Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data.

Abstract

A 2-step approach for obtaining internal consistency reliability estimates with item-level missing data is outlined. In the 1st step, a covariance matrix and mean vector are obtained using the expectation maximization (EM) algorithm. In the 2nd step, reliability analyses are carried out in the usual fashion using the EM covariance matrix as input. A Monte Carlo simulation examined the impact of 6 variables (scale length, response categories, item correlations, sample size, missing data, and missing data technique) on 3 different outcomes: estimation bias, mean errors, and confidence interval coverage. The 2-step approach using EM consistently yielded the most accurate reliability estimates and produced coverage rates close to the advertised 95% rate. An easy method of implementing the procedure is outlined.