Background. Familismo or familism is a cultural value frequently seen in Hispanic cultures, in which a higher emphasis is placed on the family unit in terms of respect, support, obligation and reference. Familism has been implicated as a protective factor against mental health problems and may foster the growth and development of children. This study aims at measuring the size of the relationship between familism and mental health outcomes of depression, suicide, substance abuse, internalizing and externalizing behaviors. Methods. Thirty-nine studies were systematically reviewed to assess the relationship between familism and mental health outcomes. Data from the studies were comprised and organized into 5 categories: depression, suicide, internalizing symptoms, externalizing symptoms, and substance use. The Cohen’s d of each value (dependent variable in comparison to familism) was calculated. Results were weighted based on sample sizes (n) and total effect sizes were then calculated. It was hypothesized that there would be a large effect size in the relationship between familism and depression, suicide, internalizing and externalizing symptoms and substance use in Hispanics. Results. The meta-analysis showed small effect sizes in the relationship between familism and depression, suicide and internalizing behaviors. And no significant effects for substance abuse and externalizing behaviors. Discussion. The small effects found in this study may be explained by the presence of moderator variables between familism and mental health outcomes (e.g., communication within the family). In addition, variability in the Latino samples and in the measurements used might explain the small and non-significant effects found.

In a frequentist framework, the exact fit of a structural equation model (SEM) is typically evaluated with the chi-square test and at least one index of approximate fit. Current Bayesian SEM (BSEM) software provides one measure of overall fit: the posterior predictive p value (PPP χ2 ). Because of the noted limitations of PPP χ2 , common practice for evaluating Bayesian model fit instead focuses on model comparison, using information criteria or Bayes factors. Fit indices developed under maximum-likelihood estimation have not been incorporated into software for BSEM. We propose adapting 7 chi-square-based approximate fit indices for BSEM, using a Bayesian analog of the chi-square model-fit statistic. Simulation results show that the sampling distributions of the posterior means of these fit indices are similar to their frequentist counterparts across sample sizes, model types, and levels of misspecification when BSEMs are estimated with noninformative priors. The proposed fit indices therefore allow overall model-fit evaluation using familiar metrics of the original indices, with an accompanying interval to quantify their uncertainty. Illustrative examples with real data raise some important issues about the proposed fit indices' application to models specified with informative priors, when Bayesian and frequentist estimation methods might not yield similar results. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Suicide has been the second leading cause of death for 18- to 24-year-olds in the United States since 2011. The stress experienced by undergraduate college students has the potential to increase one’s risk for suicide. Resilience theory was used as a theoretical framework to examine the interplay between risk and protective factors. A cross-sectional and correlational design was used to assess the mediating effects of positive thinking and/or social support on suicide resilience in 131 college students 18 to 24 years old who completed an online survey. The study found an indirect effect of self-esteem on suicide resilience through positive thinking and social support indicating that as self-esteem increases, positive thinking and social support also increase, which leads to an increase in resilience. The study also found a direct effect of self-esteem, positive thinking, and social support on suicide resilience. The findings inform the development of tailored interventions to build suicide resilience in college students.

Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing. Five methods may be used to deal with ordinal missing data in ME/I testing, including the continuous full information maximum likelihood estimation method (FIML), continuous robust FIML (rFIML), FIML with probit links (pFIML), FIML with logit links (lFIML), and mean and variance adjusted weight least squared estimation method combined with pairwise deletion (WLSMV_PD). The current study evaluates the relative performance of these methods in producing valid chi-square difference tests ( $\Delta {\chi }^2$) and accurate parameter estimates. The result suggests that all methods except for WLSMV_PD can reasonably control the type I error rates of $\Delta {\chi }^2$ tests and maintain sufficient power to detect noninvariance in most conditions. Only pFIML and lFIML yield accurate factor loading estimates and standard errors across all the conditions. Recommendations are provided to researchers based on the results.