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journal article Open Access Dec 26, 2021

An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation

Steffen Nestler ORCID
Statistics in Medicine Vol. 41 No. 3 pp. 471-482 · Wiley
View at Publisher Save 10.1002/sim.9280
Abstract
Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed‐effects model that allows to examine hypotheses concerning between‐person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that—in addition to the random effects for the mean structure—also includes a random effect for the within‐subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach.
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Metrics
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Citations
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References
Details
Published
Dec 26, 2021
Vol/Issue
41(3)
Pages
471-482
License
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Authors
S
Steffen Nestler

Statistik und Psychologische Methoden, Institut für Psychologie Universität Münster Münster Germany

Cite This Article
Steffen Nestler (2021). An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation. Statistics in Medicine, 41(3), 471-482. https://doi.org/10.1002/sim.9280
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