The Effects of Auxiliary Variables on Coefficient Bias and Efficiency in Multiple Imputation

Current research on multiple imputation suggests that including auxiliary variables in the imputation model may increase the accuracy and efficiency of coefficient estimation, yet few studies have actually tested this principle for regression analysis. This article uses data from the 2008 General Social Survey to present results from simulations that vary in three respects: (a) three types of auxiliary variables (variables related to the mechanism of missingness, variables related to the variable/varaibles being imputed, and extraneous variables); (b) three levels of missing data (10 percent, 20 percent, and 30 percent missing); and (c) two assumptions of missing (missing completely at random and missing at random). Results show that the inclusion of any type of auxiliary variable does not appreciably impact the coefficient bias or efficiency in this simulation, regardless of the amount of missing data or the assumption of missing. Hence, the inclusion of auxiliary variables may not be necessary in many analytic situations.