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PreprintCited by 12Mental Health Research TopicsStatistical Methods and Bayesian InferencePsychometric Methodologies and Testing

A Primer on Two-Level Dynamic Structural Equation Models for Intensive Longitudinal Data

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McNeish, Daniel·2018
DOI 10.31234/osf.io/j56bm
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Abstract

Technological advances have led to an increase in intensive longitudinal data and the statistical literature on modeling such data is rapidly expanding, as are software capabilities. Common methods in this area are related to time-series analysis, a framework that historically has received little exposure in psychology. There is a scarcity of psychology-based resources introducing the basic ideas of time-series analysis, especially for datasets featuring multiple people. We begin with basics of N=1 time-series analysis and build up to complex dynamic structural equation models available in the newest release of Mplus. The goal is to provide readers with a basic conceptual understanding of common models, template code, and result interpretation. We provide short descriptions of some advanced issues, but our main priority is to supply readers with a solid knowledge base so that the more advanced literature on the topic is more readily digestible to a larger group of researchers

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  • Intensive longitudinal designs can help uncover potential real-time causal variance attributable to a construct that would be missed when it is measured at relatively lengthy intervals such as one academic semester or year.

    “Intensive longitudinal designs can help uncover potential”

    From Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda (2022) · cites this paper as McNeish & Hamaker, 2019

  • Non-linear developmental trends cannot be captured by low-frequent measurements.

    “This may also help when developmental trajectories are characterized by non-linear trends that cannot be captured by low-frequent measurements (McNeish & Hamaker, 2019).”

    From Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda (2022) · cites this paper as McNeish & Hamaker, 2019

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Research works printed but not linked: 5

  1. Asparouhov, T. & Muthén, B.O. (2015). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. In J.R. Harring, L.M. Stapleton & S.N. Beretvas (Eds.), Advances in Multilevel Modeling for Educational Research (pp. 163-192). Information Age Publishing.
  2. In A. Maydeu-Olivares & J.J. McArdle (Eds.), Advances in psychometrics: A Festschrift for Roderick P. McDonald (pp. 415-452).
  3. Croon, M. (2002). Using predicted latent scores in general latent structure models. In G. Marcoulides & I. Moustaki (Eds.), Latent variable and latent structure modeling (pp. 195-223).
  4. Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77, 1-35.
  5. Hamilton, J.D. (1994). Time Series Analysis. Princeton, NJ: Princeton University Press.

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Authors
McNeish, D.
Affiliations
Arizona State University (US)
Corresponding author
McNeish, Daniel
ORCID
McNeish, Daniel
Publisher
Center for Open Science
Published
2018-11-28
DOI
10.31234/osf.io/j56bm
OpenAlex
W4232723833
Cited by
12
References
84
Citations by year
2025: 6 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 1
Type
posted content
Language
en
Open access
Licence
Locations
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
  • OSF Preprints (OSF Preprints) (submittedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
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, time series analysis, N-of-1 design, Mplus, multilevel modeling, repeated measures

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