Missing data frequently reduce the applicability of clinically collected data in research requiring multivariate statistics. In data imputation, missing values are replaced by predicted values obtained from models based on auxiliary information. Our aim was to complete a clinical child neuropsychological data set containing 5.2% of missing observations. This was to be used in research requiring multivariate statistics. We compared four data imputation methods by artificially deleting some data. A real-donor imputation method which preserved the parameter estimates and which predicted the observed values with acceptable accuracy was used to complete the data set. In addressing the lack of studies with regard to treatment of missing data in neuropsychological data sets, this study presents information on the outcomes of applying data imputation methods to such data. The imputation modeling described can be applied to a variety of clinical neuropsychological data sets.