Examining Tradeoffs of Exposure Control and Collateral Information with Multidimensional Forced Choice Computerized Adaptive Testing

Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Psychology

Major Professor

Stephen Stark, Ph.D.

Committee Member

Georgia Chao, Ph.D.

Committee Member

Daniel Griffin, Ph.D.

Committee Member

Marina Bornovalova, Ph.D.

Committee Member

Sean Joo, Ph.D.

Keywords

Faking, Item Response Theory (IRT), Multi-Unidimensional Pairwise Preference (MUPP), Noncognitive Assessment

Abstract

Multidimensional forced choice (MFC) testing has been proposed as an alternative to single statement (SS) Likert-type measures to reduce response biases in noncognitive measurement. Research progress has been made on MFC computerized adaptive testing (CAT) to improve testing efficiency. CAT enhances efficiency by successively selecting items that are most informative at each respondent’s estimated trait score. In MFC CAT, this causes some forced choice items and the statements composing them to be frequently exposed while others are rarely used, which adversely affects test security and costs. This research develops an exposure control method for MFC CAT based on the Multi-Unidimensional Pairwise Preference model (MUPP; Stark et al., 2005). Because the method is intended to prevent the overuse of the most informative items and statements, it tends to decrease overall measurement accuracy and precision. Thus, a second purpose of this research is to examine the extent to which these losses in accuracy and precision might be offset by incorporating collateral information. The effectiveness of the exposure control method and the incorporation of collateral information in MFC CAT was investigated in a Monte Carlo study that also manipulated test length and the correlation between dimensions. A byproduct of this research is an MFC CAT algorithm that improves test security and cost-effectiveness, while simultaneously maintaining measurement accuracy and precision.

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