Missing completely at random

In statistical analysis, data-values in a data set are missing completely at random (MCAR) if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest.

Missing at random (MAR) is the alternative, suggesting that what caused the data to be missing does not depend upon the missing data itself. An example of this is accidentally omitting an answer on a questionnaire. Not missing at random (NMAR) is data that is missing for a specific reason. An example of this is if a question on a questionnaire has been skipped deliberately by the participant. This data (unlike MAR and MCAR) must be replaced or the entire case deleted before running any analyses.

See also

• Missing data

References

• Weiner, I. B., Freedheim, D.K., Velicer, W. F., Schinka, J. A., & Lerner, R. M. (2003). Handbook of Psychology. John Wiley and Sons: USA
• Little, Roderick J. A.; Rubin, Donald B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley. ISBN 0-471-18386-5. 

Further reading

• Heitjan, D. F.; Basu, S. (1996). "Distinguishing "Missing at Random" and "Missing Completely at Random"". The American Statistician 50 (3): 207–213. doi:10.2307/2684656. JSTOR 2684656.  edit