The goal of this two-day workshop was to help participants gain the theoretical background and applied skills to be able to address interesting research questions using latent class analysis (LCA). By the end of the workshop, they were able to fit preliminary latent class models to their own data. Participants became familiar with all of the LCA concepts covered in the recent book co-authored by Dr. Lanza and published by Wiley, Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences1.
Workshop time was spent in lecture, software demonstrations, computer exercises, and discussion. At the workshop, participants were provided with a hard copy of all lecture notes, select computer exercises and output, and suggested reading lists for future reference. The software used in this course was PROC LCA, a downloadable add-on procedure for SAS to estimate latent class models; this software is developed at the Penn State Methodology Center. The second afternoon was reserved for participants to apply the concepts learned in class to their own data, and the presenters were available for consultation during that period.
The prerequisite for this workshop was graduate-level statistics training for the behavioral or health sciences up through linear regression (usually two semesters of course work). Basic familiarity with SAS and logistic regression was helpful, but not a prerequisite.
Participants were strongly encouraged to bring a laptop so that they can conduct the computer exercises, and to bring a copy of their data so that they may conduct analyses using their own data on the second day. To conduct analyses at the workshop, SAS Version 9 for Windows must be installed on the laptop prior to arrival. In addition, approximately one week prior to the workshop participants were sent an email requesting that they download and install PROC LCA. Participants verified that data use agreements permit them to bring their own data to the workshop. A simulated data set was made available to those who do not bring their own data.
- Introduction to latent class analysis (LCA) and the LCA model
- Model interpretation, model selection, model identification
- Multiple-groups LCA
- Measurement invariance across groups
- Review of logistic regression
- LCA with covariates
- LCA with distal outcomes
In addition to the above topics, there were five hands-on computer exercises, open discussion times, and question/answer periods.
1Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York: Wiley.