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The detection and modeling of direct effects in latent class analysis

Janssen, Jeroen H. M., van Laar, Saskia, de Rooij, Mark J., Kuha, Jouni ORCID: 0000-0002-1156-8465 and Bakk, Zsuzsa (2018) The detection and modeling of direct effects in latent class analysis. Structural Equation Modeling. ISSN 1070-5511

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Identification Number: 10.1080/10705511.2018.1541745


Several approaches have been proposed for latent class modeling with external variables, including one-step, two-step and three-step estimators. However, very little is known yet about the performance of these approaches when direct effects of the external variable to the indicators of latent class membership are present. In the current article, we compare those approaches and investigate the consequences of not modeling these direct effects when present, as well as the power of residual and fir statistics to identify such effects. The results of the simulations show that not modeling direct effect can lead to severe parameter bias, especially with a weak measurement model. Both residual and fit statistics can be used to identify such effects, as long as the number and strength of these effects is low and the measurement model is sufficiently strong.

Item Type: Article
Official URL:
Additional Information: © 2018 Taylor & Francis Group, LLC
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Date Deposited: 29 Oct 2018 10:00
Last Modified: 20 Oct 2021 03:09

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