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Nov. 7 Applied Research Talk: Latent Class Analysis

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Researchers who conduct analyses on large data sets may benefit from a presentation on latent class analysis (LCA) and how to implement LCA on an empirical data set using Mplus software.

LCA provides researchers valuable insights into various types of respondents and constructing future intervention strategies for them. Caihong R. Li will lead the discussion about LCA and teach us how to conduct LCA in Mplus.
LCA, a latent variable modeling approach, is used to classify people into groups that are similar on unobserved constructs, based on their response patterns. In LCA, group membership is unseen and is indicated by observed variables. LCA is a model-based approach and thus the results from LCA can be replicated in other samples.
This presentation is the second in the “Applied Quantitative and Psychometric Series (AQPS)” series. These talks are provided by the Applied Psychometric Strategies (APS) lab, under the direction of Dr. Michael Toland.
Even if you don’t work on large data sets or know what LCA means, this talk will make you think differently about research. Bring your lunch and join us!