for week one and two (N = 1,048). These ICCs are .36
and .27, respectively, indicating that 64% of the variation in positive affect and 73% of the
variation in state extraversion was due to within-person variation in those constructs. Next, we
calculated the ICC for cumulative counterdispositional extraversion (N = 138). This ICC
equaled .17, implying that 83% of the variation in cumulative counterdispositional extraversion
lies within the individual. Means, standard deviations, ICCs and correlations between
extraversion and positive affect are shown in Table 3.
Hypotheses tests. We first tested the within-person relationship between cumulative
counterdispositional extraversion and positive affect (i.e., Hypothesis 1). To this end, we
regressed positive affect on the person-centered counterdispositional extraversion index (See
Model 1 in Table 2 for the full results). A fixed slope model revealed that counterdispositional
extraversion was not significantly related to positive affect (B = -.05, p = .703, 95% CI
[-.292, .197])
6
. A comparison of the residual variances of the null-model and the fixed slope
model revealed that only .18% of the within-person variance in positive affect was predicted by
within-person variance in counterdispositional extraversion
7
.
Next, we tested whether cumulative negative deviations from one’s average state
extraversion level related differently to positive affect than cumulative positive deviations from
the average state extraversion level. Regarding cumulative negative deviations from one’s
average state level, we found a negative relation with levels of positive affect that approached
conventional levels of statistical significance (B = -.20, p = .099, 95% CI [-.434, .035]). In terms
of effect size, a comparison of the residual variances of the null-model and the fixed slope model
6
Only the fixed slope model was tested because there were not enough observations to estimate a random slope
model.
7
The analysis with the corrected index of cumulative counterdispositional extraversion confirms this finding,
showing that counterdispositional extraversion was not significantly related to positive affect (B = <-.01, p = .999,
95% CI [-1.820, 1.816]).
showed that 3.84% of the variance in positive affect was predicted by negative
counterdispositional extraversion.
For the cumulative positive deviations from one’s trait extraversion level, we found a
positive relationship with positive affect, and this association again approached conventional
levels of statistical significance (B = .27, p = .094, 95% CI [-.041, .574]), with this predictor
accounting for 3.96% of the variance in level of positive affect. When including both predictors
simultaneously in one model, however, positive (B = .19, p = .268, 95% CI [-.144, .525]) and
negative (B = - .14, p = .285, 95% CI [-.395, .115]) counterdispositional extraversion were no
longer related to positive affect (see Table 4 model 2 for full results).
As a final analysis, we tested whether the relationship between cumulative (positive and
negative) counterdispositional extraversion and the level of positive affect was different for
people with different levels of average state extraversion. This analysis revealed that average
state extraversion did not interact with cumulative counterdispositional extraversion (B = -.10, p
= .558, 95% CI [-.438, .236])
8
, nor with negative counterdispositional extraversion (B = .06, p
= .752, 95% CI [-.304, .422), or positive counterdispositional extraversion (B = .36, p = .434,
95% CI [-.539, 1.258]). In other words, we found no support for the idea that the relationship
between (positive and negative) counterdispositional extraversion and positive affect was
different for people with different levels of average state extraversion (see Table 4 model 3 and
model 4 for full results).
Sensitivity analysis
8
The analysis with the corrected index of counterdispositional extraversion confirms this finding, showing that
individual differences in average state extraversion do not moderate the effect of counterdispositional extraversion
on positive affect (B = .11, p = .930, 95% CI [-2.272, 2.485]).
To evaluate the robustness of our findings, we tested whether different inclusion criteria
altered our findings. In particular, we tested whether the results changed when adopting a
minimum of five (three participants and 23 observations deleted) or six (six participants and 121
observations deleted) observations per three-day period. Results showed that with a minimum of
five observations, positive (B = .30, p = .051, 95% CI [.005, .604]) and negative (B = -.22, p
= .064, 95% CI [-.444, .009]) counterdispositional extraversion still approached conventional
levels of significance. However, when using a minimum of six observations, the effect of
positive (B = .27, p = .107, 95% CI [-.054, .602]) and negative (B = -.19, p = .129, 95% CI
[-.440, .053])) counterdispositional extraversion on positive affect did no longer hold. Full results
are shown in Table S5 (for a minimum of five observations a week) and S6 (for a minimum of
six observations a week).
Discussion
In sum, the findings of study 2 showed that acting counterdispositionally was unrelated to
positive affect. However, when further inspecting this result, the effect of both positive and
negative counterdispositional behavior on positive affect approached conventional levels of
significance. Ergo, during days in which participants behaved more often in a more introverted
way than usual, they reported marginally lower levels of positive affect, while during days that
they more often behaved in a more extraverted way than usual, they experienced marginally
higher levels of positive affect. Moreover, and in line with our findings from Study 1, no strong
evidence was found for interaction effects with the average level of state extraversion.
General discussion
The positive association between extraversion and positive affect is by now well-established
(e.g., Fleeson et al., 2002; Smillie et al., 2015). Because of this association, some researchers
have even alluded to a possible causal connection between extraversion and wellbeing (e.g.,
Leikas & Ilmarinen, 2017). Against this background, the goal of the present study was to add
more depth to our understanding of the link between extraversion and wellbeing by looking at
how cumulative deviations from one’s average state extraversion level over the course of a week
relate to weekly fluctuations in positive feelings. This is an important contribution because
behavioral phenomena are typically described and explained without reference to time. This is
unfortunate because research shows that findings at the within-person level can dramatically
differ from findings at the between-person level (Roe, 2005) and combined results of the past,
current, and future can impact affect to a different extent than right after the behavior (Luhmann,
Orth, Specht, Kandler, & Lucas, 2014).
Whereas the large majority of research has focused on the short-term benefits of
extraverted behavior, we hypothesized that counterdispositional extraversion might have
wellbeing-related costs in the long run due to the cumulation of costs following from acting out
of character. Looking beyond the initial increase in positive affect that extraverted behavior
typically provides (Smillie et al., 2015) we found no support for this hypothesis. Yet, our
findings did reveal a different pattern of associations. In periods during which participants
behaved more often in a more introverted way than they typically do, they reported lower levels
of positive feelings. Moreover, in periods they more often behaved in a more extraverted way
than usual, they experienced higher levels of positive feelings. Importantly, those findings were
robust to several design choices and they held across different time windows and studies. An
important sidenote here is that the associations with positive feelings (Study 1) were markedly
stronger than those with positive affect (Study 2), where the relationships only approached
conventional levels of significance. One reason for this difference might be that positive feelings
and positive affect tap into different aspects of the construct space. Whereas we measured
positive feelings by asking how people felt, positive affect was measured using a set of specific
positive emotions, including feeling active, determined, attentive, inspired and alert. Hence,
counterdispositional extraversion might relate to this general feeling state, rather than to the
specific feeling states covered by positive affectivity. Finally, the evidence for a moderating role
of average state extraversion was less convincing, with the moderating effects not replicating
across sensitivity analyses and across studies. On balance, our findings on the cumulative effects
of counterdispositional extraversion are generally in line with the “more is better” idea,
according to which higher levels of extraversion are beneficial for wellbeing, while we found
little evidence for a moderating role of trait extraversion.
Combined with findings of previous research, our results provide a rich and nuanced
perspective on the association between extraversion and positive feelings. Studies on momentary
relationships show that, when people behave in an extraverted way, they also experience higher
levels of positive affect (e.g., Fleeson et al., 2002; Smillie et al., 2015; Wilt, Noftle, Fleeson, &
Spain, 2012). Yet, this positive association does not always last. Leikas and Ilmarinen (2017) and
Pickett et al. (2020b) demonstrated that instances of (counterdispositional) extraversion can lead
to later levels of fatigue and depletion (as indexed by lower levels of vitality). Hence, the
question becomes whether on balance those negative delayed effects might cancel out or even
overshadow the positive concurrent effects. Our findings show that this is not the case. When
looking at cumulative counterdispositional extraversion over a longer period of time, increased
levels of extraversion are associated with higher levels of positive feelings. Thus, -while
depending on the timeframe the association between extraversion and positive feelings can
reverse- on balance higher levels of extraversion seem to go hand in hand with higher levels of
positive feelings.
Research Contributions
The contributions of this study to the literature of counterdispositional behavior are
threefold. First, to the best of our knowledge, this is the first study to look at the consequences of
within-person fluctuations in the amount of counterdispositional extraversion shown over a
longer period of time. This is important given that prior work showed that the consequences of
counterdispositional extraversion can differ depending on the time frame that is adopted (Leikas
& Ilmarinen, 2017; Pickett et al., 2020b). By doing so, we were able to show that within-person
variation in cumulative counterdispositional extraversion relates to within-person variation in
positive feelings.
Second, by decomposing the overall counterdispositional extraversion index into an
index of positive and an index of negative counterdispositional extraversion, we offered a more
comprehensive picture of how different types of counterdispositional extraversion relate to
positive feelings. Moreover, our findings convincingly demonstrate that an undifferentiated
counterdispositional extraversion index might lead to different conclusions. That is, whereas the
analysis of the general index suggested that deviating more from one’s trait extraversion level is
costly for one’s positive feelings (in Study 1 but not in Study 2), delving into the decomposed
indices revealed a drastically different story. Such incongruences suggest that, by lumping these
effects together into a general index, one effect might conceal or outweigh the other, which gives
a distorted picture of what is actually happening.
Third, although previous studies have typically adopted experimental designs to evaluate
the effects of counterdispositional extraversion, the current study uses a more fine-grained,
continuous measure of counterdispositional extraversion. More specifically, whereas
counterdispositional behavior has typically been approached as a quasi-categorical construct in
which dispositional introverts and extraverts were respectively exposed to a high extraversion
and introversion condition (e.g., Whelan, 2014; Zelenski et al., 2012; Gallagher et al., 2011,
study 1), the current study used a more comprehensive measure by tracking participants state
extraversion level over time, and by comparing one’s state extraversion levels to one’s average
state extraversion level. This is an important contribution, since theoretically every deviation
from the trait level should matter, with larger deviations being more depleting.
Limitations and Future Research
Despite the contributions of our study, it is subject to a number of limitations as well.
First, we relied on self-reports to measure extraversion and positive feelings. Although this way
of measurement is not uncommon with these constructs (Costa & McCrae, 1992a; Sandvik,
Diener, & Seidlitz, 2009), two potential problems associated with using self-reports are people’s
limited introspective ability and self-serving biases. Yet, since the time between the
measurements was relatively short, and people needed to report about how they behaved and felt
at that point in time, it is unlikely that problems with introspective ability confound our findings.
Moreover, our study pertained to within-person differences, which implies that we compared
participants to themselves. The implication thereof is that differences between individuals in the
extent they hold self-serving biases cannot affect our findings because such differences are
removed by person-centering the data.
A second limitation that might be addressed in future research is the conceptualization of
our dependent variable (positive feelings). In the present study, we did not take into account that
depletion has both an affective and cognitive component. Such distinction might be considered
when examining the cost of counterdispositional behavior in future research.
A third limitation concerns the fact that we measured counterdispositional extraversion
by comparing participants state extraversion scores to their average level of state extraversion.
Although we are not the first ones to argue that the average state level is a meaningful descriptor
of one’s personality dispositions (e.g., Fleeson, 2001, Furr, 2009, Shoda, LeeTiernan, & Mischel,
2002, Sosnowska, Kuppens, De Fruyt, & Hofmans, 2020), the average of one’s states is not
identical to a trait score as measured by a traditional personality questionnaire. As Rauthmann et
al. (2018) argue, differences between both measurement methods show because (a) short ESM
studies might not be long enough to approximate the trait well, (b) there is a difference in
bandwidth because states are often measured using shorter instruments than traits, and (c) states
are more tied to actual behavior while traditional personality questionnaires tap into how people
think they behave, feel and think. In our studies, (a) and (b) are less of an issue because we
obtained many observations per participant and measured their personality states using validated
instruments. Regarding (c), we feel that the average state level is a sensible point of reference for
computing counterdispositional extraversion because traditional personality questionnaires tap
into how people think they behave, feel and think, while state measurements are much closer to
one’s actual behavior. Provided that one has enough representative repeated measures, the
average state level thus offers a purer measure of one’s behavioral homebase, which makes it the
primary candidate as the point of reference when calculating counterdispositional extraversion
scores.
Furthermore, it should be noted that causality could not be established with our study
design. Although ESM data allow for testing within-person associations (Conner & Lehman,
2011), the lack of randomization implies that we cannot make any claims about the causal nature
of our relations. In addition, the sample size of our second study was rather small. This was
particularly an issue for the detection of cross-level interactions. Yet, in terms of effect sizes,
those interactions were extremely small in Study 2 and they showed to be fairly unstable in the
first study as well. Hence, the combined evidence does not point toward important cross-level
interactions. Lastly, combining the findings of people from different countries (each with their
own language) might have introduced some unmeasured biases to our study.
Conclusion
What happens when people act out of character? The current study examined this issue
by focusing on the cumulative effects of counterdispositional extraversion over the course of
several weeks. By taking such a long-term approach, our findings showed that in weeks when
people acted more extraverted than usual, they experienced more positive feelings, whereas
cumulated negative deviations were associated with less positive feelings during that week.
These findings contribute to a deeper understanding of what it means for people to repeatedly act
in a counterdispositional manner.
References
Abdel-Khalek, A. M. (2006). Measuring happiness with a single-item scale. Social Behavior and
Personality: an international journal, 34, 139-150.
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.
Arend, M. G., & Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on
Monte Carlo simulation. Psychological methods, 24, 119.
Baird, B. M., Le, K., & Lucas, R. E. (2006). On the nature of intraindividual personality
variability: Reliability, validity, and associations with well-being. Journal of Personality
and Social Psychology, 90(3), 512527.Barrick, M. R., & Mount, M. K. (2000). Select on
conscientiousness and emotional stability. Handbook of principles of organizational
behavior (pp. 19-39), John Wiley & Sons.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting linear mixed-effects models
using lme4. arXiv preprint arXiv:1406.5823.
Cain, S. (2012). The power of introverts. TED: Ideas Worth Spreading.
Carver, C. S., Sutton, S. K., & Scheier, M. F. (2000). Action, emotion, and personality:
Emerging conceptual integration. Personality and Social Psychology Bulletin, 26, 741-751.
Cooper, A. B., Sherman, R. A., Rauthmann, J. F., Serfass, D. G., & Brown, N. A. (2018).
Feeling good and authentic: Experienced authenticity in daily life is predicted by positive
feelings and situation characteristics, not trait-state consistency. Journal of Research in
Personality, 77, 57-69.
Conner, T. S., & Lehman, B. J. (2011). Getting started: Launching a study in daily life.
Handbook of research methods for studying daily life (pp. 89107). New York, NY:
Guilford Press
Costa, P. T., & McCrae, R. R. (1980). Influence of extraversion and neuroticism on subjective
well-being: happy and unhappy people. Journal of personality and social psychology, 38(4),
668-678.
Costa, P. T., & McCrae, R. R. (1992a). Four Ways Five Factors are Basic. Personality and
Individual Differences, 13, 653- 665.
Depue, R., & Collins, P. (1999). Neurobiology of the structure of personality: Dopamine,
facilitation of incentive motivation, and extraversion. Behavioral and Brain Sciences, 22,
491517.
Diener, E., Heintzelman, S., Kushlev, K., Tay, L., & Wirtz, D. (2016). Findings all psychologists
should know from the new science on subjective well-being. Canadian
Psychology/Psychologie Canadienne, 58, 87-104.
Diener, E., Sandvik, E. D., Pavot, W., & Fujita, F. (1992). Extraversion and subjective well-
being in a US national probability sample. Journal of research in personality, 26(3), 205-
215.
Diener, E., Scollon, C. N., & Lucas, R. E. (2009). The evolving concept of subjective well-being:
The multifaceted nature of happiness. In Assessing well-being (pp. 67-100). Springer,
Dordrecht.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three
decades of progress. Psychological bulletin, 125, 276-302.
Fleeson, W. (2001). Toward a structure-and process-integrated view of personality: Traits as
density distributions of states. Journal of personality and social psychology, 80, 1011-1027.
Fleeson, W., & Gallagher, P. (2009). The implications of Big Five standing for the distribution
of trait manifestation in behavior: Fifteen experience-sampling studies and a meta-analysis.
Journal of Personality and Social Psychology, 97, 10971114.
Fleeson, W., & Jayawickreme, E. (2015). Whole trait theory. Journal of Research in Personality,
56, 8292.
Fleeson, W., Malanos, A., & Achille, N. (2002). An intraindividual process approach to the
relationship between extraversion and positive affect: Is acting extraverted as&quot;
good&quot; as being extraverted? Journal of Personality and Social Psychology, 83, 1409
1422.
Fleeson, W., & Wilt, J. (2010). The relevance of Big Five trait content in behavior to subjective
authenticity: Do high levels of within‐person behavioral variability undermine or enable
authenticity. Journal of Personality, 78, 13531382.
Gable, S., Reis, H., & Elliot, A. (2000). Behavioral activation and inhibition in everyday life.
Journal of Personality and Social Psychology, 78, 11351149.
Gallagher, Matthew Patrick (2010). Contra-Trait Effort and Trait Stability: A Self-Regulatory
Personality Process. Dissertation, Duke University.
Gallagher, P., Fleeson, W., & Hoyle, R. H. (2011). A Self-Regulatory Mechanism for
Personality Trait Stability. Social Psychological and Personality Science, 2, 335342.
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel
confirmatory factor analysis framework. Psychological methods, 19, 72-91.
GitHub, Inc. (2016). PACO (4.2.25) [Mobile application software].
Godin, I., Kittel, F., Coppieters, Y., & Siegrist, J. (2005). A prospective study of cumulative job
stress in relation to mental health. BMC public health, 5, 67.
Gosling, S., Rentfrow, P., & Swann, W. (2003). A very brief measure of the Big-Five personality
domains. Journal of Research in Personality, 37, 504528.
Gross, J. J., Sutton, S. K., & Ketelaar, T. (1998). Relations between Affect and Personality:
Support for the Affect-Level and Affective-Reactivity Views. Personality and Social
Psychology Bulletin, 24, 279288.
Headey, & Wearing, B. (1989). Personality, life events, and subjective well-being: Toward a
dynamic equilibrium model. Journal of Personality and Social Psychology, 57, 731739.
Heller, D., Komar, J., & Lee, W. B. (2007). The dynamics of personality states, goals, and well-
being. Personality and Social Psychology Bulletin, 33(6), 898-910.
Hoekstra, H., & De Fruyt, F. (2014). NEO-PI-3 en NEO-FFI-3: persoonlijkheidsvragenlijsten:
handleiding. Hogrefe.
Hoffman, L., & Stawski, R. S. (2009). Persons as contexts: Evaluating between-person and
within-person effects in longitudinal analysis. Research in Human Development, 6, 97-
120.
Howell, R. T., Ksendzova, M., Nestingen, E., Yerahian, C., & Iyer, R. (2017). Your personality
on a good day: How trait and state personality predict daily well-being. Journal of Research
in Personality, 69, 250-263.
Jacques-Hamilton, R., Sun, J., & Smillie, L. D. (2019). Costs and benefits of acting extraverted:
A randomized controlled trial. Journal of Experimental Psychology: General, 148, 1538-
1556.
Judge, T., Simon, L., Hurst, C., & Kelley, K. (2014). What I experienced yesterday is who I am
today: Relationship of work motivations and behaviors to within-individual variation in the
five-factor model of personality. Journal of Applied Psychology, 99, 199221.
Juster, R. P., McEwen, B. S., & Lupien, S. J. (2010). Allostatic load biomarkers of chronic stress
and impact on health and cognition. Neuroscience & Biobehavioral Reviews, 35, 2-16.
Kernis, M. H., & Goldman, B. M. (2005). Authenticity, social motivation, and psychological
adjustment. Social motivation: Conscious and unconscious processes, 210-227.
Leikas, S., & Ilmarinen, V. (2017). Happy Now, Tired Later? Extraverted and Conscientious
Behavior Are Related to Immediate Mood Gains, but to Later Fatigue. Journal of
Personality, 85, 603615.
Little, B. R. (2008). Personal Projects and Free Traits: Personality and Motivation Reconsidered.
Social and Personality Psychology Compass, 2, 12351254.
Lucas, R. E. (2018). Exploring the associations between personality and subjective well-being.
In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of well-being. Salt Lake City, UT: DEF
Publishers.
Lucas, R. E., Le, K., & Dyrenforth, P. S. (2008). Explaining the extraversion/positive affect
relation: Sociability cannot account for extraverts' greater happiness. Journal of
personality, 76(3), 385-414.
Lucas, R., & Fujita, F. (2000). Factors influencing the relation between extraversion and pleasant
affect. Journal of Personality and Social Psychology, 79, 10391056.
Luhmann, M., Orth, U., Specht, J., Kandler, C., & Lucas, R. E. (2014). Studying changes in life
circumstances and personality: It's about time. European Journal of Personality, 28, 256-
266.
Lykken, D., & Tellegen, A. (1996). Happiness Is a Stochastic Phenomenon. Psychological
Science, 7, 186189.
Margolis, S., & Lyubomirsky, S. (2019). Experimental manipulation of extraverted and
introverted behavior and its effects on well-being. Journal of Experimental Psychology:
General, 149(4), 719731.
McCabe, K. O., & Fleeson, W. (2012). What is extraversion for? Integrating trait and
motivational perspectives and identifying the purpose of extraversion. Psychological
science, 23(12), 1498-1505.
McCrae, R. R., Costa, P. T., John, O. P., Robins, R. W., & Pervin, L. A. (1999). Handbook of
personality: Theory and research. New York, NY: Guilford.
McNiel, J. M., & Fleeson, W. (2006). The causal effects of extraversion on positive affect and
neuroticism on negative affect: Manipulating state extraversion and state neuroticism in an
experimental approach. Journal of Research in Personality, 40, 529-550.
Michalos, A.C. (2014). Encyclopedia of Quality of Life and Well-Being Research. Dordrecht,
Netherlands: Springer.
Moskowitz, D., & Coté, S. (1995). Do interpersonal traits predict affect? A comparison of three
models. Journal of Personality and Social Psychology, 69, 915-924.
Muthén, L.K. and Muthén, B.O. (1998-2010), Mplus User's Guide, 6th ed., Muthén and
Muthén, Los Angeles, CA.
Oerlemans, W. G., & Bakker, A. B. (2014). Why extraverts are happier: A day reconstruction
study. Journal of Research in Personality, 50, 11-22.
Pavot, W., Diener, E., & Fujita, F. (1990). Extraversion and happiness. Personality and
Individual Differences, 11, 12991306.
Pavot, W., & Diener, E. (1993). The affective and cognitive context of self-reported measures of
subjective well-being. Social Indicators Research, 28, 1-20.
Pickett, J., Hofmans, J., & De Fruyt, F. (2019). Extraversion and performance approach goal
orientation: an integrative approach to personality. Journal of Research in Personality, 82,
103846.
Pickett, J., Hofmans, J., Debusscher, J., & De Fruyt, F. (2020a). Counterdispositional
Conscientiousness and Well-being: How Does Acting Out of Character Relate to Positive
and Negative Affect At Work? Journal of Happiness Studies, 21, 1463-1485.
Pickett, J., Hofmans, J., Feldt, T., & De Fruyt, F. (2020b). Concurrent and lagged effects of
counterdispositional extraversion on vitality. Journal of Research in Personality, 87, 1-23.
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions
in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of
Educational and Behavioral Statistics, 31, 437-448.
Rauthmann, J. F., Horstmann, K. T., & Sherman, R. A. (2018). Do self-reported traits and
aggregated states capture the same thing? A nomological perspective on trait-state
homomorphy. Social Psychological and Personality Science, 10(5), 596-611.
Roe, R. A. (2005). No more variables, please. Giving time a place in work and organizational
psychology. Convivence in Organizations and Society, 11-20.
Sandvik, E., Diener, E., & Seidlitz, L. (2009). Subjective well-being: The convergence and
stability of self-report and non-self-report measures. Assessing well-being (pp. 119-138).
Springer, Dordrecht.
Saucier, G. (1994). Mini-Markers: A Brief Version of Goldberg’s Unipolar Big-Five Markers.
Journal of Personality Assessment, 63, 506516.
Schlegel, R., Hicks, J., Arndt, J., & King, L. (2009). Thine own self: True self-concept
accessibility and meaning in life. Journal of Personality and Social Psychology, 96, 473-
490.
Seitz, N. E., Westbrook, C. J., & Noble, B. F. (2011). Bringing science into river systems
cumulative effects assessment practice. Environmental Impact Assessment Review, 31, 172-
179.
Smillie, L. D., Cooper, A. J., Wilt, J., & Revelle, W. (2012). Do extraverts get more bang for the
buck? Refining the affective-reactivity hypothesis of extraversion. Journal of personality
and social psychology, 103(2), 306-326.
Smillie, L. D., DeYoung, C. G., & Hall, P. J. (2015). Clarifying the relation between
extraversion and positive affect. Journal of personality, 83(5), 564-574.
Sosnowska, J., Hofmans, J., & De Fruyt, F. (2019). Relating emotional arousal to work vigour: A
dynamic systems perspective. Personality and Individual Differences, 136, 178-183.
Sosnowska, J., Kuppens, P., De Fruyt, F., & Hofmans, J. (2019). A dynamic systems approach to
personality: The Personality Dynamics (PersDyn) model. Personality and individual
differences, 144, 11-18.
Steel, P., Schmidt, J., & Shultz, J. (2008). Refining the relationship between personality and
subjective well-being. Psychological Bulletin, 134, 138161.
Sun, J., Kaufman, S. B., & Smillie, L. D. (2018). Unique associations between big five
personality aspects and multiple dimensions of well‐being. Journal of personality, 86(2),
158-172.
Terracciano, A., McCrae, R. R., Brant, L. J., & Costa, P. T. (2005). Hierarchical linear modeling
analyses of the NEO-PI-R Scales in the Baltimore Longitudinal Study of Aging. Psychology
and Aging, 20, 493506.
van Allen, Z. M., & Zelenski, J. M. (2018). Testing Trait-State Isomorphism in a New Domain:
An Exploratory Manipulation of Openness to Experience. Frontiers in Psychology, 9, 1964.
Whelan, D. (2014). Extraversion and counter-dispositional behavior: Exploring consequences
and the impact of situation-behavior congruence (Doctoral dissertation, Carleton
University).
Wilt, J., Noftle, E. E., Fleeson, W., & Spain, J. S. (2012). The dynamic role of personality states
in mediating the relationship between extraversion and positive affect. Journal of
Personality, 80, 1205-1236.
Zelenski, J., Santoro, M., & Whelan, D. (2012). Would introverts be better off if they acted more
like extraverts? Exploring emotional and cognitive consequences of counterdispositional
behavior. American Psychological Association, 12, 290303.
Zelenski, J., Whelan, D., Nealis, L., Besner, C., Santoro, M., & Wynn, J. (2013). Personality and
affective forecasting: Trait introverts underpredict the hedonic benefits of acting
extraverted. Journal of Personality, 104, 10921108.
Table 1
Descriptive statistics, ICC, and zero-order correlations for the study variables. Within-person
correlations are above and between-person correlations are below the diagonal.
M
SDwithin
SDbetween
1
2
1. Extraversion (state)
4.91
.74
1.03
-
.38**
2. Positive feelings
5.02
.97
1.23
.53**
-
Note: *** p<.001; ** p<.01; * p<.05.
Table 2
Multilevel regression parameters relating weekly counterdispositional extraversion to positive feelings.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
4.95
.09
[4.78, 5.11]
4.94
.09
[4.76, 5.11]
2.20
.47
[1.27, 3.12]
2.18
.48
[1.25, 3.11]
Ext
-.38
.13
[-.64, -.13]
-
-
-
-.07
.97
[-1.97, 1.83]
-
-
-
Ext_neg
-
-
-
-.70
.14
[-.98, -.42]
-
-
-
-2.86
1.12
[-5.04, -.66]
Ext_pos
-
-
-
.69
.19
[.31, 1.06]
-
-
-
-1.78
1.08
[-3.89, .33]
Trait
-
-
-
-
-
-
.57
.10
[.38, .75]
.57
.10
Trait * Ext
-
-
-
-
-
-
-.06
.19
[-.44, .31]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.42
.22
[-.01, .85]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.50
.23
[.06, .95]
Var
comp
Var
comp
Var
comp
Var
Com
p
Random
-
-
-
-
Intercept
.53
-
-
.57
-
-
.35
-
-
.39
-
-
Ext
.31
-
-
-
-
-
-
-
-
-
-
-
Ext_neg
-
-
-
.58
-
-
-
-
-
-
-
-
Ext_pos
-
-
-
.71
-
-
-
-
-
-
-
-
Trait
-
-
-
-
-
-
-
-
-
-
-
-
Trait * Ext
-
-
-
-
-
-
.29
-
-
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.60
-
-
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.39
-
-
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
Table 3
Descriptive statistics, ICC, and zero-order correlations for the study variables. Within-person
correlations are above and between-person correlations are below the diagonal.
M
SDwithin
SDbetween
1
2
1. Extraversion (state)
3.64
.73
.88
-
.14***
2. Positive affect
3.33
.77
.91
.34**
-
Note: *** p<.001; ** p<.01; * p<.05.
Table 4
Multilevel regression parameters relating counterdispositional extraversion to positive affect.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
3.32
.07
[3.18, 3.46]
3.32
.07
[3.18, 3.46]
2.23
.48
[1.37, 3.25]
2.31
.48
[1.37, 3.25]
Ext
-.05
.12
[-.29, .20]
-
-
-
.35
.69
[-1.00, 1.70]
-
-
-
Ext_neg
-
-
-
-.14
.13
[-.39, .11]
-
-
-
-.34
.76
[-1.83, 1.16]
Ext_pos
-
-
-
.19
.17
[-.14, .53]
-
-
-
-1.08
1.63
[-4.27, 2.11]
Trait
-
-
-
-
-
-
.27
.13
[.02, .53]
.27
.13
[.02, .53]
Trait*ext
-
-
-
-
-
-
-.11
.17
[-.44, .24]
-
-
-
Trait *ext_neg
-
-
-
-
-
-
-
-
-
.06
.19
[-.30, .42]
Trait * ext_pos
-
-
-
-
-
-
-
-
-
.36
.46
[-.54, 1.26]
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = average state extraversion.
Figure 1. State personality distributions for three different (hypothetical) individuals. The dotted
lines represent the average state (or trait) levels.
Figure 2. Histogram of the slopes relating positive feelings and counterdispositional
extraversion.
Figure 3. Histogram of the slopes predicting positive feelings from positive (left) and negative
(right) counterdispositional extraversion.
Figure 4. Simple slopes plot relating negative (left) and positive (right) counterdispositional
extraversion to positive feelings for people low (-1 SD) and high (+1 SD) on average state
extraversion.
Supplementary materials
Table S1
Multilevel regression parameters relating weekly counterdispositional extraversion to positive feelings. Minimum of three
observations per week.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
4.92
.09
[4.75, 5.09]
4.92
.09
[4.74, 5.10]
1.97
.49
[1.02, 2.93]
1.58
.51
[.59, 2.57]
Ext
-.31
.11
[-.53, -.10]
-
-
-
.39
.80
[-1.18, 1.96]
-
-
-
Ext_neg
-
-
-
-.60
.13
[-.85, -.35]
-
-
-
-1.90
.94
[-3.75, -.05]
Ext_pos
-
-
-
.74
.16
[.43, 1.06]
-
-
-
-.99
.97
[-2.90, .920]
Trait
-
-
-
-
-
-
.61
.10
[.41, .80]
.69
.10
Trait * Ext
-
-
-
-
-
-
-.14
.16
[-.45,.17]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.26
.19
[-.11,.62]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.36
.21
[-.04,.76]
Var
comp
Var
comp
Var
comp
Var
comp
Random
-
-
-
-
Intercept
.50
-
-
.59
-
-
.30
-
-
.37
-
-
Ext
.22
-
-
-
-
-
-
-
-
-
-
-
Ext_neg
-
-
-
.45
-
-
-
-
-
-
-
-
Ext_pos
-
-
-
.34
-
-
-
-
-
-
-
-
Trait
-
-
-
-
-
-
-
-
-
-
-
-
Trait * Ext
-
-
-
-
-
-
.21
-
-
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.44
-
-
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.17
-
-
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
Table S2
Multilevel regression parameters relating weekly counterdispositional extraversion to positive feelings. Minimum of four observations
per week.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
4.99
.08
[4.83, 5.15]
4.99
.08
[4.82, 5.15]
2.32
.49
[1.36, 3.27]
2.32
.49
[1.36, 3.28]
Ext
-.34
.13
[-.60, -.09]
-
-
-
.49
1.0
[-1.49, 2.46]
-
-
-
Ext_neg
-
-
-
-.53
.13
[-.79, -.28]
-
-
-
-2.66
1.1
[4.74, -.57]
Ext_pos
-
-
-
.89
.19
[.52, 1.27]
-
-
-
-2.91
1.2
[-5.27, -.54]
Trait
-
-
-
-
-
-
.55
.10
[.35, .74]
.54
.10
[.35, .74]
Trait * Ext
-
-
-
-
-
-
-.17
.20
[-.56, .22]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.41
.21
[.01, .82]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.79
.25
[.29, 1.28]
Var
comp
Var
comp
Var
comp
Var
comp
Random
-
-
-
-
Intercept
.47
-
-
.49
-
-
.31
-
-
.34
-
-
Ext
.32
-
-
-
-
-
-
-
-
-
-
-
Ext_neg
-
-
-
.43
-
-
-
-
-
-
-
-
Ext_pos
-
-
-
.60
-
-
-
-
-
-
-
-
Trait
-
-
-
-
-
-
-
-
-
-
-
-
Trait * Ext
-
-
-
-
-
-
.31
-
-
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.44
-
-
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.31
-
-
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
Table S3
Multilevel regression parameters relating weekly counterdispositional extraversion to positive feelings. Minimum of five observations
per week.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
4.99
.08
[4.83, 5.15]
4.99
.08
[4.83, 5.15]
2.46
.47
[1.55, 3.38]
2.50
.46
[1.59, 3.41]
Ext
-.36
.14
[-.63, -.08]
-
-
-
.80
1.08
[-1.32, 2.92]
-
-
-
Ext_neg
-
-
-
-.57
.14
[-.85, -.30]
-
-
-
-2.45
1.14
[-4.69, -.21]
Ext_pos
-
-
-
.88
.19
[.50, 1.26]
-
-
-
-2.52
1.25
[-4.96, -.07]
Trait
-
-
-
-
-
-
.52
.09
[.33, .70]
.51
.09
[.33, .70]
Trait * Ext
-
-
-
-
-
-
-.23
.21
[-.65, .19]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.37
.22
[.07, .81]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.70
.26
[.19, 1.22]
Var
comp
Var
comp
Var
comp
Var
comp
Random
-
-
-
-
Intercept
.47
-
-
.50
-
-
.32
-
-
.35
-
-
Ext
.39
-
-
-
-
-
-
-
-
-
-
-
Ext_neg
-
-
-
.52
-
-
-
-
-
-
-
-
Ext_pos
-
-
-
.58
-
-
-
-
-
-
-
-
Trait
-
-
-
-
-
-
-
-
-
-
-
-
Trait * Ext
-
-
-
-
-
-
.37
-
-
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.52
-
-
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.33
-
-
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
Table S4
Multilevel regression parameters relating two-day counterdispositional extraversion to positive feelings.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
4.99
.09
[4.82, 5.16]
4.99
.09
[4.82, 5.16]
2.39
.52
[1.37, 3.41]
2.4
.52
[1.38, 3.42]
Ext
-.32
.10
[-.51, -.13]
-
-
-
.39
.67
[-.96, 1.73]
-
-
-
Ext_neg
-
-
-
-.48
.09
[-.65, -.30]
-
-
-
-1.27
.67
[-2.59, .05]
Ext_pos
-
-
-
.84
.14
[.57, 1.12]
-
-
-
-1.23
.81
[-2.82, .37]
Trait
-
-
-
-
-
-
.53
.11
[ .33, .74]
.53
.11
[.32, .73]
Trait * Ext
-
-
-
-
-
-
-.14
.14
[-.41, .12]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.15
.13
[-.10, .41]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.43
.17
[.09, .76]
Var
comp
Var
comp
Var
comp
Var
comp
Random
-
-
-
-
Intercept
.53
-
-
.54
-
-
.38
-
-
.40
-
-
Ext
.28
-
-
-
-
-
-
-
-
-
-
-
Ext_neg
-
-
-
.21
-
-
-
-
-
-
-
-
Ext_pos
-
-
-
.34
-
-
-
-
-
-
-
-
Trait
-
-
-
-
-
-
-
-
-
-
-
-
Trait * Ext
-
-
-
-
-
-
.27
-
-
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.20
-
-
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.20
-
-
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
Table S5
Multilevel regression parameters relating counterdispositional extraversion to positive affect. Minimum of five observations per three
days.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
3.33
.07
[3.19, 3.47]
3.33
.07
[3.19, 3.47]
2.29
.47
[1.36, 3.23]
2.30
.48
[1.36, 3.23]
Ext
-.05
.12
[-.29, .19]
-
-
-
.37
.67
[-.94, 1.68]
-
-
-
Ext_neg
-
-
-
-.15
.13
[-.39, .10]
-
-
-
-.46
.74
[-1.91, .98]
Ext_pos
-
-
-
.22
.17
[-.11, .55]
-
-
-
-1.84
1.69
[-5.16, 1.48]
Trait
-
-
-
-
-
-
.28
.13
[.03, .54]
.28
.13
[.03, .54]
Trait * Ext
-
-
-
-
-
-
-.11
.17
[-.43, .22]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.10
.18
[-.25, .45]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.59
.48
[-.35, 1.53]
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
Table S6
Multilevel regression parameters relating counterdispositional extraversion to positive affect. Minimum of six observations per three
days.
Model 1
Model 2
Model 3
Model 4
Parameter
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Coeff
SE
CI
Fixed
Intercept
3.31
.07
[3.17, 3.46]
3.31
.07
[3.17, 3.46]
2.33
.50
[1.36, 3.31]
2.33
.50
[1.36, 3.31]
Ext
-.05
.14
[-.32, .22]
-
-
-
.07
.73
[-1.36, 1.50]
-
-
-
Ext_neg
-
-
-
-.13
.14
[-.40, .15]
-
-
-
-.68
.77
[-2.20, .84]
Ext_pos
-
-
-
.20
.19
[-.17, .57]
-
-
-
-2.71
1.83
[-6.30, .87]
Trait
-
-
-
-
-
-
.27
.14
[.01, .53]
.27
.14
[.01, .53]
Trait * Ext
-
-
-
-
-
-
-.03
.18
[-.38, .32]
-
-
-
Trait * Ext_neg
-
-
-
-
-
-
-
-
-
.16
.19
[-.21, .53]
Trait * Ext_pos
-
-
-
-
-
-
-
-
-
.82
.51
[-.19, 1.83]
Note. Ext = Counterdispositional extraversion. Ext_pos = Positive counterdispositional extraversion. Ext_neg = Negative counterdispositional
extraversion. Trait = Average state extraversion.
... Second, subjective well-being was assessed with a single-item measure. Although single-item measure has shown satisfactory reliability and validity in previous research (Abdel-Khalek, 2006), and been widely used in many empirical studies (e.g., Buijs et al., 2021;Du et al., 2019;Kuijpers et al., 2022), it is impossible to examine its internal consistency and important to replicate the present findings with better-validated multi-item measures. Third, it should be noted that our findings may not be generalized to adolescents from other cultures and the generalizability should be cautious, given that cultural differences in educational expectations (Pinquart & Ebeling, 2020). ...
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... While dispositional control is a strong predictor, so is context control or the skills one acquires to feel in control in a given situation (Gibbons, 2008;Maddi, 2002).Given the potential context control has over dispositional control in improving coping, it is this that is measured. Other important Big Five traits (McCrae & Costa, 2004) linked to successful coping include extraversion (Kuijpers et al., 2021), levels of emotional stability and openness (Vollrath & Torgersen, 2000). In education contexts, openness is important if learning is to expand; and both learnt and dispositional optimistic thinking strategies have been associated with performance, course satisfaction and well-being (Schwarzer, 1994;Seligman, 2008). ...
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Thesis
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... Important personality ingredients related to coping include those measured by the Big Five (McCrea & Costa, 2004), including extraversion (e.g. Kuijpers et al., 2021) and conscientiousness, levels of emotional stability and openness (e.g. Vollrath & Torgersen, 2000)in education contexts, openness is important if learning is to expand; and optimistic thinking strategies have been associated with improved well-being, performance and health (e.g. ...
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