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A Primer to Latent Profile and Latent Class Analysis

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Methods for Researching Professional Learning and Development

Part of the book series: Professional and Practice-based Learning ((PPBL,volume 33))

Abstract

This chapter gives an applied introduction to latent profile and latent class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals are probing whether and, if so, how many latent classes can be identified in the data and estimating their proportional size and response profiles. Moreover, latent class membership can serve as a predictor or outcome for external variables. Substantively, LPA/LCA adopt a person-centred approach that is useful for analysing individual differences in learning prerequisites, processes, or outcomes. This chapter provides a conceptual overview of LPA/LCA, a nuts-and-bolts discussion of the steps and decisions involved in their application, and illustrative examples using available data and the R statistical environment.

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Notes

  1. 1.

    That is, residual correlations among the indicators are zero given class membership. Local independence is a default assumption in many latent variable models, such as factor analysis or IRT models, but can be relaxed.

  2. 2.

    Using composite scores of categorical items as indicators will turn an LCA into an LPA.

  3. 3.

    Discussing details of estimation is beyond the scope of this chapter; for a tractable introduction, see Masyn (2013).

  4. 4.

    The maximum number of probed classes frequently hinges on practical issues, such the occurrence of convergence problems or other issues as more classes are extracted (e.g., occurrence of small class sizes; see step 3). If possible, researchers should consider what maximum number of classes may be of theoretical interest.

  5. 5.

    In this and the following example, the maximum number of latent classes to be extracted was chosen for practical reasons (i.e., estimation time when readers reproduce the analyses).

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Author information

Authors and Affiliations

  1. University of Erfurt, Erfurt, Germany

    Johannes Bauer

Authors
  1. Johannes Bauer
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Editor information

Editors and Affiliations

  1. Institute of Educational Science, Paderborn University, Paderborn, Germany

    Michael Goller

  2. Centre for Transformative Innovation, Swinburne University of Technology, Melbourne, VIC, Australia

    Eva Kyndt

  3. Department of Education, University of Jyväskylä, Jyväskylä, Finland

    Susanna Paloniemi

  4. Department of Education, University of Oslo, Blindern, Oslo, Norway

    Crina Damşa

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Bauer, J. (2022). A Primer to Latent Profile and Latent Class Analysis. In: Goller, M., Kyndt, E., Paloniemi, S., Damşa, C. (eds) Methods for Researching Professional Learning and Development. Professional and Practice-based Learning, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-08518-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-08518-5_11

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