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HD FS 597F - Latent Class Analysis for Cross-sectional and Repeated Measures Data
Course Dates: January 19, 26, February 2, 9, 16, 23
Prerequisites: Prerequisites are three semesters of graduate-level statistics for the social and behavioral sciences, or three semesters of graduate-level statistics and a demonstrated interest in the social and behavioral sciences. Basic familiarity with SAS is helpful, but not a prerequisite. If you are unsure whether you have the necessary background to take this course, contact Dr. Stephanie Lanza at email@example.com.
Course Outline/Syllabus: The goal of this short course is to help students gain the background and skills to be able to address interesting research questions using latent class and latent transition analysis. Latent class theory is conceptually similar to factor analysis. However, in latent class theory, latent variables are categorical, and individuals are sorted into mutually exclusive and exhaustive latent classes based on a set of item responses. Latent class analysis identifies underlying subgroups in data and estimates their prevalence, while simultaneously adjusting for measurement error. Latent class models can be used to estimate change over time in latent class membership using longitudinal data, in a variation called latent transition analysis (LTA). In addition, multiple-groups analyses can be performed, and covariates can be introduced to predict latent class membership and transitions over time in latent class membership.
Class time will be spent in lecture, discussion, and software demonstrations. Laboratory exercises will be provided for students to do outside of class. In addition, students will be required to do a small project. The software used in this course will be two downloadable add-on procedures for SAS Version 9 for Windows: PROC LCA (for latent class analysis) and PROC LTA (for latent transition analysis).