摘要
目前调节效应检验主要是基于截面数据,本文讨论纵向(追踪)数据的调节效应分析。如果自变量X和因变量Y有纵向数据,调节效应可分为三类:调节变量Z不随时间变化、Z随时间变化、调节变量从自变量或因变量中产生。评介了基于多层模型、多层结构方程模型、交叉滞后模型和潜变量增长模型的纵向数据的多种调节效应分析方法。调节效应的分解和潜调节结构方程法的使用是纵向数据的调节效应分析的两大特点。对基于四类模型的调节效应分析方法进行综合比较后,总结出一个纵向数据的调节效应分析流程。随后用实际例子演示如何进行纵向数据的调节效应分析,并给出相应的Mplus程序。随后展望了纵向数据的调节效应分析的拓展方向,例如基于动态结构方程模型的密集追踪数据的调节效应分析。
At present,the analysis of moderating effect is mainly based on cross sectional data.This article discusses how to analyze the moderating effect with longitudinal data.If the independent variable X and the dependent variable Y are longitudinal data,longitudinal moderation model can be divided into three categories according to the type of moderator:time-invariant moderator,time-variant moderator,and moderator generated from X or Y.Four types of longitudinal moderation analysis approaches are summarized:1)Multilevel model(MLM);2)Multilevel structural equation model(MSEM);3)Cross-lagged model(CLM);4)Latent growth model(LGM).It is found that the decomposition of the moderating effect and the use of latent moderating structural equation(LMS)method are the two characteristics of the moderation analysis for longitudinal data.A procedure for longitudinal moderation analysis is proposed.An example is illustrated with the software Mplus.Directions for future research on moderation analysis of longitudinal data are discussed,such as the moderation analysis for intensive longitudinal data based on the dynamic structural equation model.
作者
方杰
温忠麟
FANG Jie;WEN Zhonglin(Institute of New Development&Department of Applied Psychology,Guangdong University of Finance&Economics,Guangzhou 510320,China;Center for Studies of Psychological Application&School of Psychology,South China Normal University,Guangzhou 510631,China)
出处
《心理科学进展》
CSSCI
CSCD
北大核心
2022年第11期2461-2472,I0001,I0002,I0003,共15页
Advances in Psychological Science
基金
国家自然科学基金项目(32171091)
国家社会科学基金项目(17BTJ035)资助。
关键词
纵向数据
调节效应
多层模型
多层结构方程模型
交叉滞后模型
潜变量增长模型
longitudinal data
moderation effect
multilevel model
multilevel structural equation model
cross-lagged model
latent growth model
