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, and occur entirely at random. When data are MCAR, the analyses performed on the data are unbiased; however, data are rarely MCAR.
Missing at random (MAR) is an alternative, and occurs when the missingness is related to a particular variable, but it is not related to the value of the variable that has missing data.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 (ie. the value of the variable that's missing is related to the reason it's missing). An example of this is if certain question on a questionnaire tend to be skipped deliberately by participants with certain characteristics.
See also 
- Polit DF Beck CT (2012). Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th ed. Philadelphia, USA: Wolters Klower Health, Lippincott Williams & Wilkins.
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.
- 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.
|This statistics-related article is a stub. You can help Wikipedia by expanding it.|