Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina GrĂ¼n, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory | Mazza | Journal of Statistical Software
Authors: Angelo Mazza, Antonio Punzo, Brian McGuire
Title: KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory
Abstract: Item response theory (IRT) models are a class of statistical models used to describe the response behaviors of individuals to a set of items having a certain number of options. They are adopted by researchers in social science, particularly in the analysis of performance or attitudinal data, in psychology, education, medicine, marketing and other fields where the aim is to measure latent constructs. Most IRT analyses use parametric models that rely on assumptions that often are not satisfied. In such cases, a nonparametric approach might be preferable; nevertheless, there are not many software implementations allowing to use that.

To address this gap, this paper presents the R package KernSmoothIRT . It implements kernel smoothing for the estimation of option characteristic curves, and adds several plotting and analytical tools to evaluate the whole test/questionnaire, the items, and the subjects. In order to show the package's capabilities, two real datasets are used, one employing multiple-choice responses, and the other scaled responses.

Page views:: 1519. Submitted: 2012-05-08. Published: 2014-06-30.
Paper: KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory     Download PDF (Downloads: 1454)
Supplements:
KernSmoothIRT_6.1.tar.gz: R source package Download (Downloads: 231; 68KB)
v58i06.R: R example code from the paper Download (Downloads: 233; 1KB)

DOI: 10.18637/jss.v058.i06

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.