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Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious and error-prone task. This article provides an overview of methods used to probe interaction effects and describes a unified collection of freely available online resources that researchers can use to obtain significance tests for simple slopes, compute regions of significance, and obtain confidence bands for simple slopes across the range of the moderator in the MLR, HLM, and LCA contexts. Plotting capabilities are also provided.

The social distancing protocols (SDPs) implemented as a response to the COVID-19 pandemic may seriously influence peoples’ mental health. We used a sample of 4361 Norwegian adults recruited online and stratified to be nationally representative to investigate the evolution of anxiety following each modification in national SDPs across a 20-month period from the onset of the pandemic to the reopening of society and discontinuation of SDPs. The mean anxiety level fluctuated throughout the observation period and these fluctuations were related to the stringency of the modified SDPs. Those with a high initial level almost in unison showed a substantial and lasting decrease of anxiety after the first lifting of SDPs. A sub-group of 9% had developed a persistent anxiety state during the first 3 months. Younger age, pre-existing psychiatric diagnosis, and use of unverified information platforms proved to predict marked higher anxiety in the long run. In conclusion, individuals with a high level of anxiety at the outbreak of the pandemic improved when the social distancing protocols were lifted. By contrast, a sizeable subgroup developed lasting clinical levels of anxiety during the first 3 months of the pandemic and is vulnerable to prolonged anxiety beyond the pandemic period.

Poor physiological self-regulation has been proposed as a potential biological vulnerability for adolescent suicidality. This study tested this hypothesis by examining the effect of parasympathetic stress responses on future suicide ideation. In addition, drawing from multilevel developmental psychopathology theories, the interplay between parasympathetic regulation and friendship support, conceptualized as an external source of regulation, was examined. At baseline, 132 adolescent females ( M age = 14.59, SD = 1.39) with a history of mental health concerns participated in an in vivo interpersonal stressor (a laboratory speech task) and completed self-report measures of depressive symptoms and perceived support within a close same-age female friendship. Respiratory sinus arrhythmia (RSA) was measured before and during the speech task. Suicide ideation was assessed at baseline and at 3, 6, and 9 months follow-up. The results revealed that females with greater relative RSA decreases to the laboratory stressor were at higher risk for reporting suicide ideation over the subsequent 9 months. Moreover, parasympathetic responses moderated the effect of friendship support on suicide ideation; among females with mild changes or higher relative increases in RSA, but not more pronounced RSA decreases, friendship support reduced risk for future suicide ideation. Findings highlight the crucial role of physiological and external regulation sources as protective factors for youth suicidality.

Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator’s consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-the-art MML estimation procedures such as the Metropolis–Hastings Robbins–Monro (MH-RM) algorithm as well as approximate MML estimation procedures such as variational inference (VI) are computationally time-consuming when the sample size and the number of latent factors are very large. In this work, we investigate a deep learning-based VI algorithm for exploratory item factor analysis (IFA) that is computationally fast even in large data sets with many latent factors. The proposed approach applies a deep artificial neural network model called an importance-weighted autoencoder (IWAE) for exploratory IFA. The IWAE approximates the MML estimator using an importance sampling technique wherein increasing the number of importance-weighted (IW) samples drawn during fitting improves the approximation, typically at the cost of decreased computational efficiency. We provide a real data application that recovers results aligning with psychological theory across random starts. Via simulation studies, we show that the IWAE yields more accurate estimates as either the sample size or the number of IW samples increases (although factor correlation and intercepts estimates exhibit some bias) and obtains similar results to MH-RM in less time. Our simulations also suggest that the proposed approach performs similarly to and is potentially faster than constrained joint maximum likelihood estimation, a fast procedure that is consistent when the sample size and the number of items simultaneously tend to infinity.

While past research has suggested possible seasonal trends in crime rates, this study employs a novel methodology that directly models these changes and predicts them with explanatory variables. Using a nonlinear latent curve model, seasonal fluctuations in crime rates are modeled for a large number of communities in the U.S. over a three-year period with a focus on testing the theoretical predictions of two key explanations for seasonal changes in crime rates: the temperature/aggression and routine activities theories. Using data from 8,460 police units in the U.S. over the 1990 to 1992 period, we found that property crime rates are primarily driven by pleasant weather, consistent with the routine activities theory. Violent crime exhibited evidence in support of both theories.

The person-oriented approach seeks to match theories and methods that portray development as a holistic, highly interactional, and individualized process. Over the past decade, this approach has gained popularity in developmental psychopathology research, particularly as model-based varieties of person-oriented methods have emerged. Although these methods allow some principles of person-oriented theory to be tested, little attention has been paid to the fact that these methods cannot test other principles, and may actually be inconsistent with certain principles. Lacking clarification regarding which aspects of person-oriented theory are testable under which person-oriented methods, assumptions of the methods have sometimes been presented as testable hypotheses or interpreted as affirming the theory. This general blurring of the line between person-oriented theory and method has even led to the occasional perception that the method is the theory and vice versa. We review assumptions, strengths, and limitations of model-based person-oriented methods, clarifying which theoretical principles they can test and the compromises and trade-offs required to do so.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

Objectives. We used nationally representative data to examine whether individuals’ sexual and drug behavior patterns account for racial disparities in sexually transmitted disease (STD) and HIV prevalence. Methods. Data were derived from wave III of the National Longitudinal Study of Adolescent Health. Participants were aged 18 to 26 years old; analyses were limited to non-Hispanic Blacks and Whites. Theory and cluster analyses yielded 16 unique behavior patterns. Bivariate analyses compared STD and HIV prevalences for each behavior pattern, by race. Logistic regression analyses examined within-pattern race effects before and after control for covariates. Results. Unadjusted odds of STD and HIV infection were significantly higher among Blacks than among Whites for 11 of the risk behavior patterns assessed. Across behavior patterns, covariates had little effect on reducing race odds ratios. Conclusions. White young adults in the United States are at elevated STD and HIV risk when they engage in high-risk behaviors. Black young adults, however, are at high risk even when their behaviors are normative. Factors other than individual risk behaviors and covariates appear to account for racial disparities, indicating the need for population-level interventions.

Better understanding individual differences in social, cognitive, and behavioral processes is a core goal of much psychological theory and research. Although great progress has been made toward this goal, I argue here that the classical design and analysis approach that dominates individual difference research, namely, the collection of single-time-point data and application of standard linear regression models, potentially limits further advances. In particular, the opportunity to evaluate individual differences in psychological processes is restricted by the estimation of a single effect to represent the relationship between variables. I discuss alternative analysis and design options that offer the opportunity to more fully examine individual differences in psychological processes.

This paper contributes new evidence on the association between immigrant status and health by describing and attempting to explain patterns of co-occurring sex and drug use behaviors among Asian and Latino adolescents in the United States. Nine patterns of sex and drug use behaviors were identified from a cluster analysis of data from 3,924 Asian and Latino youth (grades 7-12) who participated in the National Longitudinal Study of Adolescent Health (Add Health). The relationship between immigrant status and risk cluster membership was evaluated with multinomial logistic regression. Compared to foreign-born youth, U.S. born Asian and Latino adolescents were more likely to engage in sex and drug risk behaviors. Family and residential characteristics associated with immigrant status partly accounted for this finding. The results indicate that among Asian and Latino adolescents, assimilation to U.S. risk behavior norms occurs rapidly and is evident by the second generation.