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. Previous experience with SAS is highly desirable. Both basic and advanced features of PROC LCA will be covered. 

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.  

If you prefer Stata or Mplus, you can get equivalent program code for these packages on request. However, the instructor will not discuss these packages in class, nor will she be available to assist participants who choose to do the exercises with these packages. 


Location, Format and materials

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

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 $995.00 includes all seminar materials. The early registration fee of $895 is available until November 7.

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 $137 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STSH06 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, November 6, 2018.

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 

“This class was very comprehensive and informative. Stephanie did an amazing job at covering such a large amount of information in 2 days. She is a clear instructor and her use of examples really helps to solidify the content. The style and approach to the course was not intimidating and created an atmosphere of comfort that encouraged questions and collaboration. Highly recommend.”
  Cassie West, University of North Texas

“This class was excellent. Stephanie is a great instructor who can teach complex content in a way that is easy for the learners to understand. I would highly recommend this course to anyone who thinks they will use LCA in their career.”
  Susan Frase, Oregon Health and Science University

“This course provides an excellent overview of Latent Class Analysis. Stephanie is an excellent teacher and is always available to answer questions or provide resources for further investigation if she’s not sure of the consensus. I highly recommend this course to anyone looking for detailed application of latent class analysis.”
  Carmen Tekwe, Texas A&M University

“I highly recommend this course. I learned basic concepts of LCA and was able to apply them to actual research questions.”
  Bo Young Nam, University of Maryland

“Dr. Lanza presented information in this course in an easily digestible fashion. Her thoughtfulness towards LCA/LTA and her careful approach towards model building was impressive and refreshing.”
  Samuel Meisel, University at Buffalo

“Stephanie was a clear, thorough, patient teacher. While I had read several LCA papers, I still did not fully understand. In two days, she helped explain LCA in a way that made me realize my inaccurate points of understanding and feel confident that I could understand how to utilize and apply LCA.”
  Gracelyn Cruden, University of North Carolina, Chapel Hill