Coefficient alpha and related internal consistency reliability coefficients.

TLDR

The study examined when coefficient alpha and related reliability coefficients underestimate a measure’s reliability. Simulations revealed that while alpha is generally robust, it can seriously underestimate reliability and lead to overestimated corrected correlations; most other coefficients performed poorly, except stratified alpha, maximal reliability, and maximized lambda4, which yielded more accurate estimates.

Abstract

The author studied the conditions under which coefficient alpha and 10 related internal consistency reliability coefficients underestimate the reliability of a measure. Simulated data showed that alpha, though reasonably robust when computed on n components in moderately heterogeneous data, can under certain conditions seriously underestimate the reliability of a measure. Consequently, alpha, when used in corrections for attenuation, can result in nontrivial overestimation of the corrected correlation. Most of the coefficients studied, including lambda 2, did not improve the estimate to any great extent when the data were heterogeneous. The exceptions were stratified alpha and maximal reliability, which performed well when the components were grouped into two subsets, each measuring a different factor, and maximized lambda 4, which provided the most consistently accurate estimate of the reliability in all simulations studied.