Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and

Jaime A Cid1, Alina A von Davier1

  • 1Educational Testing Service, Princeton, NJ, USA.

Summary

Kernel equating (KE) can be biased by extreme scores. This study explores atypical score effects on KE and introduces Epanechnikov and adaptive kernels to reduce boundary bias in test score equating.

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