ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2018, Vol. 26 ›› Issue (12): 2272-2280.doi: 10.3724/SP.J.1042.2018.02272

• Research Method • Previous Articles    

Regression mixture modeling: Advances in method and its implementation

WANG Meng-Cheng1,2,3(), BI Xiangyang4()   

  1. 1. Department of Psychology, Guangzhou University
    2. The Center for Psychometric and Latent Variable Modeling, Guangzhou University
    3. The Key Laboratory for Juveniles Mental Health and Educational Neuroscience in Guangdong Province, Guangzhou University, Guangzhou 510006, China
    4. School of Sociology, China University of Political Science and Law, Beijing 102249, China
  • Received:2017-03-04 Online:2018-12-15 Published:2018-10-30
  • Contact: Meng-Cheng WANG,Xiangyang BI;


The person-centered methods, including latent class analysis (LCA) and latent profile analysis (LPA), are increasingly popular in recent years. Researchers often add covariate variables (i.e., predictor and distal variables) into LCA and LPA models. This kind of models are also called regression mixture models. In this paper, we introduce several new methods. Those methods include (1) the LTB method proposed by Lanza, Tan and Bray (2013) to model categorical outcome variables; and (2) the BCH method proposed by Bolck, Croon and Hagenaars (2004) to deal with continuous distal variables. Using an empirical example, we demonstrate the process of analyses in Mplus. The future directions of those new methods were also discussed.

Key words: person-centered method, mixture modeling, latent class analysis, latent variable modeling, Mplus

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