At the frontiers of modeling intensive longitudinal data. Dynamic structural equation models for the affective measurements from the COGITO study.

Autor/inn/en	Hamaker, Ellen L.; Asparouhov, Tihomir; Brose, Annette; Schmiedek, Florian; Muthén, Bengt
Titel	At the frontiers of modeling intensive longitudinal data. Dynamic structural equation models for the affective measurements from the COGITO study.
Quelle	In: Multivariate behavioral research, 53 (2018) 6, S. 820-841 PDF als Volltext (1); PDF als Volltext (2) Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1532-7906
DOI	10.1080/00273171.2018.1446819
Schlagwörter	Längsschnittuntersuchung; Mehrebenenanalyse; Strukturgleichungsmodell; Zeitreihenanalyse; Datenanalyse; Daten; Modellierung
Abstract	With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2023/1