Derailment and depression in college: Tests of 3-year predictive capacity and moderation by self-reflection, brooding, perfectionism, and cognitive flexibility.

While rich with opportunities for self-exploration, the transition to and through college is stressful, often associated with the onset or exacerbation of mental illness. Attending to these characteristics, this preregistered study asked whether derailment-or difficulties reconciling perceived identity change-in freshman year predicts senior depressive symptoms, and how individual risks for depression relate to this association. Derailment and depressive symptoms evidenced significant 3-year stability, and these constructs had positive cross-sectional associations in both freshman and senior year. Freshman derailment failed to predict senior depressive symptoms for the average student, but individual differences in self-reflection moderated the association: freshman derailment positively predicted senior depression among those lowest in self-reflection. Together, this study suggests derailment and depressive symptoms are consistently related at critical points of transition, and some individual differences in cognition may help predict their long-term association. While useful for understanding nuances between derailment and depression, these findings also inform ways of attending to and supporting college students through periods of transition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Psychological Science in the Wake of COVID-19: Social, Methodological, and Metascientific Considerations.

The COVID-19 pandemic has extensively changed the state of psychological science from what research questions psychologists can ask to which methodologies psychologists can use to investigate them. In this article, we offer a perspective on how to optimize new research in the pandemic's wake. Because this pandemic is inherently a social phenomenon-an event that hinges on human-to-human contact-we focus on socially relevant subfields of psychology. We highlight specific psychological phenomena that have likely shifted as a result of the pandemic and discuss theoretical, methodological, and practical considerations of conducting research on these phenomena. After this discussion, we evaluate metascientific issues that have been amplified by the pandemic. We aim to demonstrate how theoretically grounded views on the COVID-19 pandemic can help make psychological science stronger-not weaker-in its wake.

Invariance of the Derailment Scale-6: Testing the measurement and correlates of derailment across adulthood.

Derailment, a newly mapped construct describing difficulty reconciling perceived changes in identity and self-direction, offers researchers a unique lens for understanding adjustment. As identity development is a lifelong process, establishing the impartiality of the primary measure for derailment across a broad age range is necessary. Across a cross-sectional sample of emerging, young, and midlife adults (N = 1,420), we tested preregistered hypotheses concerning whether the Derailment Scale-6 (DS-6) demonstrates measurement invariance, and the extent to which levels and correlates of derailment differ with age. The DS-6 exhibited configural, metric, and partial scalar invariance across the three age groups. In terms of effect size, statistical differences detected at the scalar level were hardly larger than negligible noninvariance effects, suggesting in sum that the DS-6 is adequate for assessing and comparing derailment among those roughly 18-70 years. In general, derailment was related to greater distress and identity exploration, and lower identity commitment, sense of purpose in life, and future time perspective (FTP). We further found that derailment shares a modest negative association with age; it is more positively associated with identity exploration among younger individuals; and it is more negatively associated with identity commitment, sense of purpose in life, and FTP among older individuals. With the broadest age range and most highly powered set of tests studying derailment to date, this investigation bolsters confidence in our tools for assessing derailment and has taken the initial steps toward sketching the form and function of perceived identity change over the lifespan. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Taboo Against Explicit Causal Inference in Nonexperimental Psychology.

Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects-the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.

Positive affect and chronic pain: a preregistered systematic review and meta-analysis.

Chronic noncancer pain (CNCP) is a significant health burden among adults. Standard behavioral therapies typically focus on targeting negative affect (NA) and yield only modest treatment effects. The aims of this study were to systematically review and investigate the association between positive affect (PA) and pain severity among adults with CNCP. Databases that were searched included MEDLINE (PubMed), PsycINFO, CINAHL, ProQuest Dissertations and Theses, OLASTER, Open Grey, and PsyArXiv (inception to July 23, 2019). We analyzed studies that: (1) used observational, experimental, or intervention study designs; (2) enrolled individuals with CNCP (pain ≥ 12 weeks); and (3) reported full quantitative results on outcomes. Two researchers independently screened articles, extracted data, and assessed the risk of bias. The main meta-analysis was followed by subgroup analyses. All analyses were performed using random-effects models. Formal tests for heterogeneity (Q-statistic; I) and publication bias (p-curve and p-uniform*) were performed. We meta-analyzed 29 studies with 3521 participants. Results demonstrated that PA inversely impacts pain severity in people with CNCP (r = -0.23). Subgroup analyses showed a significant effect for gender and marginally significant effects for age in studies that adjusted for NA. On average, effect sizes for observational studies were larger in studies with a higher proportion of female respondents and in studies that did not adjust for NA. Finally, larger effect sizes were found in intervention studies with older compared with younger samples.

The Assumptions of Direction Dependence Analysis.

Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions.

Analysis of Variance Models with Stochastic Group Weights.

The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This article is about stochastic group weights in ANOVA models - a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments. We propose two new ways to incorporate stochastic group weights in the tests of average effects - one based on the general linear model and one based on multigroup structural equation models (SEMs). We show in simulation studies that our methods have nominal type I error rates in experiments with stochastic group weights while classic approaches show an inflated type I error rate. The SEM approach can additionally deal with heteroscedastic residual variances and latent variables. An easy-to-use software package with graphical user interface is provided.

Local fit evaluation of structural equation models using graphical criteria.

Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow derivation of implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally underidentified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation. (PsycINFO Database Record

Does Gender of Administrator Matter? National Study Explores U.S. University Administrators' Attitudes About Retaining Women Professors in STEM.

Omnipresent calls for more women in university administration presume women will prioritize using resources and power to increase female representation, especially in STEM fields where women are most underrepresented. However, empirical evidence is lacking for systematic differences in female vs. male administratorsÂS attitudes. Do female administrators agree on which strategies are best, and do men see things differently? We explored United States college and university administratorsÂS opinions regarding strategies, policies, and structural changes in their organizations designed to increase women professorsÂS representation and retention in STEM fields. A comprehensive review of past research yielded a database of potentially-effective, recommended policies. A survey based on these policies was sent to provosts, deans, associate deans, and department chairs of STEM fields at 96 public and private research universities across the U.S. These administrators were asked to rate the quality and feasibility of each strategy; 474 provided data, of which 334 contained complete numerical data used in the analyses. Our data revealed that female (vs. male) administrators believed the 44 strategies were higher in quality overall-but not higher in feasibility-with 9 strategies perceived differently by women and men, after imposing conservative statistical controls. There was broad general agreement on the relative-quality rankings of the 44 strategies. Women (vs. men) gave higher quality ratings to increasing the value of teaching, service, and administrative experience in tenure/promotion decisions, increasing flexibility of federal-grant funding to accommodate mothers, conducting gender-equity research, and supporting shared tenure lines enabling work-life balance. Women (vs. men) believed it was more feasible for men to stop the tenure clock for 1 year for childrearing and for universities to support requests for shared tenure lines, but less feasible for women to chair search committees. Our national survey thus supported the belief that placing women into administration creates greater endorsement of strategies to attract and retain women in STEM, although the effectiveness of these strategies was outside the scope of this research. Topics of disagreement between women and men are potentially important focuses of future policy, because female administrators may have insights into how to retain women that male administrators do not share.

The effects of exposure to objective coherence on perceived meaning in life: a preregistered direct replication of Heintzelman, Trent & King (2013).

Having a sense of meaning in life (MIL) has been acknowledged as a catalyst to psychological flourishing. As such, understanding ways to promote MIL represents a worthy goal for those interested in bolstering positive outcomes. This study sought to replicate the findings of Heintzelman, Trent & King (2013 Psychol. Sci.24, 991-998 (doi:10.1177/0956797612465878)), who found that MIL could be influenced by external stimulation. Their findings suggest that exposure to coherent stimuli produces significantly higher MIL scores than exposure to incoherent stimuli. Using materials and methodology provided by the corresponding author of the original paper, this study attempted to directly test this manipulation under conditions with increased statistical power. All tests, however, failed to replicate. Possible explanations for these discrepant findings are discussed, and potential future directions for this area of the literature are proposed.

What is the biological reality of gene-environment interaction estimates? An assessment of bias in developmental models.

BACKGROUND: Standard models used to test gene-environment interaction (G × E) hypotheses make the causal assumption that there are no unobserved variables that could be biasing the interaction estimate. Whether this assumption can be met in nonexperimental studies is unclear because the interactive biological pathways from genetic polymorphisms and environments to behavior, and the confounders that can be introduced along these pathways, are often not delineated. This is problematic in the context of studies focused on caregiver-child dyads, in which common genes and environments induce gene-environment correlation. To address the impact of sources of bias in G × E models specifically assessing the interaction between child genotype and caregiver behavior, we provide a causal framework that integrates biological and statistical concepts of G × E, and assess the magnitude of bias introduced by various confounding pathways in different causal circumstances. METHODS: A simulation assessed the magnitude of bias introduced by four types of confounding pathways in different causal models. Unadjusted and adjusted statistical models were then applied to the simulated data to assess the efficacy of these procedures to capture unbiased G × E estimates. Finally, the simulation was run under null effects of the genotype to assess the impact of biasing sources on the false-positive rate. RESULTS: Common environmental pathways between caregiver and child inflated G × E estimates and raised the false-positive rate. Evocative effects of the child also inflated G × E estimates. CONCLUSIONS: Gene-environment interaction studies should be approached with consideration to the causal pathways at play and the confounding opportunities along these pathways to facilitate the inclusion of adequate statistical controls and correct inferences from study findings. Bridging biological and statistical concepts of G × E can significantly improve research design and the communication of how a G × E process fits into a broader developmental framework.

I wish I had (not) taken a gap-year? The psychological and attainment outcomes of different post-school pathways.

Existing gap-year research indicates a number of benefits of a gap-year at the end of school and before university enrollment. Life span theory of control, however, suggests that direct goal investment, rather than delay, at developmental transitions is associated with more adaptive outcomes. Comparing these perspectives, the authors undertook 2 studies: 1 in Finland (N = 384, waves = 3) and 1 in Australia (N = 2,259, waves = 5) both with an initial time wave in the last year of high school. The authors explored the effects of a gap-year on both psychological and attainment outcomes using an extensive propensity score matching technique. The Finnish study found no difference in growth in goal commitment, effort, expectations of attainment and strain, or in actual university enrollment in those planning to enter university directly versus those who plan to take a gap-year. The Australian study found no difference in growth in outlooks for the future and career prospects, and life satisfaction between gap-year youth and direct university entrants. However, the study did find that gap-year students were more likely to drop out of a university degree. Implication for theory and practice are discussed.

Propensity scores as a basis for equating groups: basic principles and application in clinical treatment outcome research.

A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome.

Theory and Analysis of Total, Direct, and Indirect Causal Effects.

Mediation analysis, or more generally models with direct and indirect effects, are commonly used in the behavioral sciences. As we show in our illustrative example, traditional methods of mediation analysis that omit confounding variables can lead to systematically biased direct and indirect effects, even in the context of a randomized experiment. Therefore, several definitions of causal effects in mediation models have been presented in the literature (Baron & Kenny, 1986 ; Imai, Keele, & Tingley, 2010 ; Pearl, 2012 ). We illustrate the stochastic theory of causal effects as an alternative foundation of causal mediation analysis based on probability theory. In this theory we define total, direct, and indirect effects and show how they can be identified in the context of our illustrative example. A particular strength of the stochastic theory of causal effects are the causality conditions that imply causal unbiasedness of effect estimates. The causality conditions have empirically testable implications and can be used for covariate selection. In the discussion, we highlight some similarities and differences of the stochastic theory of causal effects with other theories of causal effects.

A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems.

The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved consists of including many covariates as so-called auxiliary variables. These variables are either included based on data considerations or in an inclusive fashion; that is, taking all available auxiliary variables. In this article, we point out that there are some instances in which auxiliary variables exhibit the surprising property of increasing bias in missing data problems. In a series of focused simulation studies, we highlight some situations in which this type of biasing behavior can occur. We briefly discuss possible ways how one can avoid selecting bias-inducing covariates as auxiliary variables.

Personality traits and living arrangements in young adulthood: selection and socialization.

Based on the social investment principle and theories of social relationship differentiation, the present study was conducted to investigate whether personality differences in high school predict young adults' living arrangements (with roommates or a romantic partner, alone, or staying with parents) 2 years later (selection) and whether these different social contexts provoke long-term personality changes (socialization). Using data from a 3-wave longitudinal study of 8,052 high school graduates in Germany, multinomial logistic regression analyses revealed substantial selection effects of the Big Five traits on living arrangements. Propensity score matching was applied to provide a strong test of the socialization effects of these living arrangements. Young adults who lived with roommates showed increases in Openness but the smallest increases in Conscientiousness. Living with a romantic partner was the most beneficial arrangement for the development of Conscientiousness. These results highlight the importance of social contexts for personality development in young adulthood.

Continuously Cumulating Meta-Analysis and Replicability.

The current crisis in scientific psychology about whether our findings are irreproducible was presaged years ago by Tversky and Kahneman (1971), who noted that even sophisticated researchers believe in the fallacious Law of Small Numbers-erroneous intuitions about how imprecisely sample data reflect population phenomena. Combined with the low power of most current work, this often leads to the use of misleading criteria about whether an effect has replicated. Rosenthal (1990) suggested more appropriate criteria, here labeled the continuously cumulating meta-analytic (CCMA) approach. For example, a CCMA analysis on a replication attempt that does not reach significance might nonetheless provide more, not less, evidence that the effect is real. Alternatively, measures of heterogeneity might show that two studies that differ in whether they are significant might have only trivially different effect sizes. We present a nontechnical introduction to the CCMA framework (referencing relevant software), and then explain how it can be used to address aspects of replicability or more generally to assess quantitative evidence from numerous studies. We then present some examples and simulation results using the CCMA approach that show how the combination of evidence can yield improved results over the consideration of single studies.

Understanding the durability of a fire department wellness program.

OBJECTIVES: To understand the influences associated with durability and diffusion of benefits of a fire service wellness program. METHODS: Qualitative assessment of group interviews. RESULTS: Five years following a controlled worksite wellness trial, behavioral improvements were durable and had diffused to control participants. These findings were associated with firefighters' team orientation, enacted healthy norms and competitiveness regarding the results of annual health assessments. The original intervention trial appeared to initiate individual change that coalesced into group effects. Secondary influences included increasing public awareness about health, newly hired younger firefighters, and a modicum of administrative support. Culture shift was achieved at the workplace. CONCLUSIONS: Although the fire service is a unique occupation, these findings suggest general strategies to achieve durable positive health change in other occupational settings.

Ambulatory monitoring in the genetics of psychosomatic medicine.

Psychosomatic disorders are composed of an array of psychological, biologic, and environmental features. The existing evidence points to a role for genetic factors in explaining individual differences in the development and maintenance of a variety of disorders, but studies to date have not shown consistent and replicable effects. As such, the attempt to uncover individual differences in the expression of psychosomatic disorders as a function of genetic architecture requires careful attention to their phenotypic architecture or the various intermediate phenotypes that make up a heterogeneous disorder. Ambulatory monitoring offers a novel approach to measuring time-variant and situation-dependent intermediate phenotypes. Recent examples of the use of ambulatory monitoring in genetic studies of stress reactivity, chronic pain, alcohol use disorders, and psychosocial resilience are reviewed in an effort to highlight the benefits of ambulatory monitoring for genetic study designs.

Military training and personality trait development: does the military make the man, or does the man make the military?

Military experience is an important turning point in a person's life and, consequently, is associated with important life outcomes. Using a large longitudinal sample of German males, we examined whether personality traits played a role during this period. Results indicated that personality traits prospectively predicted the decision to enter the military. People lower in agreeableness, neuroticism, and openness to experience during high school were more likely to enter the military after graduation. In addition, military training was associated with changes in personality. Compared with a control group, military recruits had lower levels of agreeableness after training. These levels persisted 5 years after training, even after participants entered college or the labor market. This study is one of the first to identify life experiences associated with changes in personality traits. Moreover, our results suggest that military experiences may have a long-lasting influence on individual-level characteristics.