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Using Item Scores and Distractors to Detect Test Speededness.

Kylie Gorney¹, James A Wollack¹, Daniel M Bolt¹

¹Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, USA.

Applied Psychological Measurement

|October 9, 2023

Summary

This summary is machine-generated.

Detecting test speededness is crucial for accurate results. A new change-point analysis (CPA) procedure using item scores and distractors improves speeded examinee detection and parameter estimation.

Keywords:

change-point analysis distractor analysis nested logit model test speededness

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Area of Science:

Psychometrics
Educational Measurement
Statistical Analysis

Background:

Test speededness, where time limits affect performance, can bias examinee and item parameter estimates.
Accurate detection of speeded examinees is essential for valid test results.
Existing methods for detecting speededness have limitations.

Purpose of the Study:

To develop a novel change-point analysis (CPA) procedure for detecting test speededness.
To improve the accuracy of identifying speeded examinees and estimating the point at which speededness occurs.
To leverage information from item distractors, which are commonly available in multiple-choice tests.

Main Methods:

Development of a new CPA procedure incorporating both item scores and distractor information.
Extensive simulations to evaluate the performance of the proposed CPA procedure.
Application of the CPA procedure to a real-world dataset.

Main Results:

The new CPA procedure demonstrates improved detection of speeded examinees compared to existing methods.
The procedure yields more accurate change-point estimates.
Item distractor information significantly contributes to the improved detection of speededness.

Conclusions:

The proposed CPA procedure offers a more effective method for detecting test speededness.
Utilizing distractor information in CPA is a valuable approach for improving the accuracy of psychometric analyses.
This method has practical implications for ensuring the validity of large-scale assessments.