Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus
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Summary
This article discusses alternatives to single-step mixture modeling for latent class predictor variables in several different settings, including latent class analysis, latent transition analysis, and growth mixture modeling.
- Type
- article
- Published
- 2014-07-03
- Cited by
- 3,390
- References
- 13
- OpenAlex
- https://openalex.org/W2147200107
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:122769054
Keywords
Latent variable, Latent class model, Class (philosophy), Mathematics, Latent variable model
References
- The Sage handbook of quantitative methodology for the social sciences
- Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach
- Residual Diagnostics for Growth Mixture Models
- Latent Class Modeling with Covariates: Two Improved Three-Step Approaches
- Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
- Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators
- Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches
- Multiple Imputation For Nonresponse In Surveys
- Multiple Imputation for Nonresponse in Surveys
- Latent Class Analysis Results to Variables not Included in the Analysis
- Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data
- Growth mixture modeling
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