Latent Class Analysis - Online Course
Thursday, January 22 –
Saturday, January 24, 2026
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar is part of our Measurement and Psychometrics Certification Program, a series of 4 expert-led courses designed to provide advanced training and build specialized skills in measurement science. Contact us to learn how to complete your certification and access special pricing.
Latent Class Analysis (LCA) provides empirical researchers in the social, behavioral, and health sciences with a critical lens through which to inspect their data. This method has the ability to uncover hidden structures and patterns related to complex phenomena. For example, LCA enables social scientists to discover subgroups within a population that share similar patterns of behaviors or attitudes. Researchers also can gain a more nuanced understanding of human behavior by using LCA to characterize patterns of intersecting behaviors that confer a high risk of adverse outcomes.
LCA can be viewed as a special kind of structural equation modeling in which the latent variables are categorical rather than continuous. This seminar will give you the theoretical background and applied skills to address interesting research questions using LCA. Topics include model identification, model selection, model interpretation, multiple-groups LCA, measurement invariance across groups, LCA with covariates and outcomes, and latent profile analysis (LPA). The format will combine lectures, software demonstrations, computer exercises, and discussion. There will be opportunities to discuss how LCA can be applied in your own research.
Using LCA to gain new insight into how different components interact and influence outcomes, researchers can gain a more comprehensive understanding of phenomena under investigation. Applying this technique to empirical data can inform theory, contribute to evidence-based decision-making, and guide development of interventions tailored to specific subgroups within a population. Ultimately, LCA empowers empirical researchers in the social, behavioral, and health sciences to extract new insights from their data and contribute innovative findings that advance science.
Starting January 22, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
Computing
All examples and exercises will be demonstrated using Mplus. Seminar exercises can be conducted using the latest version of the freely available Mplus demo version. Syntax to complete exercises also will be provided in SAS, Stata, and Latent Gold.
Basic familiarity with Mplus is desirable, but even novice Mplus users with access to the software will be able to follow the lectures and complete the exercises.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
All examples and exercises will be demonstrated using Mplus. Seminar exercises can be conducted using the latest version of the freely available Mplus demo version. Syntax to complete exercises also will be provided in SAS, Stata, and Latent Gold.
Basic familiarity with Mplus is desirable, but even novice Mplus users with access to the software will be able to follow the lectures and complete the exercises.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
Who should register?
If you plan to analyze empirical 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/protection exposure, mood, political alignment, personality, and many other hard-to-measure constructs.
You should have a good working knowledge of the principles and practice of multiple regression; familiarity with logistic regression is helpful.
If you plan to analyze empirical 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/protection exposure, mood, political alignment, personality, and many other hard-to-measure constructs.
You should have a good working knowledge of the principles and practice of multiple regression; familiarity with logistic regression is helpful.
Seminar outline
Day 1: Introduction to latent class analysis (LCA)
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- Conceptual introduction to LCA
- Parameters estimated in LCA
- Technical considerations: model identification, model selection
Day 2: Adding grouping variables and covariates
-
- Including a grouping variable
- Review of logistic regression
- Predicting latent class membership
Day 3: Adding outcomes and latent profile analysis (LPA)
-
- Predicting an outcome from latent class membership
- Conceptual introduction to LPA
Day 1: Introduction to latent class analysis (LCA)
-
- Conceptual introduction to LCA
- Parameters estimated in LCA
- Technical considerations: model identification, model selection
Day 2: Adding grouping variables and covariates
-
- Including a grouping variable
- Review of logistic regression
- Predicting latent class membership
Day 3: Adding outcomes and latent profile analysis (LPA)
-
- Predicting an outcome from latent class membership
- Conceptual introduction to LPA
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.