Latent Class Analysis
A 2-Day Seminar Taught by Stephanie Lanza, Ph.D.
Latent Class Analysis (LCA) is an intuitive and rigorous tool for uncovering hidden subgroups in a population. It can be viewed as a special kind of structural equation modeling in which the latent variables are categorical rather than continuous. This two-day seminar will give you the theoretical background and applied skills to address interesting research questions using LCA. You will also be introduced to latent transition analysis (LTA), a longitudinal extension of latent class analysis.
Other topics include model interpretation, model selection, model identification, multiple-groups LCA, measurement invariance across groups, LCA with covariates and distal outcomes. The seminar will combine lectures, software demonstrations, computer exercises, and discussion. There will be opportunities for participants to discuss how LCA and LTA can be applied in their own research.
Who should attend?
If you plan to analyze cross-sectional or longitudinal data and believe that there are meaningful subgroups of individuals characterized by the intersection of multiple characteristics, this seminar is for you. These subgroups might be defined by patterns of problem behavior, risk exposure, product preference, political alignment, and many other hard-to-measure constructs.
Participants should have a good working knowledge of the principles and practice of multiple regression; familiarity with logistic regression is helpful.
All examples and exercises will use SAS and the free add-on procedure PROC LCA developed by Dr. Lanza and her colleagues. No previous experience with SAS is required. Both basic and advanced features of PROC LCA will be covered. If you prefer Stata or Mplus, you can get equivalent program code for these packages on request.
This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments. To do the exercises, you will need to bring your own laptop computer with a recent version of SAS and the free add-on procedure, PROC LCA, installed. PROC LCA can be downloaded at http://methodology.psu.edu/downloads/proclcalta. Power outlets will be provided at each seat.
Schedule and materials
The class will meet from 9 to 5 each day with a 1-hour lunch break.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and Lodging
The fee of $895 includes all course materials.
Lodging Reservation Instructions
A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut St., Philadelphia, PA at a nightly rate of $142 for a Standard room. This hotel is about a 5-minute walk from the seminar location. To register, you must call 203-905-2100 during business hours and identify yourself with Statistical Horizons and give the group code STA113 . The room block will expire when it is full or on October 13, 2014. There is a shortage of hotel rooms in Philadelphia for these dates so please make your reservation as early as possible.
- Introduction to latent class analysis
- Latent class homogeneity and separation
- Model identification, selection, starting values
- Multiple-groups LCA
- Measurement invariance across groups
- Brief review of binary and multinomial logistic regression
- LCA with covariates
- LCA with a distal outcome
- Introduction to latent transition analysis (LTA)
- LCA and LTA in professional writing and grant proposals
“This workshop helped me build on my foundational knowledge of LCA and LTA to become a more informed, critical, and sophisticated user of the latent class models. I learned about state-of-the-art advances in checking models identification, assessing measurement nuance, and using latent class as a predictor of distal outcomes.”
Danielle McCarthy, Rutgers University
“This course provides an excellent introduction to LCA/LTA with hands-on examples that allow attendees to look at these methods from an applied real-world perspective. Dr. Lanza speaks authoritatively from an amazing experience base.”
Lori Anderson, Kansas State University
“If you are interested in latent class analysis, this course is informative and introduces a SAS procedure for analysis with lots of code and output explanation. Stephanie is very knowledgeable and helpful.”
Stephanie Pugh, American College of Radiology