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Johan Braeken¹, Muirne C S Paap^2,3

¹University of Oslo, Norway.

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|August 16, 2021

Summary

This summary is machine-generated.

New item selection methods for multidimensional computerized adaptive tests (MCATs) address precision differences. These approaches lead to shorter, more effective tests with fixed marginal precision.

Keywords:

computerized adaptive testing fixed precision item selection rules multidimensional IRT variable length

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Area of Science:

Psychometrics
Educational Measurement
Computerized Adaptive Testing

Background:

Fixed-precision multidimensional computerized adaptive tests (MCATs) are increasingly utilized.
Current item selection rules optimize multivariate precision, while stopping rules target fixed marginal precision per dimension.
This creates an asymmetry between selection and stopping criteria in MCATs, unlike in unidimensional tests.

Purpose of the Study:

To address the selection-stopping asymmetry in fixed-precision between-item MCATs.
To introduce and evaluate novel item-selection approaches.
To develop effective MCATs with fixed marginal precision that are shorter in test length.

Main Methods:

Discussion of the selection-stopping asymmetry and its implications.
Introduction of three alternative, computationally inexpensive item-selection methods.
Evaluation of the proposed methods for fixed-marginal-precision MCATs.

Main Results:

The proposed alternative item-selection approaches are computationally inexpensive and easy to implement.
These methods result in effective fixed-marginal-precision MCATs.
The new approaches yield shorter test lengths compared to current methods.

Conclusions:

The identified selection-stopping asymmetry in MCATs can be effectively addressed.
Alternative item-selection strategies offer practical advantages for developing efficient MCATs.
The proposed methods improve the precision and reduce the length of fixed-precision MCATs.