Event Phone: 1-610-715-0115

Cancellation 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).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Stephanie Lanza, Ph.D. and Bethany Bray, Ph.D.

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.