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) - Hecht, Martin; Zitzmann, Steffen
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.