Treffer: Intensive Longitudinal Mediation in Mplus.

Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral research. These intensive longitudinal data differ in structure and focus relative to traditionally collected panel data. As a result, existing methodological resources do not necessarily extend to nuances present in the recent influx of intensive longitudinal data and designs. In this tutorial, we first cover potential limitations of traditional longitudinal mediation models to accommodate unique characteristics of intensive longitudinal data. Then, we discuss how recently developed dynamic structural equation models (DSEMs) may be well-suited for mediation modeling with intensive longitudinal data and can overcome some of the limitations associated with traditional approaches. We describe four increasingly complex intensive longitudinal mediation models: (a) stationary models where the indirect effect is constant over time and people, (b) person-specific models where the indirect effect varies across people, (c) dynamic models where the indirect effect varies across time, and (d) cross-classified models where the indirect effect varies across both time and people. We apply each model to a running example featuring a mobile health intervention designed to improve health behavior of individuals with binge eating disorder. In each example, we provide annotated Mplus code and interpretation of the output to guide empirical researchers through mediation modeling with this increasingly popular type of longitudinal data. Technological innovations have changed how researchers collect data. Historically, collecting data densely over time often required participants to frequently meet with the research team or rely heavily on recall. Now, smartphones and wearables make it much simpler and less invasive to collect dense streams of data on participants throughout the day and in real time. This change in data collection techniques has corresponded to changes in the types of research questions that can feasibly explored as well as approaches to statistical modeling. The focus of this article is on mediation modeling with these intensive longitudinal data. Mediation is a popular method to understand causal mechanisms of one variable on another through a third intermediary variable. Mediation is particularly useful with longitudinal data because there is temporal precedence such that it is clear which variables are causes and which variables are effects. However, the existing literature on longitudinal mediation mostly addresses more traditional data structures where each person is measured relatively few times over a longer period of time. This article discusses unique considerations when fitting mediation models to intensive longitudinal data that are increasingly common in psychology and behavioral sciences. The article provides multiple example analyses using a mobile health intervention and provides in-depth details about the statistical notation for these models, how the models are setup, how to fit the models in the Mplus software program, and how to interpret the results and the associated software output. [ABSTRACT FROM AUTHOR]

Volltext ist im Gastzugang nicht verfügbar. Melden Sie sich für Vollzugriff an.