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.[1] When data are MCAR, the analyses performed on the data are unbiased; however, data are rarely MCAR.[1]
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.[1]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).[1] An example of this is if certain question on a questionnaire tend to be skipped deliberately by participants with certain characteristics.
See also [edit]
References [edit]
Further reading [edit]
- 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. |
