Non-Parametric Tests

Saiyidi Mat Roni () and Hadrian Geri Djajadikerta ()
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Saiyidi Mat Roni: Edith Cowan University, School of Business and Law
Hadrian Geri Djajadikerta: Edith Cowan University, School of Business and Law

Chapter Chapter 10 in Data Analysis with SPSS for Survey-based Research, 2021, pp 219-260 from Springer

Abstract: Abstract In this final chapter we demonstrate some of the under-appreciated non-parametric tests. These are the cousins (not siblings) of the parametric ones. For example, if your dataset does not meet the assumptions for the t-test (parametric), then Mann-Whitney test (non-parametric) in this section can be an alternative for you. Similarly, if you want to check if BMW, Ferrari, and Mercedes F1 cars are different speed-wise, you typically use ANOVA (parametric) when the speed data is normally distributed (because there are three groups to compare). You can also Kruskal-Wallis test if the data is non-normal. We also walk you through the non-parametric tests of correlation and the rarely used but very helpful Jonckheere-Terpstra test.

Keywords: Chi-square; Jonckheere-Terpstra; Kendall tau; Kruskal-Wallis; Mann-Whitney; Non-parametric; Spearman rho; Wilcoxon (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-0193-4_10

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DOI: 10.1007/978-981-16-0193-4_10

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