Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects. Issue 6 (2nd November 2021)

• Record Type: Journal Article
• Authors: Hecht, Martin; Zitzmann, Steffen
• Is Part Of: Structural equation modeling. Volume 28:Issue 6(2021)
• Page Start: 894
• Page End: 902
• Publication Date: 2021-11-02
• Subjects: Continuous-time modeling -- cross-lagged effects -- statistical power -- time series; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1914627
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Cross-lagged panel models have been commonly applied to investigate the dynamic interplay of variables. In such discrete-time models, the size of the cross-lagged effects depends on the length of the time interval between the measurement occasions. Continuous-time modeling allows to explore this interval dependence of cross-lagged effects and thus to identify the maximal "peak" cross-lagged effects. To detect these peak effects, sufficient statistical power is needed. Based on results from a simulation study, we employed machine learning algorithms to identify a highly accurate prediction model. Results are incorporated into a Shiny App (available at https://psychtools.shinyapps.io/ContinuousTimePowerCalculation ) for easy power calculations. Although limitations apply, our results might be helpful for study planning.