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Modelling non‐ignorable missing‐data mechanisms with item response theory models
Author(s) -
Holman Rebecca,
Glas Cees A. W.
Publication year - 2005
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.2005.tb00312.x
Subject(s) - missing data , computer science , econometrics , data mining , scale (ratio) , statistics , machine learning , mathematics , physics , quantum mechanics
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.