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