Field experiments in education frequently assign entire groups such as schools to treatment or control conditions. These experiments incorporate sometimes a longitudinal component where for example students are followed over time to assess differences in the average rate of linear change, or rate of acceleration. In this study, we provide methods…

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…

The impact of socially desirable responding or faking on noncognitive assessments remains an issue of strong debate. One of the main reasons for the controversy is the lack of a statistical method to model such response sets. This article introduces a new way to model faking based on the assumption that faking occurs due to an interaction between…

This study considers the problem of testing the difference between treatment and control groups on m [greater than or equal to] 2 measures when it is assumed a priori that the treatment group will perform better than the control group on all measures. Two procedures are investigated that do not rest on the assumptions of covariance homogeneity or…

The Dunnett procedure for comparing several treatment means with a control is applied to the problem of comparing several treatment variances with the variance of a control. Appropriate critical values are specified and an example is provided. (Author)