The reciprocal impacts of adversity and personality traits: A prospective longitudinal study of growth, change, and the power of personality

Abstract

Objective:

We integrate the study of post-traumatic growth with personality science by examining reciprocal effects of adversity and core personality traits. We implemented conceptual (i.e., incorporating personality traits into the study of adversity-related growth, examining trait-specific and configural personality change, and adopting a cumulative approach to adversity) and methodological (i.e., three-wave prospective design, assessing many life events, sampling from populations likely to experience adversity) innovations to advance the study of personality development and of the generality of adversity-related growth.

Method:

A diverse sample (41% non-white, 48% low-income, 68% female, M_age = 44, 30% diagnosed with Borderline Personality Disorder) participated in a three-wave prospective longitudinal study (N = 258–128 across waves). Participants completed a personality inventory and a battery of life-event surveys (including 105 events) at each wave.

Results:

Personality was generally stable from trait-specific and configural perspectives, and all traits were correlated with adversity. All traits, particularly extraversion and conscientiousness, predicted adversity. Adversity predicted increases in emotionality and decreases in agreeableness.

Conclusions.

Although personality growth is not the typical response to adversity within a 3-year period, findings underscore the real-world impact of personality, and they provide some support for developmental theories emphasizing the effects of life events.

Adults typically become more emotionally stable over time (Bleidorn, Kandler, Riemann, Angleitner, & Spinath, 2009; Roberts, Walton, & Viechtbauer, 2006), but individuals differ in the degree to which their stability increases (Roberts & DelVecchio, 2000; Schwaba & Bleidorn, 2018). Moreover, some people become less stable over time, while others show no systematic changes. Similarly, individuals typically become more agreeable and conscientious, but some become less so or remain the same. The causes of such changes are not fully clear, but some researchers posit that life events influence developmental trajectories (Asselmann & Specht, in press; Denissen, Luhmann, Chung, & Bleidorn, 2018; Denissen, Ulferts, Lüdtke, Muck, & Gerstorf, 2014; Jackson, Thoemmes, Jonkmann, Lüdtke, & Trautwein, 2012; Specht, Egloff, & Schmukle, 2011). One perspective, post-traumatic growth (PTG), highlights the potential positive transformative effects of adversity arising from traumatic life events (e.g., Tedeschi & Calhoun, 2004). Some research supports PTG theory, but greater integration with personality science may facilitate conceptual and methodological advances in this area of research (Jayawickreme & Blackie, 2014; Tennen & Affleck, 2009). The current prospective longitudinal study integrates PTG with personality science by examining reciprocal effects of adversity and core personality traits, with attention to two conceptualizations of personality change.

This work has important implications for PTG theory and for a broader understanding of personality development. First, we hope to expand the theoretical basis of PTG. We integrate core personality traits into the study of growth through adversity, we conceptualize psychological change in two distinct ways (including one new to PTG research), and we examine “cumulative adversity” rather than single events. These conceptual advances can shed new light on the psychological characteristics that respond to adversity and the generality or typicality of growth-through-adversity. PTG theory suggests that adversity can lead to growth (Tedeschi & Calhoun, 2004), but research has not yet revealed whether personality growth is the norm or the exception following cumulative adversity. We address this issue, which has theoretical implications, as well as clinical interventions for individuals facing adverse life events.

Second, this work takes a methodologically rigorous approach to the study of growth through adversity. Using methods that are atypical in PTG research (e.g., a prospective longitudinal design, avoiding retrospective assessments of change, see below), we avoid several key problems that otherwise obscure findings related to psychological change.

Third, this work expands our understanding of personality’s impact and development. Evidence that personality shapes life events (e.g., in the form of experiencing adversity) deepens our understanding of personality’s real-world power and impact (Heckman & Kautz, 2012; Moffitt et al., 2011; Ozer & Benet-Martinez, 2006; Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007; Soto, 2019). In addition, evidence that life events shape personality informs theories of personality development. Most theories agree that at least some life events can shape personality traits; however, they seem to place differential weight on the extent to which personality traits are sensitive to environmental influences like life events. For example, Social Investment Theory proposes that personality trait development is “largely the result of experiences in universal social roles in young adulthood” (Roberts, Wood, & Lodi-Smith, 2004). This implies that events such as entering the workforce, getting married, and starting a family among others, can influence personality traits and that personality traits are somewhat sensitive to the environment. Alternatively, the Five-Factor Theory “does not admit of any influence of the environment on personality traits” (McCrae & Costa, 2003, p. 193). This relatively extreme view implies that life events can only rarely ever influence personality traits and that such traits are relatively resistant to environmental influence. Accumulating evidence is inconsistent with such extreme views, and it suggests that environmental factors do indeed affect personality. However, the apparent effects of environmental factors are generally small and inconsistent, and much work in the area remains to be done (Bleidorn, Hopwood, & Lucas, 2018). The current work informs personality theory by examining the reciprocal effects of personality traits and adversity.

Growth in Response to Adversity

Several theoretical perspectives on PTG exist, but their common assertion is that adversity can lead to positive psychological change (e.g., Joseph & Lindley, 2005; Tedeschi & Calhoun, 2004). As individuals cope with significant challenges, they may revise their assumptions about themselves, the world, and/or their purpose. These processes can generate growth in psychosocial characteristics such as greater appreciation of life, more intimate social relationships, heightened feelings of personal strength, greater engagement with spiritual questions, the recognition of new possibilities for their lives, meaning and purpose in life, and/or psychological maturity. Empirical findings are consistent with some of these ideas. Indeed, a recent meta-analysis suggests growth in social relationships, self-esteem, and environmental mastery following negative life events (Mangelsdorf, Eid, & Luhmann, 2019).

Although several methodological issues complicate the interpretation of this research (Jayawickreme & Blackie, 2014; Mangelsdorf et al, 2019; Tennen & Affleck, 2009), such findings have several implications. First, some people see themselves as growing through adversity, consistent with PTG theory. Second, growth may occur in some but not all psychosocial dimensions. Third, additional psychosocial dimensions, beyond those examined thus far, might be important for growth in response to adversity.

Opportunities for Conceptual Advances

Core personality traits.

By integrating core personality traits into the study of adversity, we can better understand both the scope of PTG and, potentially, its underlying mechanisms. PTG research has focused mainly on adversity’s effects on eudaimonic well-being, including characteristics such as appreciation for life, engagement with spiritual questions, self-acceptance, and positive relationships (Jayawickreme & Blackie, 2014). Traits such as extraversion, emotionality, conscientiousness, agreeableness, openness to experience, and honesty/humility are not generally seen as direct indicators of such characteristics, but they do reflect core personological differences in consistent patterns of thought, feelings, motivation, and behavior (Ashton et al., 2004).

Although not yet integrated into PTG theories, such traits may play an important role in the PTG process (Jayawickreme & Blackie, 2014), in at least four possible ways. First, they may be mediators through which adversity enhances well-being and psychosocial resources. That is, adversity might lead to increases in traits such as agreeableness, openness, or conscientiousness (or decreases in negative emotionality), which affect individuals’ interpersonal environments and cognitive/affective tendencies, and thus shape well-being. Second, core personality traits may change in response to changes in well-being and psychosocial resources. Such effects may be a downstream consequence of PTG as typically conceived. Third, core personality traits may change independently of the characteristics typically considered as outcomes of PTG. That is, they may represent entirely different “target outcomes” (Mangelsdorf et al., 2019) in which adversity produces psychological change. Fourth, core personality traits may contribute to the experience of adversity, thereby triggering potential growth processes. In sum, there is reason to suspect that core personality traits play a role in PTG.

Some empirical work implies connection between core traits and PTG. For example, research suggests that: a) individuals high in emotionality (also labeled negative emotionality or neuroticism) are relatively likely to experience negative events as compared to emotionally stable individuals, and b) individuals high in extraversion are relatively likely to experience positive life events, as compared to introverts (Lüdtke, Roberts, Trautwein, & Nagy, 2011; Vaidya, Gray, Haig, & Watson, 2002; but see Kandler, Bleidorn, Riemann, Angleitner, & Spinath, 2012; Löckenhoff, Terracciano, Patriciu, Eaton, & Costa, 2009). Thus, at least some core personality traits are linked to the experience of life events. Moreover, extraversion, conscientiousness, agreeableness, openness, and emotionality are associated with individuals’ beliefs that they have grown from trauma (Evers et al., 2001; Tedeschi & Calhoun, 1996). In sum, PTG theory can advance by examining these connections, and a key step in this direction is to explore reciprocal longitudinal links between adversity and changes in core personality traits. We aim to accomplish this key step.

Conceptualizations of change/stability.

A second way in which personality psychology may offer new insights into PTG is via multiple conceptualizations of psychological change. Personality changes in different ways (Block & Robins, 1993; Caspi & Roberts, 1999), and a deep perspective on PTG requires attention to such differences.

Configural change (also called ipsative or profile change) is a global approach across a set of characteristics, conceptualizing change at the level of an individual (rather than in terms of differences among individuals on a particular trait). As shown in Figure 1‘s panel A, personality configuration refers to the relative ordering of traits within a person. The solid line represents (hypothetical) Person 1’s personality profile as measured at Time 1 of a longitudinal study. This person is higher on openness than on agreeableness, conscientiousness, and emotionality, and higher on those traits than on honesty/humility and extraversion. Person 2 (dashed line) has a different configuration, represented by a different pattern of high/low trait levels.

Figure 1.

Two Ways of Conceptualizing Personality Change in Relation to Adversity

Note. P = Person

From this perspective, change is viewed as a shift in an individual’s personality configuration. Comparing Figure 1‘s panels A and B, Person 1’s configuration changes dramatically across time. At Time 2, openness is now lower than several other traits (rather than being higher than all other traits), and emotionality is now much lower than all other traits. The correlation between Person 1’s Time 1 scores and his/her Time 2 scores is r = −.08, and this near-zero correlation indicates dramatic change in his/her personality configuration. In contrast, Person 2’s configuration remains completely stable – the relative ordering of his/her traits at Time 2 is identical to the ordering at Time 1. This stability is reflected in a perfect positive correlation between his Time 1 and Time 2 scores (r = 1.00).

Adversity may have broad or global effects, impacting several traits in different ways – perhaps increasing some and decreasing others (e.g., Person 1). Moreover, adversity might affect different people in different ways, which could be reflected in an association between adversity and configural stability/change.

An association between adversity and configural change would suggest either (or both) of two possibilities: a) adversity has widespread effects on personality, affecting a range of core traits; and b) personality instability across a range of traits triggers adversity in some way (e.g., personality instability reflects a maladaptive period of personality reconfiguration, triggering behaviors and events that create adversity). These possibilities have been examined in only one recent study (Pusch, Mund, Hagemeyer, & Finn, 2019), with none of six assessed events affecting personality configurations. More research is needed in order to fully understand the links between adversity and personality configurations, and researchers have recently called for such work (Bleidorn et al., 2018).

Figure 1‘s Panel C illustrates a second way of conceptualizing change, in terms of individual traits. In this example, eight participants are observed at two times, with agreeableness assessed at both times. As Figure 1C shows, four participants (solid lines) experienced adversity between assessments, whereas four others (dashed lines) experienced no adversity. On average, those who experienced adversity increased in agreeableness; however, the four who experienced no adversity showed no general tendency to increase or decrease in agreeableness on average, Such “trait-specific” results suggest that adversity leads to increased agreeableness, on average.¹

These conceptualizations reflect what has been called “actual growth” or “measured growth” in the PTG literature (Jayawickreme & Blackie, 2014; Tennen & Affleck, 2009). In the PTG literature, studies more typically evaluate “perceived growth” or “retrospectively assessed growth,” asking participants to report their beliefs about post-traumatic growth. Thus, connections between adversity and actual growth are not well-established in the PTG literature.

Beyond actual growth, associations between adversity and individual traits could suggest two other possibilities: a) adversity can limit or lessen typical growth patterns observed for traits across adulthood (e.g., smaller increases in emotional stability, agreeableness, and conscientiousness); and b) adversity can decrease trait levels. These possibilities have been examined for single life events (e.g., Denissen et al., 2018). For example, Jackson and colleagues (2012) found that military recruitment reduced rates of increase in agreeableness. However, no study has evaluated these associations using the cumulative adversity approach described next.

Cumulative adversity.

A “cumulative adversity” approach to life events is a third advance. As noted previously (Jayawickreme & Blackie, 2014; Mangelsdorf et al., 2019), PTG studies typically focus on single traumatic events (e.g., studying participants who have survived a natural disaster). Although informative, this may miss a broader perspective on adversity.

As often implemented in the study of stress (e.g., Holmes & Rahe, 1967; Lewinsohn, Joiner, & Rohde, 2001), a broader perspective conceptualizes adversity as an accumulation of negative events, rather than in terms of a single traumatic event. Some traumatic experiences unfold over time (e.g., the trauma of extended service in war), while many others are linked to specific, discrete events that are severely painful (e.g., a sexual assault). Although such discrete traumatic events can have dramatic effects, an accumulation of negative (but perhaps not “traumatic”) events may be just as, or even more, impactful. Not only can one adverse event trigger others, but meaningful adversity can arise from events not typically considered “traumatic” (Seery, Holman, & Silver, 2011). Moreover, conceptualizing adversity cumulatively in terms of “differences among people in the total amount of adversity experienced” provides built-in comparative scaling. By studying only those participants who have experienced a single traumatic event (Jayawickreme & Blackie, 2014; Mangelsdorf et al., 2019), the typical PTG study lacks variability in adversity and thus cannot compare participants across different levels of adversity. A cumulative adversity approach avoids this problem, as participants naturally differ in the amount of adversity experienced over a given time period.

Ideally, a cumulative adversity approach is based upon assessments of a wide range of life events, which has important advantages. For example, it allows researchers to examine both negative events (e.g., death of a spouse) and positive events (e.g., getting married), consistent with recent recommendations (Mangelsdorf et al., 2019). Adversity emerging from negative events may be mitigated by benefits associated with positive events (Longua, DeHart, Tennen, & Armeli, 2009), and assessment of diverse events affords insight into this possibility. Moreover, assessment of many events allows reliable differentiation between dependent and independent events (Conway, Boudreaux, & Oltmanns, 2018; Hammen, 1991). Dependent events are those that are ostensibly influenced by an individual’s actions (e.g., getting fired due to being unreliable and unproductive), whereas independent events are those that arise from causes outside of the individual’s actions (e.g., getting “let go” when a company goes bankrupt). Fully understanding the reciprocal connections between personality and adversity requires attention to this distinction. Failing to do so may obscure findings, particularly in terms of personality’s impact on adversity. Unfortunately, few studies have attended to this distinction (cf., Conway et al., 2018; Kandler et al., 2012; Perry, Lavori, Pagano, Hoke, & O’Connell, 1992).

Methodological Issues

As PTG research has evolved, researchers have noted opportunities for methodological improvement (e.g., Coyne & Tennen, 2010; Jayawickreme & Blackie, 2014; Mangelsdorf et al., 2019; Tennen & Affleck, 2009). One major methodological concern is under-utilization of prospective longitudinal designs. To disentangle cause and effect, personality (or whatever target outcome is of interest) must be measured before and after occurrences of adversity. Unfortunately, such designs are atypical in PTG work, limiting the field’s ability to make causal inferences. More narrowly, although even more waves of observation are desirable, researchers suggest designs with at least three waves of observation (Bleidorn et al., 2018; Tennen & Affleck, 2009). Among other advantages, this allows evaluation of replicability of effects.

A second methodological issue is the assessment of change, as most previous PTG research does so retrospectively in terms of “perceived growth” (e.g., beliefs about psychological change in response to events). This suffers from biases and that prevent clear conclusions about PTG (Tennen & Affleck, 2009). A better strategy is to measure personality (or again, whatever outcome is of interest) in terms of “current standing” both before and after the occurrence of adversity. A prospective longitudinal design facilitates this.

A third methodological issue is sampling that aims for diversity in the experience of adverse events. As noted earlier, PTG research typically holds adversity constant, by including only participants who have experienced a single (to the researchers’ knowledge) event. Ideally, a prospective study would include participants who experience considerable adversity, as well as those who experience little or no meaningful adversity. Such sampling ensures the variability necessary for the detection of effects related to adversity.

Fourth, a study of cumulative adversity should assess many events. Studies taking a cumulative approach typically rely on “event checklists,” with participants noting which events occurred during some time period (Conway et al., 2018; Seery et al., 2011). Ideally, such checklists include many events, providing a wide-ranging assessment of adversity. If a checklist includes few events, then participants might have experienced adverse events omitted from the checklist. In such cases, researchers underestimate those participants’ adversity, which obscures the measurement of adversity and limits researchers’ ability to detect true effects (Furr, 2018). Unfortunately, event checklists often include few events. Seery et al. (2011) used a checklist of 37 events, and they noted that this was “more types of adversity than is typical” (p. 1037).

Fifth and relatedly, some events are more challenging and traumatic than others, and cumulative adversity should be sensitive to such differences. However, event checklists are often scored simply by counting the number of events that occurred (e.g., Conway et al., 2018; Seery et al., 2011). This is reasonable if the events are equally traumatic or challenging, but it may obscure meaningful differences among a wider range of events. For example, two individuals might each report a single event on an event checklist – one suffering a violent sexual assault, and another experiencing a lawsuit. Simply counting (i.e., unit-weighting) the number of events equates the two, leaving the two individuals with identical “adversity” scores. Assuming that the sexual assault was significantly more traumatic and psychologically damaging than the lawsuit, the equivalence of the two individuals’ adversity scores misrepresents a psychologically significant difference in true level of adversity that they experienced. In such cases, a preferable approach is to differentially scale events in terms their level of adversity.

Present Study

The current study implements all conceptual and methodological innovations described above. Our broad goals are to advance the methodological basis of PTG research, to integrate it with personality science, and ultimately, to advance theory in both PTG and personality psychology. Conceptually, we examined the reciprocal impacts of core personality traits and cumulative adversity, with attention to both configural and trait-specific personality change.

Methodologically, the present three-wave prospective study sampled from a population likely to experience diverse levels of adversity, it assessed personality in terms of “current standing” (rather than in terms of retrospective change), and it assessed a wide range of events examined in a manner sensitive to their differential adversity. Our battery of events included 105 events, far beyond the typical checklist. Moreover, it included both positive and negative events, both dependent and independent events, and both major and more minor events.

A prospective study must sample within populations that are likely to experience adversity. Our sample reflects this in two important ways. First, it includes many participants with Borderline Personality Disorder (BPD) and other psychopathology. Clinical populations, particularly individuals BPD, tend to experience high rates of stress and adversity (Pagano et al., 2004; Shevlin, Dorahy, Adamson, & Murphy, 2007; Wingenfeld et al., 2011). Second, the experience of adverse events differs across racial groups and/or economic standing (Dowd, Palermo, Chyu, Adam, & McDade, 2014; Lipsky, Kernic, Qiu, & Hasin, 2016; Roberts, Gilman, Breslau, Breslau, & Koenen, 2011; Turner & Avison, 2003), and our sample is quite diverse with regard to race and income. These characteristics suggest that our participants will vary dramatically in the experience of adversity.

Finally, we take a normative approach to scaling the adversity of life events, with independent judges rating the likely negative or positive impact of each event (e.g., Hammen, 1991). No method of measuring adversity avoids all problems, but a normative approach reduces a significant problem that arises if participants rate their own perceptions of adversity. Such self-ratings have “been rejected as confounding measurement of the event with emotional reaction to the event” (p. 199, Kessler, 1997). Indeed, some traits (e.g., emotionality) or biases (e.g., acquiescence, extremity) could influence participants’ ratings on an adversity measure, even if there is no true link between personality and the actual experience of adversity. By affecting both participants’ personality ratings and their adversity ratings, such biases could create spurious (or spuriously large) correlations between those sets of ratings. A normative approach avoids this problem, and we revisit its merits and limitations in the discussion.

Key Hypotheses and Questions

Several key hypotheses and questions were examined. In terms of configural personality change-in-response-to-adversity, theories emphasizing the relatively high potential for life events to influence personality suggest (H1) that adversity will be linked to configural change, as greater adversity may have broad effects in reshaping personality.

From a trait-specific perspective on personality and personality change, insight into adversity’s effect on personality requires analyses that disentangle the reciprocal effects of adversity and personality. Thus, one issue is whether personality predicts subsequent cumulative adversity. Since personality can have significant, broad life consequences (Heckman & Kautz, 2012; Ozer & Benet-Martinez, 2006; Soto, 2019), a general hypothesis (H2) is that such effects will exist for at least some core traits. Viewing some traits as buffering against adversity, we would additionally hypothesize (H3) negative associations between adversity and honesty/humility, extraversion, conscientiousness, agreeableness, and perhaps openness along with a positive association between adversity and emotionality. A related hypothesis (H4) is that such effects will be stronger for dependent adversity than for independent adversity.

A second key question is whether adversity predicts changes in personality. Again, and as consistent with our Figure 1C, some theories emphasize a relatively high potential for life events to influence personality. These theories suggest (H5) that adversity will predict personality change in one or more traits. As an extension of H5 and also as consistent with our Figure 1C, if psychological growth (as opposed to change in general) is a general or typical response to adversity, then we would expect (H6) adversity to predict personality change in a positive or adaptive direction (e.g., less emotional, more agreeable, more conscientious).

METHODS

Participants

Participants (n = 271) in this longitudinal study were community and clinical adults recruited at mental health clinics, a human services agency, and via posted flyers and advertisements. As part of a larger study on BPD, some individuals were eligible for participants only if they exhibited (via a brief questionnaire, Zanarini et al. 2003) elevated BPD symptomatology. However, to ensure a wide range of psychopathology and well-being, other individuals were eligible regardless of BPD symptomatology.

Initially, 282 participants entered the three-wave study, with waves spaced at 1.5-year intervals. Accounting for missing data on relevant measures, attrition, and replacement recruitment, current analyses are based upon N = 258, N = 173, and N = 128 participants at waves 1, 2, and 3 respectively. Participants received compensation of up to $170 (in gift cards) for completing the first wave and up to $155 for each subsequent wave.

Table 1 (top) presents demographics. The sample is racially and economically diverse, mostly female, and (on average) middle-aged. Borderline pathology varied considerably across participants, with symptom counts ranging from 0–9 in each wave, out of 9 DSM-5 symptoms American Psychiatric Association, 2013). Approximately 30% of participants were diagnosed with BPD (i.e., 5 or more symptoms) at Wave 1, via a diagnostic interview conducted by PhD or Masters-level clinicians and/or social workers.

Table 1.

Mean (SD) or Frequencies of Key Variables

	Wave
	1	2	3
Demographics
Race (% Non-white)	41.2%	46.6%	46.3%
Income (% below 25K)	47.7%	51.7%	50.0%
Gender (% Female)	67.8%	70.7%	70.1%
Age	43.8 (11.3)	45.2 (11.5)	47.3 (10.6)
BDP # Symptoms (9 possible)	3.1 (2.6)	2.2 (2.4)	1.5 (1.8)
Personality Traits
Honesty-Humility	3.61 (.60)	3.60 (.49)	3.65 (.58)
Emotionality	3.45 (.56)	3.36 (.55)	3.18 (.58)
Extraversion	3.11 (.63)	3.16 (.60)	3.18 (.66)
Agreeableness	2.87 (.61)	2.96 (.52)	2.97 (.60)
Conscientiousness	3.50 (.56)	3.50 (.50)	3.51 (.55)
Openness	3.40 (.63)	3.35 (.61)	3.39 (.64)
Cumulative Adversity
Dependent		−.99 (5.76)	−1.38 (6.11)
Independent		5.72 (7.26)	5.21 (5.21)

Measures

Analyses are based upon two sets of measures collected as part of a larger study. The first is a personality inventory, which participants completed at each wave. The second includes several life-event surveys. We examine these surveys at waves 2 and 3, in which participants reported on life events that had occurred since the previous wave.

HEXACO Personality Inventory Revised - 100 (HEXACO).

The HEXACO-PI is a 100-item scale assessing the six HEXACO traits (Lee & Ashton, 2004). Participants completed the HEXACO at each wave, indicating agreement with each item on a 1 (strongly disagree) to 5 (strongly agree) scale. Internal consistency reliability estimates were acceptable for all traits at all waves, with alphas ranging from .77 to .87.

Life Event Assessment (LEA).

The LEA (adapted from Dohrenwend et al., 1978) is a checklist of eighty-five life events reflecting seven life domains (i.e., work/school, financial matters, crime/legal matters, health, love relations, family/living matters, social)..

Stressful Life Event Screening Questionnaire (SLESQ).

The SLESQ (Goodman, Corcoran, Turner, Yuan, & Green, 1998) includes thirteen events typically associated with trauma (e.g., physically assault, being robbed or mugged, sexually assault).

Midlife in the United States National Survey (MIDUS).

This survey assesses social, psychological, and behavioral factors related to mental and physical health (Brim, Ryff, & Kessler, 2004). We used two items for the present analysis (i.e., “Have you been diagnosed with a physical health problem?” and “Did you receive any government aid?”).

Background Information Schedule (BIS).

The BIS is a structured interview assessing demographics, premorbid functioning, and treatment history in BPD patients for the McLean Study of Adult Development (Zanarini, Temes, Frankenburg, Reich, & Fitzmaurice, 2018). We used an abbreviated self-report version of the BIS that included five event items involving conflict, criminality, and childbirth; and six demographic and treatment history items.

Coding of events as dependent or independent.

In total, we used 105 events from the LEA, SLESQ, MIDUS, and BIS. Four independent raters (graduate students and faculty in psychology) rated each in terms of its likely dependence (i.e., event is influenced to some degree by the individual’s actions) versus independence (i.e., event occurs independent of the individual’s thoughts and behaviors) using a 0 (= most independent) to 10 (= most dependent) scale. Interrater agreement was excellent (absolute intraclass correlation = .94) so the ratings were averaged to create a dependence score for each event.

Events were then categorized as either dependent (N = 71 having dependence scores > 5) or independent (N = 32 having dependence scores < 5)². For example, “started a business or profession” had a dependence rating of 9.5 and was thus categorized as a dependent event. With a dependence rating of 1.0, “lost home through disaster” was categorized as independent.

Adversity scores for each event.

Using a method similar to Hammen (1991), the raters also rated the valence of the likely impact (−5 = most positive to +5 = most negative) of each event. Interrater agreement was excellent (absolute intraclass correlation = .97), so ratings were averaged to create an adversity score for each event. For example, “Spouse/mate died” had a score of +4.5 (reflecting a strongly adverse impact), whereas “Graduated from school/training program” was −3.5, reflecting a highly positive event.

Cumulative adversity.

Two cumulative adversity scores were created for each participant at both waves 2 and 3. A dependent adversity score was created as a weighted sum of the dependent events that a participant reported occurring between waves. For example, if a participant in wave 2 reported recently getting divorced (adversity score = 3) and getting fired (adversity score = 3.5), s/he would have a wave 2 dependent adversity score of 6.5. Similarly, an independent adversity score was created as a weighted sum of the independent events that a participant reported occurring between waves. Thus, each participant had two dependent adversity scores (one at wave 2 and one at wave 3) and two independent adversity scores that reflect the likely cumulative amount of adversity that she/he experienced between waves.

Results

Table 1 presents means and standard deviations of key variables, with three notable points. First, there was little systematic change in mean levels of personality traits. Second, independent adversity scores are fully in the “negative” range, as our life event surveys included no events that were considered both independent and positively impactful. Third, and most importantly, there is considerable between-person variability in adversity. A close look reveals, for example, that dependent adversity scores ranged from −20.25 to 18.38 at wave 2. Thus, some participants experienced large amount of adversity, while others experienced minimal adversity or even mainly positive events. This variability is important because such differences in adversity might be related to (i.e., result from or contribute to) differences in personality.

Configural Stability/Change and Cumulative Adversity

Table 2 (top half) summarizes associations between cumulative adversity and configural stability (versus change). For each participant, we correlated her/his personality trait profile (i.e., across the six HEXACO traits) from one wave with her/his trait profiles from the other two waves. These correlations are interpretable as configural stability scores, with large positive values indicating that a participant’s personality configuration was stable across waves.

Table 2.

Configural Personality Stability, Correlations Between Cumulative Adversity and Configural Stability, and Trait-specific Stability

		Correlation with Dependent Adversity Occurring Between		Correlation with Independent Adversity Occurring Between
	Mean (range)	Waves 1 & 2	Waves 2 & 3	Waves 1 & 2	Wave 2 & 3
Configural stability
Waves 1 to 2	.79*** (−.34, .99)	.14†	.04	−.02	−.05
Waves 2 to 3	.75*** (−.76, .99)	−.14	−.02	−.04	.04
Waves 1 to 3	.71*** (−.80, .99)	.06	−.02	−.01	.06

Trait-specific Stability	Wave 1 to Wave 2	Wave 2 to Wave 3	Wave 1 to Wave 3
Honesty-Humility	.68***	.61***	.64***
Emotionality	.79***	.83***	.74***
Extraversion	.80***	.79***	.72***
Agreeableness	.78***	.76***	.77***
Conscientiousness	.77***	.77***	.72***
Openness	.82***	.81***	.78***

Note.

^***p < .001,

^†p < .10

On average, there was a high degree of configural stability. As Table 2 shows, mean configural stability correlations ranged from $\overline{r}$ = .71 to $\overline{r}$ = .79. However, stability varied considerably across participants. For example, across wave 1 and 2, participants’ configural stability ranged from r = −.34 to r = .99. Thus, participants’ personality configurations were generally very stable, but some participants’ configuration changed dramatically. Such changes might be related to the experience of adversity.

As Table 2 additionally shows, however, this is not the case – configural stability/change was not significantly linked to cumulative adversity. We examined correlations between participants’ (Fisher-transformed) stability scores and their adversity scores. None were significant at p < .05.³ Thus, personality configuration is independent of cumulative adversity. However, particular traits may be systematically related to adversity, and we next turn to that.

Trait-specific Personality Stability

Table 2 (bottom half) reveals considerable stability of personality traits. Stability correlations ranged between r = .61 and r = .83, with $\overline{r}$ = .75, which is comparable to previous findings under a similar time frame (e.g., Milojev & Sibley, 2014).

When combined with the stability of trait means across time (see again Table 1), these correlations suggest that very few participants experienced dramatic changes in personality across waves. Although there were some individual differences in the patterns of change (as demonstrated by the fact that the stability correlations are < 1.0), the vast majority of participants ended the study with trait levels that were quite similar to where they began. This in turn suggests that individual differences in adversity could have produced (at most) only limited differences in unique patterns of personality change. If many participants did experience different levels of adversity, those experiences likely had little, if any effect on personality across most participants. However, those who experienced the most personality change may have been those who had experienced the greatest adversity.

Personality and Adversity: Bivariate Associations

Table 3 (left half) presents correlations between personality and cumulative adversity. This provides initial insights regarding the links between personality and adversity, revealing four important points. First, all personality traits show some connection to adversity. All but one (Honesty/Humility) trait was significantly (p < .05) correlated with adversity in at least one wave. Many correlations are between r = |.10| and r = |.30|. Based upon practical considerations and upon effects often found in personality/social psychology, these could be considered small to large effects (Funder & Ozer, 2019). Thus, personality appears to be linked to adversity.

Table 3.

Personality and Adversity: Trait-specific correlations and Longitudinal Path Modeling

	Correlations								Longitudinal Path Modeling
	Personality At								Effect of Personality on Adversity				Effect of Adversity on Personality
	Wave 1 With Adversity Between Waves 1 and 2		Wave 2 With Adversity Between Waves 1 and 2		Wave 2 With Adversity Between Waves 2 & 3		Wave 3 With Adversity Between Waves 2 & 3		Path A Personality At Wave 1 With Adversity Between Waves 1 & 2		Path C Personality At Wave 2 With Adversity Between Waves 2 & 3		Path B Adversity Between Waves 1 & 2 With Personality At Wave 2		Path D Adversity Between Waves 2 & 3 With Personality At Wave 3
Dependent Events
Honesty/Humility	−.10		−.15	†	−.15	†	−.03		−.12		−.14	†	−.08	†	.08
Emotionality	.03		.00		.02		.15		.01		.05		.02		.10	*
Extraversion	−.25	**	−.18	*	−.21	*	−.21	*	−.24	**	−.18	*	.02		−.14	†
Agreeableness	−.19	*	−.12		−.09		−.16	†	−.17	*	−.13	†	.02		−.12	*
Conscientiousness	−.14	†	−.15	†	−.21	*	−.29	**	−.17	*	−.26	**	−.03		−.07
Openness	−.06		−.11		−.19	*	−.21	*	−.04		−.19	*	−.04		−.06
Independent Events
Honesty-Humility	.00		−.04		.05		.01		.02		.11		−.03		.00
Emotionality	.03		.08		.21	*	.23	*	.08		.18	*	.07		.08	†
Extraversion	−.06		−.11		−.03		.04		−.04		−.02		−.11	†	.06
Agreeableness	−.11		−.10		−.08		−.18	*	−.11		−.05		−.03		−.11	*
Conscientiousness	−.11		−.06		.09		.04		−.06		.08		.02		−.04
Openness	−.05		−.02		.13		.15		−.11		.12		.04		.07

Note.

^**p < .01,

^*p < .05,

^†p < .10

Second, personality appears more closely linked to dependent adversity than to independent adversity. Across the 24 dependent adversity correlations in the top left-half of Table 3, mean |r| = .14 and over half (14) are at least marginally significant at p < .10. In contrast, across the 24 independent adversity correlations (Table 3 bottom left-half), mean |r| = .08 and only three are at least marginally significant. This difference underscores the distinction between dependent and independent events, and it validates our panel’s ratings of the 105 events.

Third, extraversion and conscientiousness are most consistently linked to (dependent) adversity. Both are negatively correlated with dependent adversity across all waves, with $\overline{r}$ = −.21 and $\overline{r}$ = −.20 respectively. Emotionality was not significantly related to dependent adversity at any wave, though it was significantly linked with independent adversity. All other traits showed some links to dependent adversity, though less consistently across waves.

Fourth, correlations were generally in directions inconsistent with the possibility that growth is the typical response to adversity in the timeframe of the study. High levels of honesty/humility, extraversion, agreeableness, conscientiousness, and openness are generally seen as more preferable than low levels. For these “positively-oriented” traits, every correlation with dependent adversity is negative, with 9/20 being significant at p < .05. This suggests that a relatively high level of adversity is associated with relatively low scores on the five “positive” traits. These results seem inconsistent with the possibility that trauma or negative life experiences generally trigger positive growth in personality within the period of this study.

Overall, bivariate associations reveal connections between personality and adversity. Clarifying the causal processes underlying these connections requires additional analyses.

Longitudinal Modeling Linking Personality Traits and Cumulative Adversity

Figure 2 presents the general model testing the longitudinal connections between personality and adversity, as applied to each personality trait. Analyses were conducted using MPlus 7.31 with MLR estimation. Fit indices for all models were strong (i.e., all RMSEA < .04, CFI > .97, SRMR < .03). Of primary interest are paths linking personality traits to stress, labelled A, B, C, and D in Figure 2 and Table 3.

Figure 2.

Longitudinal Model Testing Reciprocal Processes Linking Personality and Life Event Impact

Notes. The tested models also include a path from Wave 1 Personality to Wave 3 Personality. The letters (A, B, C, and D) refer to the paths as labeled in Table 3; they do not imply that paths with identical letters were constrained to equality in the estimation process.

Preliminary correlational analysis reveals that both dependent and independent adversity were somewhat stable across waves (r = .21, p < .05 and r = .32, p < .01 respectively). Thus, participants who experienced a relatively great adversity between waves 1 and 2 generally experienced a relatively great adversity between waves 2 and 3. These stability correlations are substantially smaller than those for personality (which again ranged between r = .61 and r = .83), indicating more idiosyncratic patterns of change in adversity than in personality. This suggests the potential for greater effects of personality on adversity than the reverse.

Table 3‘s top-right half (paths A and C) reveals significant links between personality and subsequent dependent adversity. Four traits predicted subsequent experiences of dependent adversity at least marginally significantly. Most notably, relatively high levels of extraversion and conscientiousness at waves 1 and 2 predicted relatively less dependent adversity at subsequent waves. Honesty/humility, agreeableness, and openness had similar trends, though not consistently across waves. Emotionality had no predictive links with dependent adversity.

Not surprisingly, there were minimal links between personality and subsequent independent adversity. The bottom-right half (again paths A and C) of Table 3 reveals only one significant (p < .05) effect, as relatively high levels of Emotionality at wave 2 predicted relatively high levels of later independent adversity.

Table 3 (paths B and D) also reveals some evidence that adversity may shape personality. Seven (of 24) effects were at least marginally significant at p < .10. Most notably, both types of adversity (dependent and independent), as experienced between waves 2 and 3, significantly predicted greater emotionality and lower agreeableness at wave 3 (path D). Adversity was also (marginally) significantly predictive of later honesty/humility and extraversion across at least one wave. Adversity predicted neither conscientiousness nor openness.

DISCUSSION

The current study reveals whether growth in core personality traits is a typical response to adversity and whether personality traits affect the adversity that one experiences. We addressed these issues by implementing several conceptual and methodological innovations consistent with current personality science. Conceptually, we expanded the potential scope of PTG to include core personality traits, we examined two conceptualizations of personality change, and we adopted a cumulative approach to adversity. Methodologically, we implemented rigorous design (e.g., prospective longitudinal), assessment (e.g., avoiding assessment of “retrospective change,” using differential scaling of more than 100 events), and sampling (e.g., recruiting participants diverse in the experience of adverse life events) procedures consistent with recent recommendations by researchers studying PTG and/or personality development.

Several key findings emerged. First, personality was stable across the three years of our study. This is consistent with previous examinations of personality stability (e.g., Milojev & Sibley, 2014) and was consistent across both configural and trait-specific perspectives. Second, all personality traits were linked to cumulative adversity in some way. Third, several traits affected the experience of adversity. Most notably, relatively high levels of extraversion and conscientiousness at one point in time led to relatively low levels of adversity at later points in time. Consistent with expectations, these effects emerged for dependent, rather than independent adversity. Fourth, some evidence indicated that cumulative adversity affects personality. Although configural personality stability was not linked to adversity, trait-specific analysis suggests that adversity may increase emotionality and decrease agreeableness.

Implications for PTG

Contrary to hypothesis H6, results suggest that growth, in terms of core personality traits, is not a general or typical reaction to adversity, at least within the 3-year study timeframe. Our findings suggest two possibilities: (1) individuals who experienced relative high levels of adversity tended to become more emotional (e.g., more fearful and anxious) and less agreeable (e.g., less forgiving and patient); or (2) individuals who experienced relative high levels of adversity tended to have less growth in these traits. The latter possibility is improbable given that the respective mean levels of emotionality and agreeableness are relatively unchanging across waves. PTG theorists acknowledge that adversity is typically accompanied by distress and anxiety (Tedeschi & Calhoun, 2004), and the link between adversity and emotionality validates this observation. However, the current finding that adversity is associated with less agreeableness suggests that growth in areas related to “warmer, more intimate relationships with others” (p. 6, Tedeschi & Calhoun, 2004) is not the typical reaction to adversity.

Although this finding implies that growth-through-adversity is not the norm within a three-year period, it is not inconsistent with the idea that some people grow through adversity. Such growth may be a real but rare reaction to adversity.

This leads to questions regarding who grows through adversity and why. The current study was not designed (in terms of assessments, sample size, etc.) to test theory-guided hypotheses about moderators of the adversity-growth association. However, future research could profitably address this issue, and we can envision several classes of moderators. Demographic variables have received some attention in this context (Costa et al., 2000; Galdiolo & Roskam, 2014; Jokela, 2009; Specht, Egloff, & Schmukle, 2011). This attention has focused primarily on age and gender; however, demographic variables such as SES, education, and race are linked to stress and/or personality (e.g., Damian, Su, Shanahan, Trautwein, & Roberts, 2015; Kristenson, Eriksen, Sluiter, Starke, & Ursin, 2004; McAbee & Oswald, 2013) and should be examined as well⁴. Relational variables such as marital status/quality and perceived quality of social network may also help identify individuals who grow in response to adversity (Hill, Weston, & Jackson, 2018; Solomon & Jackson, 2013). Indeed, Tedeschi and Calhoun (2004) posit that those who have strong social networks are more likely to grow from adversity. Despite such suggestions and despite theory-relevant and empirically-known associations with adversity and personality, relational variables have received limited attention as moderators. Third, intrapersonal variables related to personality and self-related processes (e.g., history of adversity, coping strategies, ruminative or affective tendencies) may facilitate positive responses to adversity (MacFarlane et al., 2005; Shand, Cowlishaw, Brooker, Burney, & Ricciardelli, 2015). One interesting possibility is that personality traits themselves (e.g., openness, emotionality) moderate the effect of adversity on characteristics such as meaning in life and sense of strength. Finally, psychotherapeutic interventions may facilitate growth in response to adversity (Roberts, Luo, Briley, Chow, Su, & Hill, 2017; Nelson, 2011).

We should acknowledge limitations and caveats that may qualify conclusions regarding personality growth in response to adversity. First, although our approach to assessing adversity reduces potential confounding effects of personality, no approach avoids all problems. Our approach was normative, as participants’ adversity scores were based upon the negativity (versus positivity) of the impacts of events, in the judgment of our raters. Of course, any individual’s experience of, say, losing a job, might differ from the “normatively expected” experience. Thus, some participants’ adversity scores might overestimate their true adversity, whereas other participants’ scores might underestimate their true adversity. If this is the case, then our observed effects may underestimate the actual connections between adversity and personality (i.e., through attenuation due to random measurement error; Furr, 2018). Importantly, this issue is unlikely to change the direction of effects, for example, changing a truly positive correlation into a negative correlation. We thus do not suspect that our normative approach obscures a broader generality of growth-through-adversity. It is fair to note that our approach does not completely eliminate the potential confounding of personality in the assessment of adversity. When using event checklists, participants may interpret the event descriptors idiosyncratically (Dohrenwend, 2006). If interpretational tendencies reflect one or more personality traits, then personality may be somewhat confounded with our assessment of adversity. However, if interpretational tendencies are not linked with personality, then there is no personality confounding in the adversity scores.

Second, the timeframe in which growth occurs after adversity is unclear and may differ across people and events (Tedeschi, Park, & Calhoun, 1998). Some evidence suggests that growth may unfold only after two or more years after experiencing adversity (e.g., Helgeson, Reynolds, & Tomich, 2006). In the present study, waves were spaced more closely at approximately 1.5 years. Thus, the present study may have been too short to detect emerging growth. Indeed, a period of distress and difficulty likely follows significant adversity prior to growth. Some, or perhaps many, of our participants may have been on this trajectory, with our analyses capturing the distress and difficulty phase of the process. However, our path models (see Fig. 2) fit extremely well without a path from “Adversity between Waves 1 & 2” to “Wave 3 personality.” This suggests that such a longer-term effect was trivially small. Nevertheless, growth might take even longer than the 3-year period covered in the current study. Relatedly, our treatment of adversity does not reveal precisely when participants experienced adverse events. It is possible that some events occurred more than a year prior to assessment, whereas others occurred only shortly before assessment. Our approach aggregates adverse events without regard to their precise timing, which prevents us from examining whether personality changes in anticipation of an event (e.g., planning to have a child; Denissen et al., 2018).

Third, although the present study included three waves of observation of personality, it included only two waves of observation of adversity. Although this is a significant improvement over much PTG research, additional waves would offer important benefits (e.g., ability to examine change over extended time periods, insight into more complex patterns of change, etc.; Willett, Singer, & Martin, 1998). One implication of the two-wave treatment of adversity is that the current analyses may not fully separate between-person effects and within-person effects in the connection between personality and adversity. Our analyses control for “initial levels” and thus at least partially account for global between-person differences in personality and adversity, but some effects in Table 3 may reflect the effects of any global differences that are not fully reflected in initial levels (Hamaker, Kuiper, & Grasman, 2015). In addition, although HEXACO scores were acceptably reliable, any lack of reliability could contribute to such problems. Future research should include at least three waves in assessing both personality and adversity (and ideally more than three waves) when replicating the current pattern of findings.

Our results reflect the generality with which core personality traits grow through adversity. Other characteristics (e.g., spirituality, appreciation of life, meaning, etc.) may grow in response to adversity more frequently or more rapidly than do personality traits such as agreeableness or extraversion.

Implications for Personality Science

The current study has implications for personality science. In terms of personality’s impact on adversity, we had several hypotheses. Based upon previous findings (Heckman & Kautz, 2012; Moffitt et al., 2011; Ozer & Benet-Martinez, 2006; Roberts et al., 2007; Soto, 2019), we expected (H2) that at least some personality traits would predict adversity. Moreover, if such effects were found, the same previous findings suggest (H3) that honesty/humility, extraversion, agreeableness, conscientiousness, and openness would negatively predict adversity, whereas emotionality would be positively predictive. Finally, we expected (H4) these associations to be stronger for dependent events than for independent events.

Results generally supported these hypotheses. Five traits predicted dependent adversity at least marginally significantly, and the sixth (emotionality) predicted independent adversity. Thus, although some effects did not recur across waves, all six traits had some predictive connection to adversity. In addition, the direction of effects was consistent with expectations – higher levels of honesty/humility, extraversion, agreeableness, conscientiousness, and openness predicted less adversity, whereas higher levels of emotionality predicted more adversity. Extraversion and conscientiousness had the most consistent effects on adversity, as their effects recurred across waves. Thus, core personality traits, particularly extraversion and conscientiousness, seem to shape the amount of life adversity that individuals experience.

These findings support the general argument that personality has significant consequences for individuals’ lives. Moreover, they are consistent with previous findings showing personality’s impact and power (Heckman & Kautz, 2012; Moffitt et al., 2011; Ozer & Benet-Martinez, 2006; Roberts et al., 2007; Soto, 2019). By demonstrating that personality shapes an accumulation of events, current findings complement previous work showing that personality affects different events and outcomes.

As the most consistent predictors of cumulative adversity, extraversion and conscientiousness may be the personality traits with the most widespread impact on life events and outcomes. Both of these traits are linked to behaviors, events, and outcomes from many life domains, including physical health, academics, work, marriage, and mental health (Roberts, Lejuez, Krueger, Richards, & Hill, 2014; Moffitt et al., 2011; Wilt & Revelle, 2009). The current findings underscore the widespread impact of those traits, suggesting greater attention to the psychosocial mechanisms through which their impact unfolds.

The lack of association between emotionality and dependent adversity is surprising, given previous findings linking emotionality (or neuroticism) and adversity (e.g.. Kendler, Gardner, & Prescott, 2003; Lüdtke et al., 2011; Vaidya et al., 2002; but see Kandler et al, 2012; Löckenhoff et al., 2009). This discrepancy might reflect differences in the assessment of adversity. Our normative approach is different from previous studies, and it likely reduces the confounding of personality in assessments of adversity potentially present in other studies. Thus, previous findings may not reflect links between emotionality and the objective number or kind of events, but instead reflect links between emotionality and the description of those events as being stressful or challenging (Dohrenwend, 2006).

Moving to personality development, results revealed considerable stability in personality across the three-year timeframe of the study. On average, participants’ overall personality configuration was highly stable, and differences between participants (in specific traits) were also quite stable. This is consistent with the literature on stability of personality traits and the general conceptualization of personality as stable patterns of thoughts, feelings, and behaviors. That said, some change did occur, and adversity might have played a role in these changes.

At the trait level, some findings were consistent with the expectation (H5) that adversity would predict changes in personality. Specifically, participants who experienced relatively great adversity between waves 2 and 3 became more emotional and less agreeable by wave 3. These effects occurred for both dependent and independent adversity, which reflected completely different life events. In contrast, at the configural level, stability was unrelated to adversity, not supporting hypothesis H1. These null findings were consistent with the single other study evaluating life event-personality profile associations (Pusch et al., 2019).

These results provide mixed messages regarding personality change. The fact that some traits changed subsequent to adversity supports theories emphasizing the impact of life events (e.g., Social Investment Theory; Roberts et al., 2004). However, these effects were not large, they did not recur across waves, and they were the exception – most paths linking adversity to subsequent personality were non-significant. Moreover, analysis of configural stability provided essentially no support for theories linking personality change to adversity. Such findings are more consistent with theories emphasizing personality stability (e.g., McCrae & Costa, 2003).

Overall, we see the findings as being modestly supportive of the idea that life events can shape personality. Not only do effects recur for both emotionality and agreeableness, but all effects at p ≤ .10 are in the same psychological direction. That is, they all suggest that adversity is a generally negative experience that shapes personality in ways typically seen as not positive, with increases in emotionality and decreases in agreeableness, honesty/humility, and extraversion. Moreover, as described earlier, methodological limitations (e.g., imprecision in the assessment of adversity, the timing of personality assessments in relation to the experience of adversity) may attenuate the observed effects as compared to true effects. Thus, although results are not robustly supportive of life event effects on personality, we believe that they are encouraging with regard to such theories.

Additional Limitations and Future Directions.

A few additional limitations and directions for future research should be considered. First, although our sample was diverse in important ways, it was homogeneous in others (e.g., all were residents of a single geographic region, relatively few participants identified as Asian or Hispanic). This limits the generalizability of our results and suggests a need to examine other populations. Second, attrition occurred and affected power and precision of statistical estimates, particularly for later time points. Third, many of the events in our battery of assessments were rarely experienced during the study. This restricted our ability to examine individual events or classes of events, but our cumulative approach ensured reasonable variability in participants’ adversity scores. Fourth, our assessment of adversity required recall and, as noted earlier, might be affected by idiosyncratic interpretational differences among participants. Although not unique to our assessments, it introduces potential imprecision (Dohrenwend, 2006).

An important consideration for future research is whether to focus on specific events that might be transformative or to take a broad approach as in the current study. In both PTG and personality development research more generally, studies often focus on specific events, roles, or transitions (e.g., starting work or having children; see Bleidorn et al., 2018 and Mangelsdorf et al., 2019 for reviews). Such work facilitates theoretical focus on the meaning and implications of those specific events, and on their links to personality. In contrast, the study of stress and coping often takes a broader approach in which multiple events are assessed and aggregated in terms of cumulative adversity or stress (e.g., Hammen, 1991; Seery et al., 2010). Given the psychological, physiological, and social effects of stress in general, this approach can shed light on personality’s role in a highly consequential global domain. The current study parallels this line of work.

Complementing these two extremes, a potentially fruitful additional approach would be to measure broadly at multiple levels of abstraction. At a measurement phase, assessing a large number of events allows researchers to delve deeply into important distinctions that might be lost if assessing fewer events (e.g., events related to different types of relationships, rather than “relationship challenges” as a single event). Assessing many events also allows researchers insight into classes of events that might be missed in narrower assessments (e.g., covering financial hardship, legal troubles, physical health, and relational disturbances, rather than only one or two of these). Such broad measurement provides analytic flexibility, in that it allows examination at a broad level (as in the current study), at a narrow level (focusing on specific events), or a mid-range level. A potentially useful mid-range approach would be to examine “cumulative adversity” within classes of events, allowing insight into (for example) overall financial adversity, overall relational adversity, and so on.

Conclusion

The reciprocal impact of personality and life events has theoretical and practical implications. What drives idiosyncratic patterns of personality change? In what ways does our personality affect our lives and the lives of those around us? Is a growth a typical response to adversity? The current study reveals that personality does indeed affect our lives, in terms of the degree of adversity we are likely to experience. It also suggests that the experience of adversity can trigger changes in some aspects of our personalities, though “growth” does not seem to be the typical pattern of personality change.