Bayes estimates, Gibbs sampler, item response theory (IRT), Markov chain Monte Carlo, multilevel model, two-parameter normal ogive model,

Bayes estimates, full-information factor analysis, Gibbs sampler, item response theory, Markov chain Monte Carlo, multidimensional item response theory, normal ogive model,

A model‐based procedure for assessing the extent to which missing data can be ignored and handling non‐ignorable missing data is presented. The procedure is based on item response theory modelling. As an example, the approach is worked out in detail in conjunction with item response data modelled using the partial credit and generalized partial credit models. Simulation studies are carried out to assess the extent to which the bias caused by ignoring the missing‐data mechanism can be reduced. Finally, the feasibility of the procedure is demonstrated using data from a study to calibrate a medical disability scale.

No abstract