This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust…

Treatments in randomized studies are often targeted to key mediating variables. Researchers want to know if the treatment is effective and how the mediators affect the outcome. The data are often analyzed using structural equation models (SEMs), and model coefficients are interpreted as effects. However, only assignment to treatment groups is…

Considers the estimation of polynomial structural models and shows a limitation of an existing method. Introduces a new procedure, the generalized appended product indicator procedure, for nonlinear structural equation analysis. Addresses statistical issues associated with the procedure through simulation. (SLD)

The Programme for International Student Assessment comparative study of reading performance among 15-year-olds is reanalyzed using statistical procedures that allow the full complexity of the data structures to be explored. The article extends existing multilevel factor analysis and structural equation models and shows how this can extract richer…

Conducted a meta-analysis focusing on the explanation of empirical Type I error rates for six principal classes of estimators. Generally, chi-square tests for overall model fit were found to be sensitive to nonnormality and the size of the model for all estimators, with the possible exception of elliptical estimators with respect to model size and…

Considers an approach to validating the selection of education indicators by incorporating them into a multilevel structural model and using the estimates from that model in policy-relevant simulations. The potential of this approach is demonstrated with data from the National Education Longitudinal Study of 1988. (SLD)