Abstract
Possible time trends are a common violation of the stationarity assumption, which is crucial in intensive longitudinal data analysis. Here we focus on the detrending issue in intensive longitudinal dyadic data analysis using Dynamic Structural Equation Modeling (DSEM). We first adjusted Savord et al. (2023) DSEM extension of the Actor-Partner Interdependence Model to better capture the interdependence between dyad members. Based on the adjusted model, using a simulation study, we investigated the influence of ignoring trends and compared two detrending practices—using residual DSEM (RDSEM) to separate time effects from the within-level autoregression or adding time covariate in autoregressive equations. Recommendations about whether and how to detrend are discussed.
Published Version
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