Latent Class Analysis

A 2-Day Seminar Taught by Stephanie Lanza, Ph.D.

Read reviews of this seminar

To see a sample of the course materials, click here.


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.


Computing

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

Note: PROC LCA does not function with the free University Edition of SAS.  


Location, Format and materials

The seminar meets Friday, December 8 and Saturday, December 9 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 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 $995 includes all course materials. The early registration fee of $895.00 is available until November 8. 

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $134. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STAT12 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, November 7.

If you make reservations after the cut-off date ask for the Statistical Horizon’s room rate (do not use the code) and they will try to accommodate your request.


Outline

  1. Introduction to latent class analysis
  2. Latent class homogeneity and separation
  3. Model identification, selection, starting values
  4. Multiple-groups LCA
  5. Measurement invariance across groups
  6. Brief review of binary and multinomial logistic regression
  7. LCA with covariates
  8. LCA with a distal outcome
  9. Introduction to latent transition analysis (LTA)
  10. LCA and LTA in professional writing and grant proposals

COMMENTS FROM RECENT PARTICIPANTS 

“Dr. Lanza took a topic with which I have long-struggled to fully grasp and made it seem simple, straight-forward, and clear. I have much more confidence to try this on my own.”
  Yessenia Castro, University of Texas at Austin

“This course was remarkable in that both the foundations of LCA and the most cutting-edge techniques were covered in just 2 days. Highly recommend!”
  Lyndsey Miller, University of Utah, College of Nursing

“Stephanie Lanza is a fabulous instructor. She breaks down complex topics so that it is easy to understand and apply. She also does a great job of highlighting ways that these methods can be used in practice to advance our respective fields. Thanks for a great workshop.”
  May Chen, University of North Carolina at Chapel Hill

“This is a great introductory course on LCA for individuals who have no prior knowledge or experience with learning LCA. Dr. Lanza is a wonderful instructor. Her pace is comfortable, which makes it easier to digest some new concepts related to LCA. She also provides codes to LCA in various statistical packages (ex. Stata). Importantly, there’s a textbook that accompanies her instruction to serve as a further reference.”
  Caroline Lim, University of South Carolina, School of Social Work

“If you’re interested in developmental studies, this would be a very useful tool. Prof. Lanza is very good at communicating complicated statistical methods to applied researchers.”
  Yudong Zhang, University of Chicago

Instructor was very knowledgeable, engaging, and patient. Concepts were clearly explained, involved SAS syntax and theory as well as additional material to take home (Including SAS files and macro). Instructor was willing to answer questions about your own projects during her break (which I imagine must have been exhausting for her!).”
  Uriyoan Colon-Ramos

“This course is a great practical course for LCA. I feel ready to use it and can further explore the field.”
  Eric Rubenstein, University of North Carolina