This Monte Carlo study investigates the beneficiary effect of including auxiliary variables during estimation of confirmatory factor analysis models with multiple imputation. Specifically, it examines the influence of sample size, missing rates, missingness mechanism combinations, missingness types (linear or convex), and the absence or presence of the auxiliary variables on convergence failure, bias, standard error, and confidence interval coverage of parameters. Including auxiliary variables in the imputation model is found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR and absence of auxiliary variables in the imputation model. The results of this study can be applied to test validity studies where item selection is needed because of the presence of many alternative items (e.g., instrument development from an item bank). Implications and recommendations for proper imputation are discussed. (Contains 6 tables and 1 note.)