Disentangling the discrimination and mental health relationship among diverse college students: Moderation analyses of shame-proneness across race, gender, and race-by-gender interactions.

OBJECTIVE: This study investigated shame-proneness as a moderating risk factor within the relationship between perceived discrimination and mental health outcomes. Moderation across race, gender, and race-by-gender intersections was also examined. METHOD: Bayesian analysis was employed to examine moderation among African, Latinx, and Asian descent college students (N = 295). RESULTS: Shame-proneness had a moderating role contingent on participants' social identities. Higher shame-proneness moderated the discrimination-anxiety relationship for the African American sample and African American women and moderated the discrimination-depression relationship for African American women and men, respectively. CONCLUSIONS: The present study advances our understanding of the association between discrimination and negative mental health outcomes. African American participants with high shame-proneness were uniquely impacted by discrimination. Researchers, clinicians, and university officials are encouraged to develop culturally informed interventions and services to support this population. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Probability of superiority for comparing two groups of clusters.

The probability of superiority (PS) has been recommended as a simple-to-interpret effect size for comparing two independent samples-there are several methods for computing the PS for this particular study design. However, educational and psychological interventions increasingly occur in clustered data contexts; and a review of the literature returned only one method for computing the PS in such contexts. In this paper, we propose a method for estimating the PS in clustered data contexts. Specifically, the proposal addresses study designs that compare two groups and group membership is determined at the cluster level. A cluster may be: (i) a group of cases with each case measured once, or (ii) a single case with each case measured multiple times, resulting in longitudinal data. The proposal relies on nonparametric point estimates of the PS coupled with cluster-robust variance estimation, such that the proposed approach should remain adequate regardless of the distribution of the response data. Using Monte Carlo simulation, we show the approach to be unbiased for continuous and binary outcomes, while maintaining adequate frequentist properties. Moreover, our proposal performs better than the single extant method we found in the literature. The proposal is simple to implement in commonplace statistical software and we provide accompanying R code. Hence, it is our hope that the method we present helps applied researchers better estimate group differences when comparing two groups and group membership is determined at the cluster level.