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Interactions Between Termination Criteria and Ability Estimators in Computerized Adaptive Testing.

Xinyu Liu¹, David J Weiss¹

¹University of Minnesota, Minneapolis, USA.

Educational and Psychological Measurement

|June 15, 2026

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Choosing the right estimator and stopping rule is crucial for efficient computerized adaptive testing (CAT). Weighted Likelihood Estimation (WLE) with standard error of measurement (SEM) or fixed-length rules works best for high-quality item banks, while other rules are better for lower-quality ones.

Area of Science:

Psychometrics
Educational Measurement
Computerized Adaptive Testing (CAT)

Background:

Computerized adaptive testing (CAT) optimizes measurement by tailoring items to examinees.
The effectiveness of CAT relies on ability estimators and termination criteria.
Limited research exists on the interaction of these components across diverse item banks.

Purpose of the Study:

To investigate the interactive effects of ability estimators and termination criteria on CAT performance.
To evaluate these interactions across varying item bank sizes and information distributions.
To identify optimal CAT configurations for different item bank characteristics.

Main Methods:

Simulation study evaluating four ability estimators (MLE, WLE, MAP, EAP).

Keywords:

ability estimation computerized adaptive testing item bank characteristics item response theory measurement precision stopping rules

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Assessed four termination criteria (fixed-length, SEM, MI, Δθ).

Tested across low- (100-item) and high- (500-item) information banks with flat and peaked distributions using the three-parameter logistic model.

Main Results:

Optimal CAT configuration depends on item bank size and shape.
WLE proved the most robust estimator, mitigating MLE boundary issues and Bayesian shrinkage bias.
In high-information banks, SEM and fixed-length rules minimized bias and RMSE.
In low-information peaked banks, the Δθ rule with WLE balanced accuracy and efficiency, avoiding inefficient test elongation.

Conclusions:

No single CAT design fits all scenarios; optimization is context-dependent.
For high-quality item banks, WLE with SEM or fixed-length rules is recommended.
For lower-quality banks, Δθ or hybrid SEM rules are advised to prevent inefficient test length.