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 EM approach 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.
Original language | English (US) |
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Pages (from-to) | 419-436 |
Number of pages | 18 |
Journal | Educational and Psychological Measurement |
Volume | 64 |
Issue number | 3 |
DOIs | |
State | Published - Jun 2004 |
Keywords
- EM algorithm
- Maximum likelihood
- Missing data
- Reliability
- Reliability generalization
ASJC Scopus subject areas
- Education
- Developmental and Educational Psychology
- Applied Psychology
- Applied Mathematics