Don’t Forget the Model in Your Model-based Reliability Coefficients: A Reply to McNeish ()

Abstract

McNeish (2018) advocates that researchers abandon coefficient alpha in favor of alternative reliability measures, such as the 1-factor reliability (coefficient omega), a total reliability coefficient based on an exploratory bifactor solution (“Revelle’s omega total”), and the glb (“greatest lower bound”). McNeish supports this argument by demonstrating that these coefficients produce higher sample values in several examples. We express three main disagreements with this article. First, we show that McNeish exaggerates the extent to which alpha is different from omega when unidimensionality holds. Second, we argue that, when unidimensionality is violated, most alternative reliability coefficients are model-based, and it is critical to carefully select the underlying latent variable model rather than relying on software defaults. Third, we point out that higher sample reliability values do not necessarily capture population reliability better: many alternative reliability coefficients are upwardly biased except in very large samples. We conclude with a set of alternative recommendations for researchers.

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