ABSTRACT Cronbach’s alpha (α) is a widely-used measure of reliability used to quantify the amount of random measurement error that exists in a sum score or average generated by a multi-item measurement scale. Yet methodologists have warned that α is not an optimal measure of reliability relative to its more general form, McDonald’s omega (). Among other reasons, that the computation of is not available as an option in many popular statistics programs and requires items loadings from a confirmatory factor analysis (CFA) have probably hindered more widespread adoption. After a bit of discussion of α versus , we illustrate the computation of using two structural equation modeling programs (Mplus and AMOS) and the MBESS package for R. We then describe a macro for SPSS and SAS (OMEGA) that calculates in two ways without relying on the estimation of loadings or error variances using CFA. We show that it produces estimates of that are nearly identical to when using CFA-based estimates of item loadings and error variances. We also discuss the use of the OMEGA macro for certain forms of item analysis and brief form construction based on the removal of items from a longer scale.

中文翻译：

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使用Omega而不是Cronbach的Alpha来评估可靠性。但…

摘要克伦巴赫（Cronbach）的alpha（α）是一种广泛使用的可靠性度量，用于量化由多个项目的度量标准生成的总和或平均值中存在的随机测量误差的数量。然而，方法学家警告说，相对于更普遍的形式麦当劳的omega（），α并不是可靠性的最佳度量。除其他原因外，在许多流行的统计程序中不能作为一种选择来计算，并且需要从确认因子分析（CFA）中加载项目可能阻碍了更广泛的采用。在对α与进行了一些讨论之后，我们说明了使用两个结构方程建模程序（Mplus和AMOS）以及针对R的MBESS程序包的计算。然后，我们描述了SPSS和SAS（OMEGA）的宏，该宏以两种方式进行计算，而无需依赖使用CFA进行的载荷或误差方差的估计。我们证明了它产生的估计值几乎与使用基于CFA的项目装载和误差差异估计值相同。我们还讨论了将OMEGA宏用于某些形式的项目分析和基于从较长范围内删除项目的简短形式构造。