Abstract
A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that the EMapproach yields (a) less bias in reliability estimates, (b) dramatically reduces cross-sample fluctuation of estimates, and (c) yields more accurate confidence intervals. Implications for reliability reporting practices are discussed, and the EM procedure is demonstrated using a heuristic data set.
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