Hierarchical Linear Models for Longitudinal Data (Boulder, CO)
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. The workshop will consider the formulation of statistical models for repeated measures longitudinal data, where individuals, families, dyads, or organizations are followed over time. Interest centers on the shape of the average trajectory, the heterogeneity around the mean growth curve, and individual and contextual characteristics that predict differences in change. Topics include an introduction to the basic two-level model for polynomial growth functions, an introduction to discontinuous (piecewise) growth models that incorporate multiple growth segments, models for accelerated longitudinal designs, checking model assumptions, model comparison tests, multiparameter hypothesis testing, the incorporation of time-varying predictors, and multivariate models for growth, with consideration of a variety of alternative covariance structures including compound symmetry, autoregressive structures, and heterogeneous level-1 variance. If time permits, we will consider the multivariate outcomes model for longitudinal dyads.
The emphasis is on the interpretation of computer output and the reporting of results rather than estimation theory and derivation. Participants should have strong backgrounds in multiple regression analysis. Note there is some overlap of the content of this course with the 5-day workshop on Introduction to Hierarchical Linear Models (HLM 1).
Fee: Members = $1300; Non-members = $2600