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Power and Sample Size Computation for Wald Tests in Latent Class Models

Author

Listed:
  • Dereje W. Gudicha

    (Tilburg University)

  • Fetene B. Tekle

    (Janssen Pharmaceutica)

  • Jeroen K. Vermunt

    (Tilburg University)

Abstract

Latent class (LC) analysis is used by social, behavioral, and medical science researchers among others as a tool for clustering (or unsupervised classification) with categorical response variables, for analyzing the agreement between multiple raters, for evaluating the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and for modeling heterogeneity in developmental trajectories. Despite the increased popularity of LC analysis, little is known about statistical power and required sample size in LC modeling. This paper shows how to perform power and sample size computations in LC models using Wald tests for the parameters describing association between the categorical latent variable and the response variables. Moreover, the design factors affecting the statistical power of these Wald tests are studied. More specifically, we show how design factors which are specific for LC analysis, such as the number of classes, the class proportions, and the number of response variables, affect the information matrix. The proposed power computation approach is illustrated using realistic scenarios for the design factors. A simulation study conducted to assess the performance of the proposed power analysis procedure shows that it performs well in all situations one may encounter in practice.

Suggested Citation

  • Dereje W. Gudicha & Fetene B. Tekle & Jeroen K. Vermunt, 2016. "Power and Sample Size Computation for Wald Tests in Latent Class Models," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 30-51, April.
    • Handle: RePEc:spr:jclass:v:33:y:2016:i:1:d:10.1007_s00357-016-9199-1
    DOI: 10.1007/s00357-016-9199-1
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    References listed on IDEAS

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    Cited by:

    1. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    2. Panayiota Metallidou & Dimitrios Stamovlasis, 2020. "University Students’ Perfectionistic Profiles: Do They Predict Achievement Goal Orientations and Coping Strategies?," Journal of Educational and Developmental Psychology, Canadian Center of Science and Education, vol. 10(2), pages 1-57, November.
    3. Anastasia Burkovskaya & Adam Teperski & Kadir Atalay, 2022. "Framing and insurance choices," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(2), pages 311-337, June.
    4. Douglas L. Steinley, 2018. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 1-4, April.

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