Abstract

This dissertation compares the parameter estimates obtained from two item response theory (IRT) models: the 1-PL IRT model and the MC1-PL IRT model. Several scenarios were explored in which both unidimensional and multidimensional item-level and personal-level data were used to generate the item responses. The Monte Carlo simulations mirrored the real-life application of the two correlated dimensions of Necessary Operations and Calculations in the basic mathematics domain. In all scenarios, the MC1-PL IRT model showed greater precision in the recovery of the true underlying item difficulty values and person theta values along each primary dimension as well as along a second general order factor. The fit statistics that are generally applied to the 1-PL IRT model were not sensitive to the multidimensional item-level structure, reinforcing the requisite assumption of unidimensionality when applying the 1-PL IRT model.

Degree

PhD

College and Department

David O. McKay School of Education; Instructional Psychology and Technology

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2004-12-08

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd646

Keywords

Item Response Theory, Dimensionality, Multidimensional, Goodness of Fit, Fit statistics

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