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McCrae (2020) argues that it is premature to explore interventions focused on personality change. In his commentary, he suggests that interventions should be promoted only if their effects in self-report data are confirmed by the additional opinion of informants. We agree with the essence of his position and would go further by envisioning a new framework for rigorous collaborative research on personality change (Bleidorn et al., 2020). We nevertheless maintain that policymakers would benefit from considering the additional opinion of personality scientists. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The importance of personality for predicting life outcomes in the domains of love, work, and health is well established, as is evidence that personality traits, while relatively stable, can change. However, little is known about the sources and processes that drive changes in personality traits and how such changes might impact important life outcomes. In this paper, we make the case that the research paradigms and methodological approaches commonly used in personality psychology need to be revised to advance our understanding of the sources and processes of personality change. We propose Longitudinal Experience-Wide Association Studies as a framework for studying personality change that can address the limitations of current methods, and we discuss strategies for overcoming some of the challenges associated with Longitudinal Experience-Wide Association Studies. © 2020 European Association of Personality Psychology.

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = -0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Personality traits are powerful predictors of outcomes in the domains of education, work, relationships, health, and well-being. The recognized importance of personality traits has raised questions about their policy relevance, that is, their potential to inform policy actions designed to improve human welfare. Traditionally, the use of personality traits in applied settings has been predicated on their ability to predict valued outcomes, typically under the assumption that traits are functionally unchanging. This assumption, however, is both untrue and a limiting factor on using personality traits more widely in applied settings. In this article, we present the case that traits can serve both as relatively stable predictors of success and actionable targets for policy changes and interventions. Though trait change will likely prove a more difficult target than typical targets in applied interventions, it also may be a more fruitful one given the variety of life domains affected by personality traits. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

How do life events affect life satisfaction? Previous studies focused on a single event or separate analyses of several events. However, life events are often grouped non-randomly over the lifespan, occur in close succession, and are causally linked, raising the question of how to best analyze them jointly. Here, we used representative German data (SOEP; N = 40,121 individuals; n = 41,402 event occurrences) to contrast three fixed-effects model specifications: First, individual event models in which other events were ignored, which are thus prone to undercontrol bias; second, combined event models which controlled for all events, including subsequent ones, which may induce overcontrol bias; and third, our favored combined models that only controlled for preceding events. In this preferred model, the events of new partner, cohabitation, marriage, and childbirth had positive effects on life satisfaction, while separation, unemployment, and death of partner or child had negative effects. Model specification made little difference for employment- and bereavement-related events. However, for events related to romantic relationships and childbearing, small but consistent differences arose between models. Thus, when estimating effects of new partners, separation, cohabitation, marriage, and childbirth, care should be taken to include appropriate controls (and omit inappropriate ones) to minimize bias. Plain language summary: How do different life events (e.g., marriage and childbirth) affect life satisfaction? To answer this question, past studies focused on a single event or separate analyses of several events. In reality, however, life events are often grouped together as they happen over the lifespan, occur in close succession, and are linked through common causes. The current paper aims to analyze life events jointly using representative German data (SOEP; N = 40,121 individuals; n = 41,402 event occurrences). We compare three different models: First, models with each life event by itself (other events are ignored but might still bias results through undercontrol bias). Second, combined models which controlled for all other life events regardless of when they occurred (these may also introduce bias, namely, overcontrol bias). Third, the model we favored which only controlled for any preceding (but not succeeding) life events. In this preferred model, the events of new partner, cohabitation, marriage, and childbirth had positive effects on life satisfaction, while separation, unemployment, and death of partner or child had negative effects. The choice of model made little difference for employment- and bereavement-related events. However, for events related to romantic relationships and childbearing, small but consistent differences arose between models. Thus, when estimating effects of new partners, separation, cohabitation, marriage, and childbirth on life satisfaction, care should be taken to include appropriate controls (and omit inappropriate ones) to minimize bias that potentially occurs due to other life events. [ABSTRACT FROM AUTHOR]

Objective: Entringer et al. used longitudinal data from a German panel study to examine reciprocal causal effects between personality and religiosity, along with cultural moderators of these effects. The current paper examines the robustness of the original effects to alternative model specifications. Method: We reanalyzed the same four‐wave data spanning 12 years (total N = 46,316), first replicating the original cross‐lagged panel analyses and then extending these analyses in three ways: Using a random‐intercept cross‐lagged panel model, using observed rather than latent variables, and modeling each trait individually rather than simultaneously. Results: Correlations between personality and religiosity were all small in size, even when aggregating over 12 years. Lagged effects were very small, and none was robust across all model specifications. Cultural moderators also depended on model specifications. Conclusions: The very small size of these reciprocal effects, along with their sensitivity to model specifications, suggest that conclusions about causal effects of personality and religiosity should be drawn very cautiously. [ABSTRACT FROM AUTHOR]

ObjectiveTheory and research have emphasized the impact of life events on personality trait change. In this article, we review prospective research on personality trait change in response to nine major life events in the broader domains of love and work.MethodWe expected to find that life events lead to personality trait change to the extent that they have a lasting influence on individuals' thoughts, feelings, and behavior. Moreover, we predicted that love-related life events such as marriage or parenthood would be more strongly related to changes in traits that emphasize affective content, whereas work-related life events would be more likely to lead to change in traits that reflect behavioral or cognitive content.ResultsThe current state of research provided some evidence that life events can lead to changes in personality traits and that different life events may be differently related to specific trait domains.ConclusionsA more general conclusion emerging from this review is that the evidence for the nature, shape, and timing of personality trait change in response to life events is still preliminary. We discuss the implications of the results for theory and research and provide directions for future studies on life events and personality trait change.

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences—conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects.

We evaluated how the number of response options affects the psychometric properties of the Big Five Inventory-2 (BFI-2). Using two large samples collected from a market research company (N s = 893 and 1,213), we tested how different response options of the BFI-2 influenced scale score distributions, internal consistency estimates, convergent validity correlations, and criterion validity correlations. Results suggest that score distributions were impacted by the number of response options such that ceiling and floor effects were more common when using two or three response options than when using more options. Estimates of Cronbach's alpha were generally lower with fewer scale points as compared with more scale points, but these effects disappeared when ordinal alpha was used. There were no systematic effects of response options on convergent validity and criterion validity correlations. Given these results, there seems to be few psychometric reasons for deciding whether to administer personality items with five, six, or seven scale points. [ABSTRACT FROM AUTHOR]

The present study explored how life satisfaction changes before and after childbirth among first-time parents from a nationally representative, longitudinal study of Germans. Life satisfaction increased before pregnancy to a peak just after birth and then returned to the baseline level within 2 years postpartum. The 2 members of the same couple reacted and adapted to childbirth in similar ways, suggesting that something about the event itself (rather than the individuals' personality) affects individual differences in these responses. However, some personality characteristics (including neuroticism and openness) did moderate reaction and adaptation effects. (Contains 2 figures, 4 tables, and 5 footnotes.)

Major life events can trigger personality trait change. However, a clear, replicable pattern of event-related personality trait change has yet to be identified. We examined whether the perception of major life events is associated with personality trait change. Therefore, we assessed young adults' personality traits at five measurement occasions within 1 year. At the second measurement occasion, we also assessed their perception of a recently experienced major life event using the Event Characteristics Questionnaire. Contrary to our expectations, perceived impact of the event was not associated with the amount of personality trait change, but perceived valence was associated with changes in agreeableness and neuroticism. Exploratory analyses revealed some weak associations between other perceived event characteristics and the amount of personality trait change as well as interactions between perceived event characteristics and event categories in predicting changes in neuroticism. In general, effect sizes were small, and associations depended on the time interval between pre-event and post-event personality assessment. Results indicate that perceived event characteristics should be considered when examining event-related personality trait change. [ABSTRACT FROM AUTHOR]

Although aging is associated with declines in many life domains, overall life satisfaction does not appear to decline sharply with age. One explanation for this paradoxical finding is that several life domains improve with age such that increases in certain domains balance the decreases in others. Because different issues are problematic at different life stages, it is likely that specific domains display different life trajectories compared to overall life satisfaction. The observed pattern for overall life satisfaction is likely due to a bottom-up approach. Life and domain satisfaction data from 8 years of the British Household Panel Study were analyzed to evaluate this hypothesis. Results indicated that satisfaction with some life domains increased after middle age (e.g. social life), whereas satisfaction with other life domains decreased (e.g. health). Additionally, results illustrated that although domain satisfaction scores demonstrate distinct trajectories, the aggregate of these distinct domains resembled the overall life satisfaction trajectory. These findings have implications for top-down and bottom-up models of life satisfaction.

Life satisfaction is often assessed using single-item measures. However, estimating the reliability of these measures can be difficult because internal consistency coefficients cannot be calculated. Existing approaches use longitudinal data to isolate occasion-specific variance from variance that is either completely stable or variance that changes systematically over time. In these approaches, reliable occasion-specific variance is typically treated as measurement error, which would negatively bias reliability estimates. In the current studies, panel data and multivariate latent state-trait models are used to isolate reliable occasion-specific variance from random error and to estimate reliability for scores from single-item life satisfaction measures. Across four nationally representative panel studies with a combined sample size of over 68,000, reliability estimates increased by an average of 16% when the multivariate model was used instead of the more standard univariate longitudinal model.

The current study takes a novel approach to studying the correlates of subjective well-being. Unlike previous studies, which typically examine group-level well-being at the state or national level, we analyzed correlates of well-being at the county level within the United States. Using nationally representative data, we found that reliable variance in well-being exists across counties. Furthermore, this variance was associated with a number of objective factors, including income, population density, health and education. Continued study of these factors at the regional level may have important implications for developing community programs and public policy.

Two large-scale, nationally representative panel studies (the German Socio Economic Panel Study and the British Household Panel Study) were used to assess changes in life satisfaction over the lifespan. The cross-sectional and longitudinal features of these studies were used to isolate age-related changes from confounding factors including instrumentation effects and cohort effects. Although estimated satisfaction trajectories varied somewhat across studies, two consistent findings emerged. First, both studies show that life satisfaction does not decline over much of adulthood. Second, there is a steep decline in life satisfaction among those older than 70. The British data also showed a relatively large increase in satisfaction from the 40s to the early 70s. Thus, age differences in well-being can be quite large and deserve increased empirical and theoretical attention.

This article uses dyadic latent panel analysis (DLPA) to examine environmental influences on well-being. DLPA requires longitudinal dyadic data. It decomposes the observed variance of both members of a dyad into a trait, state, and an error component. Furthermore, state variance is decomposed into initial and new state variance. Total observed similarity between members of a dyad is decomposed into trait similarity, initial state similarity, new state similarity, and error similarity. Dyadic similarity in new state variance reveals that both members of a dyad change in the same direction, which is a strong indication of environmental effects. DLPA is used to examine environmental influences on life satisfaction and domain satisfaction based on 22 annual assessments of married couples in the German Socio-Economic Panel Study (Ns = 607-740). The results show high similarity in new state variance for life satisfaction and objectively identical domains (household income, housing), and less similarity for objectively less similar domains (recreation, health). This finding provides strong evidence for environmental influences on well-being. In addition, the results show high trait similarity. The implications of the latter finding for interpretation of behavioral genetics studies of well-being are discussed.

Cross-sectional age differences in the Big Five personality traits were examined in a nationally representative sample of Australians (N = 12,618; age range = 15-84). Extraversion, Neuroticism, and Openness were negatively associated with age, whereas Agreeableness and Conscientiousness were positively associated with age. Effect sizes comparing the youngest and the oldest sample members were usually medium to large in size. Item-level analysis revealed that although most personality descriptors showed patterns similar to those exhibited by their respective global traits, this was not always the case. Thus, investigations of narrower aspects of personality may reveal more nuanced age differences and may begin to explain contradictory results from different studies. (Contains 3 tables and 5 figures.)