Structural equation modeling in medical research: a primer

<p>Abstract</p> <p>Background</p> <p>Structural equation modeling (SEM) is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables. Similar but more powerful than regression analyses, it examines linear causal relationships among variables, while simultaneously accounting for measurement error. The purpose of the present paper is to explicate SEM to medical and health sciences researchers and exemplify their application.</p> <p>Findings</p> <p>To facilitate its use we provide a series of steps for applying SEM to research problems. We then present three examples of how SEM has been utilized in medical and health sciences research.</p> <p>Conclusion</p> <p>When many considerations are given to research planning, SEM can provide a new perspective on analyzing data and potential for advancing research in medical and health sciences.</p

Mother and Adolescent Reports of Associations Between Child Behavior Problems and Mother-Child Relationship Qualities: Separating Shared Variance from Individual Variance

This study contrasts results from different correlational methods for examining links between mother and child (N = 72 dyads) reports of early adolescent (M = 11.5 years) behavior problems and relationship negativity and support. Simple (Pearson) correlations revealed a consistent pattern of statistically significant associations, regardless of whether scores came from the same reporter or from different reporters. When correlations between behavior problems and relationship quality differed, within-reporter correlations were always greater in magnitude than between-reporter correlations. Dyadic (common fate) analyses designed for interdependent data decomposed within-reporter correlations into variance shared across reporters (dyadic correlations) and variance unique to specific reporters (individual correlations). Dyadic correlations were responsible for most associations between adolescent behavior problems and relationship negativity; after partitioning variance shared across reporters, no individual correlations emerged as statistically significant. In contrast, adolescent behavior problems were linked to relationship support via both shared variance and variance unique to maternal perceptions. Dyadic analyses provide a parsimonious alternative to multiple contrasts in instances when identical measures have been collected from multiple reporters. Findings from these analyses indicate that same-reporter variance bias should not be assumed in the absence of dyadic statistical analyses

The psychology of passion: A meta-analytical review of a decade of research on intrapersonal outcomes

It is just over a decade since Vallerand et al. (J Personal Soc Psychol 85:756–767, 2003) introduced the dualistic model of passion. In this study, we conduct a meta-analytical review of relationships between Vallerand et al’s two passions (viz. harmonious and obsessive), and intrapersonal outcomes, and test the moderating role of age, gender, domain, and culture. A systematic literature search yielded 94 studies, within which 27 criterion variables were reported. These criterion variables derived from four research areas within the intrapersonal sphere: (a) well-/ill-being, (b) motivation factors, (c) cognitive outcomes and, (d) behaviour and performance. From these areas we retrieved 1308 independent effect sizes and analysed them using random-effects models. Results showed harmonious passion positively corresponded with positive intrapersonal outcomes (e.g., positive affect, flow, performance). Obsessive passion, conversely, showed positive associations with positive and negative