异方差多水平模型的精确标准误差:一种计算效率更高的折刀技术

Psych Pub Date : 2023-07-21 DOI:10.3390/psych5030049
Steffen Zitzmann, Sebastian Weirich, Martin Hecht
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引用次数: 1

摘要

在随机效应模型、层次线性模型或多层模型中,通常假设高层单元内的方差是同方差的,这意味着它们在这些单元之间是相等的。然而,这一假设在研究中经常被违背。根据违反的程度,这可能导致更高级别参数的偏差标准误差,从而导致不正确的推断。在这篇文章中,我们描述了一种获得标准误差的重采样技术——齐兹曼折刀。我们进行了蒙特卡罗模拟研究,将该技术与常用的delete-1折刀、Mplus中的鲁棒标准误差以及常用的delete-1折刀的改进版本进行了比较。研究结果表明,在具有高水平异方差的相当小的样本中,重采样技术明显优于稳健的标准误差。此外,Zitzmann的折刀比两个版本的delete-1折刀表现得更好,速度也快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accurate Standard Errors in Multilevel Modeling with Heteroscedasticity: A Computationally More Efficient Jackknife Technique
In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster.
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