Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach (Chapel Hill, NC)
A powerful method for analyzing longitudinal data is Latent Growth Curve Models (LGCM). LGCM allow each case in a sample to have individual trajectories ("latent curves" or "growth curves") representing change over time. In addition to mapping these trajectories, LGCM allow researchers to examine the determinants of these trajectories or to relate the trajectories of one variable to those of another. The approach to LGCM in this course draws on the strengths of structural equation models (SEM), and the primary goal is to introduce participants to the theory and application of LGCM. The course begins with a conceptual introduction to LGCM, a description of research questions that are well suited for the technique, and a review of SEM. The remainder of the course will cover the following topics: LGCM for a single variable with and without predictors of differences in trajectories; modeling nonlinear trajectories; the LGCM for multiple variables; the relation between the parameters governing the trajectories in two or more variables; incorporating predictors of multiple trajectories; and extensions to the LGCM.
Prerequisites: The workshop assumes that participants have prior training and experience with SEM software.
Fee: Members = $1700; Non-members = $3200