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In prevention trials, outcomes of interest frequently include data that are best quantified as proportion scores. In some cases, however, proportion scores may violate the statistical assumptions underlying common analytic methods. In this paper, we provide guidelines for analyzing frequency and proportion data as primary outcomes. We describe standard methods including generalized linear regression models to compare mean proportion scores, and examine tools for testing normality and other assumptions for each model. Recommendations are made for instances when the assumptions are not met, including transformations for proportions scores that are non-normal. We also discuss more sophisticated analytical tools to model change in proportion scores over time. The guidelines provide ready-to-use analytical strategies for frequency and proportion data that are commonly encountered in prevention science.

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Structural equation modeling (SEM) is the term for a broadly applicable set of statistical techniques that allow researchers to precisely represent constructs of interest, measure the extent to which data are consistent with a proposed conceptual model, and to adjust for the influence of measurement error. Although SEM may appear intimidating at first glance, it can be made accessible to researchers. The current manuscript provides a non-technical overview of SEM and its major constructs for a novitiate user. Concepts are illustrated using a simple example, representing a potential study performed in the field of youth and family research. The purpose of this manuscript is to offer interested scholars a conceptual overview and understanding of research questions and issues that may be addressed with this family of techniques.

The aim of this study was to examine the psychometric properties of the Acceptance and Action Questionnaire-II (AAQ-II) among Hispanic college students (N ϭ 104). Consistent with previous studies, the AAQ-II enjoyed excellent internal consistency in this sample and fit a 1-factor solution. However, different method effects than those used in previous investigations had to be used to obtain a proper fit. High psychological inflexibility was associated with higher symptoms of depression, anxiety, and stress and lower levels of life satisfaction and mindfulness. The AAQ-II added to prediction of life satisfaction and psychological distress above and beyond measures of mindfulness and thought suppression. This study provides initial evidence that the AAQ-II may be a valid and reliable measure in Hispanic college student populations.