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Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus

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Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus. / Speyer, Lydia Gabriela; Murray, Aja Louise; Kievit, Rogier.
In: Multivariate Behavioral Research, 14.02.2024.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Speyer LG, Murray AL, Kievit R. Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus. Multivariate Behavioral Research. 2024 Feb 14. Epub 2024 Feb 14. doi: 10.1080/00273171.2023.2288575

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@article{979d188389184584b9ca04c8bae90341,
title = "Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus",
abstract = "Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person{\textquoteright}s level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.",
keywords = "Arts and Humanities (miscellaneous), Experimental and Cognitive Psychology, General Medicine, Statistics and Probability",
author = "Speyer, {Lydia Gabriela} and Murray, {Aja Louise} and Rogier Kievit",
year = "2024",
month = feb,
day = "14",
doi = "10.1080/00273171.2023.2288575",
language = "English",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Psychology Press Ltd",

}

RIS

TY - JOUR

T1 - Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data

T2 - A Two-Level Dynamic Structural Equation Modelling Approach in Mplus

AU - Speyer, Lydia Gabriela

AU - Murray, Aja Louise

AU - Kievit, Rogier

PY - 2024/2/14

Y1 - 2024/2/14

N2 - Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person’s level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.

AB - Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person’s level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.

KW - Arts and Humanities (miscellaneous)

KW - Experimental and Cognitive Psychology

KW - General Medicine

KW - Statistics and Probability

U2 - 10.1080/00273171.2023.2288575

DO - 10.1080/00273171.2023.2288575

M3 - Journal article

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

ER -