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Item selection methods in multidimensional computerized adaptive testing for forced-choice items using Thurstonian

Qin Wang¹, Yi Zheng², Kai Liu¹

¹Jiangxi Normal University, Nanchang, China.

Behavior Research Methods

|February 7, 2023

Summary

This summary is machine-generated.

New KL-information methods improve multidimensional computerized adaptive testing for forced-choice items (MFC-CAT). These methods offer more accurate trait estimation and better item pool usage compared to traditional Fisher information approaches.

Keywords:

Fisher information Forced-choice items Item selection methods Kullback–Leibler information MFC-CAT Thurstonian IRT model

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Area of Science:

Psychometrics
Educational Measurement
Statistical Modeling

Background:

Multidimensional computerized adaptive testing for forced-choice items (MFC-CAT) merges benefits of MFC items and CAT.
Existing MFC-CAT designs rely on Fisher information (FI) for item selection, which can be unstable early in testing.

Purpose of the Study:

To propose and evaluate new KL-information-based item selection methods for MFC-CAT.
To compare these novel methods against existing FI-based methods.

Main Methods:

Developed KL-information methods (MFC-KI, MFC-K B , MFC-KLP) based on the Thurstonian IRT (TIRT) model.
Conducted three simulation studies, including one with real data, across 3- and 5-dimensional MFC-CAT scenarios.
Compared performance based on trait estimation accuracy and item pool usage.

Main Results:

KL-based methods are feasible for MFC-CAT, providing acceptable trait estimation and uniform item pool utilization.
MFC-K B and MFC-KLP demonstrated superior performance over FI-based methods.
The proposed methods achieved more accurate trait estimation and balanced item pool usage.

Conclusions:

KL-information-based item selection methods are a viable advancement for MFC-CAT.
MFC-K B and MFC-KLP represent significant improvements for trait estimation and test efficiency in MFC-CAT.