ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2022, Vol. 30 ›› Issue (8): 1715-1733.doi: 10.3724/SP.J.1042.2022.01715

• Section of Research Methods • Previous Articles     Next Articles

Methodological research and model development on structural equation models in China’s mainland from 2001 to 2020

WANG Yang1, WEN Zhonglin2(), LI Wei3,4, FANG Jie5   

  1. 1School of Public Administration, Guangdong University of Finance, Guangzhou 510521, China
    2School of Psychology/Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
    3School of Education Science and Technology, Northwest Minzu University, Lanzhou 730124, China
    4Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
    5Institute of New Development & Department of Applied Psychology, Guangdong University of Finance & Economics, Guangzhou, 510320, China
  • Received:2021-12-28 Online:2022-08-15 Published:2022-06-23
  • Contact: WEN Zhonglin


Structural equation modeling (SEM) is an important statistical method in social science research. In the first two decades of the 21st century, great progress has been made in methodological research on SEM in China’s mainland. The publications cover five aspects: model development, parameter estimation, model evaluation, measurement invariance and the special data processing in SEM.

SEM development includes the research on measurement models, structural models, and complete models, as well as the SEM in population heterogeneity studies and longitudinal studies. The research on the measurement models involves bi-factor model, exploratory structural equation model, measurement models for special design (e.g., random intercept factor analysis model, fixed-links model, and the Thurston model), and formative measurement models. The research on the structural models involves the actor-partner interdependence model. The research on the complete models focuses on item parceling. The SEM in the study of population heterogeneity involves latent class/profile model, factor mixture model, and multi-level latent class model. The SEM in longitudinal studies includes models describing development trajectories and differences, such as the latent growth model, the piecewise growth model, the latent class growth model, the growth mixture model, the piecewise growth mixture model, the latent transition model and the cross-lagged model.

The publications on parameter estimation methods mainly involve the introduction of methodology (including the partial least square method and the Bayesian method) and the comparison of different parameter estimation methods. Advances in the model evaluation include fit indices and their corresponding critical values, selection of fit indices, model evaluation criteria beyond fit indices, and comparison and selection among alternative models. The development of measurement invariance involves three topics: (1) the introduction of different models with testing process and model evaluation criteria for measurement invariance analysis; (2) measurement invariance analysis in a particular model or data (e.g., second order factor model and ordered categorical data); (3) new methods of measurement invariance analysis (e.g., alignment and projection method). In addition, research into special data processing methods in SEM addresses issues of missing data, non-continuous data, non-normal data, and latent variable scores.

Finally, recent advances in SEM methodological research abroad are introduced to help researchers understand some cutting-edge topics in this field, which offers implications for future directions of SEM methodological research.

Key words: structural equation model, model development, parameter estimation, model evaluation, measurement invariance

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