Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…
By Andrew F. Hayes, Jacob J Coutts. 2020
2,324 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Social Psychology
View Paper: https://www.semanticscholar.org/paper/5f87861a7f1992009fdf0aa943bd9df914b2f1bf
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In Computer Networks and Communications
In Management Science and Operations Research
In Social Psychology
Citations in the corpus
- Alpha, Omega, and H Internal Consistency Reliability Estimates: Reviewing These Options and When to Use Them (2021, methodology): “Coefficient omega is a generalized form of coefficient alpha, as omega is robust to most of alpha’s statistical assumptions and offers more practical utility when compared to alpha since alpha is just a unique case of omega (Hayes & Coutts, 2020; McDonald, 1999).”