Introduces methods, theory and applications of statistical models, from linear models (simple and multiple linear regression), to hierarchical linear models. Topics such as estimation, residual ...

This course will discuss what mixed models are, why they are called "mixed" models, what is a "random factor", and why. The primary focus will be for the researcher to understand when he or she should ...

This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual ...

This research evaluated the usefulness of 3 approaches for predicting college grades: (a) traditional regression models, (b) high-school-effects models, and (c) hierarchical linear models. Results of ...