Psychology & Brain Sciences 891J - S-Hierarchical Linear Modeling

The hierarchical linear model provides a conceptual framework and a flexible set of analytic tools to study a variety of educational, social and developmental processes. One set of applications focuses on data in which persons are clustered within social contexts such as couples, families, classrooms, schools, or neighborhoods. A second set of applications concerns individual growth or change over time. Interest focuses on the shape of mean growth, the variability in individual growth curves around the mean growth curve, and person-level characteristics that predict differences in growth curves.
The course will consider the formulation of statistical models for these applications. Topics include an introduction to the basic two-level model for continuous outcomes, assessment of fit, checking model assumptions, single and multiparameter hypothesis testing, and the extension to three-level models. Special topics include univariate and multivariate models for dyads.
Participants will be exposed to a wide variety of examples, with emphasis on the interpretation and reporting of results. Each class session will be divided into a lecture-demonstration, where basic conceptual material will be presented and discussed, and a computer lab time, where participants will use the HLM5 program to develop models and become familiar with the features of the program. A basic understanding of statistical inference and skill in interpreting results from multiple regression are strongly suggested as preparation for the course.

Open to Graduate Psychology majors only. A course in multiple regression (Psych 641/642 or equivalent) is required