Structural Equation Modeling A Multidisciplinary Journal· 2014Q1
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M<i>plus</i>
- 3,399citations
- Q1SCImago
- 2014year
Short summary
A three-step approach for incorporating auxiliary variables in mixture modeling is validated, showing it performs well even when its assumptions are violated, such as with direct effects from predictors to latent class indicators.
AI-generated from the title and abstract; the full text is not read.
Key points
- A three-step method for latent class predictor variables is effective in mixture modeling.
- The method performs well even when assumptions are violated, such as direct predictor effects on indicators.
- The Lanza, Tan, and Bray (2013) method for distal variables is analyzed under various conditions.
- Standard errors are developed for the Lanza method, addressing a previous limitation.
AI-generated from the title and abstract; the full text is not read.
Abstract
This article discusses alternatives to single-step mixture modeling. A 3-step method for latent class predictor variables is studied in several different settings, including latent class analysis, latent transition analysis, and growth mixture modeling. It is explored under violations of its assumptions such as with direct effects from predictors to latent class indicators. The 3-step method is also considered for distal variables. The Lanza, Tan, and Bray (2013) method for distal variables is studied under several conditions including violations of its assumptions. Standard errors are also developed for the Lanza method because these were not given in Lanza et al. (2013).
The authors' abstract, as published at the source. Structural Equation Modeling A Multidisciplinary Journal, 2014 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics