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Modeling Skipped and Not‐Reached Items Using IRTrees
Author(s) -
Debeer Dries,
Janssen Rianne,
Boeck Paul
Publication year - 2017
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12147
Subject(s) - missing data , computer science , item response theory , test (biology) , process (computing) , reading (process) , statistical hypothesis testing , econometrics , data mining , machine learning , statistics , artificial intelligence , psychometrics , mathematics , programming language , paleontology , political science , law , biology
When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree‐based IRT framework for modeling responses and omissions jointly, taking into account that test takers as well as items can contribute to the two types of omissions. The proposed framework covers several existing models for missing responses, and many IRTree models can be estimated using standard statistical software. Further, simulated data is used to show that ignoring missing responses is less robust than often considered. Finally, as an illustration of its applicability, the IRTree approach is applied to data from the 2009 PISA reading assessment.