Little's test of missing completely at random

Cheng Li ()
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Cheng Li: Northwestern University

Stata Journal, 2013, vol. 13, issue 4, 795-809

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. Copyright 2013 by StataCorp LP.

Keywords: mcartest; CDM; MAR; MCAR; MNAR; chi-squared; missing data; missing-value patterns; multivariate; power (search for similar items in EconPapers)
Date: 2013
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