Abstract
AbstractThe classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581–592, 1976), is often not required for valid inference ignoring the missingness process. Neither are other assumptions sometimes believed to be necessary that result from misunderstandings of MAR. We discuss three strategies that allow us to use standard estimators (i.e., ignore missingness) in cases where missingness is usually considered to be non-ignorable: (1) conditioning on variables, (2) discarding more data, and (3) being protective of parameters.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,General Psychology
Reference43 articles.
1. Allison, P. D. (1987). Estimation of linear models with incomplete data. In C. C. Clogg (Ed.), Sociological methodology 1987 (pp. 71–103). Washington, DC: American Sociological Association.
2. Allison, P. D. (2000). Multiple imputation for missing data: A cautionary tale. Sociological Methods & Research, 28, 301–309.
3. Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage.
4. Anderson, T. W. (1957). Maximum likelihood estimates for the multivariate normal distribution when some observations are missing. Journal of the American Statistical Association, 52, 200–203.
5. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243–277). Mahwah, NJ: Erlbaum.
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