This article describes how to tests the null hypothesis that missing data is Missing Completely At Random (MCAR). A p.value of less than 0.05 is usually interpreted as being that the missing data is not MCAR (ie, is either Missing At Random or nonignorable). See What are the Different Types of Missing Data? for more information about MCAR and other types of missing data.
Requirements
A Displayr document with a data set.
Method
 Go to Anything > Data > Missing Data > Little's MCAR Test.
 Under Inputs > Variables select the variables you want to analyze and click Calculate to run the function.
OPTIONAL: You can adjust the following settings to modify the calculation and output:

Output
 Summary  Shows a nicely formatted table of the test results (default).
 R  The original textbased output from the LittleMCAR function.
 Variable names  Display Variable Names in the output, instead of Variable Labels.
 More decimal places  Display numeric values with 8 decimal places.
 Filter  The data is automatically filtered using any filters prior to estimating the model.
Technical details:
This test starts by using the EM algorithm to estimate the means and covariances. These estimates are approximate, and, consequently, the test is also approximate (it may get slightly different results on different computers).
This test does not take weights into account.
Acknowledgements:
Uses the LittleMCAR function from the R Package BaylorEdPsych.
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202.