Hecht, Martin
[Author];
Zitzmann, Steffen
[Author]
Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects
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Media type:
E-Article
Title:
Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects
Contributor:
Hecht, Martin
[Author];
Zitzmann, Steffen
[Author]
imprint:
Humboldt-Universität zu Berlin, 2021-05-14
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
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. ; Peer Reviewed