The table below provides an outline of the topics covered in the LDA workshop. LDA encompasses random effect models for change, latent growth curve, latent change score models, and survival analysis and other models for event occurrence. Briefly, day 1 introduces the kinds of research questions approached using LDA; an introduction to Mplus software; using Mplus software; and the importance of workflow; finally, LDA models are introduced. By the end of day 1 we have demonstrated that the latent growth curve model can be specified in a way that produces the same parameter estimates as mixed effect models for change (e.g., SAS/PROC MIXED, Stata/xtmixed). Day 2 expands on latent growth curve models, discusses data handling issues, model fit assessment, and latent classes for growth. Day 3 covers latent change score models, survival analysis, complex models, complex sampling weights, and Bayesian data analysis. Note: MPlus v8 includes new routines for intensive longitudinal data, and Alden Gross from Hopkins will showcase some of these features.

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