Below is a list of various tutorials on YouTube in English. This list is constantly updated and expanded.
RStudio – graphical user interface for R Statistics
- How To Install R On Your Computer
- User-friendly interface for R Statistics – RStudio (download + installation)
- RStudio introduction: All basics in just 3 Minutes
Importing data into R
- Import data from an Excel worksheet into R
- Import data from a CSV-file into R
- Import data from a TXT-file into R
- Import data from an SPSS-file into R
One Sample tests
Chi-Square Goodness of Fit-Test
R Statistics
- Chi Square Goodness of Fit – calculate required sample size with G*Power
- Chi Square Goodness of Fit in R – calculation and interpretation
- Effect Size for Chi Square goodness of fit test
- Reporting Chi-Square Goodness-of-fit test R
SPSS
- Chi Square Goodness of Fit in SPSS – calculation and interpretation
- How to calculate the effect size for Chi Square goodness of fit test in SPSS
- Reporting results of the Chi-Square Goodness-of-fit test SPSS
One Sample Wilcoxon-Test
R Statistics
- One sample Wilcoxon signed rank-test – calculate required sample size with G*Power
- One sample Wilcoxon signed-rank test in R – calculation and interpretation
- How to calculate the effect size for the one sample Wilcoxon test in R
- Reporting results from the one sample Wilcoxon test in R
SPSS
- One sample Wilcoxon signed-rank test in R – calculation and interpretation
- How to calculate the effect size for the one sample Wilcoxon test in SPSS
- Reporting results from one sample Wilcoxon signed-rank test in SPSS
One sample t-Test
R Statistics
- One sample t-Test – calculate required sample size with G*Power
- Testing the normality assumption for the one sample t-test in R
- One sample t-Test in R – calculation and interpretation
- Effect size for one sample t-test in R
- Reporting one sample t-test – results from R
SPSS
- One sample t-Test – calculate required sample size with SSS
- Reporting results from one sample t-test in SPSS
Two sample tests (independent samples)
Two sample t-Test (Independent samples t-Test)
R Statistics
- Two sample t-Test – calculate required sample size with G*Power
- Two sample t-Test in R – calculation and interpretation
- Two sample t-test in R – checking normality assumption
- Checking the homogeneity of variance assumption for the two sample t-test in R
- Two sample t-Test in R – calculation and interpretation
- Effect size for the two sample t-test in R
- Reporting two sample t-test – results from R
SPSS
- Independent samples t-Test – calculate required sample size with SPSS
- Independent samples t-test in SPSS – checking normality assumption
- Independent samples t-Test in SPSS – calculation and interpretation
- Reporting independent samples t-test – results from SPSS
Mann-Whitney-Wilcoxon-Test/Mann-Whitney-U-Test
- Mann-Whitney-Wilcoxon-test – calculate required sample size with G*Power
- Mann-Whitney-Wilcoxon-test in R – calculation and interpretation
- Effect size of the Mann-Whitney-Wilcoxon-test in R
- Reporting Mann-Whitney-Wilcoxon-test – results from R
Three or more sample tests (independent samples)
One-way ANOVA
- One-Way ANOVA – calculate required sample size with G*Power
- One-way ANOVA in R – checking normality assumption
- One-way ANOVA in R – checking homogeneity of variance assumption
- One-Way ANOVA in R – calculation and interpretation
- Post hoc tests for the One-Way ANOVA in R
- Effect size for post-hoc-tests of the One-Way ANOVA in R
- Effect size Eta-squared for the One-Way ANOVA in R
- Welch-ANOVA in R – calculation and interpretation
- Reporting One-Way ANOVA – results from R
Kruskal-Wallis-Test
- Kruskal-Wallis-Test – calculate required sample size with G*Power
- Kruskal-Wallis-Test in R – calculation and interpretation
- Post hoc tests for the Kruskal Wallis-test in R
- Effect size for post-hoc-tests of the Kruskal-Wallis-test in R
- Effect size of the Kruskal-Wallis-test in R
- Reporting Kruskal-Wallis-test – results from R
Paired tests for two measurements (dependent samples)
Paired samples t-Test (Dependent t-Test)
R Statistics
- Paired t-Test – calculate required sample size with G*Power
- Paired t-Test in R – checking normality assumption
- Paired t-Test in R – calculation and interpretation
- Effect size for the Paired t-Test in R
- Reporting Paired t-Test – results from R
SPSS
- Paired samples t-Test – calculate required sample size with SPSS
- Paired samples t-Test in SPSS – checking normality assumption
- Paired samples t-Test in SPSS – calculation and interpretation
- Reporting Paired samples t-Test – results from SPSS
Wilcoxon-Test (Wilcoxon signed-rank test)
- Paired Samples Wilcoxon-test – calculate required sample size with G*Power
- Paired Samples Wilcoxon-test in R – calculation and interpretation
- Effect size of the Paired Samples Wilcoxon-test in R
- Reporting Paired Samples Wilcoxon-test – results from R
Paired tests for at least three measurements (dependent samples)
Repeated Measures ANOVA
- Repeated Measures ANOVA – calculate required sample size with G*Power
- Repeated Measures ANOVA in R – checking normality assumption
- Repeated Measures ANOVA in R – calculation and interpretation
- Post hoc tests for the Repeated Measures ANOVA in R
- Effect size for post-hoc-tests of the Repeated Measures ANOVA in R
- Effect size Eta-squared for the Repeated Measures ANOVA in R
- Reporting Repeated Measures ANOVA – results from R
Friedman Test/ANOVA
- Friedman ANOVA – calculate required sample size with G*Power
- Friedman ANOVA in R – calculation and interpretation
- Post hoc tests for the Friedman ANOVA in R
- Effect size for post-hoc-tests of the Friedman ANOVA in R
- Effect size Kendall’s W for the Friedman ANOVA in R
- Reporting Friedman ANOVA – results from R
Mixed approach (between-subject factor and within-subject factor
Mixed ANOVA
- Mixed ANOVA – calculate required sample size with G*Power
- Mixed ANOVA in R – calculation and interpretation
- Mixed ANOVA in R – checking normality assumption
- Mixed ANOVA in R – Homogeneity of Covariance Matrices
