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Summary
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Computerized adaptive testing (CAT) offers efficient and accurate trait estimation. Standard error (SE) stopping rules demonstrated superior performance in measurement precision and efficiency compared to other methods.

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Computerized Adaptive Testing (CAT)

Background:

  • Computerized adaptive testing (CAT) presents an efficient alternative to traditional paper-and-pencil assessments.
  • Stopping rules are crucial for optimizing CAT efficiency and accuracy.

Purpose of the Study:

  • To compare the efficiency and precision of three variable-length stopping rules (standard error [SE], minimum information [MI], and change in trait [CT]) in CAT.
  • To evaluate the impact of a maximum item limit on these stopping rules.

Main Methods:

  • A simulation study was conducted to compare variable-length stopping rules (SE=.3, MI=.7, CT=.02) against fixed-length linear tests (10 and 20 items).
  • The study examined performance with and without a maximum of 20 items imposed on the adaptive tests.

Main Results:

  • Minimum information (MI) rules led to longer tests and biased trait estimates.
  • Change in trait (CT) rules resulted in biased estimates at the upper trait range and increased standard errors.
  • Standard error (SE) rules exhibited balanced measurement precision and efficiency across the trait scale.

Conclusions:

  • Standard error (SE) stopping rules are recommended for computerized adaptive testing due to their optimal balance of precision and efficiency.
  • The choice of stopping rule significantly impacts the accuracy and length of adaptive assessments.