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Using the General Diagnostic Model to Measure Learning and Change in a Longitudinal Large-Scale Assessment IRT

Author(s):
von Davier, Matthias; Xu, Xueli; Carstensen, Claus H.
Publication Year:
2009
Report Number:
RR-09-28
Source:
ETS Research Report
Document Type:
Report
Page Count:
22
Subject/Key Words:
Item Response Theory (IRT), Growth Models, Multidimensional Item Response Theory (MIRT), Longitudinal Models, Diagnostic Models, Large-Scale Assessment

Abstract

To illustrate the models presented in this paper, we applied the models to the PISA-I-Plus data for assessing learning and change across multiple subpopulations. The results indicate that (a) the Embretson-type model with multiple-group assumptions fits the data better than the other models investigated, and (b) the higher performing group shows larger improvement at Time Point 2 than the lower performing group.

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