Selection of the Number of Participants in Intensive Longitudinal Studies: A User-Friendly Shiny App and Tutorial for Performing Power Analysis in Multilevel Regression Models That Account for Temporal Dependencies
Selection of the Number of Participants in Intensive Longitudinal Studies: A User-Friendly Shiny App and Tutorial for Performing Power Analysis in Multilevel Regression Models That Account for Temporal Dependencies
复制标题
DOI：
10.1177/2515245920978738
复制
发表时间：
2021-01-01
影响因子：
13.6
通讯作者：
Ceulemans, Eva
中科院分区：
文献类型：
--
作者：
Lafit, Ginette;Adolf, Janne K.;Ceulemans, Eva
近年来，经验抽样法等收集密集纵向数据的程序越来越受欢迎。使用这种设计收集的数据使研究人员能够研究心理功能的动态以及这些动态在个体之间的差异。为此，数据通常采用多级回归模型进行建模。当研究人员设计密集的纵向研究时，出现的一个重要问题是如何确定所需的参与者数量，以检验关于这些模型参数的特定假设。密集纵向研究的功效计算具有挑战性，因为分层数据结构中重复观察嵌套在个体中，并且因为这些数据中通常存在序列依赖性。因此，我们提出了一个用户友好的应用程序和一步一步的教程，用于对一组在密集的纵向研究中流行的模型进行基于模拟的功率分析。由于许多研究使用相同的采样方案（即，固定数量的至少近似等距的观察），我们假设该协议是固定的，并且关注参与者的数量。所有包含的模型都通过假设序列相关误差或包含自回归效应来明确解释数据中的时间依赖性。
In recent years, the popularity of procedures for collecting intensive longitudinal data, such as the experience-sampling method, has increased greatly. The data collected using such designs allow researchers to study the dynamics of psychological functioning and how these dynamics differ across individuals. To this end, the data are often modeled with multilevel regression models. An important question that arises when researchers design intensive longitudinal studies is how to determine the number of participants needed to test specific hypotheses regarding the parameters of these models with sufficient power. Power calculations for intensive longitudinal studies are challenging because of the hierarchical data structure in which repeated observations are nested within the individuals and because of the serial dependence that is typically present in these data. We therefore present a user-friendly application and step-by-step tutorial for performing simulation-based power analyses for a set of models that are popular in intensive longitudinal research. Because many studies use the same sampling protocol (i.e., a fixed number of at least approximately equidistant observations) within individuals, we assume that this protocol is fixed and focus on the number of participants. All included models explicitly account for the temporal dependencies in the data by assuming serially correlated errors or including autoregressive effects.