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We hope that you find the information here to be a helpful supplement to the book, particularly as you begin to apply LCA and LTA in your research.

The Methodology Center at Penn State maintains a website containing information for getting started with LCA and LTA. Click here for more information. Note that answers to a variety of frequently asked questions about LCA, LTA, and the SAS software we distribute appear here.

Methodology MinutesNew book on LCA & LTA - an interview with the authors!

Host Michael Cleveland interviews Linda Collins and Stephanie Lanza, authors of the new book, Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. It is available for download at Methodology Minutes.
About the Book

On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data.
Free Software

PROC LCA and PROC LTA are SAS® procedures for latent class analysis (LCA) and latent transition analysis (LTA) developed by The Methodology Center at The Pennsylvania State University. These add-on procedures are available for download free of charge. Simply register on the site, then download and install the program on a Windows® computer running SAS Version 9.1 or newer. Once installed, the SAS user can access these procedures just as they can any other procedures that are native to the SAS system. These straightforward procedures make it possible to pre-process data, fit a variety of latent class and latent transition models, and post-process the results without leaving the SAS environment.
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