CoChat
Sign Up
Cited by 67Mental Health Research TopicsStatistical Methods and Bayesian InferencePsychometric Methodologies and Testing

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

Open access
Ryan, Oisín, Kuiper, Rebecca M., Hamaker, Ellen L.·2018·Continuous Time Modeling in the Behavioral and Related Sciences
DOI 10.1007/978-3-319-77219-6_2

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

Integrity signals

Registry

Crossref · 10.1007/978-3-319-77219-6_2

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Methods exist to consider associations on multiple time scales, but they further increase the complexity of person-specific networks, which are already difficult for patients to understand.

    “There are methods to consider associations on multiple time scales [43]. However, such methods further increase the complexity of PSNs, which are already difficult to understand for patients.”

    From Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway (2020) · cites this paper as [43]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Ryan, O., Kuiper, R. M., & Hamaker, E. L.
Affiliations
Utrecht University (NL)
Corresponding author
Ryan, Oisín
ORCID
Venue
Continuous Time Modeling in the Behavioral and Related Sciences
Publisher
Springer International Publishing
Published
2018
Pages
27-54
DOI
10.1007/978-3-319-77219-6_2
OpenAlex
W2895832607
Cited by
67
References
60
Citations by year
2026: 5 · 2025: 9 · 2024: 10 · 2023: 12 · 2022: 9 · 2021: 11 · 2020: 7 · 2019: 1 · 2018: 3
FWCI
31.79
Type
book chapter
Language
en
Open access
Licence
Locations
  • Springer eBooks (publishedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • PsyArXiv (OSF Preprints) (submittedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, public-domain)
  • Utrecht University Repository (Utrecht University) (submittedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Keywords
intensive longitudinal data, ecological momentary assessment, cognitive processes, dynamical systems, impulse response functions, vector fields

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review