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MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Individual Differences in Event-Related Personality Changes: A Systematic Review and
Coordinated Data Analysis
Peter Haehner, Amanda J. Wright, Michael D. Krämer, Wiebke Bleidorn
Department of Psychology, University of Zurich, Switzerland
Draft version November 5, 2024. This paper is submitted for publication but has not
yet been peer reviewed.
Abstract: Life events have been theorized to cause personality trait changes. However, people
differ in their personality trait trajectories after experiencing important life events. Although
several studies have examined the sources of these individual differences, a replicable set of
variables explaining individual differences in the reaction to major life events has yet to be
identified. In a systematic literature review (Study 1), we integrated existing evidence on
potentially important moderators of event-related changes in the Big Five personality traits
(23 studies, Ntotal = 82,374). This review showed that (1) only a limited set of moderators has
been linked to individual differences in event-related personality trait changes so far, and (2) that
there are vast differences in the methods used to examine these effects across studies,
complicating meta-analytical integration. To overcome these limitations, we conducted a
coordinated data analysis (Study 2), generating novel and robust evidence for associations
between a broad set of psychological, demographic, event-related, and contextual characteristics
and event-related personality trait changes. Across eight large-scale panel studies
(Ntotal = 90,934, Nobs = 391,024), we found several replicable moderators of event-related
personality changes. For example, age and psychological variables such as life satisfaction
moderated personality trait changes across datasets. Even though the effect sizes of the
moderators were (very) small (bMedian = 0.007, ΔR2Median = 0.16%), the findings of the coordinated
data analysis contribute to a more comprehensive understanding of event-related personality
change, critical for the advancement of contemporary personality development theories.
Keywords: personality change; life events; individual differences; moderator; coordinated data
analysis
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Author Note
Peter Haehner https://orcid.org/0000-0002-3896-6172
Amanda J. Wright https://orcid.org/0000-0001-8873-9405
Michael D. Krämer https://orcid.org/0000-0002-9883-5676
Wiebke Bleidorn https://orcid.org/0000-0003-3795-8143
This paper uses data from the German Family Panel pairfam, coordinated by Josef Brüderl, Sonja
Drobnič, Karsten Hank, Johannes Huinink, Bernhard Nauck, Franz J. Neyer, and Sabine Walper. The study
was funded from 2004 to 2022 as a priority program and long-term project by the German Research
Foundation (DFG). This paper uses data from the Health and Retirement Study (HRS). The HRS is sponsored
by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University
of Michigan. This paper uses unit record data from Household, Income and Labour Dynamics in Australia
Survey (HILDA) conducted by the Australian Government Department of Social Services (DSS). The
findings and views reported in this paper, however, are those of the authors and should not be attributed to
the Australian Government, DSS, or any of DSS’ contractors or partners. DOI:
https://doi.org/10.26193/R4IN30. This paper uses data from the Longitudinal Internet studies for the Social
Sciences (LISS panel) managed by the non-profit research institute Centerdata (Tilburg University, the
Netherlands). Funding for the panel's ongoing operations comes from the Domain Plan SSH and ODISSEI
since 2019. The initial set-up of the LISS panel in 2007 was funded through the MESS project by the
Netherlands Organization for Scientific Research (NWO). This paper uses data from the German Socio-
Economic Panel (GSOEP). We thank everyone at the German Institute for Economic Research (DIW)
involved in making data from the GSOEP available to the research community. This paper uses publicly
available data from the MIDUS study. Since 1995 the MIDUS study has been funded by the following: John
D. and Catherine T. MacArthur Foundation Research Network, National Institute on Aging (P01-
AG020166), and National institute on Aging (U19-AG051426). This paper uses data from the Children of
the NLSY79 survey. The Children of the NLSY79 survey is sponsored and directed by the U.S. Bureau of
Labor Statistics and the National Institute for Child Health and Human Development. The survey is managed
by the Center for Human Resource Research (CHRR) at The Ohio State University and interviews are
conducted by the National Opinion Research Center (NORC) at the University of Chicago. This paper uses
data from the National Educational Panel Study (NEPS; Blossfeld & Roßbach, 2019). The NEPS is carried
out by the Leibniz Institute for Educational Trajectories (LIfBi, Germany) in cooperation with a nationwide
network.
We would like to thank Marco Altorfer for his support in double-checking the data extraction process
for the coordinated data analysis. We have no conflicts of interest to report. Analysis scripts, coding
information, and supplemental materials are available at https://osf.io/9nqt3/. The systematic review was
preregistered at https://osf.io/ytsgk. The coordinated data analysis was preregistered at https://osf.io/3t5ax.
Peter Haehner: Conceptualization (lead), Data curation (lead), Formal analysis (lead),
Methodology (lead), Project administration (lead), Writing – original draft (lead). Amanda J. Wright:
Conceptualization (supporting), Methodology (supporting), Writing - Review & Editing (equal). Michael D.
Krämer: Conceptualization (supporting), Methodology (supporting), Writing - Review & Editing (equal).
Wiebke Bleidorn: Conceptualization (supporting), Methodology (supporting), Writing – review and editing
(equal), Supervision (lead).
Correspondence concerning this article should be addressed to Peter Haehner, Department of
Psychology, University of Zurich, Binzmühlestrasse 14/7, CH-8050 Zürich, Switzerland. Email:
p.haehner@psychologie.uzh.ch.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Individual Differences in Event-Related Personality Changes: A Systematic Review and
Coordinated Data Analysis
Although personality traits are relatively stable compared to other constructs, it is now
well established that traits change across the entire lifespan (Bleidorn et al., 2022; Damian et
al., 2019; Seifert et al., 2023). Major life events such as job loss or divorce have been discussed
as possible causes of personality trait changes (Bleidorn et al., 2018, 2020; Luhmann et al.,
2014), and several studies have examined whether major life events lead to mean-level changes
in personality traits (e.g. Asselmann & Specht, 2021b; Denissen et al., 2019; Van Scheppingen
et al., 2016). However, findings are inconsistent across studies, suggesting that major life events
have only small, if any, average effects on personality traits (Bleidorn et al., 2018, 2020; Bühler
et al., 2024).
In contrast to the mixed evidence for event-related mean-level changes in personality
traits, existing research consistently found that people differ in their personality trajectories
before and after experiencing major life events (Bleidorn et al., 2020; Denissen et al., 2019;
Schwaba & Bleidorn, 2018; Van Scheppingen et al., 2016). For example, there is evidence that
some people decrease in conscientiousness after experiencing a job loss, whereas others do not
change in this trait, and some people even seem to increase in conscientiousness after losing
their job (Denissen et al., 2019; Haehner, Bleidorn, et al., 2024).
Several studies have examined potential sources of these individual differences in
personality trait change (Asselmann & Specht, 2021b; Haehner, Bleidorn, et al., 2024; Schwaba
et al., 2023). However, a replicable set of variables that moderate changes in personality traits
in response to major life events is still missing. The lack of replicable evidence for moderator
effects may have to do with two challenges that complicate this line of research. First, as most
life events occur only infrequently, large-scale longitudinal studies are needed to detect
moderators of event-related personality changes with sufficient statistical power (Bleidorn et
al., 2020; Haehner, Bleidorn, et al., 2024). Second, integrating findings, for example with meta-
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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analytic tools, is necessary to identify replicable sources of individual differences and possible
reasons for discrepant results across studies (Buecker et al., 2023; Siddaway et al., 2019).
However, existing studies on moderators of event-related changes differ in their research
designs and approaches (e.g., analytical strategy), which precludes a systematic integration of
effect sizes (e.g., Asselmann & Specht, 2021b; Haehner, Bleidorn, et al., 2024; Reitz, Luhmann,
et al., 2022).
To overcome these challenges and advance our understanding of why people differ in
event-related personality trait changes, the present study used a twofold approach. First, we
conducted a systematic review to identify and summarize existing research on moderators of
event-related personality changes. Second, we conducted a coordinated data analysis (Graham
et al., 2022) using data from eight large-scale panel studies to identify variables that are
consistently linked to individual differences in event-related personality changes across
datasets. In doing so, we aim to identify a replicable set of moderators that shape the ways in
which personality traits change in response to major life events. This knowledge is critical for
the development and refinement of theories of personality development (Bleidorn, 2024).
Major Life Events and Personality Change
Major life events can be defined as discrete status transitions that mark the beginning or
end of a specific status (Bleidorn et al., 2018; Luhmann et al., 2012). This definition includes
events such as marriage, divorce, retirement, or graduation. Several theoretical approaches to
personality development converge on the idea that major life events can cause changes in
personality traits (Ormel et al., 2017; Specht et al., 2014). For example, the Neo-Socioanalytic
Theory (Roberts & Nickel, 2017) suggests that changes and investments in normative social
roles can foster personality change by eliciting changes in people’s routine thoughts, feelings,
and behaviors that are relevant to that role (social investment principle). Since many major life
events encompass such changes in social roles (e.g., becoming a jobholder or becoming a
parent), they may serve as triggers for personality trait changes (Lodi-Smith & Roberts, 2007).
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Similarly, the TESSERA framework (Wrzus & Roberts, 2017) suggests that major life events
can lead to personality change if they sustainably modify everyday sequences of personality
state expression. In particular, event-related personality change is expected to occur if life
events repeatedly change TESSERA sequences, that is, sequences of triggering situations,
expectations, state expression, and reactions. Finally, the Integrative State-Trait Process Model
(Geukes et al., 2018) predicts that major life events can lead to changes in biological
mechanisms (e.g., hormone levels) or environmental structures (e.g., social roles). These
changes, in turn, can cause shifts in the expression of personality states in everyday life and, if
reinforced, subsequently manifest as long-term personality trait development. In summary, the
common theme across different theories on personality development is that major life events
can drive personality changes by leading to lasting changes in the expression of personality
states in everyday life (Bleidorn et al., 2020).
In contrast to these consensual theoretical predictions, the empirical evidence for the
effects of life events on changes in personality traits is less consistent. For example, whereas
some studies found the birth of a child to be associated with a decrease in extraversion
(Asselmann & Specht, 2021b), other studies found no effects of childbirth on change in any of
the Big Five traits (Denissen et al., 2019; Van Scheppingen et al., 2016). Recently, Bühler et
al. (2024) meta-analytically integrated findings from 44 longitudinal studies on event-related
mean-level changes in personality traits. The results showed that, first, major life events are
only weakly related to mean-level changes in personality traits (0.02 ≤ |d| ≤ 0.09), and second,
that there is substantial heterogeneity across studies in the effect size estimates. Based on these
findings, some scholars concluded that major life events are unrelated to personality trait change
(Hopwood et al., 2024).
However, it may be premature to conclude that life events are not important for
personality trait change, because there is consistent evidence that people differ substantially in
how their personality traits change in the context of major life events (Bleidorn et al., 2020;
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Denissen et al., 2019; Schwaba et al., 2023; Van Scheppingen et al., 2016). Focusing on the
average change trajectory may thus be insufficient because this heterogeneity implies that the
effects of life events could differ across people. Thus, to better understand event-related
personality trait changes, we suggest to capitalize on this variability by examining individual
differences in the reaction to major life events and the variables that can explain these
differences.
Understanding Individual Differences in Event-Related Personality Trait Change
Although people differ in their personality trait development throughout the lifespan
(Bleidorn et al., 2021; Haehner, Hopwood, et al., 2024; Schwaba & Bleidorn, 2018), individual
differences in personality trajectories tend to become more pronounced in the context of major
life events (Denissen et al., 2019; Wright & Jackson, 2024). Thus, examining the sources of
individual differences in event-related personality trait changes seems warranted to better
understand why people differ in their reaction to major life events (Haehner, Bleidorn, et al.,
2024). For example, differences in psychological characteristics like perceived control and
personal sense of mastery or demographic characteristics like gender and education could
explain why some people are better equipped to deal with major life events and thus show more
favorable personality trait changes.
A better understanding of the relevant moderators of event-related personality trait
changes is important for several reasons. First, findings on moderators of event-related
personality trait changes could advance theoretical accounts of personality development
through concrete predictions about the conditions that may or may not lead to trait change after
the occurrence of a major life event (Haehner, Kritzler, et al., 2024). Second, as existing
research has consistently shown that there are substantial individual differences in how people
react to major life events, identifying factors that relate to differential change trajectories could
help clarify conflicting findings and gain a more fine-grained picture of event-related
personality changes (Bleidorn et al., 2020, 2022; Denissen et al., 2019; Haehner, Bleidorn, et
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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al., 2024). Third, information about the sources of individual differences of event-related
personality changes could be used to prevent potentially unwanted personality trait changes
(Bleidorn et al., 2019). For example, preventative measures could be employed if people with
certain demographic characteristics are more likely to increase in their neuroticism after
experiencing widowhood (Matz et al., 2023).
Empirical Research on Moderators of Event-Related Personality Change: Challenges and
Future Directions
Although understanding moderators of event-related personality changes is important,
empirical research on this topic is still limited, which may be a consequence of the specific data
requirements that need to be fulfilled to examine these effects. First, a rigorous test of event-
related personality changes requires prospective, longitudinal studies, ideally with several
measurement occasions before and after a major life event to examine non-linear personality
changes and anticipation effects of major life events (Bleidorn et al., 2020). Second, to reliably
detect individual differences in personality trait trajectories, large sample sizes are needed as
most life events occur infrequently (Luhmann et al., 2014). Third, compared to the examination
of mean-level changes, even larger sample sizes are needed for quantifying effects of
moderators because effect sizes of variables explaining individual differences tend to be
relatively small (Haehner, Bleidorn, et al., 2024).
Large-scale panel datasets such as the German Socio-Economic Panel or the
Longitudinal Internet Studies for the Social Sciences generally are well-suited to examine
sources of individual differences in personality trait changes as they fulfill these data
requirements (Luhmann et al., 2014). Some initial studies have already taken advantage of these
datasets to examine, for example, whether certain demographic characteristics moderate event-
related personality changes (e.g., Asselmann & Specht, 2020b, 2021b; Schwaba & Bleidorn,
2019). However, despite their common goal, findings are not necessarily consistent across
studies. For instance, some studies have found gender differences in personality changes after
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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unemployment (Boyce et al., 2015) while others have not (Gnambs & Stiglbauer, 2019; Specht
et al., 2011). Such diverging findings could be explained by the reliance on different subsamples
within a single dataset; different datasets that vary in their country of origin and study design;
or different analytical strategies that are used to estimate event-related change and moderators
thereof.
Thus, a systematic review and integration of the existing research is needed to identify
moderators that can explain individual differences in personality trait changes observed across
samples, events, and study designs (Bühler et al., 2024). In particular, a meta-analytic
aggregation of effect size estimates across samples would increase the statistical power needed
to draw conclusions about the relevance of certain moderators (Buecker et al., 2023; Siddaway
et al., 2019). However, for a meaningful meta-analytic aggregation, effect size estimates need
to be comparable across studies. This is a challenge for longitudinal research on event-related
changes as different statistical methods and effect size estimates have been used to examine
sources of individual differences (e.g., subgroup analyses in multilevel models versus variables
predicting a latent personality change score; Asselmann & Specht, 2020; De Vries et al., 2021).
Results from these different statistical methods cannot be meaningfully integrated in a meta-
analysis because a uniform standardized effect size is missing. Thus, in addition to providing
an overview of the existing evidence on individual differences in event-related personality
changes, research is necessary that (1) examines a broad set of possible moderators, (2) draws
on data sources that fulfill the mentioned design requirements, and (3) uses compatible
statistical methods to allow an integration of findings.
Identifying Candidate Moderators of Event-Related Personality Changes
To guide further research on the sources of individual differences in event-related
personality trait changes, the literature on event-related changes in other constructs may provide
some indication on possible moderators for personality changes (cf. Haehner, Bleidorn, et al.,
2024). Theories and research in the fields of subjective well-being and mental health (Abramson
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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et al., 1989; A. T. Beck & Bredemeier, 2016; Halford & Sweeper, 2013; Houwen et al., 2010;
Park, 2010; Stroebe et al., 2006) have highlighted four clusters of moderators that may also be
relevant for event-related personality trait changes: demographic variables, psychological
variables, event-related variables, and contextual variables.
Demographic Variables
First, demographic differences in age, gender, or socio-economic status may explain
why people react differently to major life events. For gender, different social expectations may
lead men and women to experience different personality trait changes in the context of major
life events, and, for some events in particular (e.g., pregnancy), biological differences
associated with sex may also play a role (Asselmann & Specht, 2020b, 2021b). For example,
during pregnancy, hormonal changes and social expectations differ between men and women,
which likely is related to how expecting mothers and fathers think, feel, and behave (Asselmann
& Specht, 2021b; Costantine, 2014; Kaźmierczak & Karasiewicz, 2019). Similarly, biological
changes and changing social expectations may also lead to age-graded effects of life events on
personality trait change. For example, some events, like parenthood or widowhood, are
normative in certain life phases and may have different effects on people in that life phase
compared to younger or older people (Haehner, Schaefer, et al., 2024; Hutteman et al., 2014).
Finally, people’s personality trait changes in response to major life events may also depend on
their socio-economic status (Mancini et al., 2011). For example, the experience of
unemployment may be less stressful for people with high educational achievements and
sufficient financial resources, which may, in turn, buffer people from potentially negative event
effects on their personality traits.
Psychological Variables
Psychological variables, which refer to internal processes, attitudes, or characteristics
of a person, may influence how people react to major life events. There is some evidence that
a personal sense of mastery and internal control beliefs may be relevant for how people deal
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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with major life events (Reitz, Den Boer, et al., 2022). For example, people who feel more
competent in dealing with changing environments may be better off after experiencing major
life events and may thus show more adaptive personality trait changes (Haehner, Bleidorn, et
al., 2024; Reitz, Den Boer, et al., 2022). Similarly, it has been suggested that religious beliefs
might help people deal with stressful experiences (Stroebe et al., 2006). Religion can provide a
meaning system of the world and immediate environment, which may help people to make
sense of major life transitions (Park, 2010; Stroebe et al., 2006). As such, higher levels in
religiosity may be related to more adaptive personality trait changes (Bleidorn et al., 2024;
Entringer et al., 2021).
Event-Related Variables
Differences in the ways an event is experienced may moderate event-related personality
changes (Haehner, Kritzler, et al., 2024; Luhmann et al., 2021). The perception of major life
events captures people’s subjective experience of an event and there is initial evidence that
individual differences in how people perceive major life events are related to differential trait
changes after experiencing such events (Fassbender et al., 2022; Haehner, Bleidorn, et al., 2024;
Haehner et al., 2023; Schwaba et al., 2023). Furthermore, major life events also differ in their
objective characteristics. For example, the loss of a job may differ in its objective characteristics
such as the reason for the job loss or the duration of previous employment. These objective
characteristics may shape people change in their personality traits after the occurrence of life
events (Haehner, Bleidorn, et al., 2024; Haehner, Kritzler, et al., 2024; Lawes et al., 2024).
Similarly, the reaction to an important event may be qualified by previous experiences with
similar events or depend on whether an event occurred at a normative age or not (Haehner,
Schaefer, et al., 2024; Krämer et al., 2024). For instance, there is initial evidence that repeated
occurrences of unemployment lead to more pronounced decreases in well-being (Luhmann &
Eid, 2009) and that well-being gains after childbirth are more pronounced for earlier-born
children (Krämer et al., 2024).
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Contextual Variables
Finally, the reaction to an event likely also depends on the broader environmental
context in which an event occurs (Bleidorn et al., 2020; Haehner, Bleidorn, et al., 2024). For
example, cultural differences could explain individual differences in event-related personality
changes (Bleidorn et al., 2020). Societal expectations regarding how people experience and
react to certain life events differ across cultures (McAdams, 2001; Ngo & Le, 2007), which
may be related to differences in personality trait changes (Bleidorn et al., 2013). Furthermore,
in research on event-related changes in mental health, people’s social network and social
support have been among the most promising variables to explain individual differences in
changes in these constructs (e.g., Abramson et al., 1989; Scott et al., 2020; Stroebe et al., 2006).
For example, social support may buffer potential harmful consequences of negative life events
on mental health (Lin et al., 2013; Stroebe et al., 2006).
The Present Study
Although there are several candidate moderators of event-related personality trait
changes, robust evidence on which variables predict individual differences in event-related
personality trait changes is still missing. To reach this goal, we first conducted a systematic
review of the existing literature to summarize and integrate what is already known about the
sources of individual differences in the reaction to major life events. In particular, we aimed to
address three research questions with this review: (1) Which variables have been examined as
potential sources of individual differences in event-related personality changes? (2) Which
statistical models and effect size estimates have been used to describe individual differences in
the reaction to major life events? (3) Which variables moderate event-related personality
change?
Second, to test a broader set of possible moderating variables and enrich the existing
empirical work, we conducted a coordinated data analysis across eight large-scale panel studies
examining various potential moderators of event-related personality trait changes. This
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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coordinated data analysis provided results on moderators of event-related personality changes
using the same statistical models across datasets so that findings could be integrated using meta-
analytic tools, thus showing which variables are consistently related to individual differences
in event-related personality changes.
Transparency and Openness
The systematic literature review was preregistered at
https://osf.io/ytsgk/?view_only=7a36970bc32f4585a3840d5c242b204e and the coordinated
data analysis was preregistered at
https://osf.io/3t5ax/?view_only=60fcaa7641574bf19a1c6aa4e1ceafb0. Deviations from these
preregistrations are summarized in an HTML document with Supplemental Materials, uploaded
to OSF. Furthermore, this OSF project contains (1) detailed coding instructions and a file with
the extracted data of the systematic review, and (2) R scripts, a codebook, and detailed coding
information for the coordinated data analysis. Data used in the coordinated data analysis cannot
be shared publicly due to legal constraints, but they are available to researchers after signing
user contracts with the respective providers (see below for details). Ethical review was not
required as this project is based on secondary data only. For all empirical studies in this article,
we report how we determined our sample size, all data exclusions, all manipulations, and all
measures in the study.
Study 1: Systematic Review
The aim of the systematic review was to summarize and integrate existing research on
moderators of event-related personality changes. The review was conducted in accordance with
the PRISMA guidelines (Page et al., 2021) and preregistered in accordance with the PRISMA-P
checklist (Moher et al., 2016).
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Methods
Search Strategy
We conducted a systematic literature search on PsycINFO on September 4, 2023. We
used personality-related (e.g., “personality” or “Big Five”), event-related (e.g., “life event*” or
“transition”), and design-related search terms (e.g., “longitudinal” or “prospective”; see
Section 1 of the Supplemental Materials for details). Furthermore, we also executed a backward
search by screening the reference lists of existing reviews on event-related personality changes
(Bleidorn et al., 2018; Bühler et al., 2024; Specht, 2017). Figure 1 summarizes our literature
search and study selection.
Inclusion Criteria
Studies that were eligible to be included in the review fulfilled the following criteria:
(1) empirical, quantitative study with human subjects (e.g., no reviews or qualitative studies),
(2) written in English or German, (3) longitudinal study (i.e., two or more assessments of the
same people), (4) examination of the effects of a single life event (e.g., job loss, divorce), (5)
prospective design (i.e., at least one measurement occasion took place before the event occurred
and at least one measurement occasion took place after the event occurred), (6) assessment of
Big Five personality traits in accordance with general design requirements (i.e., longitudinally,
prospectively, using the same instrument), and (7) assessment of a moderation effect: the
association between at least one variable and personality changes from pre-event to post-event
assessments has been examined.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Figure 1
PRISMA Flowchart on Study Inclusion
Search
Step 1: ScreenedStep 2: Eligibility
Included
Database sear ch
(PsycINFO)
(k= 2,502)
Screening reference list of
relevan t reviews (k= 167)
Titl es an d a bst ra ct s
screened
(k= 2,590)
Records excluded
(k= 2,446)
Full texts evaluated
for eligibility
(k= 136)
Full texts excluded (k= 113)
for the following reasons:
-Not empirical, quantitative (k= 16)
-Not in English or German (k= 1)
-Not longitudina l (k= 15)
-Not prospe ctive (k= 5)
-No life event (k= 26)
-No assessment of traits (k= 10)
-Assessment of traits not matching
design req uirements (k= 7)
-No mod eration effect (k = 33)
Studies included in systematic review
(k= 23)
Full text unavailable
(k= 8)
Records after duplicates remov ed
(k= 2,590)
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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Coding Procedure
Coding was done in a two-step procedure. In Step 1, we evaluated eligibility for
inclusion based on titles and abstracts. In this step, we decided to be rather overinclusive: We
included all studies in Step 2 that examined event-related personality trait changes regardless
of whether testing a moderation effect was mentioned. In Step 2, we evaluated eligibility for
inclusion based on full texts. For eligible studies, we coded information about the sample (e.g.,
sample size, age information, sample type), the study design (e.g., number of measurement
occasion, time lag between assessments), the assessment of personality traits (e.g., measures),
the examined moderator (e.g., type of moderator), the analyses (e.g., model type, effect sizes),
and the results. More details about the coding procedure can be found in the coding instructions
on OSF.
Eligibility screening was done by four independent, trained coders (three post-docs, and
one graduate student). In both steps, 100 studies were double-coded to examine intercoder
agreement. Intercoder agreement regarding study inclusion was 97% in Step 1 and 96% in
Step 2. Data extraction of eligible articles was done by three independent, trained coders (three
post-docs) and one third of the eligible studies was double coded. Intercoder agreement ranged
from 83% (e.g., for mean age) to 100% (e.g., for the number of personality assessments), with
an average intercoder agreement of 98%. Divergent coding was resolved through discussion.
Results
The systematic review is accompanied by an interactive ShinyApp (https://life-event-
research.shinyapps.io/Review_Moderators/), which allows users to filter, select, and sort the
review results according to their personal interests (e.g., restricting findings to certain life events
or sorting results based on the used dataset).
Overview of Included Articles and Datasets
The systematic literature search identified 23 eligible studies (22 journal articles, 1
dissertation), with a total of 103 sample-event combinations. Table 1 provides an overview of
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
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the sample and study design characteristics of these studies. Studies were published between
2000 and 2022 (M = 2017, SD = 4.80), and relied on 13 different datasets, six of which were
based on large-scale representative panel studies such as the German Socio-Economic Panel.
Furthermore, datasets stemmed from only five different Western countries: Germany (k = 5),
Netherlands (k = 3), US (k = 2), UK (k = 1), and Australia (k = 1). On average, personality traits
were assessed three times (SD = 1.29) with an average time lag between the first and the last
personality assessment of 4.52 years (SD = 2.95). Sample sizes from included studies ranged
from 168 to 5,005 (M = 1,160, SD = 1,205). Finally, the mean age of samples was 36.78 years
(SD = 14.60) and samples on average consisted of 53% female participants (SD = 9.63).
Which Variables Have Been Examined as Moderators of Event-Related Personality
Changes?
Table 2 summarizes the moderators identified in the systematic literature review.
Overall, 25 different variables have been examined as possible sources of individual differences
in personality changes, with most variables belonging to the demographic or event-related
cluster. Generally, age (37 samples) and gender (35 samples) were examined most frequently
as moderators of personality trait changes. All other moderators have each been examined in
only one study and in less than five different subsamples.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
17
Table 1
Studies and Datasets Included in the Systematic Review
Study
Dataset
Country
N
Personality
assessments
Life events
Moderators
Anger et al. (2017)
GSOEP
Germany
336
2
Unemployment
Education, Unemployment experience
Asselmann & Specht (2020a)
GSOEP
Germany
9088
4
Marriage, Separation, Divorce
Gender
Asselmann & Specht (2021a)
GSOEP
Germany
18507
4
First job, Retirement
Gender, Age, Employment before/after transition
Boyce et al. (2015)
GSOEP
Germany
922
2
Unemployment, Reemployment
Gender, Unemployment duration
Bühler et al. (2022)
GSOEP
Germany
15832
4
Relationship, Cohabitation, Marriage, Separation,
Divorce, Widowhood
Age
Bühler et al. (2022)
HILDA
Australia
12713
4
Relationship, Cohabitation, Marriage, Separation,
Divorce, Widowhood
Age
Bühler et al. (2022)
UndSoc
UK
3409
2
Cohabitation, Marriage, Separation, Divorce,
Widowhood
Age
Chopik (2018)
CLOC
US
510
3
Widowhood
Caregiver status, Expectedness of death
Costa et al. (2000)
UNCAHS
US
719
2
Promotion, Divorce
Gender
Deventer et al. (2019)
TOSCA
Germany
4636
3
Graduation
Age, Gender
Galdiolo & Roskam (2014)
Ad-hoc
408
3
Childbirth
Gender
Gnabms & Stiglbauer (2019)
NEPS
Germany
10010
2
Unemployment
Gender, Previous unemployment experience
Pusch et al. (2019)
Pairfam
Germany
4888
2
First residence, Separation, First job, Leaving
parental home, Relationship, Childbirth
Age group
Reitz et al. (2022)
GradLife
Netherlands
440
5
First job
Gender, Work-related mastery
Rohr et al. (2013)
GSOEP
Germany
496
2
Becoming a caregiver
Gender
Schwaba & Bleidorn (2019)
LISS
Netherlands
6210
6
Retirement
Gender, Early retirement, Age, Time spent
volunteering, Subjective health, Days per week
performing physical activities, Social connectedness,
Financial hardship, Other Big Five traits
Specht et al. (2011)
GSOEP
Germany
8044
2
Marriage, Cohabitation, Divorce, Separation,
Widowhood, Leaving parental home, Child leaves
home, Childbirth, Death of parent,
Unemployment, Retirement, First job
Gender
Specht et al. (2013)
GSOEP
Germany
2113
2
Marriage, Childbirth, First job
Life satisfaction
Spikic et al. (2021)
GSOEP
Germany
1382
2
Divorce
Gender, Separation duration
Spikic et al. (2021)
HILDA
Australia
752
2
Divorce
Gender, Separation duration
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
18
Spikic et al. (2021)
UndSoc
UK
182
2
Divorce
Gender
Wagner et al. (2015)
TOSCA
Germany
176
3
Relationship
Age
Wortman (2017)
GSOEP
Germany
5370
3
Marriage, Childbirth
Gender, Age, Normativeness
Wortman (2017)
HILDA
Australia
4374
3
Marriage, Childbirth
Gender, Age, Normativeness
de Vries et al. (2021)
GEPP
Germany
1442
2
Graduation, Leaving parental home
Event perception (valence)
den Boer et al. (2019)
CAMOR
Netherlands
1226
3
First job
Work commitment, Work reconsideration
van Dijk et al. (2020)
CAMOR
Netherlands
2094
6
Leaving parental home, Relationship, First job,
Marriage
Age
van Scheppingen et al. (2016)
HILDA
Australia
1668
2
Childbirth
Age, Time since event, Gender
Note. This table provides an overview of the studies and datasets that were included in the systematic review. Sample sizes are collapsed across all included life events.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
19
Table 2
Identified Moderators of Event-Related Personality Trait Changes
Moderator
Frequency
% Significant
Demographic variables
Age
Gender
Education
73
37
35
1
2%
11%
—
Psychological variables
Life satisfaction
Other Big Five traits
Subjective health
Work commitment
Work reconsideration
Work-related mastery
8
3
1
1
1
1
1
20%
—
—
—
—
—
Event-related variables
Occurrence of event at normative age
Type of employment before/after transition
Event perception
Duration of separation
Unemployment duration
Time since event occurrence
Unemployment experience
Caregiver status
Early retirement
Expectedness of death
Previous event experience
18
4
2
2
2
2
1
1
1
1
1
1
10%
0%
10%
30%
40%
—
—
—
—
—
—
Contextual variables
Performing physical activities
Social connectedness
Financial hardship
Time spent volunteering
4
1
1
1
1
—
—
—
—
Note. Frequency refers to the number of available sample-event combinations for a certain moderator (with a total
number of k = 103). In the column “% Significant”, we provide an overview of how frequently a moderator was
significantly related to personality trait changes in the included studies. We provide this estimate only for
moderators that have been examined in more than one sample-event combination. This column is intended to
provide a brief (and approximate) overview of the results. More details on the results can be found in the ShinyApp.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
20
Which Statistical Methods Have Been Used to Examine Moderators of Event-Related
Personality Trait Changes?
Different statistical methods have been used to examine moderators of event-related
personality trait changes. Each study in our review used a single method, with the most common
being latent change score models (8 studies), multilevel models (6 studies), and latent growth
curve models (5 studies). Regarding effect size estimates, most studies provided some form of
path coefficient or regression coefficient as an effect size estimate (17 studies); that is, a
coefficient quantifying the relationship between a certain moderator and personality trait
changes. However, in most studies, effect size estimates were not standardized (15 studies). In
particular, no study provided an effect size estimate with a fixed value range (e.g., R2).
What Are Relevant Moderators of Event-Related Personality Change?
In the following, we briefly describe the results for the different moderator domains.
More details about the methods and results of the included studies can be found in the ShinyApp
and in Section 1 of the Supplemental Materials. To gauge the relevance of a moderator, we
included a percentage of significant results across traits, studies, and events (e.g., 10% if a
moderator was significant in 1 of 10 statistical tests) for each moderator in Table 2.
Demographic Moderators. Age, gender, and education have been examined as
demographic moderators. First, there was no consistent evidence that age moderated event-
related personality changes. Across the 37 samples, there were only two studies reporting that
age moderated changes in conscientiousness in the context of a new relationship (Bühler et al.,
2022) and in context of childbirth (Van Scheppingen et al., 2016). However, other studies
examining the same events did not identify such effects (e.g., Pusch et al., 2019; Wortman,
2017). Likewise, for other life events no significant moderation effects of age were found.
Second, there was evidence that gender moderated event-related personality trait
changes—at least for some events. Specifically, Wortman (2017) found in two samples that
men show greater declines in agreeableness after marriage than women. Furthermore, Specht
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
21
et al. (2011) found that men – compared to women – become more open after a separation,
more conscientious after widowhood, and less emotionally stable after moving out from
parental home. However, results were partly inconsistent across studies (Gnambs & Stiglbauer,
2019; Specht et al., 2011) and for some events, such as first job or retirement, there seem to be
no gender differences (e.g., Asselmann & Specht, 2021a; Schwaba & Bleidorn, 2019; Specht
et al., 2011).
Third, educational background has been examined as a moderator in only one study on
personality trait changes following unemployment (Anger et al., 2017). This study compared
personality trait changes of subsamples with high versus low education and found that openness
increased after unemployment only for individuals with a high level of education.
Psychological Moderators. Life satisfaction, work-related psychological variables,
personality traits, and subjective health have been examined as psychological moderators. First,
regarding life satisfaction, Specht et al. (2013) found that people with higher levels of life
satisfaction experienced stronger event-related increases in agreeableness after marriage and
childbirth. Second, there was initial evidence that different work-related psychological (work
commitment, work reconsideration, and work-related mastery) moderated personality trait
changes during the transition to the first job (den Boer et al., 2019; Reitz, Den Boer, et al.,
2022). Third, Schwaba and Bleidorn (2019) examined whether the other Big Five traits and
subjective health moderated personality trait changes during the transition to retirement.
However, no moderation effects were found in this study.
Event-Related Moderators. Time since event occurrence, objective event
characteristics, event perception, and event normativeness were examined as event-related
moderators. First, there was little evidence that objective event characteristics moderated
personality trait changes. For example, in the context of retirement and new employment,
neither early retirement status (yes versus no) nor the type of employment (full time versus not
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
22
full time) moderated personality trait changes (Asselmann & Specht, 2021a; Schwaba &
Bleidorn, 2019).
Second, variables related to the timing of life events or the duration of previous phases
before a transition were associated with personality trait changes and the result pattern was
relatively consistent across events and studies (Anger et al., 2017; Boyce et al., 2015; Spikic et
al., 2021; Van Scheppingen et al., 2016). For example, Boyce et al. (2015) found that a longer
unemployment duration was associated with a stronger increase in openness and agreeableness
after experiencing the events unemployment or reemployment.
Third, the perception of life events seemed to predict personality changes for some
events. Perceiving the event graduation more positively was associated with stronger increases
in extraversion and emotional stability, whereas the perceived valence of the event leaving the
parental home was unrelated to personality changes (De Vries et al., 2021).
Contextual Moderators. Contextual moderators such as financial hardship and social
connectedness have only been examined in one study on personality trait changes during the
transition to retirement (Schwaba & Bleidorn, 2019). This study found no associations between
the examined contextual moderators and individual differences in personality traits changes.
Summary. Age and gender emerged as the most frequently examined moderators to
explain individual differences in personality trait trajectories, with gender being the most
promising candidate for moderating personality trait changes in the context of some major life
events such as marriage or leaving the parental home.
However, conclusions about the relevance of specific moderators of event-related
personality trait changes may be premature given that the systematic review indicated several
limitations of existing research. First, except age and gender, other moderators have only been
examined in one single study. Thus, more research replicating potential moderator effects in
independent samples is needed. Second, only a limited set of moderators has been tested in
relation to specific life events and possibly relevant moderators such as social support or income
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
23
have not been tested at all. Third, even for age and gender, it was not possible to meta-
analytically integrate findings across studies, because existing studies differed substantially in
their assessment of moderators of event-related personality trait changes and no standardized
effect size estimates were provided. Thus, additional empirical evidence on the moderators of
event-related personality change is needed to draw robust conclusions.
Study 2: A Coordinated Data Analysis
The systematic review identified several limitations in the existing research on
moderators of event-related personality trait changes, most prominently the lack of effect sizes
and the use of diverging methodological approaches. To overcome these limitations, we
conducted a coordinated data analysis across eight large-scale panel studies that allowed us to
generate comprehensive evidence on moderators of event-related personality trait changes
(Graham et al., 2022). Specifically, we examined a broad set of theoretically and empirically
derived moderators across different life events by using the same statistical model. We then
integrated the findings across datasets using meta-analytic tools to identify robust moderators
of event-related personality trait changes.
Methods
We used eight representative panel studies for the coordinated data analysis: (1) Health
and Retirement Study (HRS; Juster & Suzman, 1995), (2) Panel Analysis of Intimate
Relationships and Family Dynamics (PAIRFAM; Huinink et al., 2011), (3) German Socio-
Economic Panel (GSOEP; Liebig et al., 2021), (4) Household, Income and Labour Dynamics
in Australia (HILDA; Watson & Wooden, 2012), (5) Longitudinal Internet Panel for the Social
Sciences (LISS; Scherpenzeel, 2010), (6) Midlife in the United States (MIDUS; Radler, 2014),
(7) National Longitudinal Surveys (NLSY; Bureau Labor of Statistics et al., 2023), and (8)
National Education Panel Study (NEPS; Blossfeld & Rossbach, 2019). We selected these
datasets because they (a) used random sampling methods, (b) had a baseline sample size of
N > 5,000 to ensure a sufficient number of event occurrences, and (c) repeatedly assessed
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
24
personality traits. In the following, we describe the participants, procedures, and measures in
each of these panel studies in more detail. Additional information on data cleaning, the extracted
variables, and the harmonization procedure can be retrieved from OSF.
Participants and Procedures
Health and Retirement Study (HRS). HRS is an ongoing longitudinal panel study of
a random sample of inhabitants from the United States over age 50. Data collection began in
1992, with assessments every two years. Initially, people born between 1931 and 1941 were
included in the panel but every 6 years the sample is complemented by younger birth cohorts.
Data can be accessed here and a list of prior publications using this dataset can be found here.
We used the HRS public use file and the RAND Longitudinal File 2020 for our analyses.
Panel Analysis of Intimate Relationships and Family Dynamics (PAIRFAM).
PAIRFAM is an ongoing longitudinal panel study of a random sample of individuals from three
birth cohorts in Germany (1971-1973, 1981-1983, 1991-1993). Data collection began in 2008,
with assessments every year. In 2019, a fourth birth cohort (2001-2003) was added. However,
data from this birth cohort could not be used for the present study as personality traits were only
assessed once in this refreshment sample. Data can be accessed here and a list of prior
publications using this dataset can be found here. We used Release 14.0 for our analyses
(Brüderl et al., 2023).
German Socioeconomic Panel (GSOEP). GSOEP is an ongoing longitudinal panel
study of a representative sample of individuals living in German households. Data collection
began in 1984, with assessments every year. Refresher samples are regularly added to the panel
and we included all subsamples except of the refugee samples, which differed in their data
collection procedures and target population. Data can be accessed here and a list of prior
publications using this dataset can be found here. We used Version 38.1 for our analyses
(Goebel et al., 2023).
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
25
Household, Income and Labour Dynamics in Australia (HILDA). HILDA is an
ongoing longitudinal panel study of a representative sample of individuals living in Australian
households. Data collection began in 2001, with assessments every year. Refresher samples are
regularly added to the panel. Data can be accessed here and a list of prior publications using
this dataset can be found here. We used Release 22 for our analyses.
Longitudinal Internet Panel for the Social Sciences (LISS). LISS is an ongoing
longitudinal panel study of a representative sample of individuals living in Dutch households.
Data collection began in 2007, with assessments every year. Refresher samples are regularly
added to the panel. Data can be accessed here and a list of prior publications using this dataset
can be found here. We used data collected until September 2023 for our analyses.
Midlife in the United States (MIDUS). MIDUS is an ongoing longitudinal panel study
of a national probability sample of people aged between 25 and 75 in the United States. Data
collection began in 1995, with assessments every 8 to 9 years. Data can be accessed here and a
list of prior publications using this dataset can be found here. We used data from MIDUS 1
Core, MIDUS 2 Core, and MIDUS 3 Core (Brim et al., 1999; Ryff et al., 2007, 2015).
National Longitudinal Survey of Youth (NLSY). NLSY is an ongoing longitudinal
study of children and young adults in the United States. Specifically, we used the child and
young adult cohorts of this longitudinal study (NLSY79-CA), which includes biological
children of the women of the NLSY79 cohort. We focused on this sample because it features
frequent personality assessments. Data collection began in 1986, with assessments every two
years. Data can be accessed here and a list of prior publications using this dataset can be found
here. We used data released in April 2023 for our analyses.
National Education Panel Study (NEPS): NEPS is an ongoing longitudinal panel
study of individuals from six cohorts in Germany. For the present study, we used the student
cohort – a probability sample of first‐year students of the academic year 2010/2011– and the
adult cohort – a probability sample of individuals born between 1944 and 1986. Data collection
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
26
began in 2010, with assessments up to 3 times per year. Data can be accessed here and a list of
prior publications using this dataset can be found here. We used data from Version 14.0.0 of
the adult cohort (Artelt & NEPS, National Educational Panel Study, 2023) and Version 19.0.0
of the student cohort (Artelt & NEPS, National Educational Panel Study, 2024).
Measures
Details on the extracted variables from the different panel studies are available in the
codebook. To ensure reproducibility of our analyses, we also prepared a coding document with
further details on the data extraction and harmonization strategy.
Life Events. We identified 10 life events that were frequently considered in existing
research on event-related personality trait changes: new romantic relationship, marriage,
childbirth, separation, divorce, widowhood, graduation, new employment, unemployment, and
retirement. This selection of life events is in line with a recent meta-analysis on mean-level
changes in personality traits in the context of life events (Bühler et al., 2024). Table 3 provides
operational definitions of these life events and summarizes which events were extracted from
which study becazse not all life events could be identified in every panel study.
Personality Traits. In all panel studies, personality traits were assessed based on the
Big Five taxonomy. Details on the measures are summarized in Table 4. We recoded
neuroticism as emotional stability to ensure that high scores for all traits tend to be associated
with positive life outcomes (Soto, 2019). The number of personality trait assessments ranged
from two in the NEPS panel to 15 in the LISS panel, with most studies assessing personality
traits at three to four assessment waves.
Moderators. Based on theoretical considerations and the systematic literature review,
we extracted demographic, psychological, event-related, and contextual moderators of event-
related personality changes. To ensure that the analyses are comparable across life events, we
focused on moderators that a) could be expected to be relevant across life events and b) were
available across different panel studies. Table 5 provides an overview of the moderators that
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
27
we extracted from the different panel studies. However, not all moderators were available in
every dataset and in some cases only potential proxies for the moderators of interest were
available. More details on the considered moderators can be found in the sections on different
panel studies in the coding document.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
28
Table 3
Overview of Life Events Considered in the Coordinated Data Analysis
Event
Operational definition
LISS
PAIRFAM
HRS
MIDUS
NEPS
GSOEP
HILDA
NLSY
New relationship
a person starts a new romantic relationship; does not
have to be the first romantic relationship
X
X
X
X
X
X
X
X
Marriage
a person gets married; event should be indicated by
changes in marital status
X
X
X
X
X
X
X
X
Childbirth
a person (or their romantic partner) gives birth to a
child; adopting a child is also included in this event
X
X
X
X
X
X
X
X
Separation
a person ends a romantic relationship and becomes
single; the previous relationship can have any duration;
for most panels, this event includes marital and non-
marital separations
X
X
X
X
X
X
X
Divorce
a person gets legally divorced
X
X
X
X
X
X
X
X
Widowhood
the spouse or romantic partner of a person dies
X
X
X
X
X
X
Graduation
a person graduates from secondary or tertiary
education (e.g., high school, university, vocational
training); evening school or other types of education
are not included
X
X
X
X
X
X
New employment
a person starts a new employment; can be the first job,
starting a new job after a period of unemployment, or
changing one’s employment
X
X
X
X
X
X
Unemployment
a person becomes unemployed; includes people who
were in education before
X
X
X
X
X
X
X
X
Retirement
a person retires; includes early retirement
X
X
X
X a
X
X
Note. An “X” indicates that a life event could be identified in a certain panel study. To ensure model convergence, we did not consider life events with a sample size of n < 50 in a
panel study.
a Retirement could only be identified in the adult cohort of the NEPS panel.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
29
Table 4
Personality Trait Assessments Across Panel Studies
Panel
Assessments
Measure
Number
of Items
Response
scale
Notes
#
Years
PAIRFAM
3
2009, 2013,
2017
Short version of the Big
Five Inventory (BFI-K;
Rammstedt & John, 2005)
21
1 – 5
Openness assessed with
five items; other traits
assessed with four items
LISS
15
2008 to
2023
50-item version of the
IPIP Big-Five inventory
(Goldberg, 1992)
50
1 – 5
HRS
4
2006/2008,
2010/2012,
2014/2016,
2018/2020
Midlife Development
Inventory (MIDI)
Personality Scales
26
1 – 4
Number of items differed
between traits and
assessments; we only
used the 26 items that
were consistent across
waves
MIDUS
3
1995, 2004,
2013
Midlife Development
Inventory (MIDI)
Personality Scales
(Lachman, 1997)
25
1 – 4
Number of items differs
between traits and
assessments; we only
used the 25 items that
were consistent across
waves
NEPS
2
2012/2013,
2015/2016
Short version of the Big
Five Inventory (BFI-10;
Rammstedt & John, 2007)
11
1 – 5
Agreeableness was
assessed with three items,
other traits assessed with
two items
GSOEP
5
2005, 2009,
2012/2013,
2017, 2019
Combination of items
from the TIPI (Gosling et
al., 2003) and BFI-25
(John et al., 2012)
15
1 - 7
A fourth openness item
was later included but we
only used consistent
items across waves
HILDA
5
2005, 2009,
2013, 2017,
2021
Adjective approach based
on Saucier (1994) and
Goldberg (1992)
28
1 - 7
Only the 28 items
recommended in the user
manual were used for the
analysis
NLSY
4
2006, 2010,
2014,
2018/2020
Items from the TIPI
(Gosling et al, 2003)
10
1 – 7
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
30
Table 5
Moderators Considered in the Coordinated Data Analysis
Domain
Variable
Scale
type
Explanation
LISS
PAIRFAM
HRS
MIDUS
NEPS
GSOEP
HILDA
NLSY
Demographic
Age
numeric
Age at time of interview in years
X
X
X
X
X
X
X
X
Gender
dummy
Categories: male, female
X
X
X
X
X
X
X
X
Education
dummy
Categories: lower (no university degree),
higher (university degree)
X
X
X
X
X
X
X
X
Income
numeric
If available, we included the net household
income (in country-specific currency); for some
panels, other indicators were used
X
X
X
X
X
X
X
X
Psychological
Life satisfaction
numeric
Scale score of all available items or responses
to single item
X
X
X
X
X
X
X
X
Subjective health
numeric
Responses to single item
X
X
X
X
X
X
X
X
Religiosity
numeric
Assessing subjective religiosity or frequency of
attending religious events
X
X
X
X
X
Sense of mastery
numeric
Scale score of all available items or responses
to single item
X
X
X
X
Perceived control
numeric
Scale score of all available items or responses
to single item
X
X
X
X
X
Big Five traits
numeric
Scale score of all available items
X
X
X
X
X
X
X
X
Event-related
Age normativity
numeric
Absolute deviation of own age at event
occurrence from mean age at event occurrence
in dataset
X
X
X
X
X
X
X
X
Previous event
experience
dummy
Categories: no (event not experienced before),
yes (event was experienced before target event
occurrence); in some panels, data on event
occurrences prior to first assessment were
available
X
X
X
X
X
X
Subsequent event
experience
dummy
Categories: no (event was experienced again),
yes (event was experienced again)
X
X
X
X
X
X
X
Contextual
Social support
numeric
Scale score of all available items or responses
to single item
X a
X a
X
X
X a
X a
Financial hardship
numeric
Scale score of all available items or responses
to single item
X a
X
X
X a
X a
X a
X a
X
Note. An “X” indicates that a moderator was extracted from a certain panel study. More details on the moderators in the different panel studies can be found in the coding document
a For this moderator, only a proxy variable was available in the respective panel study.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
31
Statistical Analysis
Data analysis was conducted in R (Version 4.3.2). The data analytic procedure
comprised three steps. First, we prepared the data of each panel study and identified the
occurrence of relevant life events. Second, we examined moderators of event-related
personality changes using multilevel models as implemented in the lmerTest package
(Kuznetsova et al., 2017). Third, we integrated the results of Step 2 using meta-analytic methods
as implemented in the metafor package (Viechtbauer, 2010).
Step 1: Data Preparation and Event Identification. To prepare the data and
harmonize variables across panel studies, we executed four data cleaning steps: (1) assigning
consistent variable names across datasets, (2) reverse-scoring of items, if necessary, (3)
calculating mean scores for all multi-item measures (e.g., personality traits), and (4)
harmonizing factors (e.g., education) across panel studies so that factor levels corresponded to
each other across datasets.
Second, we identified any of the 10 life events of interest that occurred after the first
personality assessment of a participant, with participants being able to contribute data to
multiple life events. For example, a person who experienced a marriage and a divorce could
contribute data for both events. However, within each life event category, we focused on the
first occurrence of an event to maximize the number of post-event personality assessments. For
example, if a person experienced multiple divorces after their first personality assessment, we
focused on the first divorce to scale time around the occurrence of this target event.
Third, we prepared the data for our moderator analyses across datasets. As we treated
all moderators as time-invariant, we used the score that was assessed directly before the event
occurrence for moderators that could have differed across assessments (e.g., a person’s
education at the last measurement occasion before a divorce occurred). Finally, we dummy
coded all categorical variables and z-standardized all metric variables for our analyses, except
for age and event normativeness which were not centered but scaled in units of 10 years.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
32
Step 2: Examination of Moderators of Event-Related Personality Changes. To
examine moderators of event-related personality trait changes, we used multilevel models with
measurement occasions (t) nested in participants (i). We estimated one model per Moderator x
Event x Trait x Dataset combination with personality trait scores as dependent variables
(Traitit). As Level-1 predictors, we included two linear time variables describing years from
and to event occurrence (Pre_Timeit and Post_Timeit, respectively). Finally, we included the
main effects of the moderator variables (Modi) as Level-2 predictors as well as their cross-level
interactions with each of the two Level-1 time variables. We decided to fit all models with
random intercepts only as an exploration of model convergence in the LISS and the PAIRFAM
data indicated that random slopes only converged very rarely (< 5% of cases). Example model
equations read as follows:
Level 1: 𝑇𝑟𝑎𝑖𝑡!" = 𝛽#! + 𝛽$! ∙𝑃𝑟𝑒_𝑇𝑖𝑚𝑒!" + 𝛽%! ∙𝑃𝑜𝑠𝑡_𝑇𝑖𝑚𝑒!" + 𝜀!"
Level 2: 𝛽#! = 𝛾## + 𝛾#$ ∙𝑀𝑜𝑑!+ 𝜐#!
𝛽$! = 𝛾$# + 𝛾$$ ∙𝑀𝑜𝑑!
𝛽%! = 𝛾%# + 𝛾%$ ∙𝑀𝑜𝑑!
As effect sizes of interest, we focused on the main effects of the Level-2 moderators
(γ01) and their cross-level interactions with the two Level-1 time variables (γ11 and γ21). The
main effect (γ01) describes whether a moderator is associated with individual differences in the
levels of personality traits at the time point of the event occurrence. The cross-level interactions
indicate whether a moderator is related to individual differences in personality trait changes
before (γ11) and after (γ21) the occurrence of a major life event.
Furthermore, we used nested model comparisons to compare models with and without
cross-level interactions between the moderators and the two time variables to compute
R2 difference scores. Specifically, using the r2mlm package (Shaw et al., 2022), we estimated
(1)
(2)
(3)
(4)
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
33
𝚫𝑅&$
%'() as effect size measure to quantify the extent of additional within-person variance in
personality trait changes that was explained by the cross-level interactions between the
respective moderator variables and time (Rights & Sterba, 2019, 2020).
Step 3: Meta-Analytic Aggregation of Step 2 Results. We used random-effects meta-
analyses to integrate the results across datasets for each Moderator x Event x Trait combination.
As the effect sizes of the different datasets are independent, no measures to deal with the
dependency of effect sizes were needed.
To calculate the sampling variance for the 𝚫𝑅&$
%'() values, we calculated their
bootstrapped standard errors based on 100 samples1 with replacement. That is, we re-estimated
each model 100 times by drawing as many participants with replacement from our sample as
were included in the original sample.
Additional Analyses. To examine the consistency of effects at higher levels of
aggregation, we aggregated results from Step 2 not only across datasets but also across traits
and events. Specifically, we aggregated findings across datasets and traits for each Moderator
x Event combination and across datasets, traits, and events for each Moderator x Event Type
combination. For the latter approach, we distinguished between gain-related events (starting a
new relationship, marriage, childbirth, graduation, new employment) and loss-related events
(separation, divorce, widowhood, unemployment, retirement) to aggregate findings within
these event types (Bühler et al., 2024). As the effect sizes of the different Big Five traits and
events were nested within datasets, we used the procedure recommended by Pustejovsky and
Tipton (2022) to address the dependency of effect sizes. That is, we estimated an approximative
variance-covariance matrix of the dependent effect sizes, assuming a correlation of ρ = .29 of
1 In a subset of 10 multilevel models, we examined the bootstrapped standard errors using 500 samples with
replacement to explore whether the consistency of these estimates depended on the number of bootstraps.
However, as using more bootstraps did not change our conclusions and as the total run time accumulated quickly
due to the large number of analyses, we sticked to the preregistered number of 100 bootstraps.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
34
different Big Five traits (Mount et al., 2005) and a correlation of ρ = .64 of different effect sizes
belonging to the same event type (Bühler et al., 2024). Then, we estimated random-effects meta-
analyses and calculated cluster-robust standard errors.
Furthermore, to explore the consistency of our findings across different ways to treat
time in the analyses, we repeated all analyses using a shift coefficient instead of the two linear
time variables (Pre_time and Post_time). This shift coefficient was coded with 0 at all
assessments prior to the event occurrence and 1 at all assessment after the event occurrences.
The cross-level interactions between our moderators and this shift coefficient informed us about
whether a moderator is related to individual differences in how personality shifts from pre-event
to post-events assessments.
Statistical Significance and Power. Our data analytic approach included several
analyses. However, most analyses can be considered as testing different hypotheses,
questioning the usefulness of corrections for multiple testing (Rubin, 2021). We thus decided
to take an intermediate approach by using a conservative level of significance of α = .005 for
our analyses.
To gauge the power to detect different effect sizes, we conducted several power
simulations using the R package simr (Green & MacLeod, 2016). Assuming a level of α = .005,
a power of .80, and three personality assessments per participant, the required sample sizes to
detect different effect sizes were as following: N ≈ 1100 to detect very small effects (b = 0.05),
N ≈ 300 to detect small effects (b = 0.10), and N ≈ 90 to detect medium-sized effects (b = 0.20).
Thus, we were able to detect small to medium-sized effects for most events in each dataset,
whereas the meta-analytic aggregations in Step 3 should be able to detect even very small
effects with sufficient statistical power.
Results
Given the large number of estimated statistical models (5,700 models in total), we focus
here on the meta-analytic results using standardized fixed effects as effect size estimates.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
35
Furthermore, in line with the main goal of this manuscript, we only describe findings regarding
moderators of event-related personality changes. Accompanying results for the event-related
mean-level changes in the Big Five personality traits can be found in [CITATION BLINDED
FOR REVIEW].
Further results from our coordinated data analysis can be found in the Supplemental
Materials and in an interactive ShinyApp: https://life-event-
research.shinyapps.io/CDA_Moderators/. This ShinyApp allows researchers to filter, select,
and receive illustrations for results of specific multilevel models from Step 2. For example, if a
researcher is interested in the effect size of the moderation effect of “sense of mastery” for the
event “Childbirth” in the PAIRFAM panel (e.g., for power estimations), they can retrieve the
results of this particular model in the ShinyApp. Furthermore, for each model, we included a
detailed interpretation of the model coefficients and a graphical illustration of the simple slopes
of this moderation effect.
Descriptive Statistics
As summarized in Table 6, sample sizes across the different life events ranged from
5,482 for divorce to 49,063 for new employment. Each panel study contributed data from 2,195
to 25,198 individuals to our analyses. The mean age differed across panels depending on the
target population, and for each panel study slightly more female participants were included in
the analyses (Table 7). Further descriptive statistics are presented in Section 2 of the
Supplemental Materials.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
36
Table 6
Sample Sizes for Life Events Across Panel Studies
Life event
GSOEP
HILDA
HRS
LISS
MIDUS
NEPS
PAIRFAM
NLSY
Total
Childbirth
4328
4136
1599
817
400
2975
1584
3554
19393
Divorce
1435
913
1054
368
349
330
291
742
5482
Graduation
3744
3890
2341
11815
307
4081
26178
New employment
14188
10135
2345
2647
15705
4043
49063
Marriage
3545
3146
783
1056
321
3525
1145
2440
15961
New relationship
8588
748
597
1533
178
6740
3204
1521
23109
Retirement
3781
3249
5298
1269
1088
1657
16342
Separation
1292
4032
871
185
4220
2723
522
13845
Unemployment
5536
3920
840
1551
146
5977
1410
1122
20502
Widowhood
1038
812
2968
271
346
202
5637
Note. This table provides an overview of the number of participants that contributed data to the analyses of specific
life events across the different panel studies. A participant could contribute data to the analyses of multiple life
events. An empty cell indicates that a life event could not be identified in a certain panel study or had a sample
size below n = 50 so that it was not included in our analyses. Information on the number of measurement occasions
included in the analyses can be found in Section 2 of the Supplemental Materials.
Table 7
Sample Characteristics in the Different Panel Studies
Panel
N
Mean age (SD)
% Female
LISS
6710
36.26 (18.92)
53.47
PAIRFAM
5892
24.00 (8.13)
53.00
HRS
10292
53.30 (5.67)
60.61
GSOEP
25198
32.94 (14.09)
56.23
NEPS
18149
29.24 (13.04)
57.31
HILDA
15616
32.65 (16.16)
53.34
MIDUS
2195
46.99 (12.23)
55.76
NLSY
6882
15.54 (1.88)
51.76
Note. This table provides an overview of the sample characteristics in the different panel studies. The mean age
refers to the age of the first personality assessment. Event-specific sample characteristics can be found in Section 2
of the Supplemental Materials.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
37
Moderators of Event-Related Personality Changes Aggregated Across Datasets
We first estimated individual multilevel models for each dataset, event, moderator, and
personality trait, and then aggregated findings across the eight panel studies to evaluate which
moderators were consistently related to event-related personality changes. In the following, we
refer to stronger increases or less pronounced decreases in agreeableness, continuousness,
emotional stability, extraversion, and openness as favorable personality trait changes whereas
we describe less pronounced increases or decreases as unfavorable. Although higher levels of
these traits may not be universally desirable (Klimstra & McLean, 2024), it seems to be a valid
simplification for the countries that contributed to our coordinated data analysis because in
these countries higher levels of these traits have been found to predict positive life outcomes
(E. D. Beck & Jackson, 2022; Soto, 2019; Wright & Jackson, 2023). Results on moderation
effects for specific events are summarized in Tables 8 and 9 and illustrated in Figures 2 to 11.
Interaction Effects of Moderators. Several moderation effects emerged consistently
across traits and events. First, in the domain of demographic moderators, we found that age was
most frequently associated with event-related personality trait changes. For example,
experiencing the events graduation, new employment, and unemployment at an older age was
linked to less favorable changes in conscientiousness before (-0.019 ≤ b ≤ -0.010) and after the
event occurrence (-0.016 ≤ b ≤ -0.010; Figure 12). Income only moderated personality trait
changes for work-related events (i.e., new employment, retirement, and unemployment). For
these events, a higher income was associated with more favorable personality trait changes after
the event occurrence (0.004 ≤ b ≤ 0.006). Finally, gender predicted individual differences in
personality changes only for the event new employment, such that women showed more
favorable changes in agreeableness (b = 0.005) and emotional stability (b = 0.004) after starting
a new employment than men.
Second, in the domain of psychological moderators, we repeatedly found opposite
moderation patterns for pre-event and post-event moderations. For example, higher levels of
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
38
life satisfaction were associated with more favorable changes in emotional stability and
extraversion before starting a new relationship (0.007 ≤ b ≤ 0.013) but with less favorable
changes in these traits after the event occurred (-0.009 ≤ b ≤ -0.005; Figure 12). Similarly,
higher levels of perceived control were associated with more favorable changes in emotional
stability before childbirth (b = 0.006) but with less favorable changes in emotional stability,
agreeableness, and extraversion after childbirth (b = -0.005; Figure 12). Comparable patterns
of positive pre-event moderation effects and negative post-event moderation effects also
emerged for self-rated health, mastery, and Big Five traits moderating changes in other traits.
Third, in the domain of event-related moderators, experiencing a life event at a non-
normative age tended to predict less favorable personality trait changes. For example,
experiencing a graduation at a non-normative age was associated with less favorable changes
in conscientiousness before the event occurred (b = -0.018; Figure 12). Previous and subsequent
experiences of the same life event were also occasionally related to individual differences in
personality changes. However, the effects seemed to be less consistent across events and traits.
For example, having experienced a new relationship before was associated with less favorable
changes in extraversion before the event occurred (b = -0.011) whereas previous childbirth
experiences predicted more favorable changes in extraversion after the event occurrence
(b = 0.006).
Fourth, in the domain of contextual moderators, we found some unexpected patterns.
Higher levels of social support were repeatedly associated with less favorable changes in
personality traits after the occurrence of life events. For example, higher social support
predicted less favorable changes in emotional stability, extraversion, and openness after starting
a new employment (-0.007 ≤ b ≤ -0.003; Figure 12). Similarly, financial hardship predicted
more favorable changes in emotional stability (b = 0.007) and extraversion (b = 0.003) after
retirement (Figure 12).
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
39
Significance of Interaction Effects. Across events, we found that psychological
moderators were significant in 20% of cases, contextual moderators in 9%, demographic
moderators in 6%, and event-related moderators in 4%, indicating that the examined
psychological moderators were most relevant for explaining individual differences in event-
related personality changes. When looking at individual moderators, we found that Big Five
traits were most frequently related to individual differences in event-related changes in other
traits (20% to 35% significant effects), whereas religiosity, subsequent event experiences,
gender, and education only rarely predicted individual differences in personality trait changes
(between 1% and 2% significant effects; see Section 3 of the Supplemental Materials). Overall,
13% of moderation effects were significant, which was well beyond our level of significance
(0.5%), indicating that the identified effects cannot just be explained by Type 1 error.
Effect Sizes of Interaction Effects. Effect sizes of significant moderators ranged
mostly between b25%-Quantile = 0.005 and b75%-Quantile = 0.009, with b = 0.007 as median effect
size. For a metric moderator such as life satisfaction, such an effect implies that having a life
satisfaction score of 1 SD above the average is associated with a 0.007 SD stronger increase or
decrease in a certain personality trait over 1 year compared to a person with an average life
satisfaction. Further insights into the effect sizes can be gained by the meta-analytic aggregation
of 𝚫𝑅&$
%'() estimates (see Section 4 of the Supplemental Materials). On average, significant
moderators explained between 0.09% and 0.24% of additional variance in personality trait
changes, with the largest effect sizes being found for age explaining 0.78% of variance in
conscientiousness changes after a graduation.
Main Effects of Moderators. In addition to the cross-level interactions, we also
examined the main effects of the moderators, describing whether a moderator was associated
with individual differences in the levels of personality traits at the time point of the event
occurrence. Details about these analyses are presented in Section 7 of the Supplemental
Materials. Similar to the cross-level interactions, psychological moderators were most
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
40
frequently related to individual differences in the levels of personality traits. For example,
higher levels of self-rated health, life satisfaction, mastery, and control were consistently related
to higher levels of most Big Five traits at event occurrence. Together with the findings on cross-
level interactions, these results indicate that a moderator like life satisfaction predicts positive
personality trait changes before the event occurrence, higher trait levels at the time of the event
occurrence, but negative changes after the event occurrence (see Figure 12). Furthermore, the
effect sizes of the main effects were considerably larger than the effect sizes of the cross-level
interactions (bMedian = 0.170), suggesting that the examined moderators are more strongly
related to level differences in personality traits than to individual differences in personality trait
changes.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
41
Figure 2
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Childbirth
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
42
Figure 3
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Divorce
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
43
Figure 4
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Graduation
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
44
Figure 5
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Starting a New Employment
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
45
Figure 6
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Marriage
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
46
Figure 7
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Starting a New Relationship
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
47
Figure 8
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Retirement
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
48
Figure 9
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Separation
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
49
Figure 10
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Unemployment
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
50
Figure 11
Meta-Analytic Moderator Effects of Personality Trait Changes Before and After Widowhood
Note. This figure illustrates the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable (Panel A) or the post-
event time variable (Panel B). These cross-level interactions describe whether people differ in their personality changes before and after the event depending on their score on the respective moderator.
Significant effects (α = .005) are depicted in black. C = contextual moderators, D = demographic moderators, E = event-related moderators, P = psychological moderators.
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Moderators
Interaction With Pre -Event Time Variable
A
Agre eableness
Cons cientious ness
Emotional stability
Extraversion
Openness
D
P
E
C
-0.03 0.00 0.03 -0.03 0.00 0.03 -0 .03 0.00 0.03 -0 .03 0.00 0.03 -0.03 0.00 0.03
Income
Education
Gender
Age
Openness
Extraversion
Emotional stability
Conscientiousness
Agreeableness
Contro l
Mastery
Religiosity
Self-rated health
Life satisfaction
Subsequent experience
Previous experience
Normativeness
Financial hardship
Social support
Standardized fixed effect
Moderators
Interaction With Post-Event Time Variable
B
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
51
Table 8
Significant Meta-Analytic Moderator Effects for Pre-Event Personality Trait Changes
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Childbirth
Age
0.006
[0.002, 0.01]
Life satisfaction
0.004
[0, 0.009]
0.01
[0, 0.02]
Self-rated health
0.006
[0.001, 0.012]
Mastery
0.009
[0.002, 0.016]
0.013
[0.003, 0.023]
Control
0.006
[0.001, 0.011]
Agreeableness
0.006
[0.002, 0.01]
Conscientiousness
0.008
[0, 0.016]
0.009
[0.003, 0.015]
Emotional stability
0.008
[0.002, 0.014]
0.007
[0.001, 0.013]
Extraversion
0.005
[0, 0.01]
Openness
0.007
[0.001, 0.014]
Financial hardship
-0.005
[-0.009, -0.001]
Divorce
Conscientiousness
0.01
[0, 0.021]
0.009
[0, 0.018]
Extraversion
0.007
[0.002, 0.013]
Openness
0.006
[0, 0.011]
Graduation
Age
-0.019
[-0.032, -0.006]
Self-rated health
0.005
[0, 0.009]
Normativeness
-0.018
[-0.035, -0.002]
Marriage
Age
-0.013
[-0.015, -0.01]
Conscientiousness
0.008
[0.003, 0.014]
Emotional stability
0.008
[0.001, 0.015]
Extraversion
0.004
[0.001, 0.008]
Openness
0.005
[0, 0.01]
Normativeness
-0.007
[-0.011, -0.002]
New employment
Age
-0.013
[-0.021, -0.005]
Life satisfaction
0.004
[0, 0.009]
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
52
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Self-rated health
0.008
[0.003, 0.012]
0.01
[0.003, 0.016]
0.005
[0.001, 0.009]
Agreeableness
0.006
[0, 0.011]
Extraversion
0.004
[0, 0.007]
Openness
0.005
[0.001, 0.009]
New relationship
Life satisfaction
0.007
[0.001, 0.014]
0.013
[0.008, 0.018]
0.007
[0.003, 0.012]
Self-rated health
0.007
[0.002, 0.012]
0.010
[0.005, 0.014]
Conscientiousness
0.010
[0.002, 0.018]
Emotional stability
0.004
[0, 0.008]
Extraversion
0.006
[0.002, 0.01]
0.007
[0.001, 0.013]
Previous experience
-0.011
[-0.021, -
0.001]
Social support
0.005
[0, 0.009]
Retirement
Income
0.004
[0.001, 0.008]
Self-rated health
0.004
[0.001, 0.008]
Mastery
0.005
[0, 0.01]
Conscientiousness
0.005
[0.001, 0.01]
Openness
0.005
[0.001, 0.009]
0.003
[0.001, 0.006]
Normativeness
-0.010
[-0.019, -0.001]
-0.006
[-0.011, -0.001]
Social support
0.004
[0.001, 0.007]
Separation
Self-rated health
0.005
[0.001, 0.009]
Mastery
0.014
[0.002, 0.027]
0.02
[0.006, 0.035]
Control
0.006
[0.002, 0.011]
Extraversion
0.006
[0.001, 0.01]
Social support
0.008
[0, 0.017]
Unemployment
Age
-0.010
[-0.019, -0.002]
Life satisfaction
0.004
[0, 0.007]
Self-rated health
0.009
[0.001, 0.017]
Conscientiousness
0.007
[0.001, 0.013]
Extraversion
0.004
[0.001, 0.007]
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
53
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Widowhood
Age
-0.006
[-0.01, -0.002]
-0.007
[-0.013, -0.001]
-0.006
[-0.01, -0.002]
Self-rated health
0.005
[0.001, 0.01]
0.005
[0, 0.009]
Mastery
0.008
[0.001, 0.015]
0.007
[0.001, 0.013]
0.007
[0.001, 0.013]
Control
0.007
[0.002, 0.012]
Extraversion
0.007
[0, 0.013]
Openness
0.007
[0.002, 0.012]
Note. This table summarizes the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the pre-event time variable. For
each trait, we included the standardized fixed effect with its 99.5% confidence interval. These cross-level interactions describe whether people differ in their personality changes
before the event occurrence depending on their score on the respective moderator. This table only includes significant moderation effects (p < .005). Results for all moderation
effects can be found in Section 3 of the Supplemental Materials.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
54
Table 9
Significant Meta-Analytic Moderator Effects for Post-Event Personality Trait Changes
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Childbirth
Life satisfaction
-0.005
[-0.009, -0.001]
-0.004
[-0.008, 0]
-0.013
[-0.022, -0.004]
Mastery
-0.007
[-0.014, -0.001]
Control
-0.005
[-0.009, -0.001]
-0.005
[-0.009, -0.001]
-0.005
[-0.01, 0]
Agreeableness
-0.009
[-0.016, -0.002]
-0.008
[-0.013, -0.002]
Emotional stability
-0.006
[-0.01, -0.002]
Extraversion
-0.008
[-0.014, -0.001]
Openness
-0.007
[-0.01, -0.003]
-0.008
[-0.011, -0.004]
-0.008
[-0.015, -0.001]
Previous experience
0.006
[0, 0.012]
Divorce
Agreeableness
-0.009
[-0.016, -0.001]
-0.009
[-0.016, -0.002]
-0.009
[-0.017, -0.001]
-0.009
[-0.015, -0.004]
Conscientiousness
-0.014
[-0.019, -0.008]
-0.011
[-0.016, -0.005]
Emotional stability
-0.007
[-0.012, -0.001]
Openness
-0.012
[-0.022, -0.002]
Graduation
Age
-0.016
[-0.028, -0.003]
Education
-0.013
[-0.02, -0.005]
Life satisfaction
-0.011
[-0.019, -0.002]
-0.005
[-0.008, -0.001]
Self-rated health
-0.003
[-0.007, 0]
Agreeableness
-0.008
[-0.012, -0.004]
-0.008
[-0.015, -0.002]
Conscientiousness
-0.006
[-0.009, -0.003]
-0.008
[-0.014, -0.001]
Emotional stability
-0.012
[-0.02, -0.003]
Extraversion
-0.007
[-0.014, 0]
Openness
-0.009
[-0.012, -0.005]
-0.008
[-0.015, -0.001]
Previous experience
-0.015
[-0.026, -0.004]
Marriage
Age
-0.003
[-0.006, 0]
Mastery
-0.009
[-0.017, 0]
Agreeableness
-0.008
[-0.011, -0.004]
-0.009
[-0.019, 0]
-0.012
[-0.022, -0.002]
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
55
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Conscientiousness
-0.006
[-0.01, -0.003]
-0.006
[-0.01, -0.003]
-0.009
[-0.016, -0.002]
Emotional stability
-0.006
[-0.011, -0.001]
-0.011
[-0.019, -0.003]
Extraversion
-0.006
[-0.01, -0.001]
Openness
-0.01
[-0.015, -0.005]
-0.007
[-0.013, -0.001]
New employment
Age
-0.014
[-0.02, -0.008]
Gender
0.005
[0, 0.01]
0.004
[0, 0.008]
Income
0.005
[0.002, 0.008]
0.003
[0, 0.006]
Life satisfaction
-0.009
[-0.017, -0.001]
-0.006
[-0.009, -0.002]
Self-rated health
-0.006
[-0.011, 0]
Control
-0.006
[-0.011, -0.002]
Agreeableness
-0.01
[-0.015, -0.005]
-0.007
[-0.012, -0.003]
Conscientiousness
-0.007
[-0.013, -0.002]
-0.006
[-0.009, -0.003]
Emotional stability
-0.004
[-0.007, -0.001]
-0.005
[-0.007, -0.002]
-0.011
[-0.019, -0.003]
Extraversion
-0.01
[-0.018, -0.003]
Openness
-0.009
[-0.013, -0.004]
-0.008
[-0.014, -0.002]
-0.009
[-0.016, -0.003]
Previous experience
-0.018
[-0.034, -0.002]
Subsequent experience
0.004
[0, 0.009]
Social support
-0.006
[-0.01, -0.002]
-0.007
[-0.012, -0.002]
-0.003
[-0.006, 0]
New relationship
Age
-0.015
[-0.026, -0.004]
Life satisfaction
-0.009
[-0.018, -0.001]
-0.005
[-0.008, -0.002]
Self-rated health
-0.009
[-0.015, -0.003]
Agreeableness
-0.011
[-0.016, -0.006]
-0.007
[-0.013, -0.001]
-0.008
[-0.012, -0.004]
Conscientiousness
-0.008
[-0.013, -0.004]
-0.008
[-0.015, -0.002]
Emotional stability
-0.011
[-0.022, 0]
Extraversion
-0.008
[-0.016, -0.001]
-0.009
[-0.018, -0.001]
Openness
-0.008
[-0.014, -0.003]
-0.008
[-0.011, -0.004]
-0.009
[-0.015, -0.003]
Social support
-0.005
[-0.008, -0.001]
Retirement
Age
-0.01
[-0.018, -0.002]
Education
0.006
[0, 0.012]
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
56
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Life satisfaction
-0.008
[-0.014, -0.002]
-0.005
[-0.009, -0.001]
Self-rated health
0.003
[0, 0.006]
Mastery
-0.005
[-0.01, 0]
-0.006
[-0.012, -0.001]
-0.007
[-0.013, -0.001]
-0.007
[-0.012, -0.003]
Control
-0.003
[-0.006, 0]
Agreeableness
-0.011
[-0.013, -0.008]
-0.003
[-0.006, -0.001]
-0.008
[-0.013, -0.003]
-0.01
[-0.014, -0.005]
Conscientiousness
-0.007
[-0.014, 0]
-0.005
[-0.007, -0.002]
-0.004
[-0.008, -0.001]
-0.004
[-0.009, 0]
Emotional stability
-0.005
[-0.009, -0.001]
Extraversion
-0.008
[-0.015, 0]
-0.004
[-0.008, -0.001]
-0.006
[-0.01, -0.003]
Social support
-0.006
[-0.009, -0.002]
Financial hardship
0.007
[0.003, 0.012]
0.003
[0, 0.005]
Separation
Age
-0.013
[-0.024, -0.002]
Religiosity
-0.005
[-0.01, 0]
Agreeableness
-0.01
[-0.016, -0.005]
-0.01
[-0.019, 0]
Conscientiousness
-0.009
[-0.014, -0.003]
-0.009
[-0.017, -0.001]
Extraversion
-0.011
[-0.021, 0]
Openness
-0.009
[-0.013, -0.005]
-0.008
[-0.015, 0]
Subsequent experience
-0.01
[-0.019, -0.001]
Social support
-0.012
[-0.022, -0.002]
Unemployment
Age
-0.01
[-0.014, -0.007]
Income
0.006
[0.001, 0.011]
Life satisfaction
-0.003
[-0.006, 0]
Agreeableness
-0.008
[-0.014, -0.002]
-0.004
[-0.007, -0.002]
-0.007
[-0.013, -0.001]
Conscientiousness
-0.006
[-0.01, -0.003]
Emotional stability
-0.006
[-0.008, -0.003]
-0.014
[-0.025, -0.003]
Extraversion
-0.004
[-0.007, 0]
Openness
-0.007
[-0.01, -0.004]
-0.005
[-0.009, -0.002]
-0.009
[-0.017, -0.001]
Social support
-0.004
[-0.008, -0.001]
Financial hardship
0.004
[0.001, 0.007]
Widowhood
Age
-0.01
[-0.018, -0.002]
-0.009
[-0.014, -0.003]
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
57
Event
Moderator
Agreeableness
Conscientiousness
Emotional stability
Extraversion
Openness
Life satisfaction
-0.008
[-0.014, -0.001]
-0.01
[-0.016, -0.005]
-0.008
[-0.017, 0]
-0.007
[-0.012, -0.001]
Control
-0.01
[-0.017, -0.004]
Agreeableness
-0.018
[-0.032, -0.004]
Conscientiousness
-0.017
[-0.03, -0.004]
-0.01
[-0.018, -0.002]
Extraversion
-0.007
[-0.015, 0]
-0.011
[-0.021, -0.001]
Normativeness
0.012
[0.003, 0.021]
Social support
-0.009
[-0.016, -0.002]
-0.011
[-0.02, -0.002]
-0.01
[-0.016, -0.003]
Financial hardship
0.007
[0.001, 0.013]
Note. This table summarizes the results from the meta-analytic aggregation across datasets on the cross-level interactions between a moderator and the post-event time variable.
For each trait, we included the standardized fixed effect with its 99.5% confidence interval. These cross-level interactions describe whether people differ in their personality changes
after the event occurrence depending on their score on the respective moderator. This table only includes significant moderation effects (p < .005). Results for all moderation effects
can be found in Section 3 of the Supplemental Materials.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
58
Figure 12
Simple Slope Plots for Selected Moderators
New relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationshipNew relationship
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
-0.8
-0.4
0.0
0.4
0.8
-5.0 -2.5 0.0 2.5 5.0
Years
Emotional stability
Satsifaction -1 SD M+1 SD
A
ChildbirthChildbirthChildbirthChildbirthChildbirthChildbirthChildbirthChildbirthChildbirthChildbirthChildbirthChildbirth
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
-0.8
-0.4
0.0
0.4
0.8
-5.0 -2.5 0.0 2.5 5.0
Years
Emotional stability
Control - 1SD M+ 1 SD
B
GraduationGraduationGraduationGraduationGraduationGraduationGraduationGraduation
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
-0.8
-0.4
0.0
0.4
0.8
-5.0 -2.5 0.0 2.5 5.0
Years
Conscientiousness
Normativeness M +/- 10 years
C
UnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemploymentUnemployment
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
Post: b = 0.003,
p < .001
-0.8
-0.4
0.0
0.4
0.8
-5.0 -2.5 0.0 2.5 5.0
Years
Conscientiousness
Age -10 years M +10 years
D
New employmentNew employmentNew employmentNew employmentNew employmentNew employmentNew employmentNew employmentNew employmentNew employmentNew employmentNew employment
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
Post: b = 0.003,
p = 0.231
-0.8
-0.4
0.0
0.4
0.8
-5.0 -2.5 0.0 2.5 5.0
Years
Extraversion
Support -1 SD M+1 SD
E
RetirementRetirementRetirementRetirementRetirementRetirementRetirementRetirementRetirementRetirementRetirementRetirement
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Pre: b = -0.002,
p = 0.231
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
Post: b = 0.003,
p = 0.001
-0.8
-0.4
0.0
0.4
0.8
-5.0 -2.5 0.0 2.5 5.0
Years
Extraversion
Hardship -1 SD M+1 SD
F
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
59
Note. Plots illustrate simple slope for selected moderators based on the meta-analytic aggregations across datasets.
Depicted coefficients are meta-analytically derived effect size estimates of cross-level interactions. Panel A
illustrates simple slopes for the moderation effect of life satisfaction for emotional stability in context of a new
relationship. Panel B illustrates simple slopes for the moderation effect of perceived control for emotional stability
in context of childbirth. Panel C illustrates simple slopes for the moderation effect of event normativeness for
conscientiousness in context of graduation. Panel D illustrates simple slopes for the moderation effect of age for
conscientiousness in context of unemployment. Panel E illustrates simple slopes for the moderation effect of social
support for extraversion in context of new employment. Panel F illustrates simple slopes for the moderation effect
of financial hardship for extraversion in context of retirement.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
60
Additional Analyses: Different Levels of Aggregation
To examine the consistency of findings across different Big Five traits and events, we
next aggregated findings across datasets and traits as well as across datasets, traits, and similar
events. Details about these analyses can be found in Section 3 of the Supplemental Materials.
Using these higher levels of aggregation, fewer significant effects emerged (across dataset and
traits: 3% significant; across datasets, traits, and events: 9% significant), suggesting that
moderator effects of event-related personality trait changes are only partly consistent across the
different Big Five traits and events.
The most consistent effect was that higher levels of agreeableness were associated with
less favorable changes in the other Big Five traits after the occurrence of loss-related events
such as divorce, and retirement and gain-related events such as new employment and new
relationship. Similarly, higher levels of conscientiousness, emotional stability, and openness
predicted less favorable changes in the other Big Five traits after the occurrence of gain-related
events, whereas higher levels of extraversion predicted less favorable changes in the other traits
after the occurrence of loss-related events.
Additional Analyses: Shift Variable
To examine whether moderators of event-related personality changes are consistent
across different ways to treat time in the analyses, we re-estimated all models using a shift
variable. This shift variable describes differences in personality traits levels before and after the
event occurrence and significant moderation effects indicate that people differ in these pre-post
differences depending on the examined moderator. Details about these analyses are presented
in Sections 5 and 6 of the Supplemental Materials.
Generally, findings with this shift coefficient were similar to the findings of our main
analysis. For instance, higher levels of a certain Big Five trait tended to predict less favorable
shifts in other Big Five traits. Similarly, higher levels of life satisfaction, mastery, and perceived
control predicted less favorable shifts in emotional stability and extraversion for events such as
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
61
widowhood, new relationship, marriage, and new employment. The most important difference
to the findings of our main analysis was that higher levels of self-rated health predicted more
favorable shifts conscientiousness for the events new employment, marriage, new relationship,
retirement, and separation. Significant moderators had a median effect size of b = 0.047,
suggesting that 1 SD difference in a metric moderator such as life satisfaction is associated with
a 0.047 SD difference in personality shifts from pre- to post-event assessments.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
62
Discussion
People differ in their personality trait changes after experiencing major life events.
Understanding which variables explain these individual differences is important to advance
theory and research on event-related personality development. Our systematic literature review
revealed, however, that existing knowledge about specific moderators of event-related
personality trait changes is limited. To date, many potentially relevant moderators had been
examined in only one sample so that the replicability of effects remains unclear.
To overcome this limitation and to identify robust moderators of event-related
personality trait changes, we conducted a coordinated data analysis across eight large-scale
panel studies. This coordinated data analysis showed that there are several replicable
moderators of event-related personality trait changes that predict individual differences in
personality development before and after experiencing these events. Specifically, psychological
characteristics such as life satisfaction, mastery, control, and Big Five traits as well as age and
normativeness moderated personality changes across several trait-event combinations.
However, most moderation effects seemed to be trait- and event specific. Furthermore, the
effect sizes of these moderation effects were (very) small (𝚫𝑅&$
%'() ≈ 0.16%, bMedian = 0.007),
suggesting that each variable accounts only for a minor proportion of the variance of personality
trait changes.
Moderators of Event-Related Personality Changes
Across the systematic review and coordinated data analysis, we identified several
replicable moderators of event-related personality changes (see Figure 13 for an overview). We
discuss our findings according to the four domains of moderating variables: demographic,
psychological, event-related, and contextual characteristics.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
63
Figure 13
Illustrative Overview of the Results
Note. This figure provides an overview of our findings across the two studies. It illustrates which psychological,
demographic, event-related, and contextual variables were consistently linked to personality trait changes before
and after the occurrence of an event across datasets. However, most moderation effects were specific to certain
trait-event combinations. Positive links are illustrated in green (dotted), negative links are illustrated in red
(dashed). Please see the online version of this article for a colored version of this figure.
Life event
Time
Personality trait
Pre-event
Post-event
Psychological variables
Demographic
variables
Event-related
variables
Contextual
variables
Age Income
Post-event
Non-normativeness
Pre-event
Social
support
Financial
hardship
Other Big
Five traits
Mastery
& control
Life
satsifaction
Self-rated
health
Positive
moderation effect
Negative
moderation effect
Most moderation effects
are specific to certain
life events and
personality traits
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
64
Demographic Moderators
For demographic moderators, the findings of the systematic review and the coordinated
data analysis partly differed from each other. Specifically, the literature review suggested that
gender moderates changes in agreeableness after marriage, conscientiousness after widowhood,
and openness after divorce (Specht et al., 2011; Wortman, 2017). In contrast, no gender
differences emerged for these events in the coordinated data analysis, questioning the
robustness of these findings.
The coordinated data analysis, however, identified several significant moderation
effects for age. An older age at event occurrence predicted less favorable personality changes
in conscientiousness after several events including graduation, new employment, or marriage.
These findings may indicate that for older individuals becoming more conscientious may be
less relevant to successfully adapt to these events. However, these findings could also be
explained by the general age-graded development of conscientiousness, which increases until
midlife and then decreases from age 50 onward (Bleidorn et al., 2022). Thus, older individuals
are generally more likely to decrease in their conscientiousness. This explanation illustrates an
important point when interpreting the findings of our coordinated data analysis. Although our
analyses focused on moderators of event-related personality development, we do not imply that
the identified moderators are only relevant in context of event-related personality development.
At least some of them could also be relevant to explain individual differences in personality
trajectories across age more generally (Haehner, Hopwood, et al., 2024; Schwaba & Bleidorn,
2018).
Finally, income was related to more favorable personality trait changes after the
occurrence of work-related events. Similarly, education was only related to personality
development after experiencing a graduation. These findings illustrate that effects within a
certain life domain (e.g., education, work) seem to be stronger than effects across life domains.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
65
Psychological Moderators
In the coordinated data analysis, the domain of psychological moderators was the most
relevant one to explain individual differences in event-related personality changes (20% of
effects were significant). A pattern that emerged quite consistently was that specific Big Five
traits predicted more favorable changes in other traits before an event occurred, higher trait
levels at event occurrence, but less favorable changes after the event occurrence. For example,
people with higher levels of conscientiousness showed more favorable changes in their
agreeableness before retirement, higher levels of agreeableness at retirement, but less favorable
changes after retirement. A similar result pattern was found for perceived control, mastery, and
life satisfaction, with these variables being associated with more favorable personality changes
before an event occurred, higher trait levels at event occurrence, but with less favorable changes
after the event occurrence.
In particular, the negative post-event moderation effects may be surprising given that
existing research generally found positive co-development among different personality traits
and well-being (Allemand & Martin, 2016; Borghuis et al., 2017; Haehner, Sleep, et al., 2024;
Reitz, Den Boer, et al., 2022). Furthermore, as summarized in the systematic review, there was
initial evidence that higher levels of life satisfaction are associated with more favorable changes
in agreeableness in context of marriage and childbirth (Specht et al., 2013). However, existing
research did not differentiate between pre- and post-event changes, such that relevant
differences between pre- and post-event moderation effects may have been overlooked.
One explanation for the opposite pattern of pre- and post-event moderation effects could
be that people with high levels of life satisfaction, mastery, or certain Big Five traits adapt to
an event before it occurs. That is, people with high levels of these psychological resources may
prepare themselves for the event occurrence and already show changes in trait-relevant
behaviors that are required to successfully master the transition (e.g., already being more
conscientious before starting a new job). As a result, people with higher levels of these variables
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
66
show – compared to people with lower levels – more favorable personality changes before the
event occurrence but less favorable changes thereafter (see Figure 12). However, an alternative,
more methodological explanation could be that the opposite pattern of pre- and post-event
moderation effects is driven by regression to the mean (Barnett, 2004). The medium-sized main
effects of our moderators (bMedian = 0.17) indicate that, for example, higher levels of life
satisfaction are associated with higher levels of emotional stability at the time point of the event
occurrence. Having high levels of emotional stability, in turn, increases the likelihood of
regressing to the mean after the event occurrence (i.e., a decrease). As such, this can produce
the finding that high levels of life satisfaction are associated with less favorable changes in
emotional stability after an event. Reducing the shared variance between personality traits and
psychological moderators would be one way to tease the two explanations apart. Therefore,
future research should move beyond self-report measures of personality traits and examine
event-related personality development with other methods such as peer reports.
Two further results from the domain of psychological moderators deserve attention.
First, religiosity did not moderate event-related personality changes across events and traits.
Religiosity thus seems to be less important for event-related personality development than other
psychological characteristics—at least in the examined countries, which can mostly be
considered as being relatively secular, and with the available operationalizations across datasets
(e.g., attendance of religious services). Second, findings from the analysis with the shift variable
show that self-rated health could be a protective factor predicting favorable personality shifts
from pre- to post-event personality assessments. One possible explanation for this effect could
be that better health helps individuals in adapting to new social roles as it facilitates the
engagement in new trait-relevant behaviors. For example, after starting a new employment,
people with a higher health status might have it easier to incorporate conscientious behavior
into their everyday life than people with lower health status. This is an important finding
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
67
because self-rated health was among the few variables that were consistently associated with
favorable personality changes.
Event-Related Moderators
For the domain of event-related moderators, the systematic literature review showed
that characteristics related to the duration of previous phases before a transition (e.g., the
employment duration before experiencing unemployment) could be relevant to understand
individual differences in personality changes (Anger et al., 2017; Boyce et al., 2015; Spikic et
al., 2021; Van Scheppingen et al., 2016). A longer duration of a previous status could, for
example, increase the (perceived) disruptiveness of a life event and thus be related to more
pronounced personality trait changes (Haehner, Kritzler, et al., 2024).
Furthermore, we found that experiencing events at a non-normative age (compared to
each sample’s mean age at event occurrence) was associated with less favorable personality
changes for some events and traits (e.g., less favorable changes in emotional stability and
openness before retirement). These findings are in line with Life Course Theory (Elder, 1998),
which posits that the effects of life events depend on social expectations regarding their timing
within people’s life course. Violating these expectations may lead to adverse consequences as
events that occur at a non-normative age are accompanied by little social guidance or even by
social sanctions (Furstenberg, 2005; Haehner, Schaefer, et al., 2024; Liefbroer & Billari, 2010;
Radl, 2012).
Finally, previous and subsequent event experiences were not consistently related to
individual differences in event-related personality changes but effects differed across events
and traits. This specificity depending on the event type is in line with the existing literature
examining the effects of repeated event occurrences on well-being (Krämer et al., 2024). For
instance, Luhmann and Eid (2009) found that repeated occurrences of unemployment predicted
more pronounced well-being changes whereas repeated occurrences of divorce were associated
with smaller well-being changes. Thus, the moderating effects of previous and subsequent event
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
68
experiences may depend on the nature of the event as for some events previous experiences
may be beneficial (e.g., childbirth), whereas for other events previous experiences may be
detrimental (e.g., unemployment).
Overall, event-related moderators were less relevant in the coordinated data analysis
than moderators from the other domains (4% of effects were significant). However, this result
may partly be explained by the limited availability of event-related moderators in large-scale
panel studies. There is, for instance, promising evidence that the subjective perception of life
events is related to personality trait changes (De Vries et al., 2021; Haehner et al., 2023;
Schwaba et al., 2023), but this variable was not assessed in the large-scale panel studies. Thus,
future research should extend the set of examined event-related moderators beyond the
variables considered in the coordinated data analysis.
Contextual Moderators
In the domain of contextual moderators, our coordinated data analysis produced some
unexpected results. Higher levels of social support and lower levels of financial hardship were
related to less favorable personality changes after the occurrence of different life events. These
findings were unexpected because social support has been identified as a protective factor
against unfavorable event-related changes in well-being and mental health (Abramson et al.,
1989; Lin et al., 2013; Scott et al., 2020; Stroebe et al., 2006).
Explanations similar to those discussed for psychological resources may apply here.
That is, higher levels of social support or less financial problems may help people to adapt to
life events before they occur, or findings may be influenced by construct overlap and regression
to the mean. Regarding the latter explanation, it seems important to note that we often had to
rely on proxy variables to identify social support and financial hardship (e.g., people’s
satisfaction with social relationships). These variables represent only rough approximations of
the social support that individuals receive. Thus, future research seems warranted that
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
69
incorporates more comprehensive assessments of contextual variables that move beyond self-
report.
Practical, Empirical, and Theoretical Implications
The findings of the present study have important implications for theory and research
on event-related personality trait changes. These implications depend on the interpretation of
the effect sizes of the identified moderation effects. In the coordinated data analysis, a
moderator accounted for about 0.16% of the variance of personality trait changes, predicted
approximately 0.01 SD differences in personality development per year, and about 0.05 SD
differences in shifts between pre- to post-event personality assessments. According to classic
conventions, these effect sizes can be described as very small (Cohen, 1988; Funder & Ozer,
2019). However, when evaluated in the context of existing research on event-related changes,
effects seem to be similar in size to moderators of other constructs (Fassbender et al., 2022;
Haehner et al., 2023; Luhmann et al., 2021). Furthermore, the effects are comparable in size to
the main effects of life events. That is, a 2-SD difference in a metric moderator, on average,
results in a similarly sized difference in personality trait changes as the average main effect of
life events (Bühler et al., 2024).
Are such small effect sizes still practically relevant? The answer to this question depends
on the practical implications one considers. For interventions, the detected effects are likely too
small to be practically relevant. If there was a single moderator that predicted strong differences
in event-related personality development, this moderator could have perhaps been used to
inform interventions attempting to prevent unfavorable personality development. The findings
of the present study question the usefulness and feasibility of such an approach as no single
moderator emerged as strong risk or protective factor for event-related personality
development. While there may be strong moderators of event-related personality development
for certain individuals, no general recommendations for intervention approaches can be given
based on our findings.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
70
However, the present effects could still be practically relevant at the population-level.
First, considering the relevance of personality traits for important life outcomes (Bleidorn et
al., 2019; Soto, 2019), predicting even small differences in event-related personality trait
change could become practically relevant due to their consequences for other variables such as
mental or physical health (Friedman et al., 2014; Friedman & Kern, 2014; Jokela et al., 2018;
Wright & Jackson, 2023). Second, moderation effects could scale across people (Anvari et al.,
2023; Götz et al., 2022). Although most life events occur relatively infrequently within an
individual life course, a sizeable proportion of the population experiences multiple important
life events over a time span of a few years (see Table 6). Third, the effects of moderators of
event-related personality trait changes may scale across the lifespan. As personality traits are
more stable than other constructs (Anusic & Schimmack, 2016; Haehner et al., 2022), the
effects of moderators of event-related personality changes may accumulate to a certain degree
across an individual’s life course (Anvari et al., 2023).
Overall, given the complexity of personality development, it seems to be a realistic
scenario that a single moderator like self-rated health may explain only a small amount of
variance in personality trait changes (Bleidorn et al., 2020; Gandhi et al., 2024). The findings
are similar in genetic research where single gene variants explain about 0.1% of variance in a
relevant phenotype (Götz et al., 2022; O’Connor, 2021; Smith-Woolley et al., 2019). These
small effects of individual genes are nonetheless considered to be practically relevant and have
resulted in a change in research approaches towards large-scale, multi-lab collaborations that
investigate the effects of a large number of genes with sufficient statistical power (Sullivan,
2010; Visscher et al., 2012). Thus, learning from this field of research, similar large-scale
research projects could help to comprehensively investigate the complex interplay of life
experiences and personal characteristics (Bleidorn et al., 2020; Dugan et al., 2023). The present
study that combined eight large-scale, multi-national datasets may be seen as initial step
towards this direction. Future research should extent the set of considered life experiences
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
71
beyond focal life events and examine personality trait development across a comprehensive set
of major and minor life experiences while considering a broad set of relevant moderators.
Another takeaway from genetic research could be the use of polygenic risk scores.
Polygenic risk scores aggregate the effects of numerous genes into a single risk score, and they
have been found to correlate higher with relevant phenotypes than single gene variants (Gandhi
et al., 2024; Plomin & Von Stumm, 2022). Similarly, aggregating the identified moderation
effects across life experiences into a poly-experience risk score could increase our predictive
accuracy of personality trait changes. This is supported by initial evidence that simultaneously
considering multiple moderators of event-related personality development increases the amount
of explained variance in personality trait changes (Haehner, Bleidorn, et al., 2024). The joint
consideration of psychological, event-related, contextual, and demographic characteristics in a
poly-experience risk score may thus help to understand a sizable proportion of the differences
in event-related personality development.
The present study also has implications for theory development in personality
psychology. First, our findings show that individual differences in event-related personality
changes are not just random noise but that there are replicable predictors of these individual
differences. Inspired by theories on event-related changes in other constructs (Abramson et al.,
1989; A. T. Beck & Bredemeier, 2016; Park, 2010; Sheldon et al., 2013), personality
development theories that seek to explain the effects of life events may thus consider
incorporating these replicable moderators into their theoretical models (see Figure 13 for an
overview). Second, the same domains of moderators – namely psychological, demographic,
event-related, and contextual characteristics – seem to be relevant across constructs, suggesting
that personality development theories could learn from theory and research on well-being and
mental health to refine their theoretical predictions (A. T. Beck & Bredemeier, 2016; Haehner,
Kritzler, et al., 2024; Wilson & Gilbert, 2008). Specifically, theories on event-related changes
in well-being describe how event-related characteristics such as novelty or variety may facilitate
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
72
or prevent the adaption to major life events (Sheldon et al., 2013; Wilson & Gilbert, 2008). To
incorporate similar hypotheses on the functional roles of relevant moderators into personality
development theories, future research testing mechanistic explanations (e.g., psychological
resources helping people to prepare for relevant life events) is needed. Third, the findings of
this study show that the relevance of moderators of event-related personality changes differs at
least partly across traits and events. Although there were some moderators with replicable
effects across traits and events, most effects were found when examining specific trait and event
combinations. These findings imply that to fully describe event-related personality
development, event- and trait-specific theories may be needed to complement more general and
overarching theoretical accounts like the TESSERA framework or the Neo-Socioanalytic
Theory (Roberts & Nickel, 2017; Wrzus & Roberts, 2017).
In summary, the present study identified both general moderators of event-related
personality changes and moderators that were specific to certain trait and event combinations.
Although the effect sizes of these moderation effects were very small, these effects may
nonetheless be practically relevant under certain conditions, inform future research, and
facilitate theory development on event-related personality changes.
Limitations and Directions for Future Research
This study represents the most comprehensive assessment of the sources of individual
differences in event-related personality changes so far. It nonetheless has several limitations.
First, we focused on personality trait development over several years with relatively
long time intervals between assessments. However, there is emerging evidence that personality
traits can change quickly and that there are sizeable individual differences in personality
development on shorter time scales (Haehner, Bleidorn, et al., 2024; Haehner, Wright, et al.,
2024; Roberts et al., 2017). Future research should thus examine moderators of event-related
personality changes over shorter time intervals between assessments to uncover whether the
effects differ across time scales (Bleidorn et al., 2020).
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
73
Second, our findings are based on datasets that used short self-report measures to assess
personality traits. Longer personality inventories would be needed to examine personality trait
changes beyond the domain level (Mõttus, 2016). Furthermore, using other-reports of
personality traits would reduce common-method variance and help to evaluate the
generalizability of effects beyond self-reports.
Third, the studies included in the systematic review and the coordinated data analysis
were all collected in democratic, Western countries. Although we tried to identify studies from
other regions of the world for the coordinated data analysis, datasets that fulfilled the
requirements to examine event-related personality changes seemed to be limited to Western
countries. It thus remains unclear whether our findings can be replicated in other cultural
contexts. As the frequency and normatively of life events differs across cultures (e.g., Ngo &
Le, 2007), one may expect that results on moderators of event-related changes also partly differ
across cultures. Future research on event-related personality development across cultures thus
is warranted to identify which effects are universal and which are culturally specific (Henrich
et al., 2010; Thalmayer et al., 2021, 2024).
Fourth, in the coordinated data analysis, we focused on moderators of personality
changes that could be relevant across life events and that were available in different panel
studies. However, there may be other relevant moderators that we could not consider in our
analyses. Future research may thus want to extend the set of examined moderators to other
psychological, demographic, event-related, and contextual variables.
Conclusion
People differ in their event-related personality development. However, the sources of
these individual differences have been unclear. To overcome this limitation and to identify
replicable moderators of event-related personality changes, we conducted a systematic review
and a coordinated data analysis. We found that age, Big Five traits, mastery, control, self-rated
health, and event normativeness were robustly associated with differential personality trait
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
74
changes across dataset. However, most moderation effects were trait- and event-specific and
each moderator explained only a very small proportion of variance in personality trait changes,
suggesting that multiple moderators may work together in shaping personality development,
with nuanced effects across events and traits. The findings of the present study may advance
personality development theories as they provide clear indications of which psychological,
demographic, event-related, and contextual variables should be considered to obtain a
comprehensive understanding of people’s personality trajectories.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
75
References
Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness depression: A theory-
based subtype of depression. Psychological Review, 96(2), 358–372.
https://doi.org/10.1037/0033-295X.96.2.358
Allemand, M., & Martin, M. (2016). On Correlated Change in Personality. European
Psychologist, 21(4), 237–253. https://doi.org/10.1027/1016-9040/a000256
Anger, S., Camehl, G., & Peter, F. (2017). Involuntary job loss and changes in personality
traits. Journal of Economic Psychology, 60, 71–91.
https://doi.org/10.1016/j.joep.2017.01.007
Anusic, I., & Schimmack, U. (2016). Stability and change of personality traits, self-esteem,
and well-being: Introducing the meta-analytic stability and change model of retest
correlations. Journal of Personality and Social Psychology, 110(5), 766–781.
https://doi.org/10.1037/pspp0000066
Anvari, F., Kievit, R., Lakens, D., Pennington, C. R., Przybylski, A. K., Tiokhin, L., Wiernik,
B. M., & Orben, A. (2023). Not all effects are indispensable: Psychological science
requires verifiable lines of reasoning for whether an effect matters. Perspectives on
Psychological Science, 18(2), 503–507. https://doi.org/10.1177/17456916221091565
Artelt, C. & NEPS, National Educational Panel Study. (2023). NEPS Starting Cohort 6:
Adults (SC6 14.0.0) (Version 14.0.0) [Dataset]. LIfBi Leibniz Institute for Educational
Trajectories. https://doi.org/10.5157/NEPS:SC6:14.0.0
Artelt, C. & NEPS, National Educational Panel Study. (2024). NEPS Starting Cohort 5: First-
Year Students (SC5 19.0.0) (Version 19.0.0) [Dataset]. LIfBi Leibniz Institute for
Educational Trajectories. https://doi.org/10.5157/NEPS:SC5:19.0.0
Asselmann, E., & Specht, J. (2020a). Taking the ups and downs at the rollercoaster of love:
Associations between major life events in the domain of romantic relationships and the
Big Five personality traits. Developmental Psychology, 56(9), 1803–1816.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
76
https://doi.org/10.1037/dev0001047
Asselmann, E., & Specht, J. (2020b). Till death do us part: Transactions between losing one’s
spouse and the Big Five personality traits. Journal of Personality, 88(4), 659–675.
https://doi.org/10.1111/jopy.12517
Asselmann, E., & Specht, J. (2021a). Personality maturation and personality relaxation:
Differences of the Big Five personality traits in the years around the beginning and
ending of working life. Journal of Personality, 89(6), 1126–1142.
https://doi.org/10.1111/jopy.12640
Asselmann, E., & Specht, J. (2021b). Testing the social investment principle around
childbirth: Little evidence for personality maturation before and after becoming a
parent. European Journal of Personality, 35(1), 85–102.
https://doi.org/10.1002/per.2269
Barnett, A. G. (2004). Regression to the mean: What it is and how to deal with it.
International Journal of Epidemiology, 34(1), 215–220.
https://doi.org/10.1093/ije/dyh299
Beck, A. T., & Bredemeier, K. (2016). A unified model of depression: Integrating clinical,
cognitive, biological, and evolutionary perspectives. Clinical Psychological Science,
4(4), 596–619. https://doi.org/10.1177/2167702616628523
Beck, E. D., & Jackson, J. J. (2022). A mega-analysis of personality prediction: Robustness
and boundary conditions. Journal of Personality and Social Psychology, 122(3), 523–
553. https://doi.org/10.1037/pspp0000386
Bleidorn, W. (2024). Toward a theory of lifespan personality trait development. Annual
Review of Developmental Psychology. https://doi.org/10.1146/annurev-devpsych-
010923-101709
Bleidorn, W., Hill, P. L., Back, M. D., Denissen, J. J. A., Hennecke, M., Hopwood, C. J.,
Jokela, M., Kandler, C., Lucas, R. E., Luhmann, M., Orth, U., Wagner, J., Wrzus, C.,
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
77
Zimmermann, J., & Roberts, B. (2019). The policy relevance of personality traits.
American Psychologist, 74(9), 1056–1067. https://doi.org/10.1037/amp0000503
Bleidorn, W., Hopwood, C. J., Back, M. D., Denissen, J. J. A., Hennecke, M., Hill, P. L.,
Jokela, M., Kandler, C., Lucas, R. E., Luhmann, M., Orth, U., Roberts, B. W.,
Wagner, J., Wrzus, C., & Zimmermann, J. (2021). Personality trait stability and
change. Personality Science, 2, e6009. https://doi.org/10.5964/ps.6009
Bleidorn, W., Hopwood, C. J., Back, M. D., Denissen, J. J. A., Hennecke, M., Jokela, M.,
Kandler, C., Lucas, R. E., Luhmann, M., Orth, U., Roberts, B. W., Wagner, J., Wrzus,
C., & Zimmermann, J. (2020). Longitudinal experience–wide association studies—A
framework for studying personality change. European Journal of Personality, 34(3),
285–300. https://doi.org/10.1002/per.2247
Bleidorn, W., Hopwood, C. J., & Lucas, R. E. (2018). Life events and personality trait
change. Journal of Personality, 86(1), 83–96. https://doi.org/10.1111/jopy.12286
Bleidorn, W., Klimstra, T. A., Denissen, J. J. A., Rentfrow, P. J., Potter, J., & Gosling, S. D.
(2013). Personality maturation around the world: A cross-cultural examination of
Social-Investment Theory. Psychological Science, 24(12), 2530–2540.
https://doi.org/10.1177/0956797613498396
Bleidorn, W., Lenhausen, M. R., Schwaba, T., & Hopwood, C. J. (2024). Psychological
change before and after religious conversion and deconversion. Journal of Personality,
92(4), 1193–1210. https://doi.org/10.1111/jopy.12881
Bleidorn, W., Schwaba, T., Zheng, A., Hopwood, C. J., Sosa, S. S., Roberts, B. W., & Briley,
D. A. (2022). Personality stability and change: A meta-analysis of longitudinal studies.
Psychological Bulletin, 148(7–8), 588–619. https://doi.org/10.1037/bul0000365
Blossfeld, H.-P., & Rossbach, H.-G. (Eds.). (2019). Education as a Lifelong Process: The
German National Educational Panel Study (NEPS) (2nd ed. 2019). Springer.
https://doi.org/10.1007/978-3-658-23162-0
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
78
Borghuis, J., Denissen, J. J. A., Oberski, D., Sijtsma, K., Meeus, W. H. J., Branje, S., Koot, H.
M., & Bleidorn, W. (2017). Big Five personality stability, change, and codevelopment
across adolescence and early adulthood. Journal of Personality and Social
Psychology, 113(4), 641–657. https://doi.org/10.1037/pspp0000138
Boyce, C. J., Wood, A. M., Daly, M., & Sedikides, C. (2015). Personality change following
unemployment. Journal of Applied Psychology, 100(4), 991–1011. psyh.
https://doi.org/10.1037/a0038647
Brim, O. G., Baltes, P. B., Bumpass, L. L., Cleary, P. D., Featherman, D. L., Hazzard, W. R.,
Kessler, R. C., Lachman, M. E., Markus, H. R., Marmot, M. G., Rossi, A. S., Ryff, C.
D., & Shweder, R. A. (1999). Midlife in the United States (MIDUS 1), 1995-1996:
Archival Version [Dataset]. ICPSR - Interuniversity Consortium for Political and
Social Research. https://doi.org/10.3886/ICPSR02760
Brüderl, J., Drobnič, S., Hank, K., Neyer, Franz. J., Walper, S., Wolf, C., Alt, P., Bauer, I.,
Böhm, S., Borschel, E., Bozoyan, C., Christmann, P., Edinger, R., Eigenbrodt, F.,
Garrett, M., Geissler, S., Gonzalez Avilés, T., Gröpler, N., Gummer, T., … Wetzel, M.
(2023). The German Family Panel (pairfam)Beziehungs- und Familienpanel (pairfam)
(Version 14.0.0) [Dataset]. GESIS. https://doi.org/10.4232/PAIRFAM.5678.14.0.0
Buecker, S., Stricker, J., & Schneider, M. (2023). Central questions about meta-analyses in
psychological research: An annotated reading list. Current Psychology, 42(8), 6618–
6628. https://doi.org/10.1007/s12144-021-01957-4
Bühler, J. L., Mund, M., Neyer, F. J., & Wrzus, C. (2022). A developmental perspective on
personality–relationship transactions: Evidence from three nationally representative
samples. Journal of Personality, 92, 202–221. https://doi.org/10.1111/jopy.12757
Bühler, J. L., Orth, U., Bleidorn, W., Weber, E., Kretzschmar, A., Scheling, L., & Hopwood,
C. J. (2024). Life events and personality change: A systematic review and meta-
analysis. European Journal of Personality, 38(3), 544–568.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
79
https://doi.org/10.1177/08902070231190219
Bureau Labor of Statistics, U.S. Department of Labor, & National Institute for Child Health
and Human Development. (2023). Children of the NLSY79, 1979-2018 [Dataset]. The
Ohio State University.
Chopik, W. J. (2018). Does personality change following spousal bereavement? Journal of
Research in Personality, 72, 10–21. https://doi.org/10.1016/j.jrp.2016.08.010
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). L. Erlbaum
Associates.
Costa, P. T. Jr., Herbst, J. H., McCrae, R. R., & Siegler, I. C. (2000). Personality at midlife:
Stability, intrinsic maturation, and response to life events. Assessment, 7(4), 365–378.
psyh. https://doi.org/10.1177/107319110000700405
Costantine, M. M. (2014). Physiologic and pharmacokinetic changes in pregnancy. Frontiers
in Pharmacology, 5. https://doi.org/10.3389/fphar.2014.00065
Damian, R. I., Spengler, M., Sutu, A., & Roberts, B. W. (2019). Sixteen going on sixty-six: A
longitudinal study of personality stability and change across 50 years. Journal of
Personality and Social Psychology, 117(3), 674–695.
https://doi.org/10.1037/pspp0000210
De Vries, J. H., Spengler, M., Frintrup, A., & Mussel, P. (2021). Personality development in
emerging adulthood—How the perception of life events and mindset affect personality
trait change. Frontiers in Psychology, 12, 671421.
https://doi.org/10.3389/fpsyg.2021.671421
den Boer, L., Klimstra, T. A., Branje, S. J. T., Meeus, W. H. J., & Denissen, J. J. A. (2019).
Personality maturation during the transition to working life: Associations with
commitment as a possible indicator of social investment. European Journal of
Personality, 33(4), 456–467. https://doi.org/10.1002/per.2218
Denissen, J. J. A., Luhmann, M., Chung, J. M., & Bleidorn, W. (2019). Transactions between
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
80
life events and personality traits across the adult lifespan. Journal of Personality and
Social Psychology, 116(4), 612–633. https://doi.org/10.1037/pspp0000196
Deventer, J., Lüdtke, O., Nagy, G., Retelsdorf, J., & Wagner, J. (2019). Against all odds – is a
more differentiated view of personality development in emerging adulthood needed?
The case of young apprentices. British Journal of Psychology, 110(1), 60–86.
https://doi.org/10.1111/bjop.12336
Dugan, K. A., Vogt, R. L., Zheng, A., Gillath, O., Deboeck, P. R., Fraley, R. C., & Briley, D.
A. (2023). Life events sometimes alter the trajectory of personality development:
Effect sizes for 25 life events estimated using a large, frequently assessed sample.
Journal of Personality. https://doi.org/10.1111/jopy.12837
Elder, G. H. (1998). The life course as developmental theory. Child Development, 69(1), 1–
12. https://doi.org/10.1111/j.1467-8624.1998.tb06128.x
Entringer, T. M., Gebauer, J. E., Eck, J., Bleidorn, W., Rentfrow, P. J., Potter, J., & Gosling,
S. D. (2021). Big Five facets and religiosity: Three large-scale, cross-cultural, theory-
driven, and process-attentive tests. Journal of Personality and Social Psychology,
120(6), 1662–1695. https://doi.org/10.1037/pspp0000364
Fassbender, I., Haehner, P., Buecker, S., & Luhmann, M. (2022). Perceived characteristics of
life events—Short-term changes in prosociality and empathy? European Journal of
Personality, 36(4), 529–542. https://doi.org/10.1177/08902070211031762
Friedman, H. S., & Kern, M. L. (2014). Personality, well-being, and health. Annual Review of
Psychology, 65(1), 719–742. https://doi.org/10.1146/annurev-psych-010213-115123
Friedman, H. S., Kern, M. L., Hampson, S. E., & Duckworth, A. L. (2014). A new life-span
approach to conscientiousness and health: Combining the pieces of the causal puzzle.
Developmental Psychology, 50(5), 1377–1389. https://doi.org/10.1037/a0030373
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense
and nonsense. Advances in Methods and Practices in Psychological Science, 2(2),
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
81
156–168. https://doi.org/10.1177/2515245919847202
Furstenberg, F. F. (2005). Non-normative life course transitions: Reflections on the
significance of demographic events on lives. Advances in Life Course Research, 10,
155–172. https://doi.org/10.1016/S1040-2608(05)10005-7
Galdiolo, S., & Roskam, I. (2014). Development of personality traits in response to childbirth:
A longitudinal dyadic perspective. Personality and Individual Differences, 69, 223–
230. https://doi.org/10.1016/j.paid.2014.06.002
Gandhi, L., Manning, B. S., & Duckworth, A. L. (2024). Effect size magnification: No
variable is as important as the one you’re thinking about—while you’re thinking about
it. Current Directions in Psychological Science, 09637214241268222.
https://doi.org/10.1177/09637214241268222
Geukes, K., van Zalk, M., & Back, M. D. (2018). Understanding personality development: An
integrative state process model. International Journal of Behavioral Development,
42(1), 43–51. https://doi.org/10.1177/0165025416677847
Gnambs, T., & Stiglbauer, B. (2019). No personality change following unemployment: A
registered replication of Boyce, Wood, Daly, and Sedikides (2015). Journal of
Research in Personality, 81, 195–206. https://doi.org/10.1016/j.jrp.2019.06.009
Goebel, J., Grabka, M. M., Liebig, S., Schröder, C., Zinn, S., Bartels, C., Franken, A., Gerike,
M., Griese, F., Halbmeier, C., Kara, S., Krause, P., Liebau, E., Nebelin, J., Petrenz,
M., Satilmis, S., Siegers, R., Steinhauer, H. W., Süttmann, F., … Deutsches Institut
Für Wirtschaftsforschung (DIW Berlin). (2023). Socio-Economic Panel, data from
1984-2021 (SOEP-Core) (Version v38.1) [Dataset]. SOEP Socio-Economic Panel
Study. https://doi.org/10.5684/SOEP.CORE.V38.1EU
Goldberg, L. R. (1992). The development of markers for the Big-Five factor structure.
Psychological Assessment, 4(1), 26–42. https://doi.org/10.1037/1040-3590.4.1.26
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the Big-Five
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
82
personality domains. Journal of Research in Personality, 37(6), 504–528.
https://doi.org/10.1016/S0092-6566(03)00046-1
Götz, F. M., Gosling, S. D., & Rentfrow, P. J. (2022). Small effects: The indispensable
foundation for a cumulative Psychological Science. Perspectives on Psychological
Science, 17(1), 205–215. https://doi.org/10.1177/1745691620984483
Graham, E. K., Willroth, E. C., Weston, S. J., Muniz-Terrera, G., Clouston, S. A. P., Hofer, S.
M., Mroczek, D. K., & Piccinin, A. M. (2022). Coordinated data analysis: Knowledge
accumulation in lifespan developmental psychology. Psychology and Aging, 37(1),
125–135. https://doi.org/10.1037/pag0000612
Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized
linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498.
https://doi.org/10.1111/2041-210X.12504
Haehner, P., Bleidorn, W., & Hopwood, C. J. (2024). Examining individual differences in
personality trait changes after negative life events. European Journal of Personality,
38(2), 209–224. https://doi.org/10.1177/08902070231156840
Haehner, P., Hopwood, C. J., Schellenberg, L., Schwaba, T., & Bleidorn, W. (2024).
Individual Differences in Personality Trait Changes Across the Lifespan: A Meta-
Analysis of Longitudinal Studies [Manuscript submitted for publication].
Haehner, P., Kritzler, S., Fassbender, I., & Luhmann, M. (2022). Stability and change of
perceived characteristics of major life events. Journal of Personality and Social
Psychology, 122(6), 1098–1116. https://doi.org/10.1037/pspp0000394
Haehner, P., Kritzler, S., & Luhmann, M. (2024). Individual differences in changes in
subjective well-being: The role of event characteristics after negative life events.
Journal of Personality and Social Psychology, 127(3), 702–729.
https://doi.org/10.1037/pspp0000511
Haehner, P., Rakhshani, A., Fassbender, I., Lucas, R. E., Donnellan, M. B., & Luhmann, M.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
83
(2023). Perception of major life events and personality trait change. European Journal
of Personality, 37(5), 524–542. https://doi.org/10.1177/08902070221107973
Haehner, P., Schaefer, B., Brickau, D., Kaiser, T., & Luhmann, M. (2024). The Perception of
Major Life Events Across the Life Course. PsyArXiv.
https://doi.org/10.31234/osf.io/8tjsg
Haehner, P., Sleep, C. E., Miller, J. D., Lynam, D. R., & Hopwood, C. J. (2024). The
longitudinal (co)development of personality traits and the level of personality
functioning after negative life events. Clinical Psychological Science, 12(4), 782–801.
https://doi.org/10.1177/21677026231197607
Haehner, P., Wright, A. J., & Bleidorn, W. (2024). A Systematic Review of Volitional
Personality Change Research. PsyArXiv. https://doi.org/10.31234/osf.io/h5db2
Halford, W. K., & Sweeper, S. (2013). Trajectories of adjustment to couple relationship
separation. Family Process, 52(2), 228–243. https://doi.org/10.1111/famp.12006
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world?
Behavioral and Brain Sciences, 33(2–3), 61–83.
https://doi.org/10.1017/S0140525X0999152X
Hopwood, C. J., Bühler, J. L., Orth, U., Scheling, L., Weber, E., Kretzschmar, A., & Bleidorn,
W. (2024). Open peer commentary and author response regarding ‘Life Events and
Personality Change: A Systematic Review and Meta-Analysis.’ European Journal of
Personality, 38(3), 569–596. https://doi.org/10.1177/08902070231211207
Houwen, K. V. D., Stroebe, M., Stroebe, W., Schut, H., Bout, J. V. D., & Meij, L. W.-D.
(2010). Risk factors for bereavement outcome: A multivariate approach. Death
Studies, 34(3), 195–220. https://doi.org/10.1080/07481180903559196
Huinink, J., Brüderl, J., Nauck, B., Walper, S., Castiglioni, L., & Feldhaus, M. (2011). Panel
Analysis of Intimate Relationships and Family Dynamics (pairfam): Conceptual
framework and design. Journal of Family Research, 23(1), 77–101.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
84
https://doi.org/10.20377/jfr-235
Hutteman, R., Hennecke, M., Orth, U., Reitz, A. K., & Specht, J. (2014). Developmental tasks
as a framework to study personality development in adulthood and old age. European
Journal of Personality. https://doi.org/10.1002/per.1959
John, O. P., Donahue, E. M., & Kentle, R. L. (2012). Big Five Inventory [Dataset].
https://doi.org/10.1037/t07550-000
Jokela, M., Airaksinen, J., Kivimäki, M., & Hakulinen, C. (2018). Is within–individual
variation in personality traits associated with changes in health behaviours? Analysis
of seven longitudinal cohort studies. European Journal of Personality, 32(6), 642–
652. https://doi.org/10.1002/per.2173
Juster, F. T., & Suzman, R. (1995). An overview of the Health and Retirement Study. The
Journal of Human Resources, 30, S7. https://doi.org/10.2307/146277
Kaźmierczak, M., & Karasiewicz, K. (2019). Making space for a new role – gender
differences in identity changes in couples transitioning to parenthood. Journal of
Gender Studies, 28(3), 271–287. https://doi.org/10.1080/09589236.2018.1441015
Klimstra, T. A., & McLean, K. C. (2024). Reconsidering normative interpretations in
personality research. European Journal of Personality, 08902070241238788.
https://doi.org/10.1177/08902070241238788
Krämer, M. D., Rohrer, J. M., Lucas, R. E., & Richter, D. (2024). Life events and life
satisfaction: Estimating effects of multiple life events in combined models. European
Journal of Personality, 08902070241231017.
https://doi.org/10.1177/08902070241231017
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in
linear mixed effects models. Journal of Statistical Software, 82(13).
https://doi.org/10.18637/jss.v082.i13
Lachman, M. (1997). The Midlife Development Inventory (MIDI) Personality Scales: Scale
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
85
Construction and Scoring.
Lawes, M., Hetschko, C., Schöb, R., Stephan, G., & Eid, M. (2024). Examining
interindividual differences in unemployment-related changes in subjective well-being:
The role of psychological well-being and re-employment expectations. European
Journal of Personality. https://doi.org/10.1177/08902070241231315
Liebig, S., Goebel, J., Schröder, C., Grabka, M., Richter, D., Schupp, J., Bartels, C., Fedorets,
A., Franken, A., Jacobsen, J., Kara, S., Krause, P., Kröger, H., Metzing, M., Nebelin,
J., Schacht, D., Schmelzer, P., Schmitt, C., Schnitzlein, D., … Deutsches Institut Für
Wirtschaftsforschung (DIW Berlin). (2021). Socio-Economic Panel, data from 1984-
2019 (SOEP-Core) (Version v36) [Dataset]. SOEP Socio-Economic Panel Study.
https://doi.org/10.5684/SOEP.CORE.V36EU
Liefbroer, A. C., & Billari, F. C. (2010). Bringing norms back in: A theoretical and empirical
discussion of their importance for understanding demographic behaviour. Population,
Space and Place, 16(4), 287–305. https://doi.org/10.1002/psp.552
Lin, N., Dean, A., & Ensel, W. M. (2013). Social Support, Life Events, and Depression.
Academic Press.
Lodi-Smith, J., & Roberts, B. W. (2007). Social investment and personality: A meta-analysis
of the relationship of personality traits to investment in work, family, religion, and
volunteerism. Personality and Social Psychology Review, 11(1), 68–86.
https://doi.org/10.1177/1088868306294590
Luhmann, M., & Eid, M. (2009). Does it really feel the same? Changes in life satisfaction
following repeated life events. Journal of Personality and Social Psychology.
https://doi.org/10.1037/a0015809
Luhmann, M., Fassbender, I., Alcock, M., & Haehner, P. (2021). A dimensional taxonomy of
perceived characteristics of major life events. Journal of Personality and Social
Psychology, 121(3), 633–668. https://doi.org/10.1037/pspp0000291
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
86
Luhmann, M., Hofmann, W., Eid, M., & Lucas, R. E. (2012). Subjective well-being and
adaptation to life events: A meta-analysis. Journal of Personality and Social
Psychology, 102(3), 592–615. https://doi.org/10.1037/a0025948
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(3), 256–266. https://doi.org/10.1002/per.1951
Mancini, A. D., Bonanno, G. A., & Clark, A. E. (2011). Stepping off the hedonic treadmill:
Individual differences in response to major life events. Journal of Individual
Differences, 32(3), 144–152. https://doi.org/10.1027/1614-0001/a000047
Matz, S. C., Beck, E. D., Atherton, O. E., White, M., Rauthmann, J. F., Mroczek, D. K., Kim,
M., & Bogg, T. (2023). Personality Science in the digital age: The promises and
challenges of psychological targeting for personalized behavior-change interventions
at scale. Perspectives on Psychological Science, 17456916231191774.
https://doi.org/10.1177/17456916231191774
McAdams, D. P. (2001). The psychology of life stories. Review of General Psychology, 5(2),
100–122. https://doi.org/10.1037/1089-2680.5.2.100
Moher, D., Stewart, L., & Shekelle, P. (2016). Implementing PRISMA-P: Recommendations
for prospective authors. Systematic Reviews, 5(1), 15. https://doi.org/10.1186/s13643-
016-0191-y
Mõttus, R. (2016). Towards more rigorous personality trait–outcome research. European
Journal of Personality, 30(4), 292–303. https://doi.org/10.1002/per.2041
Mount, M. K., Barrick, M. R., Scullen, S. M., & Rounds, J. (2005). Higher order dimensions
of the Big Five personality traits and the Big Six interests. Personnel Psychology,
58(2), 447–478. https://doi.org/10.1111/j.1744-6570.2005.00468.x
Ngo, H. M., & Le, T. N. (2007). Stressful life events, culture, and violence. Journal of
Immigrant and Minority Health, 9(2), 75–84. https://doi.org/10.1007/s10903-006-
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
87
9018-6
O’Connor, L. J. (2021). The distribution of common-variant effect sizes. Nature Genetics,
53(8), 1243–1249. https://doi.org/10.1038/s41588-021-00901-3
Ormel, J., VonKorff, M., Jeronimus, B. F., & Riese, H. (2017). Set-Point Theory and
personality development. In Personality Development Across the Lifespan (pp. 117–
137). Elsevier. https://doi.org/10.1016/B978-0-12-804674-6.00009-0
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,
Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J.,
Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson,
E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated
guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Park, C. L. (2010). Making sense of the meaning literature: An integrative review of meaning
making and its effects on adjustment to stressful life events. Psychological Bulletin,
136(2), 257–301. https://doi.org/10.1037/a0018301
Plomin, R., & Von Stumm, S. (2022). Polygenic scores: Prediction versus explanation.
Molecular Psychiatry, 27(1), 49–52. https://doi.org/10.1038/s41380-021-01348-y
Pusch, S., Mund, M., Hagemeyer, B., & Finn, C. (2019). Personality development in
emerging and young adulthood: A study of age differences. European Journal of
Personality, 33(3), 245–263. psyh. https://doi.org/10.1002/per.2181
Pustejovsky, J. E., & Tipton, E. (2022). Meta-analysis with robust variance estimation:
Expanding the range of working models. Prevention Science, 23(3), 425–438.
https://doi.org/10.1007/s11121-021-01246-3
Radl, J. (2012). Too old to work, or too young to retire? The pervasiveness of age norms in
Western Europe. Work, Employment and Society, 26(5), 755–771.
https://doi.org/10.1177/0950017012451644
Radler, B. T. (2014). The Midlife in the United States (MIDUS) Series: A National
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
88
Longitudinal Study of Health and Well-being. Open Health Data, 2, e3.
https://doi.org/10.5334/ohd.ai
Rammstedt, B., & John, O. P. (2005). Kurzversion des Big Five Inventory (BFI-K):
Diagnostica, 51(4), 195–206. https://doi.org/10.1026/0012-1924.51.4.195
Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item
short version of the Big Five Inventory in English and German. Journal of Research in
Personality, 41(1), 203–212. https://doi.org/10.1016/j.jrp.2006.02.001
RAND HRS Longitudinal File 2020 (Version 2). (2024). [Dataset]. RAND Center for the
Study of Aging.
Reitz, A. K., Den Boer, L., Van Scheppingen, M. A., & Diwan, K. (2022). Personality
maturation through sense of mastery? Longitudinal evidence from two education‐to‐
work transition studies. Journal of Personality, jopy.12789.
https://doi.org/10.1111/jopy.12789
Reitz, A. K., Luhmann, M., Bleidorn, W., & Denissen, J. J. A. (2022). Unraveling the
complex relationship between work transitions and self-esteem and life satisfaction.
Journal of Personality and Social Psychology, 123(3), 597–620.
https://doi.org/10.1037/pspp0000423
Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models:
An integrative framework for defining R-squared measures. Psychological Methods,
24(3), 309–338. https://doi.org/10.1037/met0000184
Rights, J. D., & Sterba, S. K. (2020). New recommendations on the use of R-squared
differences in multilevel model comparisons. Multivariate Behavioral Research,
55(4), 568–599. https://doi.org/10.1080/00273171.2019.1660605
Roberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., & Hill, P. L. (2017). A systematic
review of personality trait change through intervention. Psychological Bulletin,
143(2), 117–141. https://doi.org/10.1037/bul0000088
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
89
Roberts, B. W., & Nickel, L. B. (2017). A critical evaluation of the Neo-Socioanalytic Model
of personality. In Personality Development Across the Lifespan (pp. 157–177).
Elsevier. https://doi.org/10.1016/B978-0-12-804674-6.00011-9
Rohr, M. K., Wagner, J., & Lang, F. R. (2013). Effects of personality on the transition into
caregiving. Psychology and Aging, 28(3), 692–700. https://doi.org/10.1037/a0034133
Rubin, M. (2021). When to adjust alpha during multiple testing: A consideration of
disjunction, conjunction, and individual testing. Synthese, 199(3–4), 10969–11000.
https://doi.org/10.1007/s11229-021-03276-4
Ryff, C. D., Almeida, D., Ayanian, J., Binkley, N., Carr, D. S., Coe, C., Davidson, R.,
Grzywacz, J., Karlamangla, A., Krueger, R., Lachman, M., Love, G., Mailick, M.,
Mroczek, D., Radler, B., Seeman, T., Sloan, R., Thomas, D., Weinstein, M., &
Williams, D. (2015). Midlife in the United States (MIDUS 3), 2013-2014: Archival
Version [Dataset]. ICPSR - Interuniversity Consortium for Political and Social
Research. https://doi.org/10.3886/ICPSR36346
Ryff, C. D., Almeida, D. M., Ayanian, J. Z., Carr, D. S., Cleary, P. D., Coe, C., Davidson, R.
J., Krueger, R. F., Lachman, M. E., Marks, N. F., Mroczek, D. K., Seeman, T. E.,
Seltzer, M. M., Singer, B. H., Sloan, R. P., Tun, P. A., Weinstein, M., & Williams, D.
R. (2007). Midlife in the United States (MIDUS 2), 2004-2006: Archival Version
[Dataset]. ICPSR - Interuniversity Consortium for Political and Social Research.
https://doi.org/10.3886/ICPSR04652
Saucier, G. (1994). Mini-markers: A brief version of Goldberg’s unipolar Big-Five markers.
Journal of Personality Assessment, 63(3), 506–516.
https://doi.org/10.1207/s15327752jpa6303_8
Scherpenzeel, A. C. (2010). “True” longitudinal and probability-based internet panels:
Evidence from the Netherlands. In M. Das, P. Ester, & L. Kaczmirek (Eds.), Social
and Behavioral Research and the Internet (1st ed., pp. 77–104). Taylor & Francis.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
90
Schwaba, T., & Bleidorn, W. (2018). Individual differences in personality change across the
adult life span. Journal of Personality, 86(3), 450–464.
https://doi.org/10.1111/jopy.12327
Schwaba, T., & Bleidorn, W. (2019). Personality trait development across the transition to
retirement. Journal of Personality and Social Psychology, 116(4), 651–665.
https://doi.org/10.1037/pspp0000179
Schwaba, T., Denissen, J. J. A., Luhmann, M., Hopwood, C. J., & Bleidorn, W. (2023).
Subjective experiences of life events match individual differences in personality
development. Journal of Personality and Social Psychology, 125(5), 1136–1156.
https://doi.org/10.1037/pspp0000483
Scott, H. R., Pitman, A., Kozhuharova, P., & Lloyd-Evans, B. (2020). A systematic review of
studies describing the influence of informal social support on psychological wellbeing
in people bereaved by sudden or violent causes of death. BMC Psychiatry, 20(1), 265.
https://doi.org/10.1186/s12888-020-02639-4
Seifert, I. S., Rohrer, J. M., & Schmukle, S. C. (2023). Using within-person change in three
large panel studies to estimate personality age trajectories. Journal of Personality and
Social Psychology. https://doi.org/10.1037/pspp0000482
Shaw, M., Rights, J. D., Sterba, S. S., & Flake, J. K. (2022). r2mlm: An R package calculating
R-squared measures for multilevel models. Behavior Research Methods, 55(4), 1942–
1964. https://doi.org/10.3758/s13428-022-01841-4
Sheldon, K. M., Boehm, J., & Lyubomirsky, S. (2013). Variety is the Spice of Happiness: The
Hedonic Adaptation Prevention Model. Oxford University Press.
https://doi.org/10.1093/oxfordhb/9780199557257.013.0067
Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: A
best practice guide for conducting and reporting narrative reviews, meta-analyses, and
meta-syntheses. Annual Review of Psychology, 70(1), 747–770.
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
91
https://doi.org/10.1146/annurev-psych-010418-102803
Smith-Woolley, E., Selzam, S., & Plomin, R. (2019). Polygenic score for educational
attainment captures DNA variants shared between personality traits and educational
achievement. Journal of Personality and Social Psychology, 117(6), 1145–1163.
https://doi.org/10.1037/pspp0000241
Soto, C. J. (2019). How replicable are links between personality traits and consequential life
outcomes? The life outcomes of personality replication project. Psychological Science,
30(5), 711–727. https://doi.org/10.1177/0956797619831612
Specht, J. (2017). Personality development in reaction to major life events. In Personality
Development Across the Lifespan (pp. 341–356). Elsevier.
https://doi.org/10.1016/B978-0-12-804674-6.00021-1
Specht, J., Bleidorn, W., Denissen, J. J. A., Hennecke, M., Hutteman, R., Kandler, C.,
Luhmann, M., Orth, U., Reitz, A. K., & Zimmermann, J. (2014). What drives adult
personality development? A comparison of theoretical perspectives and empirical
evidence. European Journal of Personality, 28(3), 216–230.
https://doi.org/10.1002/per.1966
Specht, J., Egloff, B., & Schmukle, S. C. (2011). Stability and Change of Personality across
the Life Course: The Impact of Age and Major Life Events on Mean-Level and Rank-
Order Stability of the Big Five. Journal of Personality and Social Psychology, 101(4),
862–882. https://doi.org/null
Specht, J., Egloff, B., & Schmukle, S. C. (2013). Examining mechanisms of personality
maturation: The impact of life satisfaction on the development of the Big Five
personality traits. Social Psychological and Personality Science, 4(2), 181–189.
https://doi.org/10.1177/1948550612448197
Spikic, S., Mortelmans, D., & Pasteels, I. (2021). Does divorce change your personality?
Examining the effect of divorce occurrence on the Big Five personality traits using
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
92
panel surveys from three countries. Personality and Individual Differences, 171.
https://doi.org/10.1016/j.paid.2020.110428
Stroebe, M. S., Folkman, S., Hansson, R. O., & Schut, H. (2006). The prediction of
bereavement outcome: Development of an integrative risk factor framework. Social
Science & Medicine, 63(9), 2440–2451.
https://doi.org/10.1016/j.socscimed.2006.06.012
Sullivan, P. F. (2010). The Psychiatric GWAS Consortium: Big science comes to Psychiatry.
Neuron, 68(2), 182–186. https://doi.org/10.1016/j.neuron.2010.10.003
Thalmayer, A. G., Asatsa, S., Shino, E. N., Naudé, L., Laher, S., Hassem, T., Florence, M.,
Adonis, T.-A., Uugwanga, S. N., Rotzinger, J. S., Hofmann, D., Makunda, J., Botha,
C., Murangi, A., & Shirima, C. M. (2024). Lifespan research in Kenya, Namibia, and
South Africa: Cohort profile of the Africa long life study. Personality Science, 5.
https://doi.org/10.1177/27000710241264492
Thalmayer, A. G., Toscanelli, C., & Arnett, J. J. (2021). The neglected 95% revisited: Is
American psychology becoming less American? American Psychologist, 76(1), 116–
129. https://doi.org/10.1037/amp0000622
Van Dijk, M. P. A., Hale, W. W. I., Hawk, S. T., Meeus, W., & Branje, S. (2020). Personality
development from age 12 to 25 and its links with life transitions. European Journal of
Personality, 34(3), 322–344. psyh. https://doi.org/10.1002/per.2251
Van Scheppingen, M. A., Jackson, J. J., Specht, J., Hutteman, R., Denissen, J. J. A., &
Bleidorn, W. (2016). Personality trait development during the transition to parenthood:
A test of Social Investment Theory. Social Psychological and Personality Science,
7(5), 452–462. https://doi.org/10.1177/1948550616630032
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of
Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
93
discovery. The American Journal of Human Genetics, 90(1), 7–24.
https://doi.org/10.1016/j.ajhg.2011.11.029
Wagner, J., Becker, M., Lüdtke, O., & Trautwein, U. (2015). The first partnership experience
and personality development: A propensity score matching study in young adulthood.
Social Psychological and Personality Science, 6(4), 455–463. psyh.
https://doi.org/10.1177/1948550614566092
Watson, N., & Wooden, M. P. (2012). The HILDA Survey: A case study in the design and
development of a successful Household Panel Survey. Longitudinal and Life Course
Studies. https://doi.org/10.14301/llcs.v3i3.208
Wilson, T. D., & Gilbert, D. T. (2008). Explaining away: A model of affective adaptation.
Perspectives on Psychological Science, 3(5), 370–386. https://doi.org/10.1111/j.1745-
6924.2008.00085.x
Wortman, J. A. (2017). Personality stability and change: The role of life events and
normativeness in two longitudinal surveys (2017-01061-008) [ProQuest Information &
Learning]. https://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-
01061-008&site=ehost-live
Wright, A. J., & Jackson, J. J. (2023). Do changes in personality predict life outcomes?
Journal of Personality and Social Psychology, 125(6), 1495–1518.
https://doi.org/10.1037/pspp0000472
Wright, A. J., & Jackson, J. J. (2024). Longitudinal within-person variability around
personality trajectories. Journal of Personality and Social Psychology.
https://doi.org/10.1037/pspp0000507
Wrzus, C., & Roberts, B. W. (2017). Processes of personality development in adulthood: The
TESSERA framework. Personality and Social Psychology Review, 21(3), 253–277.
https://doi.org/10.1177/1088868316652279
MODERATORS OF EVENT-RELATED PERSONALITY CHANGE
94
