Mastering the Friedman Test in R: The Ultimate Guide to Analysis скачать в хорошем качестве

Mastering the Friedman Test in R: The Ultimate Guide to Analysis

// Friedman Test/ANOVA in R - ALL IN ONE (Calculation, Interpretation, Reporting)) // This video will help you in conducting a Friedman Test/ANOVA in R, including the calculation of post-hoc-tests, 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: ============================== 🎥    • Friedman test / Friedman test - calculate ...   The video consists of the following five parts: ===================================== 1) Calculation of the Friedman Test/ANOVA in R using the friedman_test()-function. 2) Conducting post-hoc-tests to see which pairwise comparisons show differences worth investigating further. 3) Interpretation of the results, especially the post-hoc-tests. 4) Calculation of the effect size for the post-hoc-tests of the Friedman Test. Since there is no consensus, the effect size r seemed reasonable from my perspective. Feel free to discuss this in the comments with me. (Effect size Kendall's W for the Friedman Test is shown here:    • Effect size Kendall's W for the Friedman A...  ) 5) Reporting of the results. Be aware that research field-specific standards may apply. The reporting shown is usually sufficient. General information on the Friedman Test/ANOVA ======================================== The Friedman Test/ANOVA is used to assess whether the median of the same individuals for at least three points in time are different. You have to use a dependent variable that is on at leas the ordinal scale. 📚 Sources: ========== 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: ============== 0:00 Introduction 0:17 0. Example 0:38 I. Requirements for the Friedman Test/ANOVA 0:49 II. Calculation and interpretation of the Friedman Test/ANOVA in R 2:57 III. Post-Hoc-Testing for the the Friedman Test/ANOVA in R 5:29 IV.+V. Effect size for post-hoc-tests in R and interpretation 7:15 VI. Reporting the results If you have any questions or suggestions regarding the Friedman Test/ANOVA in R, please use the comment function. Thumbs up or down to decide if you found the video helpful.#useR #statorials Support channel? 🙌🏼 =================== Paypal donation: https://www.paypal.com/paypalme/Bjoer... Amazon affiliate link: https://amzn.to/49BqP5H