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A Dynamic Stratification Method for Improving Trait Estimation in Computerized Adaptive Testing Under Item Exposure

Jyun-Hong Chen1, Hsiu-Yi Chao2, Shu-Ying Chen2

  • 1Soochow University, Taipei.

Applied Psychological Measurement
|April 29, 2020
PubMed
Summary
This summary is machine-generated.

A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), improves trait estimation in computerized adaptive testing (CAT) under strict item exposure control. This method enhances precision and accuracy compared to traditional approaches.

Keywords:
a-stratified methodcomputerized adaptive testingdominance curveshigh-stakes testingitem exposure control

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Computerized Adaptive Testing

Background:

  • Stringent item exposure control in computerized adaptive testing (CAT) significantly reduces trait estimation precision.
  • Existing item selection methods may not adequately address the trade-off between item exposure and estimation accuracy.

Purpose of the Study:

  • To introduce and evaluate a novel item selection method, the dynamic Stratification method based on Dominance Curves (SDC), for improving trait estimation in CAT under item exposure control.
  • To enhance the precision and accuracy of ability estimation in CAT when managing item usage.

Main Methods:

  • The SDC method aims to maximize the sum of test information across all examinees, differing from conventional CAT's focus on individual item information.
  • It utilizes dominance curves to stratify item pools, matching strata number to test length for optimized item quality progression.
  • A dynamic on-the-fly item-stratum adjustment process is incorporated to maximize the utility of high-quality items.

Main Results:

  • Simulation studies demonstrated the SDC's effectiveness in improving trait estimation under various levels of item exposure control.
  • The SDC method showed superior performance in terms of precision and accuracy compared to other methods, such as maximum Fisher information.
  • The SDC method effectively reduces the probability of administering high-discrimination items to examinees whose ability levels do not closely match the item difficulty.

Conclusions:

  • The dynamic Stratification method based on Dominance Curves (SDC) offers a significant advancement for trait estimation in computerized adaptive testing (CAT) under item exposure constraints.
  • SDC provides a more precise and accurate estimation of examinee traits, outperforming conventional methods in simulated environments.
  • This method presents a viable solution for balancing item exposure control with the need for reliable ability measurement in CAT.