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 non-ignorable). 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 text-based 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.