Verdam M G E
Department of Methodology and Statistics, Institute of Psychology, Leiden University, P.O. Box 9555, 2300 RB, Leiden, The Netherlands.
Medical Psychology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.
Qual Life Res. 2024 May;33(5):1241-1256. doi: 10.1007/s11136-024-03605-3. Epub 2024 Mar 1.
Statistical power for response shift detection with structural equation modeling (SEM) is currently underreported. The present paper addresses this issue by providing worked-out examples and syntaxes of power calculations relevant for the statistical tests associated with the SEM approach for response shift detection.
Power calculations and related sample-size requirements are illustrated for two modelling goals: (1) to detect misspecification in the measurement model, and (2) to detect response shift. Power analyses for hypotheses regarding (exact) overall model fit and the presence of response shift are demonstrated in a step-by-step manner. The freely available and user-friendly R-package lavaan and shiny-app 'power4SEM' are used for the calculations.
Using the SF-36 as an example, we illustrate the specification of null-hypothesis (H) and alternative hypothesis (H) models to calculate chi-square based power for the test on overall model fit, the omnibus test on response shift, and the specific test on response shift. For example, we show that a sample size of 506 is needed to reject an incorrectly specified measurement model, when the actual model has two-medium sized cross loadings. We also illustrate power calculation based on the RMSEA index for approximate fit, where H and H are defined in terms of RMSEA-values.
By providing accessible resources to perform power analyses and emphasizing the different power analyses associated with different modeling goals, we hope to facilitate the uptake of power analyses for response shift detection with SEM and thereby enhance the stringency of response shift research.
目前，关于使用结构方程模型（SEM）进行反应转移检测的统计功效报告不足。本文通过提供与SEM反应转移检测方法相关的统计检验的功效计算实例和语法来解决这一问题。
针对两个建模目标说明了功效计算和相关样本量要求：（1）检测测量模型中的错误设定，（2）检测反应转移。逐步展示了关于（精确）整体模型拟合和反应转移存在的假设的功效分析。计算使用免费且用户友好的R包lavaan和闪亮应用程序“power4SEM”。
以SF - 36为例，我们说明了原假设（H）和备择假设（H）模型的设定，以计算基于卡方的整体模型拟合检验、反应转移综合检验和反应转移特定检验的功效。例如，我们表明，当实际模型有两个中等大小的交叉载荷时，需要506个样本量才能拒绝错误设定的测量模型。我们还说明了基于RMSEA指数的近似拟合功效计算，其中H和H是根据RMSEA值定义的。
通过提供进行功效分析的可获取资源，并强调与不同建模目标相关的不同功效分析，我们希望促进使用SEM进行反应转移检测的功效分析的采用，从而提高反应转移研究的严谨性。
