Objective

Theory 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.

Method

We 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.

Results

The 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.

Conclusions

A 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.

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).

Background

DNA methylation of CpG sites on genetic loci has been linked to increased risk of asthma in children exposed to elevated ambient air pollutants (AAPs). Further identification of specific CpG sites and the pollutants that are associated with methylation of these CpG sites in immune cells could impact our understanding of asthma pathophysiology. In this study, we sought to identify some CpG sites in specific genes that could be associated with asthma regulation (Foxp3 and IL10) and to identify the different AAPs for which exposure prior to the blood draw is linked to methylation levels at these sites. We recruited subjects from Fresno, California, an area known for high levels of AAPs. Blood samples and responses to questionnaires were obtained (n = 188), and in a subset of subjects (n = 33), repeat samples were collected 2 years later. Average measures of AAPs were obtained for 1, 15, 30, 90, 180, and 365 days prior to each blood draw to estimate the short-term vs. long-term effects of the AAP exposures.

Results

Asthma was significantly associated with higher differentially methylated regions (DMRs) of the Foxp3 promoter region (p = 0.030) and the IL10 intronic region (p = 0.026). Additionally, at the 90-day time period (90 days prior to the blood draw), Foxp3 methylation was positively associated with NO₂, CO, and PM_2.5 exposures (p = 0.001, p = 0.001, and p = 0.012, respectively). In the subset of subjects retested 2 years later (n = 33), a positive association between AAP exposure and methylation was sustained. There was also a negative correlation between the average Foxp3 methylation of the promoter region and activated Treg levels (p = 0.039) and a positive correlation between the average IL10 methylation of region 3 of intron 4 and IL10 cytokine expression (p = 0.030).

Conclusions

Short-term and long-term exposures to high levels of CO, NO₂, and PM_2.5 were associated with alterations in differentially methylated regions of Foxp3. IL10 methylation showed a similar trend. For any given individual, these changes tend to be sustained over time. In addition, asthma was associated with higher differentially methylated regions of Foxp3 and IL10.

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.

Personality is not the most popular subfield of psychology. But, in one way or another, personality psychologists have played an outsized role in the ongoing credibility revolution in psychology. Not only have individual personality psychologists taken on visible roles in the movement, but our fields practices and norms have now become models for other fields to emulate (or, for those who share Baumeisters (2016, https://doi.org/10.1016/j.jesp.2016.02.003) skeptical view of the consequences of increasing rigor, a model for what to avoid). In this article we discuss some unique features of our field that may have placed us in an ideal position to be leaders in this movement. We do so from a subjective perspective, describing our impressions and opinions about possible explanations for personality psychologys disproportionate role in the credibility revolution. We also discuss some ways in which personality psychology remains less-than-optimal, and how we can address these flaws.

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).

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.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.