Accuracy of Estimates and Statistical Power for Testing Meditation in Latent Growth Curve Modeling.
Accuracy of Estimates and Statistical Power for Testing Meditation in Latent Growth Curve Modeling.
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DOI：
10.1080/10705511.2011.557334
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Cheong J
中科院分区：
文献类型：
--
作者：
Cheong J
潜在生长曲线模型（LGCM）方法已越来越多地用于研究纵向中介。然而，在 LGCM 框架中评估调解时，人们对估计的准确性和统计功效知之甚少。为了解决这些问题，我们在不同条件下进行了模拟研究，包括样本量、中介效应的效应大小、测量次数和测量变量的 R2。总体而言，结果表明，在 LGCM 框架中测试中介作用时，需要相对较大的样本才能准确估计中介效应并具有足够的统计功效。讨论了设计研究纵向中介的指南以及提高估计准确性和统计功效的方法。
The latent growth curve modeling (LGCM) approach has been increasingly utilized to investigate longitudinal mediation. However, little is known about the accuracy of the estimates and statistical power when mediation is evaluated in the LGCM framework. A simulation study was conducted to address these issues under various conditions including sample size, effect size of mediated effect, number of measurement occasions, and R2 of measured variables. In general, the results showed that relatively large samples were needed to accurately estimate the mediated effects and to have adequate statistical power, when testing mediation in the LGCM framework. Guidelines for designing studies to examine longitudinal mediation and ways to improve the accuracy of the estimates and statistical power were discussed.