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Computerized Adaptive Testing System of Functional Assessment of Stroke

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Published on: January 7, 2019

A New Stopping Rule for Computerized Adaptive Testing.

Seung W Choi¹, Matthew W Grady, Barbara G Dodd

¹Northwestern University Feinberg School of Medicine.

Educational and Psychological Measurement

|February 1, 2011

Summary

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A new stopping rule for computerized adaptive testing (CAT), the predicted standard error reduction (PSER) rule, efficiently manages test length and measurement precision. It optimizes item administration based on predictive gains in information.

Area of Science:

Psychometrics
Educational Measurement
Statistical Modeling

Background:

Computerized adaptive testing (CAT) relies on efficient stopping rules to balance test length and measurement precision.
Existing stopping rules, such as minimum standard error and minimum information rules, have limitations in optimizing item selection.
Accurate estimation of measurement error is crucial for effective adaptive testing.

Purpose of the Study:

To introduce and evaluate a novel stopping rule for CAT, termed the predicted standard error reduction (PSER) stopping rule.
To compare the performance of the PSER rule against established stopping rules using simulated data.
To assess the efficiency of the PSER rule in terms of item administration and measurement precision.

Main Methods:

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Development of the predicted standard error reduction (PSER) stopping rule, utilizing predictive posterior variance.

Simulation of adaptive tests using diverse item pools to evaluate stopping rule performance.

Comparison of PSER with minimum standard error and modified minimum information stopping rules.

Main Results:

The PSER rule demonstrated efficient use of item pools in simulated CAT environments.
PSER administered fewer items when predictive gains in information were minimal.
The rule effectively increased measurement precision when abundant information was available from items.

Conclusions:

The PSER stopping rule offers an efficient and effective alternative for managing test length in CAT.
PSER dynamically adjusts item administration based on the predictive value of additional items.
This approach enhances the precision of ability estimates in computerized adaptive testing.