Auxiliary Variables in Multiple Imputation When Data Are Missing Not at Random

作者：Mustillo Sarah^*; Kwon Soyoung

来源：Journal of Mathematical Sociology, 2015, 39(2): 73-91.

DOI：10.1080/0022250X.2013.877898

• missing data
• missing not at random
• auxiliary variables
• multiple imputation

Most current implementations of multiple imputation (MI) assume that data are missing at random (MAR), but this assumption is generally untestable. We performed analyses to test the effects of auxiliary variables on MI when the data are missing not at random (MNAR) using simulated data and real data. In the analyses we varied (a) the correlation, (b) the level of missing data, (c) the pattern of missing data, and (d) sample size. Results showed that MI performed adequately without auxiliary variables but they also had a modest impact on bias in the real data and improved efficiency in both data sets. The results of this study suggest that, counter to the concern about the violation of the MAR assumption, MI appears to be quite robust to missing data that are MNAR in analytic situations such as the ones presented here. Further, results can be made even better via the use of auxiliary variables, particularly when efficiency is a primary concern.

• 出版日期2015-4-3