Interest in modeling the structure of latent variables is gaining momentum, and many simulation studies suggest that taxometric analysis can validly assess the relative fit of categorical and dimensional models. The generation and parallel analysis of categorical and dimensional comparison data sets reduces the subjectivity required to interpret…

Cross-classified random-effects models (CCREMs) are used for modeling nonhierarchical multilevel data. Misspecifying CCREMs as hierarchical linear models (i.e., treating the cross-classified data as strictly hierarchical by ignoring one of the crossed factors) causes biases in the variance component estimates, which in turn, results in biased…

Defines a sampling taxonomy that shows the differences between and relationships among the bootstrap, the jackknife, and the randomization test. Demonstrates the usefulness of the taxonomy for teaching the goals and purposes of resampling schemes and presents univariate and multivariate examples. (SLD)

Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as…

Cross-classified random effects modeling (CCREM) is used to model multilevel data from nonhierarchical contexts. These models are widely discussed but infrequently used in social science research. Because little research exists assessing when it is necessary to use CCREM, 2 studies were conducted. A real data set with a cross-classified structure…

Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data…