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Development of an Interactive R Tool for Nonparametric Association Analysis in Experimental Data from Applied Engineering

10.24050/reia.v23i46.1935
Published 2026-09-17
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Abstract

This article presents a methodological tool designed for the calculation and interpretation of nonparametric correlation coefficients, aimed at professionals and students without formal statistical training who need to analyze associations between variables in contexts where normality assumptions are not met. The proposal includes a detailed description of the main nonparametric coefficients used in applied statistics—Spearman, Kendall, Gamma, Lambda, Cramér’s V, Phi, and Somers D—covering their theoretical foundations, applicability criteria, and the conditions that determine their appropriate use. As part of the methodological development, these coefficients were implemented in the R programming language, accompanied by practical examples with corresponding code and the creation of an interactive Shiny application. This application allows users to upload files, select variables, and visualize results without requiring programming skills, thereby enhancing accessibility in multidisciplinary environments. To validate the tool, case studies were conducted in various fields, including an analysis in materials engineering, where an inverse relationship was identified between hardness and wear resistance in thermally treated metal components. The results demonstrate that the choice of correlation coefficient significantly influences the conclusions of the analysis, particularly when working with non-normal data or categorical scales. It is concluded that the nonparametric approach offers a robust and versatile alternative for association analysis, and that the proposed tool facilitates its application in technical, educational, and scientific research requiring methodological rigor without computational complexity.

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Adrianingsih, N. Y.; Budiantara, I. N.; Purnomo, J. D. T. (2021). mixture model nonparametric regression and its application. Journal of Physics: Conference Series, 1842(1), 012044. https://doi.org/10.1088/1742-6596/1842/1/012044

Altunkaynak, B.; Gamgam, H. (2020). npordtests: an R package of nonparametric tests for equality of location against ordered alternatives. R Journal, 12(1). https://doi.org/10.32614/rj-2020-022

Bai, P.; Chen, Y.; Chen, L.; Zhang, X.; Wang, X.; Wang, X. (2024). Research on the influence of bad working state on air traffic control effect based on multi-independent sample Kruskal-Wallis test. Journal of Air Transport Management, 120, 102653. https://doi.org/10.1016/j.jairtraman.2024.102653

Bower, R.; Hager, J.; Cherniakov, C.; Gupta, S.; Cipolli, W. (2023). A case for nonparametrics. The American Statistician, 77(2), pp. 212-219. https://doi.org/10.1080/00031305.2022.2141858

Carranza, A. M.; Carranza, D. L.; León, S. L. (2023). Aplicación de las escalas de medición ordinal para interpretar coeficientes de la correlación en investigación científica. Revista Científica Searching de Ciencias Humanas y Sociales, 5(1), pp. 48-60. https://doi.org/10.46363/searching.v5i1.4

David, F. N.; Cramer, H. (1947). Mathematical methods of statistics. Biometrika, 34(3/4). https://doi.org/10.2307/2332454

de Farias, L.; de Carvalho, W. R. (2020). R-Shiny as an interface for data visualization and data analysis on the brazilian digital library of theses and dissertations (BDTD). Publications, 8(2). https://doi.org/10.3390/PUBLICATIONS8020024

Emons, W.; Sijtsma, K. (2023). Nonparametric statistical methods. International Encyclopedia of Education, 4.th ed. Elsevier, pp. 657-669. https://doi.org/10.1016/B978-0-12-818630-5.10073-9

Ewens, W. J.; Brumberg, K. (2023). Non-parametric Tests. Introductory Statistics for Data Analysis, Switzerland: Springer Nature, pp. 205-217. https://doi.org/10.1007/978-3-031-28189-1_14

Goodman, L. A.; Kruskal, W. H. (1954). Measures of association for cross classifications. Journal of the American Statistical Association, 49(268), 732-764. https://doi.org/10.2307/2281536

Goodman, L. A.; Kruskal, W. H. (1979). Measures of Association for Cross Classifications. EE. UU.: Springer. https://doi.org/10.1007/978-1-4612-9995-0_1

Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), pp. 81-93. https://doi.org/10.1093/biomet/30.1-2.81

Khedr, A.; Hilal, S. (2021). Interactive visualization for statistical modelling through a Shiny app in R. 2021 International Conference on Data Analytics for Business and Industry. Baréin: IEEE, pp. 332-337. https://doi.org/10.1109/ICDABI53623.2021.9655841

Kloke, J.; McKean, J. (2024). Nonparametric statistical methods using R. EE. UU.: Chapman y Hall/CRC. https://doi.org/10.1201/9781003039617

Kvålseth, T. O. (2018). Measuring association between nominal categorical variables: an alternative to the Goodman-Kruskal lambda. Journal of Applied Statistics, 45(6), pp. 1118-1132. https://doi.org/10.1080/02664763.2017.1346066

Madrid de la Vega, H. (2023). Gauss vs. Cramer. El Cálculo y Su Enseñanza, 19(2), pp. 1-14. https://doi.org/10.61174/recacym.v19i2.212

Mair, P.; Wilcox, R. (2020). Robust statistical methods in R using the WRS2 package. Behavior Research Methods, 52(2), pp. 464-488. https://doi.org/10.3758/s13428-019-01246-w

McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22, pp. 276-282. https://doi.org/10.11613/BM.2012.031

Mendivelso, F. (2022). Prueba no paramétrica de correlación de Spearman. Revista Médica Sanitas, 24(1). https://doi.org/10.26852/01234250.578

Miot, H. A. (2018). Análise de correlação em estudos clínicos e experimentais. Jornal Vascular Brasileiro, 17(4), pp. 275-279. https://doi.org/10.1590/1677-5449.174118

Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: the basic concept and the practical use. Korean Journal of Anesthesiology, 69(1), pp. 8-14. https://doi.org/10.4097/kjae.2016.69.1.8

Newson, R. (2002). Parameters behind “nonparametric” statistics: Kendall’s tau, Somers’ D and median differences. The Stata Journal: Promoting Communications on Statistics and Stata, 2(1), pp. 45-64. https://doi.org/10.1177/1536867X0200200103

Ozhan, A.; Tombaz, M.; Konu, O. (2021). SmulTCan: a Shiny application for multivariate survival analysis of TCGA data with gene sets. Computers in Biology and Medicine, 137. https://doi.org/10.1016/j.compbiomed.2021.104793

Polanco, C. (2024). Non-parametric statistic methods. Fundamentals of mathematics. Medical research: theory and cases, pp. 45–56. Bentham Science Publishers. https://doi.org/10.2174/9789815223132124010009

Puerto, J. A. (2018). Influencia del tiempo de revenido en la resistencia al desgaste abrasivo de un acero aisi/sae 1045 templado desde temperaturas intercríticas. Universidad Libre de Colombia, 7. https://repository.unilibre.edu.co/handle/10901/15470

R Core Team. (2025). R Core Team, R: a language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/

Ratner, B. (2009). The correlation coefficient: its values range between +1/−1, or do they? Journal of Targeting, Measurement and Analysis for Marketing, 17(2), pp. 139-142. https://doi.org/10.1057/jt.2009.5

Schober, P.; Boer, C.; Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), pp. 1763-1768. https://doi.org/10.1213/ANE.0000000000002864

Sepulveda, M. V. (2025). Kendallknight: an R package for efficient implementation of Kendall’s correlation coefficient computation. PLOS One, 20(6), e0326090. https://doi.org/10.1371/journal.pone.0326090

Shryock, D. F.; Wright, M. C.; van Mantgem, P. J.; Esque, T. C. (2023). Best practices for distributing and deploying U.S. geological survey Shiny applications. U.S. Geological Survey Techniques and Methods. https://doi.org/10.3133/tm7B2

Siegel, A. F.; Wagner, M. R. (2022). Chapter 16 - nonparametrics: testing with ordinal data or nonnormal distributions. Practical Business Statistics, pp. 511-529. https://doi.org/10.1016/B978-0-12-820025-4.00016-6

Spencer, N.; Smeeton, N. C.; Sprent, P. (2025). Applied nonparametric statistical methods. EE. UU.: Chapman and Hall/CRC. https://doi.org/10.1201/9780429326172

Suich, R. C.; Turek, R. J. (2003). An asymptotic partial correlation test for the Goodman‐Kruskal λ. British Journal of Mathematical and Statistical Psychology, 56(1), pp. 111-117. https://doi.org/10.1348/000711003321645377

Wadgave, U.; Ravindra Khairnar, M. (2019). Parametric test for non-normally distributed continuous data: for and against. Electronic Physician, 11(2), pp. 7468-7470. https://doi.org/10.19082/7468

Więckowska, B.; Kubiak, K. B.; Jóźwiak, P.; Moryson, W.; Stawińska-Witoszyńska, B. (2022). Cohen’s Kappa coefficient as a measure to assess classification improvement following the addition of a new marker to a regression model. International Journal of Environmental Research and Public Health, 19(16), 10213. https://doi.org/10.3390/ijerph191610213

Wiedermann, W.; Bonifay, W.; Huang, F. L. (2023). Advanced categorical data analysis in prevention science. Prevention Science, 24(3). https://doi.org/10.1007/s11121-022-01485-y

Wilcox, R. R.; Rousselet, G. A. (2023). An updated guide to robust statistical methods in neuroscience. Current Protocols, 3(3). https://doi.org/10.1002/cpz1.719

Withers, S. D. (2009). Categorical data analysis. International Encyclopedia of Human Geography, 1-12. https://doi.org/10.1016/B978-008044910-4.00409-0

Xia, Q.; Mudaranthakam, D. P.; Chollet-Hinton, L.; Chen, R.; Krebill, H.; Kuo, H.; Koestler, D. C. (2022). shinyOPTIK, a user-friendly R Shiny Application for visualizing cancer risk factors and mortality across the University of Kansas Cancer Center Catchment Area. JCO Clinical Cancer Informatics, 6. https://doi.org/10.1200/CCI.21.00118

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