A Continuous-Time Approach to Intensive Longitudinal Data: What, Why, and How?

Oisín Ryan (), Rebecca M. Kuiper () and Ellen L. Hamaker ()
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Oisín Ryan: Utrecht University, Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences
Rebecca M. Kuiper: Utrecht University, Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences
Ellen L. Hamaker: Utrecht University, Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences

Chapter Chapter 2 in Continuous Time Modeling in the Behavioral and Related Sciences, 2018, pp 27-54 from Springer

Abstract: Abstract The aim of this chapter is to (a) provide a broad didactical treatment of the first-order stochastic differential equation model—also known as the continuous-time (CT) first-order vector autoregressive (VAR(1)) model—and (b) argue for and illustrate the potential of this model for the study of psychological processes using intensive longitudinal data. We begin by describing what the CT-VAR(1) model is and how it relates to the more commonly used discrete-time VAR(1) model. Assuming no prior knowledge on the part of the reader, we introduce important concepts for the analysis of dynamic systems, such as stability and fixed points. In addition we examine why applied researchers should take a continuous-time approach to psychological phenomena, focusing on both the practical and conceptual benefits of this approach. Finally, we elucidate how researchers can interpret CT models, describing the direct interpretation of CT model parameters as well as tools such as impulse response functions, vector fields, and lagged parameter plots. To illustrate this methodology, we reanalyze a single-subject experience-sampling dataset with the R package ctsem; for didactical purposes, R code for this analysis is included, and the dataset itself is publicly available.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-77219-6_2

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DOI: 10.1007/978-3-319-77219-6_2

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