The Impact of Missing Data on Sample Reliability Estimates: Implications for Reliability Reporting Practices
Enders, C. K.
Educational and Psychological Measurement 64(3): 419-436
2004
ISSN/ISBN: 0013-1644 DOI: 10.1177/0013164403261050Document Number: 472294
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.