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Cited by 751

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach

Open access
Lanza, Stephanie T., Tan, Xianming, Bray, Bethany C.·2013·Structural Equation Modeling: A Multidisciplinary Journal
DOI 10.1080/10705511.2013.742377
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Abstract

Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided.

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Metadata

Authors
Lanza, S. T., Tan, X., & Bray, B. C.
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Publisher
Informa UK Limited
Published
2013-01-01
Volume
20
Issue
1
Pages
1-26
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
DOI
10.1080/10705511.2013.742377
OpenAlex
W2011949901
Cited by
751
References
11
FWCI
45.34
Type
journal article
Language
en
Open access
green

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Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2013.742377

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