A Comparison of Methods for Nonparametric Estimation of Item Characteristic Curves for Binary Items  

Oleh:

Lee, Young-Sun

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Applied Psychological Measurement vol. 31 no. 2 (Mar. 2007), page 121-134.
Topik: isotonic regression; kernel smoothing; nonparametric item response model; item response theory
Fulltext: 121.pdf (189.22KB)

Isi artikel

This study compares the performance of three nonparametric itemcharacteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict monotonicity. As the number of items and the sample size increase, the kernel smoothing and smoothed isotonic regression ICC estimation procedures yield similar results across all conditions.

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