Strong Convergence of the Coefficient Alpha Estimator for Reliability of Multiple-Component Measuring Instruments. Issue 3 (4th May 2019)

• Record Type: Journal Article
• Authors: Raykov, Tenko
• Is Part Of: Structural equation modeling. Volume 26:Issue 3(2019)
• Page Start: 430
• Page End: 436
• Publication Date: 2019-05-04
• Subjects: Coefficient alpha -- congeneric model -- convergence in probability -- measuring instrument -- population discrepancy -- reliability -- strong convergence -- weak convergence; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1515019
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : It is shown that in general the popular coefficient alpha estimator for reliability of multi-component measuring instruments converges almost surely to a quantity that is not equal to the population reliability coefficient. This convergence with probability 1 is a stronger statement than convergence in probability (consistency) and convergence in distribution for the alpha estimator, which have been studied in the past. In the special case of congeneric measures with uncorrelated errors and equal loadings on the common true score, the alpha estimator converges almost surely to the population reliability coefficient that equals population alpha, which implies also its consistency as a reliability estimator. When the loadings are unequal but sufficiently high and similar, the alpha estimator converges almost surely to population alpha that is essentially indistinguishable from the population reliability coefficient, which implies alpha's approximate consistency then. For the general case, the results entail that the alpha estimator is not a consistent estimator of reliability. The findings add to the critical literature on coefficient alpha in the general case, as well as to the justification of its use as a dependable measuring instrument reliability estimator in special cases and settings resulting under appropriate restrictive conditions, and are illustrated using a numerical example.