Latent Class Analysis

A 2-Day Seminar Taught by Stephanie Lanza, Ph.D. and Bethany Bray, 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 LCA.

Other topics include model identification, model selection, model interpretation, multiple-groups LCA, measurement invariance across groups, and 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 procedures PROC LCA and PROC LTA developed by Dr. Lanza and her colleagues.  Coverage will include both basic and advanced features of PROC LCA, but only basic features of PROC LTA. Previous experience with SAS is highly desirable. 

This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments. To complete the exercises, you will need to bring your own laptop computer with a recent version of SAS and the free add-on procedures, PROC LCA and PROC LTA, installed. Both PROCs can be downloaded at https://www.methodology.psu.edu/downloads/proclcalta/.

Note: PROC LCA and PROC LTA do not function with the free University Edition of SAS.

If you prefer Mplus, Stata, or Latent Gold, you can get equivalent program code for these packages on request. 


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.

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 $138 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 SH1205 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, November 5, 2019.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.


Outline

  1. Introduction to latent class analysis (LCA)
  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 

“If you want to know LCA in-depth it is certainly worth your time! I learned about model selection and validation while using non-traditional ordinal data in LCA.”
  Gretchen Macht, University of Rhode Island

“The course was very useful for individuals interested in latent class. Dr. Lanza is extremely knowledgeable and willing to answer or walk through any questions/problems you face. The hands-on portion of the course was also helpful. Dr. Lanza made herself available during breaks and at the end of the day for more in-depth discussions.”
  Caitlin Brady, University of Central Florida

“Thank you, Dr. Lanza, for a fantastic workshop! I started the course not knowing much about LCA but Dr. Lanza made it easy to understand how this analysis can be applied to our research and how extensions of this analysis can further serve to extract more info from our data. I am finishing this workshop infinitely more knowledgeable about LCA – excited and ready to use it in my own research!”
  Eloïse Fairbank, Concordia University

“This course was well-designed and executed. The instructor was very knowledgeable. She showed interest in attendees’ research interests and answered questions very well. The materials were clear and informative.”
  Anonymous

“This was a good orientation to LCA. I was uncertain how I would use LCA coming into the class, but got lots of great ideas over the past 2 days!”
  Rachel Pruchno, Rowan University

“This course provided many hands-on examples and statistical tools that I could use later on.”
  Jeong Lee, Iowa State University

“The instructor’s explanations are very clear. You can bring your actual data and research questions and discuss/consult with the instructor regarding your concerns. You can get a perspective on your statistical issues from the leaders of the field.”
  Evgeniya Reshetnyak, Cornell University

“I am coming away from this course with lots of ideas for my work and feeling confident that I have a good basic foundation for moving my statistics forward.”
  Elise Erickson, Oregon Health & Science University

“Excellent course! Very interactive. Great course materials that I will use in my own research.”
  Sarah Wood, Children’s Hospital of Philadelphia

“The Latent Class Analysis seminar is a great workshop. Stephanie’s passion and enthusiasm make the topic easy to understand and enjoyable. The examples provide hands-on application and she answers any and all questions thoughtfully.”
  Kalisha Bonds, Oregon Health & Science University

“Great course to learn about introductory level latent class analysis and latent transition analysis. No regrets!”
  Philseok Lee, George Mason University

“As a PhD student working on my dissertation, having an instructor walk through examples and the software made me feel more confident about completing my analysis in the coming months and truly understanding my results.”
  Hannah Klein, Temple University