Fall 2015 Classes in Methodology
If you have a question about a specific course, please contact the instructor of that course directly. If you have a general question about the suite of one-credit methodology courses, please contact Bethany Bray.
For more information about the one-credit courses, please see our frequently asked questions:
Schedule Number: TBD
Instructors: Dr. Bethany Bray, Methodology Center outreach director and research assistant professor of health and human development
Course Dates: August 27 - September 24 (5 sessions).
Time and Location: 2:30 - 5:00 PM at the Methodology Center, Note: new location
Prerequisites: (1) graduate-level statistics training for the behavioral or health sciences up through linear regression (usually two semesters of course work), (2) familiarity with latent class analysis, (3) familiarity with baseline category multinomial logistic regression, and (4) familiarity with SAS or Mplus.
Course Description: This short course builds on the knowledge and skills gained in a previous short course, “An introduction to latent class and latent profile analysis.” The goal of this short course is to help students gain advanced knowledge and skills to be able to address more complex research questions using advanced latent class and latent profile analysis techniques. Latent class and latent profile analysis are conceptually similar to factor analysis; however, in latent class and latent profile analysis, the latent variables are categorical, and individuals are sorted into mutually exclusive and exhaustive subgroups based on their sets of item responses. Latent class analysis uses categorical indicators to identify underlying subgroups in data and estimate their prevalences, while simultaneously adjusting for measurement error; latent profile analysis is conceptually similar but uses continuous indicators. These models can be extended in a variety of ways. The five lectures will cover the following topics: (1) a review of latent class and latent profile analysis basic concepts; (2) predicting distal outcomes from subgroup membership; (3) latent transition analysis and repeated measures latent class analysis as longitudinal extensions; (4) causal inference with latent class analysis; and (5) latent class moderation. Class time will be spent in lecture, discussion, and software demonstrations. Homework 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 (a) a downloadable add-on procedures for SAS Version 9 for Windows: PROC LCA (for latent class analysis), and (b) Mplus (for latent profile analysis).