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

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 r...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Statistics in medicine Ročník 41; číslo 3; s. 471 - 482
Hlavný autor: Nestler, Steffen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Wiley Subscription Services, Inc 10.02.2022
Predmet:
Bayes Theorem
Computer Simulation
Ecological Momentary Assessment
experience sampling data
Growth models
Humans
levels
mixed‐effects models
within‐person autocorrelation
within‐person variance
within-person variance
mixed-effects models
experience sampling data
levels
within-person autocorrelation
ISSN:0277-6715, 1097-0258, 1097-0258
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.9280