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A Test of Missing Completely at Random for Multivariate Data with Missing Values
Roderick J. A. Little
Journal of the American Statistical Association
Vol. 83, No. 404 (Dec., 1988), pp. 1198-1202
Stable URL: http://www.jstor.org/stable/2290157
Page Count: 5
You can always find the topics here!Topics: Statistics, Datasets, Missing data, Statistical models, Statistical estimation, Matrices, Covariance, Simulations, T tests, Standard error
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A common concern when faced with multivariate data with missing values is whether the missing data are missing completely at random (MCAR); that is, whether missingness depends on the variables in the data set. One way of assessing this is to compare the means of recorded values of each variable between groups defined by whether other variables in the data set are missing or not. Although informative, this procedure yields potentially many correlated statistics for testing MCAR, resulting in multiple-comparison problems. This article proposes a single global test statistic for MCAR that uses all of the available data. The asymptotic null distribution is given, and the small-sample null distribution is derived for multivariate normal data with a monotone pattern of missing data. The test reduces to a standard t test when the data are bivariate with missing data confined to a single variable. A limited simulation study of empirical sizes for the test applied to normal and nonnormal data suggests that the test is conservative for small samples.
Journal of the American Statistical Association © 1988 American Statistical Association