Running head: SELECTION OF AUXILIARY VARIABLES 1 Selection of auxiliary variables in missing data problems: Not all auxiliary variables are created equal

The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved, consists of including many covariates as so-called auxiliary variables. These variables are either included based on data considerations or in an inclusive fashion, i.e., taking all available auxiliary variables. However, neither approach accounts for the fact that under a wide range of circumstances there is a class of variables that, when used as auxiliary variables, will always increase bias in the estimation of parameters from data with missing values. In this paper we show that this bias exists, quantify it in a simulation study, and discuss possible ways how one can avoid selecting bias-inducing covariates as auxiliary variables.