The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the…

Rater biases are of interest to behavior genetic researchers, who often use ratings data as a basis for studying heritability. Inclusion of multiple raters for each sibling pair (M. Bartels, D. I. Boomsma, J. J. Hudziak, T. C. E. M. van Beijsterveldt, & E. J. C. G. van den Oord, 2007) is a promising strategy for controlling bias variance and may…

In practice, the inclusion of correlated residuals in latent-variable models is often regarded as a statistical sleight of hand, if not an outright form of cheating. Consequently, researchers have tended to allow only as many correlated residuals in their models as are needed to obtain a good fit to the data. The current article demonstrates that…

In current psychological research, the analysis of data from computer-based assessments or experiments is often confined to accuracy scores. Response times, although being an important source of additional information, are either neglected or analyzed separately. In this article, a new model is developed that allows the simultaneous analysis of…

An effect is a function of a cause as well as of 4 other factors: recipient, setting, time, and outcome variable. The principle of parallelism states that if a design option exists for any 1 of these 4 factors, a parallel option exists for each of the others. For example, effects are often estimated by drawing a comparison across recipients who…