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Title: Modeling non-ignorable missing for not-reached and omitted items using item response times Authors:  Chun Wang - University of Washington (United States) [presenting]
Abstract: Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit, or when the students choose to omit some items strategically. Oftentimes item nonresponses are non-random and hence the missing data mechanism needs to be properly modeled. We propose to use innovative item response time model as a cohesive missing data model to account for two most common item nonresponses: not-reached items and omitted items. Simulation studies show that the proposed approaches improve estimation precision of item parameters compared with the method based solely on observed responses (i.e., ignoring missing data). Moreover, for persons with missing data, their latent trait estimates are also less biased and more precise (i.e., lower standard error). The 2015 PISA computer-based mathematics data is analyzed to illustrate the application of the proposed method.