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Latent class analysis

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  • Kristin MacDonald

    (StataCorp LP)

Abstract

Latent class analysis (LCA) allows us to identify and understand unobserved groups in our data. These groups may be consumers with different buying preferences, adolescents with different patterns of behaviour, or different health status classifications. Stata 15 introduced new features for performing LCA. In this presentation, I will demonstrate how to use gsem with categorical latent variables to fit standard latent class models – models that identify unobserved groups based on a set of categorical outcomes. I will also show how we can extend the standard model to include additional equations and to identify groups using continuous, count, ordinal, and even survival times outcomes. We will use the results of these models to determine who is likely to be in a group and how that group’s characteristics differ from other groups.

Suggested Citation

  • Kristin MacDonald, 2018. "Latent class analysis," London Stata Conference 2018 18, Stata Users Group.
    • Handle: RePEc:boc:usug18:18
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    File URL: http://repec.org/usug2018/uk18_MacDonald.pdf
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    Cited by:

    1. Meles, Tensay Hadush & Ryan, Lisa & Mukherjee, Sanghamitra C., 2022. "Heterogeneity in preferences for renewable home heating systems among Irish households," Applied Energy, Elsevier, vol. 307(C).

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