The R Journal: accepted article

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RatingScaleReduction package: stepwise rating scale item reduction without predictability loss PDF download
Waldemar W. Koczkodaj, Feng Li and Alicja Wolny–Dominiak

Abstract 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.

Received: 2017-03-20; online 2018-06-01
CRAN packages: RatingScaleReduction, pROC, ROCR, DEoptim
CRAN Task Views implied by cited CRAN packages: MachineLearning, Multivariate, Optimization

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Waldemar W. Koczkodaj and Feng Li and Alicja Wolny–Dominiak},
  title = {{RatingScaleReduction package: stepwise rating scale item
          reduction without predictability loss}},
  year = {2018},
  journal = {{The R Journal}},
  url = {}