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The Stata Journal
Volume 13 Number 4: pp. 795-809

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Little's test of missing completely at random

Cheng Li
Northwestern University
Evanston, IL
Abstract.  In missing-data analysis, Little’s test (1988, Journal of the American Statistical Association 83: 1198–1202) is useful for testing the assumption of missing completely at random for multivariate, partially observed quantitative data. I introduce the mcartest command, which implements Little’s missing completely at random test and its extension for testing the covariate-dependent missingness. The command also includes an option to perform the likelihood-ratio test with adjustment for unequal variances. I illustrate the use of mcartest through an example and evaluate the finite-sample performance of these tests in simulation studies.
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View all articles with these keywords: mcartest, CDM, MAR, MCAR, MNAR, chi-squared, missing data, missing-value patterns, multivariate, power

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