Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions

This article proposes a new missing data imputation method based on genetic algorithms. The algorithm presented in this paper is a useful tool for the completion of missing data in knowledge and skills tests. This algorithm uses both Bayesian and Akaike’s information criterions as fitness functions and applies them to the classical item response theory models of one, two and three parameters. The results obtained by this new algorithm have been compared with those achieved by means of the Multivariate Imputation by Chained Equations (MICE) algorithm. For all the missing data ratios checked, the average incorrect imputation percentages obtained with the GA algorithm were, statistically, significantly lower than the results obtained with the MICE method. The most favorable frameworks for the use of the algorithm developed in the present research are those questionnaires in which missing answers would be considered as missing completely at random (MCAR). In other words, those questionnaires in which the same questions are present for all the examinees, but not necessarily in the same order. •A genetic algorithm for missing data imputation is proposed.•The algorithm is tested in the context of the item response theory.•Optimum parameters of the algorithm are analyzed.•The proposed algorithm performs better than MICE algorithm.