When missing values are present in item response data, there are a number of ways one might impute a correct or incorrect response to a multiple-choice item. There are significantly fewer methods for imputing the actual response option an examinee may have provided if he or she had not omitted the item either purposely or accidentally. This article applies the multiple-choice model, a multiparameter logistic model that allows for in-depth distractor analyses, to impute response options for missing data in multiple-choice items. Following a general introduction of the issues involved with missing data, the article describes the details of the multiple-choice model and demonstrates its use for multiple imputation of missing item responses. A simple simulation example is provided to demonstrate the accuracy of the imputation method by comparing true item difficulties ("p" values) and item-total correlations ("r" values) to those estimated after imputation. Missing data are simulated according to three different types of missing mechanisms: missing completely at random, missing at random, and missing not at random.