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oai:drum.lib.umd.edu:1903/116762016-03-29T05:34:08Zcom_1903_2248com_1903_1647com_1903_2col_1903_2779col_1903_3

Harring, Jeffrey R. Rupp, Andre A. Mislevy, Jessica Lynn Digital Repository at the University of Maryland University of Maryland (College Park, Md.) Measurement, Statistics and Evaluation 2011-07-07T05:40:36Z 2011-07-07T05:40:36Z 2011 http://hdl.handle.net/1903/11676 A rapidly expanding arena for item response theory (IRT) is in attitudinal and health-outcomes survey applications, often with polytomous items. In particular, there is interest in computer adaptive testing (CAT). Meeting model assumptions is necessary to realize the benefits of IRT in this setting, however. Although initial investigations of local item dependence (LID) have been studied both for polytomous items in fixed-form settings and for dichotomous items in CAT settings, there have been no publications applying LID detection methodology to polytomous items in CAT despite its central importance to these applications. The research documented herein investigates the extension of widely used methods of LID detection, Yen's Q₃ statistic and Pearson's Statistic X<super>2</super>, in this context, via a simulation study. The simulation design and results are contextualized throughout with a real item bank and data set of this type from the Patient-Reported Outcomes Measurement Information System (PROMIS). Detecting Local Item Dependence in Polytomous Adaptive Data Dissertation Educational Tests and Measurements Quantitative Psychology and Psychometrics Computer Adaptive Testing Item Response Theory Local Dependence Missing Data Polytomous Items open.access