Department of Educational Psychology and Learning Systems, College of Education, Florida State University.
Psychol Methods. 2024 Apr;29(2):308-330. doi: 10.1037/met0000498. Epub 2022 May 23.
A scale to measure a psychological construct is subject to measurement error. When meta-analyzing correlations obtained from scale scores, many researchers recommend correcting for measurement error. I considered three caveats when correcting for measurement error in meta-analysis of correlations: (a) the distribution of true scores can be non-normal, resulting in violation of the normality assumption for raw correlations and Fisher's z transformed correlations; (b) coefficient alpha is often used as the reliability, but correlations corrected for measurement error using alpha can be inaccurate when some assumptions of alpha (e.g., tau-equivalence) are violated; and (c) item scores are often ordinal, making the disattenuation formula potentially problematic. Via three simulation studies, I examined the performance of two meta-analysis approaches-with raw correlations and z scores. In terms of estimation accuracy and coverage probability of the mean correlation, results showed that (a) considering the true-score distribution alone, estimation of the mean correlation was slightly worse when true scores of the constructs were skewed rather than normal; (b) when the tau-equivalence assumption was violated and coefficient alpha was used for correcting measurement error, the mean correlation estimates can be biased and coverage probabilities can be low; and (c) discretization of continuous items can result in biased estimates and undercoverage of the mean correlations even when tau-equivalence was satisfied. With more categories and/or items on a scale, results can improve whether tau-equivalence was met or not. Based on these findings, I gave recommendations for conducting meta-analyses of correlations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
一种用于测量心理结构的量表存在测量误差。当对量表分数的相关性进行元分析时，许多研究人员建议对测量误差进行校正。在对相关性的元分析中校正测量误差时，我考虑了三个注意事项：(a)真实分数的分布可能是非正态的，从而导致原始相关系数和 Fisher's z 转换相关系数违反正态性假设；(b)通常使用系数 alpha 作为可靠性，但当 alpha 的某些假设（例如 tau 等价）被违反时，使用 alpha 校正测量误差的相关系数可能不准确；(c)项目分数通常是有序的，这使得去衰减公式可能存在问题。通过三项模拟研究，我考察了两种元分析方法——原始相关系数和 z 分数——的表现。就平均相关系数的估计准确性和覆盖率概率而言，结果表明：(a)仅考虑真实分数分布，当结构的真实分数偏斜而不是正态时，平均相关系数的估计会稍微差一些；(b)当 tau 等价假设被违反且使用系数 alpha 校正测量误差时，平均相关系数的估计可能会有偏差，覆盖率可能会很低；(c)即使满足 tau 等价，连续项目的离散化也会导致偏倚估计和平均相关系数的覆盖不足。随着量表上的类别和/或项目增多，无论 tau 等价是否得到满足，结果都可以得到改善。基于这些发现，我提出了关于进行相关性元分析的建议。（PsycInfo 数据库记录（c）2024 APA，保留所有权利）。
