The hierarchical linear model (HLM) provides a conceptual framework and a flexible set of analytic tools to study a variety of social, political, and developmental processes. One set of applications focuses on data in which persons are clustered within social contexts, such as couples, families, schools, neighborhoods, or organizations. Interest may center on the magnitude of social contextual effects on personal outcomes, the context-specificity of relationships between person background and person outcomes, or interactions between measurable features of social context and personal background.
A second set of applications concerns individual growth or change over time, where time series data are clustered within persons. 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. A third set of applications involves a combination of both of the first two types: persons changing over time who are also nested within social context. The goal is to assess the correlated and interactive effects of personal background and social context of trajectories of individual development. The course will consider the formulation of statistical models for these three applications.
Participants will be exposed to a wide variety of examples, with emphasis on the interpretation and reporting of results. Topics include an introduction to the basic two-level model for continuous outcomes, assessment of fit, checking model assumptions, single and multiparameter hypothesis testing, the extension to three-level models, and nonlinear models for binary outcomes. Participants should have strong backgrounds in multiple regression analysis.
This workshop is offered in collaboration with the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan.
Dr. Aline Sayer, University of Massachusetts Amherst