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BackgroundMediated and moderated processes that lead to intervention efficacy may underlie results of trials ruled as nonefficacious. The overall purpose of this study was to examine such processes to explain the findings of one of the largest, rigorously conducted behavioral intervention randomized controlled trials, EXPLORE.MethodsFour thousand two hundred ninety-five HIV-negative men who have sex with men (MSM) in the United States were randomized in a 2-armed trial. Participants completed follow-up and an HIV test every 6 months up to 48 months. We used multiple and causal mediation analyses to test 5 mediators, including safer sex self-efficacy and condomless receptive anal sex with HIV-positive or status-unknown partners on our primary outcome (HIV seroconversion). We also examined whether intervention effects on the mediators would be moderated by robust correlates of HIV among MSM, including stimulant use.ResultsThere were significant effects of the intervention on all hypothesized mediators. Stimulant use moderated the effect on condomless receptive anal sex In stratified multiple mediation models, we found that among MSM with low stimulant use, the intervention significantly prevented HIV by reducing condomless receptive anal sex with HIV-positive or status-unknown partners. Among MSM with higher stimulant use, there were no indirect effects of the intervention on HIV through any of the hypothesized mediators.ConclusionThe results suggest that the null effect found in the original EXPLORE trial might have occurred as a function of previously unexplored mediated and moderated processes. This study illustrates the value of testing mediated and moderated pathways in randomized trials, even in trials ruled out as nonefficacious.

When testing a statistical mediation model, it is assumed that factorial measurement invariance holds for the mediating construct across levels of the independent variable X. The consequences of failing to address the violations of measurement invariance in mediation models are largely unknown. The purpose of the present study was to systematically examine the impact of mediator noninvariance on the Type I error rates, statistical power, and relative bias in parameter estimates of the mediated effect in the single mediator model. The results of a large simulation study indicated that, in general, the mediated effect was robust to violations of invariance in loadings. In contrast, most conditions with violations of intercept invariance exhibited severely positively biased mediated effects, Type I error rates above acceptable levels, and statistical power larger than in the invariant conditions. The implications of these results are discussed and recommendations are offered.

Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to an outcome. However, current methods do not allow researchers to study the relationships between general and specific aspects of a construct to an outcome simultaneously. This study proposes a bifactor measurement model for the mediating construct as a way to parse variance and represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help determine the properties of mediated effect estimation when the mediator has a bifactor structure and a specific facet of a construct is the true mediator. This study also investigates the conditions when researchers can detect the mediated effect when the multidimensionality of the mediator is ignored and treated as unidimensional. Simulation results indicated that the mediation model with a bifactor mediator measurement model had "unbiased" and "adequate" power to detect the mediated effect with a sample size greater than 500 and medium a- and b-paths. Also, results indicate that parameter bias and detection of the mediated effect in both the data-generating model and the misspecified model varies as a function of the amount of facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis.

There is evidence for intergenerational transmission of substance use and disorder. However, it is unclear whether separation from a parent with substance use disorder (SUD) moderates intergenerational transmission, and no studies have tested this question across three generations. In a three-generation study of families oversampled for familial SUD, we tested whether separation between father (G1; first generation) and child (G2; second generation) moderated the effect of G1 father SUDs on G2 child SUDs. We also tested whether separation between father (G2) and child (G3; third generation) moderated the effect of G2 SUDs on G3 drinking. Finally, we tested whether G1-G2 or G2-G3 separation moderated the mediated effect of G1 SUDs on G3 drinking through G2 SUDs. G1 father-G2 child separation moderated intergenerational transmission. In families with G1-G2 separation, there were no significant effects of father SUD on G2 SUD or G3 drinking. However, in nonseparated families, greater G1 father SUDs predicted heightened G2 SUDs and G3 grandchild drinking. In nonseparated families, G1 father SUDs significantly predicted G2 SUDs, which predicted G3 drinking. However, G2-G3 separation predicted heightened G3 drinking regardless of G2 and G1 SUDs. Parental separation may introduce risk for SUDs and drinking among youth with lower familial risk. [ABSTRACT FROM AUTHOR]

Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method to detect the significance of the mediated effect is the bias-corrected bootstrap method. Both these methods are used for testing the significance of mediated effects in structural equation models (SEMs). A recent study by Leth-Steensen and Gallitto 2016 provided evidence that the test of joint significance was more powerful than the bias-corrected bootstrap method for detecting mediated effects in SEMs, which is inconsistent with previous research on the topic. The goal of this article was to investigate this surprising result and describe two issues related to testing the significance of mediated effects in SEMs which explain the inconsistent results regarding the power of the test of joint significance and the bias-corrected bootstrap found by Leth-Steensen and Gallitto 2016. The first issue was that the bias-corrected bootstrap method was conducted incorrectly. The bias-corrected bootstrap was used to estimate the standard error of the mediated effect as opposed to creating confidence intervals. The second issue was that the correlation between the path coefficients of the mediated effect was ignored as an important aspect of testing the significance of the mediated effect in SEMs. The results of the replication study confirmed prior research on testing the significance of mediated effects. That is, the bias-corrected bootstrap method was more powerful than the test of joint significance, and the bias-corrected bootstrap method had elevated Type 1 error rates in some cases. Additional methods for testing the significance of mediated effects in SEMs were considered and limitations and future directions were discussed.

Research Findings: The main goals of this study were to examine the factor validity of the Social Competence and Behavior Evaluation (SCBE-30) scale using exploratory factor analysis and confirmatory factor analysis and to test factor invariance across gender in a sample of Italian preschool-age children (241 boys, 252 girls). The concurrent validity of the SCBE scale was examined with measures of children's popularity and rejection. Our findings replicated a 3-factor model of the SCBE scale found in other studies with 3 correlated factors of social competence, anger-aggression, and anxiety-withdrawal. Multigroup confirmatory factor analyses provided evidence of configural, metric, and partial scalar invariance of the scale across gender. Popularity was positively related to children's social competence and negatively related to anxiety-withdrawal. Rejection was positively related to children's anger-aggression and anxiety-withdrawal and negatively related to social competence. Practice or Policy: The use of the SCBE scale in the Italian educational setting may help teachers understand children's emotional and social competencies and thereby improve social adjustment in the classroom.