Missing data and parameters estimates in multidimensional item response models

Statistical analyses of data based on surveys usually face the problem of missing data. However, some statistical methods require a complete data matrix to be applicable, hence the need to cope with such missingness. Literature on imputation abounds with contributions concerning quantitative responses, but seems to be poor with respect to the handling of categorical data. The present work aims at evaluating the impact of different imputation methods on multidimensional IRT models estimation for dichotomous data.