Effect size for post-hoc-tests of the Kruskal-Wallis-test in R
After significant post-hoc-tests for the Kruskal Wallis-test in R an effect size calculation is highly recommended. Therefore, the effect size r needs to be calculated. The effect size r is the test statistic divided by the squareroot of the sum of the two groups each time a pairwise comparison is conducted: r = z/sqrt(n1+n2) The magnitude of the effect size is classified with comparable studies or research field specific thresholds. If neither exist, you can refer to Cohen (1992): A Power Primer on page 157. ? Literature: =========== - Cohen, J. (1992): Quantitative methods in psychology: A power primer. Psychological bulletin, pp. 155-159. - Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133. The tutorial is based on my video: Post hoc tests for the Kruskal Wallis-test in R (https://youtu.be/pyLQmUfrel8). I use Dunn's tests with the dunn_test()-function of the rstatix package within it. ⏰ Timestamps: ============== 0:00 Introduction and prerequisites 0:18 Calculating r 0:40 Classifying r If you have any questions or suggestions regarding Effect size for post-hoc-tests of the Kruskal-Wallis-test in R, please use the comment function. Thumbs up or down to decide if you found the video helpful. #useR #statorials
After significant post-hoc-tests for the Kruskal Wallis-test in R an effect size calculation is highly recommended. Therefore, the effect size r needs to be calculated. The effect size r is the test statistic divided by the squareroot of the sum of the two groups each time a pairwise comparison is conducted: r = z/sqrt(n1+n2) The magnitude of the effect size is classified with comparable studies or research field specific thresholds. If neither exist, you can refer to Cohen (1992): A Power Primer on page 157. ? Literature: =========== - Cohen, J. (1992): Quantitative methods in psychology: A power primer. Psychological bulletin, pp. 155-159. - Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133. The tutorial is based on my video: Post hoc tests for the Kruskal Wallis-test in R (https://youtu.be/pyLQmUfrel8). I use Dunn's tests with the dunn_test()-function of the rstatix package within it. ⏰ Timestamps: ============== 0:00 Introduction and prerequisites 0:18 Calculating r 0:40 Classifying r If you have any questions or suggestions regarding Effect size for post-hoc-tests of the Kruskal-Wallis-test in R, please use the comment function. Thumbs up or down to decide if you found the video helpful. #useR #statorials