May 2025
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264 Reads
Acta Psychologica Sinica
Intensive longitudinal data (ILD) is increasing in fields such as psychology and management, yet research on analytical methods for ILD remains relatively scant. Traditionally, the ILD is statistically modeled as a two-level structure, with Level 1 being the time and Level 2 being individuals. Especially, existing analytical methods treat longitudinal moderated mediation as multilevel moderated mediation, without considering the lagged relationship between variables. A possible solution is to use dynamic structural equation modeling (DSEM) for ILD moderated mediation analysis. DSEM has recently been used for analyzing intensive longitudinal mediation (ILMed; McNeish & MacKinnon, 2022; Fang et al., 2024) and intensive longitudinal moderation (ILMod; Speyer et al., 2024). However, it remains unclear how DSEM can be employed in analyzing intensive longitudinal moderated mediation (ILMM). The purpose of this paper is to combine ILMed and ILMod based on DSEM and propose a method of moderated mediation analysis that takes into account the temporal order between variables. For the 1-1-1 ILMed model where all variables are measured at Level 1 (i.e., all variables are ILD), it might be moderated by variables of Level 1 or Level 2. However, for the 2-1-1 ILMed model (i.e., only the independent variable is measured at Level 2) and the 2-2-1 ILMed model (i.e., only the dependent variable is measured at Level 1), they could only be moderated by variables of Level 2. Therefore, there are four basic types of ILMM models: 2-1-1 ILMed moderated by a level 2 moderator, 2-2-1 ILMed moderated by a level 2 moderator, 1-1-1 ILMed moderated by a level 2 moderator, and 1-1-1 ILMed moderated by a level 1 moderator. This paper describes in detail how to construct the above four ILMM models with DSEM, so that empirical researchers can understand which kind of ILMM model meets their needs and how to analyze it. Mplus codes for analyzing all these ILMM models are provided. A simulation study is conducted to examine the estimation accuracy of the 1-1-1 ILMed moderated by a level 2 moderator, with the following factors taken into account: sample size (N), number of time points (T), indirect effect sizes, and Level-2 variances and covariances. Results show that the estimates for the average mediation effect components (a and b) and the average mediation effect are generally accurate when N≥100 and T≥10. However, a sufficiently large N and T (e.g., T≥20) are required in order to obtain accurate estimation of Level-2 variances. Lastly, we discuss assumptions and the extensions of ILMM based on DSEM. As usual, the models used in this paper are based on the assumption that the time series is stationary. Otherwise, residual DSEM can be employed to detrend in ILMM analysis.