Item response theory (IRT) models for unfolding processes use the responses of individuals to attitudinal tests or questionnaires in order to infer item and person parameters located on a latent continuum. Parametric models in this class use parametric functions to model the response process, which in practice can be restrictive. MUDFOLD (Multiple UniDimensional unFOLDing) can be used to obtain estimates of person and item ranks without imposing strict parametric assumptions on the item response functions (IRFs). This paper describes the implementation of the MUDFOLD method for binary preferential-choice data in the R package mudfold. The latter incorporates estimation, visualization, and simulation methods in order to provide R users with utilities for nonparametric analysis of attitudinal questionnaire data. After a brief introduction in IRT, we provide the method ological framework implemented in the package. A description of the available functions is followed by practical examples and suggestions on how this method can be used even outside the field of psychometrics.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-002.zip
mudfold, GGUM, mirt, mokken, boot, mice, gtools, glmnet, mgcv, zoo, reshape2, ggplot2, smacof
Psychometrics, Econometrics, MissingData, SocialSciences, Environmetrics, Survival, TimeSeries, Bayesian, Finance, Graphics, MachineLearning, Multivariate, OfficialStatistics, Optimization, Phylogenetics, TeachingStatistics
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For attribution, please cite this work as
Balafas, et al., "The R Journal: mudfold: An R Package for Nonparametric IRT Modelling of Unfolding Processes", The R Journal, 2020
BibTeX citation
@article{RJ-2020-002,
author = {Balafas, Spyros E. and Krijnen, Wim P. and Post, Wendy J. and Wit, Ernst C.},
title = {The R Journal: mudfold: An R Package for Nonparametric IRT Modelling of Unfolding Processes},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-002},
doi = {10.32614/RJ-2020-002},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {49-75}
}