Document Type

Dissertation

Date of Degree

Summer 2016

Degree Name

PhD (Doctor of Philosophy)

Degree In

Psychological and Quantitative Foundations

First Advisor

Michael J. Kolen

Second Advisor

Won-Chan Lee

Abstract

Dimensionality assessment provides test developers and users with a better understanding of how test scores make human abilities concrete. Issues dealt with by dimensionality assessment include, but are not restricted to, (a) whether unidimensionality holds; (b) the number of dimensions influencing test scores; and (c) the relationships among items, among underlying dimensions, and between items and dimensions. Results from dimensionality assessment allow test developers and users to carefully validate specific interpretations and uses of test scores. The widespread use of mixed-format tests complicates dimensionality assessment both conceptually and methodologically. This dissertation is the first to propose a framework tailored for exploratory type of dimensionality assessment for mixed-format tests. Based on real data from three large-scale mixed-format tests, this dissertation examined the performance of a number of popular and promising dimensionality assessment methods and procedures. Major findings were summarized, along with more extensive descriptions of the similarities and dissimilarities among methods and across different test subject areas, forms, and sample sizes. Limitations and possible further research topics were also discussed.

Pages

xvii, 196

Bibliography

180-191

Comments

This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: http://www.lib.uiowa.edu/sc/contact/

Copyright

Copyright 2016 Mengyao Zhang

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