Phadke, Chaitali2018-03-142018-03-142017-12https://hdl.handle.net/11299/194559University of Minnesota Ph.D. dissertation.December 2017. Major: Psychology. Advisor: Dr. David Weiss. 1 computer file (PDF); ix, 194 pages.The present study proposed six new omnibus hypothesis tests – F1, F2, LR, ST and two chi-squared based statistics – to measure psychometric significance of individual change when an individual is measured at two or more occasions. The hypothesis tests were evaluated on criteria of Type I error, power, and agreement between the methods in the adaptive measurement of change (AMC) framework. This study expanded on AMC research by Finkleman, Weiss and Kim-Kang (2010) and Lee (2015), by introducing more generalized methods for multi-occasion case. The omnibus tests were evaluated under various discrimination, bank type, and change conditions. The simulation results showed the LR test to achieve an optimum balance between Type I error and power. The hypothesis tests were found to be robust under most testing conditions. The tests were successfully applied to K-12 math data. The proposed methods are applicable under a variety of testing conditions in which IRT-based item parameters have been established.enAdaptive measurement of changeComputerized adaptive testsIndividual changeMeasurement of changeomnibus hypothesis testsPsychometric significance of changeThesis or Dissertation