In this chapter we will present a tutorial on the fundamentals of structural equation modeling (SEM). In recent years structural equation modeling (SEM) has grown enormously in popularity. Fundamentally, SEM is a term for a large set of techniques based on the general linear model. The goal of this chapter is to present an introduction to the field of structural equation modeling. The chapter begins with an overview of SEM that includes the purpose and goals of this statistical analysis as well as terminology unique to this technique. Following the brief overview, the process of modeling will be discussed and illustrated with an example using data from a recent D.A.R.E. evaluation. After the basic modeling process is illustrated and discussed, an extension (multilevel modeling) of the basic model is presented. Finally, we present a section that discusses future trends in SEM.

This tutorial begins with an overview of structural equation modeling (SEM) that includes the purpose and goals of the statistical analysis as well as terminology unique to this technique. I will focus on confirmatory factor analysis (CFA), a special type of SEM. After a general introduction, CFA is differentiated from exploratory factor analysis (EFA), and the advantages of CFA techniques are discussed. Following a brief overview, the process of modeling will be discussed and illustrated with an example using data from a HIV risk behavior evaluation of homeless adults (Stein & Nyamathi, 2000). Techniques for analysis of nonnormally distributed data as well as strategies for model modification are shown. The empirical example examines the structure of drug and alcohol use problem scales. Although these scales are not specific personality constructs, the concepts illustrated in this article directly correspond to those found when analyzing personality scales and inventories. Computer program syntax and output for the empirical example from a popular SEM program (EQS 6.1; Bentler, 2001) are included.

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Familism, a cultural value that emphasizes warm, close, supportive family relationships and that family be prioritized over self, has been associated with psychological health. The goal of this work was to fill a gap in the literature on how familism contributes to psychological health. Drawing from conceptual links between familism and close relationship processes, we hypothesized that familism contributes to better psychological health by facilitating closeness and social support. A university sample of U.S. women and men of Latino (n = 173), European (n = 257), and Asian (n = 642) cultural backgrounds completed measures of familism, closeness to family members, general perceived social support, and psychological health as indexed by perceived stress, general mental health, and depressive symptoms. Structural equation multiple-group modeling analyses found direct effects of familism on closeness to family members and perceived social support and an indirect effect of familism on better psychological health via greater closeness to family members and greater perceived social support. These effects did not differ by cultural background. Consistent with previous research, however, Latinos reported the highest levels of familism of the three cultural groups, and women reported higher familism and support as well as poorer psychological health than men. Discussion is focused on the implications of these findings for understanding the association of familism with psychological health and the relevance of the familism construct for diverse U.S. groups.

Although many researchers have suggested that racial discrimination has a negative impact on Black mental health, there are few empirical investigations of that possibility. The authors examined the relative contributions of racial discrimination, status variables, and ordinary stressors to symptoms among 520 Black adults. Results revealed that racial discrimination contributed significantly to symptoms and accounted for 15% of the variance in total symptoms. • racial discrimination • racism • psychiatric symptoms • BlacksNumerous studies have revealed widespread racial discrimination against Blacks (e.g.,

A model linking children's peer acceptance in the classroom to academic performance via academic self-concept and internalizing symptoms was tested in a longitudinal study. A sample of 248 children was followed from 4th to 6th grade, with data collected from different informants in each year of the study to reduce respondent bias. A path analysis supported the model; a lack of peer acceptance in the classroom in 4th grade predicted lower academic self-concept and more internalizing symptoms the following year, which in turn, predicted lower academic performance in 6th grade. An alternative path with internalizing symptoms predicting declines in peer acceptance was tested and received some support as well. Implications of the findings for schools are discussed.

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This study examined the utility of a lifetime cumulative adversities and trauma model in predicting the severity of mental health symptoms of depression, anxiety, and posttraumatic stress disorder. We also tested whether ethnicity and gender moderate the effects of this stress exposure construct on mental health using multigroup structural equation modeling. A sample of 500 low-socioeconomic status African American and Latino men and women with histories of adversities and trauma were recruited and assessed with a standard battery of self-report measures of stress and mental health. Multiple-group structural equation models indicated good overall model fit. As hypothesized, experiences of discrimination, childhood family adversities, childhood sexual abuse, other childhood trauma, and chronic stresses all loaded on the latent cumulative burden of adversities and trauma construct (CBAT). The CBAT stress exposure index in turn predicted the mental health status latent variable. Although there were several significant univariate ethnic and gender differences, and ethnic and gender differences were observed on several paths, there were no significant ethnic differences in the final model fit of the data. These findings highlight the deleterious consequences of cumulative stress and trauma for mental health and underscore a need to assess these constructs in selecting appropriate clinical interventions for reducing mental health disparities and improving human health.