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.

摘要

交叉滞后面板模型已普遍应用于研究变量的动态相互作用。在这种离散时间模型中，交叉滞后效应的大小取决于测量时机之间的时间间隔长度。连续时间建模允许探索交叉滞后效应的这种间隔依赖性，从而确定最大的“峰值”交叉滞后效应。为了检测这些峰值效应，需要足够的统计功效。根据模拟研究的结果，我们采用机器学习算法来识别高度准确的预测模型。结果被合并到一个 Shiny 应用程序中（可在 https://psychtools.shinyapps.io/ContinuousTimePowerCalculation 获得），以便于计算功率。尽管存在局限性，但我们的结果可能有助于研究计划。