// Mann-Whitney-Wilcoxon-test in R - ALL IN ONE (Calculation, Interpretation, Reporting) //
This video will help you in conducting a Mann-Whitney-Wilcoxon-test in R, including the calculation of the effect size as well as interpreting and reporting its results.
Please don't forget that an a priori sample size calculation is usually required.
Calculating the required sample size:
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🎥 https://youtu.be/Ytc3VbXNFEc
The video consists of the following four parts:
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1) Calculation of the Mann-Whitney-Wilcoxon-test in R using the wilcox_test()-function of the rstatix-package.
2) Interpretation of the results.
3) Calculation of the effect size for Mann-Whitney-Wilcoxon-test, namely the effect size r.
4) Reporting of the results. Be aware that research field-specific standards may apply. The reporting shown is usually sufficient.
General information on the Mann-Whitney-Wilcoxon-test
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The Mann-Whitney-Wilcoxon-test is a non-parametric statistical method that is used in place of the independent samples t-test when the data is not normally distributed. This test is used to assess whether the median of two independent groups is different. You can use a dependent variable that is at least on the ordinal scale.
📚 Sources:
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- Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19-24.
- Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492.
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133.
⏰ Timestamps:
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0:00 Introduction
0:20 0. Example
0:27 I. Requirements for the Mann-Whitney-Wilcoxon-test
0:35 II. Calculation of the Mann-Whitney-Wilcoxon-test in
2:02 III. interpretation of the Mann-Whitney-Wilcoxon-test in
2:34 IV. Effect size for the Mann-Whitney-Wilcoxon-test in in R
3:33 Reporting the results of the Mann-Whitney-Wilcoxon-test in R
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