At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study. (2nd November 2018)

• Record Type: Journal Article
• Authors: Hamaker, E. L.; Asparouhov, T.; Brose, A.; Schmiedek, F.; Muthén, B.
• Is Part Of: Multivariate behavioral research. Volume 53:Number 6(2018)
• Page Start: 820
• Page End: 841
• Publication Date: 2018-11-02
• Subjects: Dynamic structural equation modeling (DSEM) -- intensive longitudinal data -- dynamic multilevel modeling -- multilevel time series analysis; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2018.1446819
• Languages: English
• ISSNs: 0027-3171

Abstract

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.