Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic...

Area of Science:

• Psychology
• Behavioral Science
• Data Science

Background:

• Intensive longitudinal data (ILD) collection is enhanced by technological advances.
• ILD offers insights into moment-to-moment psychological and behavioral dynamics.
• Within-person factors can act as moderators in psychological processes.

Purpose of the Study:

• To describe the implementation, testing, and interpretation of dynamically changing within-person moderation effects.
• To utilize two-level dynamic structural equation modeling (DSEM) for analyzing these effects.
• To provide researchers with tools for understanding complex within-person interactions.

Main Methods:

• Two-level dynamic structural equation modeling (DSEM) implemented in Mplus software.
• Analysis of within-person moderation effects using empirical data.
• Illustration with an example of social media use, loneliness, and depressive symptoms.

Main Results:

• The study demonstrates how to analyze dynamic within-person moderation effects.
• It provides annotated Mplus code for practical application.
• Researchers can better isolate, estimate, and interpret within-person interaction effects.

Conclusions:

• Dynamic structural equation modeling is a valuable tool for analyzing complex within-person processes in ILD.
• The methods and code facilitate the study of momentary psychological and behavioral dynamics.
• This approach advances the understanding of how changing internal states influence external experiences.