A general non-linear multilevel structural equation mixture model

Frontiers in Psychology

Augustin Kelava¹ 

关键词: latent variables;    semiparametric;    non-linear;    mixture distribution;    structural equation modeling;    multilevel;   

DOI  :  10.3389/fpsyg.2014.00748

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, 2000), and multilevel modeling (Rabe-Hesketh et al., 2004). We present a general non-linear multilevel structural equation mixture model (GNM-SEMM) that combines recent semiparametric non-linear structural equation models (Kelava and Nagengast, 2012; Kelava et al., 2014) with multilevel structural equation mixture models (Muthén and Asparouhov, 2009) for clustered and non-normally distributed data. The proposed approach allows for semiparametric relationships at the within and at the between levels. We present examples from the educational science to illustrate different submodels from the general framework.

【 授权许可】

CC BY