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On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests.

Carl F Falk1, Leah M Feuerstahler2

  • 1McGill University, Montreal, Quebec, Canada.

Educational and Psychological Measurement
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Summary

This study shows that semi- and nonparametric item response functions perform well in computer adaptive tests (CAT). These methods offer a flexible alternative to traditional parametric models for improved assessment accuracy.

Keywords:
computer adaptive testlarge-scale testingmonotonic polynomialnonparametric IRT

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Computer adaptive tests (CAT) are widely used in large-scale assessments for item selection and respondent scoring.
  • Current CATs predominantly rely on parametric item response functions, with limited research on alternative models.
  • Investigating nonparametric and semi-parametric approaches is crucial for advancing CAT methodology.

Purpose of the Study:

  • To compare the performance of parametric item response functions against semi- and nonparametric alternatives within a CAT framework.
  • To evaluate the utility of kernel smoothing and monotonic polynomial logistic functions as nonparametric response functions in CAT.
  • To assess the impact of various simulation conditions on the performance of these different response function types.

Main Methods:

  • Computer simulations were conducted to compare parametric response functions with those estimated via kernel smoothing and monotonic polynomial logistic functions.
  • The study examined various item selection algorithms within the CAT simulations.
  • Simulation parameters included sample size, missing data, and the proportion of nonstandard items in the item pool.

Main Results:

  • Semi- and nonparametric item response functions demonstrated robust performance in CAT simulations.
  • The tested nonparametric methods proved compatible with traditional CAT item selection algorithms.
  • Performance was evaluated across diverse conditions, including varying sample sizes and data quality.

Conclusions:

  • The findings support the integration of semi- and nonparametric item response functions into computer adaptive testing.
  • These flexible models offer a viable and effective alternative to traditional parametric assumptions in CAT.
  • Further research into nonparametric methods can enhance the accuracy and adaptability of large-scale assessments.