NDLI: The Detection and Modeling of Direct Effects in Latent Class Analysis

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The Detection and Modeling of Direct Effects in Latent Class Analysis

Content Provider	Scilit
Author	Janssen, Jeroen H. M. Laar, Saskia Van Rooij, Mark J. De Kuha, Jouni Bakk, Zsuzsa
Copyright Year	2018
Description	Journal: Structural Equation Modeling: A Multidisciplinary Journal 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 fit 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.
Related Links	https://www.tandfonline.com/doi/pdf/10.1080/10705511.2018.1541745?needAccess=true
Ending Page	290
Page Count	11
Starting Page	280
ISSN	10705511
e-ISSN	15328007
DOI	10.1080/10705511.2018.1541745
Journal	Structural Equation Modeling: A Multidisciplinary Journal
Issue Number	2
Volume Number	26
Language	English
Publisher	Informa UK Limited
Publisher Date	2018-11-26
Access Restriction	Open
Subject Keyword	Journal: Structural Equation Modeling: A Multidisciplinary Journal Mathematical Social Sciences Direct Effects Latent Class Analysis Mixture Models Residual Statistics
Content Type	Text
Subject	Sociology and Political Science Economics, Econometrics and Finance Decision Sciences Modeling and Simulation

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