This study presents an innovative method for reducing the number of rating scale items without predictability loss. The “area under the receiver operator curve” method (AUC ROC) is used for the stepwise method of reducing items of a rating scale. RatingScaleReduction R package contains the presented implementation. Differential evolution (a metaheuristic for optimization) was applied to one of the analyzed datasets to illustrate that the presented stepwise method can be used with other classifiers to reduce the number of rating scale items (variables). The targeted areas of application are decision making, data mining, machine learning, and psychometrics. Keywords: rating scale, receiver operator characteristic, ROC, AUC, scale reduction.
pROC, ROCR, RatingScaleReduction, DEoptim
MachineLearning, Multivariate, Optimization
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For attribution, please cite this work as
Koczkodaj, et al., "The R Journal: RatingScaleReduction package: stepwise rating scale item reduction without predictability loss", The R Journal, 2018
BibTeX citation
@article{RJ-2018-035,
author = {Koczkodaj, Waldemar W. and Li, Feng and Wolny–Dominiak, Alicja},
title = {The R Journal: RatingScaleReduction package: stepwise rating scale item reduction without predictability loss},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-035},
doi = {10.32614/RJ-2018-035},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {43-55}
}