Intensive Longitudinal Moderation Analysis Based on Dynamic Structural Equation Models
(1. Institute of New Development, Guangdong University of Finance & Economics, Guangzhou 510320, China;
2. Center for Studies of Psychological Application & School of Psychology, South China Normal University, Guangzhou 510631, China;
3. School of Psychology, Shaanxi Normal University, Xi’an 710062, China;
4. School of Education, Ningxia University, Yinchuan 750021, China;
5. Department of Psychology, School of Educational Sciences, Hunan Normal University; Cognition and Human Behavior Key Laboratory
of Hunan Province, Changsha, 410081, China;
6. The faculty of economics, Guangdong University of Finance & Economics, Guangzhou 510320, China)
Abstract：Using dynamic structural equation models (DSEM) to conduct moderation analysis of intensive longitudinal data can deeply investigate how the dynamic relationships (i.e., autoregressive effects and lagged effects) between variables change with the variation of the moderator over a short period of time. This paper discusses in detail how to construct four intensive longitudinal moderation models (i.e., 2×(1→1), 1×(2→1), 2×(2→1), and 1×(1→1) model) based on DSEM. Two examples (with Mplus programs as Appendixes) are employed for the purpose of demonstration, one is cross-level moderation (e.g., 2×(1→1) model), and the other is within-individual level moderation (e.g., 1×(1→1) model). Finally, the direction of extension for moderation analysis of intensive longitudinal data is discussed.
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