Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
Donald B. Rubina b, Nathaniel Schenker Ac d
- aDepartment of Statistics, Harvard University, Cambridge, MA, 02138, USA
- bDepartment of Statistics, Harvard University, Cambridge, MA 02138 USA
- cStatistical Research Division, U.S. Bureau of the Census, Washington, DC, 20233, USA
- dStatistical Research Division , U.S. Bureau of the Census , Washington , DC , 20233 , USA
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Several multiple imputation techniques are described for simple random samples with ignorable nonresponse on a scalar outcome variable. The methods are compared using both analytic and Monte Carlo results concerning coverages of the resulting intervals for the population mean. Using m = 2 imputations per missing value gives accurate coverages in common cases and is clearly superior to single imputation (m = 1) in all cases. The performances of the methods for various m can be predicted well by linear interpolation in 1/(m — 1) between the results for m = 2 and m = ∞. As a rough guide, to assure coverages of interval estimates within 2% of the nominal level when using the preferred methods, the number of imputations per missing value should increase from 2 to 3 as the nonresponse rate increases from 10% to 60%.