我是否真的需要多层模型？针对组内相关系数的可忽略效应显著性检验
Do I Even Need a Multilevel Model? Negligible Effect Significance Testing for Intraclass Correlation Coefficients
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中文总结 AI 辅助
本研究针对应用研究中多层模型选择的决策问题，提出基于F统计量的组内相关系数可忽略效应显著性检验框架，还提供利用设计效应定义可忽略值的指导及对应R代码。
中文摘要 AI 辅助
在应用研究中，是否采用多层模型（MLM）的决策通常是通过将无条件组内相关系数（ICC）的点估计值与某些推荐阈值（例如0.05）进行比较来指导的。然而，这种朴素方法未考虑抽样不确定性，也未为该决策提供正式的推断依据。本研究针对ICC引入了可忽略效应显著性检验（NEST，又称等价检验），提出了一个基于F统计量的ICC枢轴量来执行该检验的框架。此外，我们还提供了指导，帮助研究者利用设计效应来定义“可忽略”，以表征可容忍的方差膨胀水平，并提供了说明所提程序的R代码。
英文摘要
In applied research, the decision to utilize multilevel modeling (MLM) is commonly guided by comparing a point estimate of the unconditional intraclass correlation coefficient (ICC) against some recommended threshold (e.g., 0.05). This naive approach, however, fails to account for sampling uncertainty and provides no formal inferential justification for a decision. The current work introduces negligible effect significance testing (NEST; or equivalence testing) for the ICC, proposing a framework that performs this test using a pivotal quantity of the ICC based on the F statistic. Furthermore, we offer guidance to help researchers define "negligible" that utilizes the design effect to characterize a tolerable level of variance inflation. R code illustrating the proposed procedure is provided.
