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Yang, Ji Seung; Zheng, Xiaying – Journal of Educational and Behavioral Statistics, 2018
The purpose of this article is to introduce and review the capability and performance of the Stata item response theory (IRT) package that is available from Stata v.14, 2015. Using a simulated data set and a publicly available item response data set extracted from Programme of International Student Assessment, we review the IRT package from…
Descriptors: Item Response Theory, Item Analysis, Computer Software, Statistical Analysis
Debeer, Dries; Janssen, Rianne; De Boeck, Paul – Journal of Educational Measurement, 2017
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…
Descriptors: Item Response Theory, Test Items, Responses, Testing Problems
Adams, Raymond J.; Lietz, Petra; Berezner, Alla – Large-scale Assessments in Education, 2013
Background: While rotated test booklets have been employed in large-scale assessments to increase the content coverage of the assessments, rotation has not yet been applied to the context questionnaires administered to respondents. Methods: This paper describes the development of a methodology that uses rotated context questionnaires in…
Descriptors: Questionnaires, Item Response Theory, Foreign Countries, Achievement Tests
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection