KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory

TL;DR

KernSmoothIRT is an R package that applies kernel smoothing techniques to estimate item characteristic curves in IRT, providing flexible nonparametric analysis tools for various fields.

Contribution

The paper introduces KernSmoothIRT, a novel R package that enables nonparametric IRT analysis through kernel smoothing, filling a gap in available software.

Findings

Successfully applied to real datasets with multiple-choice responses

Provides comprehensive plotting and evaluation tools

Enhances flexibility in IRT analysis

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 applications 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.