ISSN 1671-3710
CN 11-4766/R

心理科学进展 ›› 2022, Vol. 30 ›› Issue (8): 1703-1714.doi: 10.3724/SP.J.1042.2022.01703

• 国内心理统计方法研究热点回顾 • 上一篇    下一篇


方杰1, 温忠麟2(), 欧阳劲樱2, 蔡保贞2   

  1. 1广东财经大学新发展研究院/应用心理学系, 广州 510320
    2华南师范大学心理学院/心理应用研究中心, 广州 510631
  • 收稿日期:2021-12-29 出版日期:2022-08-15 发布日期:2022-06-23
  • 通讯作者: 温忠麟
  • 基金资助:

Methodological research on moderation effects in China’s mainland

FANG Jie1, WEN Zhonglin2(), OUYANG Jinying2, CAI Baozhen2   

  1. 1Institute of New Development & Department of Applied Psychology, Guangdong University of Finance & Economics, Guangzhou 510320, China
    2School of Psychology & Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
  • Received:2021-12-29 Online:2022-08-15 Published:2022-06-23
  • Contact: WEN Zhonglin


在心理学和其他社科研究领域, 大量实证文章建立调节效应模型, 以分析自变量对因变量的影响是如何随着调节变量的变化而改变。过去10多年, 调节效应分析成了方法学研究的一个热点。从显变量的调节效应、潜变量的调节效应、多层数据的调节效应、基于两层回归模型的单层调节分析、纵向数据的调节效应、调节和中介的整合模型六个主题系统地总结了国内调节效应分析的方法学研究的发展历程。最后对调节效应的未来研究方向做了讨论和拓展。

关键词: 调节效应, 潜变量, 类别变量, 多层数据, 纵向数据


The analysis of moderation effects has become an important statistical method in multivariate studies. Methodological research on moderation effects in China’s mainland covers the following topics: moderation effects of observed variables, latent variables, multi-level data and longitudinal data; the single-level moderation effect analysis based on a two-level regression model; the integration model of moderation and mediation (see Wen et al. 2022).

Methodological research on the moderation effect of observed variables includes three aspects: standardized resolution, simple slope test, and the moderation effect of category variables. The research on latent moderation includes three aspects too: standardized resolution, model simplification, and comparison of analytical methods. Under the normal condition, latent moderated structural equations (LMS) are recommended to estimate the moderation effect of latent variables. Otherwise, after centralizing all indicators, the unconstrained product indicator method is recommended to establish a latent moderation model; Bayesian method is an alternative, especially in the case of a small sample.

The model development of multilevel moderation effect involves the conflated multilevel model, unconflated multilevel model (UMM), and multilevel structural equation model (MSEM). All independent variables at Level-1 are not centered in the conflated multilevel model, whereas in the UMM all independent variables at level-1 are centered using group-mean, and the group mean is included at Level-2. If the group-mean was treated as a latent variable, MSEM is recommended. Further, two ways are adopted to test multilevel moderation in the multilevel structural equation model: random coefficient prediction (RCP) for cross-level moderations, and LMS for same-level moderations.

The moderation effect analysis of longitudinal data is divided into three types. The first type is moderation analysis in two-instance repeated measures designs, in which only the dependent variable is repeated measurement. In the second type, there isn’t any moderator, while both the independent and dependent variables are repeated measurement (e.g., the cross-lagged model, and the contextual moderation model). In the third type, all variables are repeated measurement, such as the latent growth model and multilevel model.

Two-level regression model is recommended to analyze the moderation effect of single-level data. It can be employed to analyze the moderation effect of both observed variables and latent variables.

Some international frontiers of methodological research on moderation analysis are briefly introduced: the combination of LMS and Bayesian method, moderation analysis of multiple moderators; moderation analysis of longitudinal data.

Key words: moderating effect, latent variable, categorical variable, multilevel data, longitudinal data