Methods and Statistics, Research Institute of Child Development and Education, University of Amsterdam, Nieuwe Achtergracht 127, 1018, WS, Amsterdam, The Netherlands.
Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands.
Behav Res Methods. 2021 Aug;53(4):1385-1406. doi: 10.3758/s13428-020-01479-0. Epub 2020 Nov 2.
Conducting a power analysis can be challenging for researchers who plan to analyze their data using structural equation models (SEMs), particularly when Monte Carlo methods are used to obtain power. In this tutorial, we explain how power calculations without Monte Carlo methods for the χ test and the RMSEA tests of (not-)close fit can be conducted using the Shiny app "power4SEM". power4SEM facilitates power calculations for SEM using two methods that are not computationally intensive and that focus on model fit instead of the statistical significance of (functions of) parameters. These are the method proposed by Satorra and Saris (Psychometrika 50(1), 83-90, 1985) for power calculations of the likelihood ratio test, and that described by MacCallum, Browne, and Sugawara (Psychol Methods 1(2) 130-149, 1996) for RMSEA-based power calculations. We illustrate the use of power4SEM with examples of power analyses for path models, factor models, and a latent growth model.
对于计划使用结构方程模型 (SEM) 分析数据的研究人员来说，进行功效分析可能具有挑战性，特别是当使用蒙特卡罗方法来获得功效时。在本教程中，我们将解释如何使用名为“power4SEM”的 Shiny 应用程序，在没有蒙特卡罗方法的情况下，针对 χ 检验和 (不) 接近拟合的 RMSEA 检验进行功效计算。power4SEM 通过两种方法为 SEM 进行功效计算，这两种方法计算量不大，并且侧重于模型拟合，而不是参数的统计显著性 (函数)。这些方法是 Satorra 和 Saris（Psychometrika 50(1)，83-90，1985）提出的用于似然比检验功效计算的方法，以及 MacCallum、Browne 和 Sugawara（Psychol Methods 1(2)，130-149，1996）提出的基于 RMSEA 的功效计算方法。我们将通过路径模型、因子模型和潜在增长模型的功效分析示例来说明 power4SEM 的使用。
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