Structural equation modeling (SEM) is a widespread approach to test substantive hypotheses in psychology and other social sciences. However, most studies involving structural equation models neither report statistical power analysis as a criterion for sample size planning nor evaluate the achieved power of the performed tests. In this tutorial, we provide a step-by-step illustration of how a priori, post hoc, and compromise power analyses can be conducted for a range of different SEM applications. Using illustrative examples and the R package semPower, we demonstrate power analyses for hypotheses regarding overall model fit, global model comparisons, particular individual model parameters, and differences in multigroup contexts (such as in tests of measurement invariance). We encourage researchers to yield reliable—and thus more replicable—results based on thoughtful sample size planning, especially if small or medium-sized effects are expected. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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有关评估结构方程模型的统计功效和确定样本量的教程。

结构方程模型 (SEM) 是检验心理学和其他社会科学中实质性假设的一种广泛使用的方法。然而，大多数涉及结构方程模型的研究既没有将统计功效分析报告为样本量规划的标准，也没有评估所执行测试的实现功效。在本教程中，我们提供了先验、事后和折衷功效分析的分步说明可以针对一系列不同的 SEM 应用进行。使用说明性示例和 R 包 semPower，我们展示了关于整体模型拟合、全局模型比较、特定个体模型参数和多组上下文差异（例如测量不变性测试）的假设的功效分析。我们鼓励研究人员根据深思熟虑的样本量规划产生可靠的——因此更可复制的——结果，尤其是在预期中小规模效应的情况下。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）