Continuous-time modeling in prevention research: An illustration. (January 2021)

• Record Type: Journal Article
• Authors: Hecht, Martin; Voelkle, Manuel C.
• Is Part Of: International journal of behavioral development. Volume 45:Number 1(2021)
• Page Start: 19
• Page End: 27
• Publication Date: 2021-01
• Subjects: Continuous-time models -- longitudinal data -- autoregressive models -- cross-lagged models; Developmental psychology -- Periodicals; Behaviorism (Psychology) -- Periodicals; 150
• DOI: 10.1177/0165025419885026
• Languages: English
• ISSNs: 0165-0254

Abstract

The analysis of cross-lagged relationships is a popular approach in prevention research to explore the dynamics between constructs over time. However, a limitation of commonly used cross-lagged models is the requirement of equally spaced measurement occasions that prevents the usage of flexible longitudinal designs and complicates cross-study comparisons. Continuous-time modeling overcomes these limitations. In this article, we illustrate the use of continuous-time models using Bayesian and frequentist approaches to model estimation. As an empirical example, we study the dynamic interplay of physical activity and health, a classic research topic in prevention science, using data from the "Midlife in the United States (MIDUS 2): Daily Stress Project, 2004–2009." To help prevention researchers in adopting the approach, we provide annotated R scripts and a simulated data set based on the results from analyzing the MIDUS 2 data.