Abstract
Survey based analyses usually face the problem of missing data. Certain statistical methods require a full data matrix to be provided, in order 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 to evaluate the impact of different imputation methods on multidimensional IRT models estimation.
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