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Hi there, I would advise not to ignore other potential independent variables/covariates, as the multiple linear regression tells you the direct effect of degree on income whilst controlling for the effect of gender on income AND the correlation of gender and degree. TLDR: always include as many variables as necessary that might explain the variance of your dependent variable. Cheers, Björn.

Glad it helped! :-) Cheers, Björn.

Yes, reporting the standardized test statistic should be sufficient in most cases since it is, at least in many reviewers eyes, mroe meaningful, aka. easier to interpret. Cheers, Björn.

Hi Carolin, unfortunately this is not as simple to explain, Bootstrapping might be a remedy. I would however prefer to do it in R to not commit any errors: th-cam.com/video/ft2IvrfhgEo/w-d-xo.html and add "conf.level = 0.95" to the formula to get the CI for the effect size r for the paired Wilcoxon test. Cheers, Björn.

Thanks for the positive feedback! I hope you'll stick around or even spread the word. ;-) Cheers, Björn.

Hi Vinda, no, unfortunately not. Eta²-Squared is only applicable to quantify effects of the Kruskal-Wallis-test itself. Post-hoc-tests should use the effect size r, which I show in this SPSS tutorial: th-cam.com/video/fAfiPoXgq3Q/w-d-xo.html Cheers, Björn.

@@statorials Thank you! I am currently doing my thesis using a hierarchical cluster analysis with Kruskal-Wallis tests to determine differences between clusters. Are you able to make a video/advise me of how to appropriately write up the results for these for APA format?

This one should come really close to APA: th-cam.com/video/IDmSnYHHqqk/w-d-xo.html Differences are nuances with reporting the medians/mean ranks and what has been used to control for alpha error inflation. APA sometimes leaves out effect sizes though, which I deem very important though. Cheers, Björn.

Hi Nicolas, 1) In order to do an a priori sample size calculation for the paired samples Wilcoxon test in G*Power you indeed need dz. According to my knowledge, a transformation is appropriate with the formula d = 2r/(sqrt(1-r²)), which I found in Cooper et al., the Handbook of Research synthesis, 2nd edition on page 234, 235. 2) Yes, there is debate whether Cohen's (be it d or r) thresholds are appropriate universally, which I think they are not. Every discipline, justifiably has "own" thresholds. For example, Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and individual differences, 102, 74-78 proposed .1, .2 and .3 for r. Cheers, Björn.

@@statorials Thank you very much for your kind answer and sahring your knowledge.

Glad you like my video on the Chi-Square independence test sample calculation! Cheers, Björn.

Glad you like my video about the ANOVA. :-) Cheers, Björn.

Simply an identifier or case number.

Hey Joanne, thanks for the feedback! If you allow a beta-error of 20% the sample size requirements for the mixed ANOVA, or any test in general, will drop rapidly. If you have 2 between-subjects factors, you need to multiply the number of values with eachother to get to the number of groups you can enter into G*Power. Let's say you have a one between-subjects factor being control and intervention group and another between-subjects factor being the gender with male and female, you would eventually have 2x2 = 4 groups. If I use 4 groups and the other parameters you mentioned, I would need 48 observations. Cheers, Björn.

It depends on the standard you have to report your Kruskal-Wallis test results. SPSS unfortunately does not provide CI for the Kruskal-Wallis-test. Cheers, Björn.

Lantz was already in the text, I highlighted it in a different section and also added Field. Cheers, Björn.

Glad you like it. Check out the slightly longer version for a few more details: th-cam.com/video/_jOzKArVLqw/w-d-xo.html Cheers, Björn.

Hi there, actually, OLS regression is the method for estimating the parameters in a linear regression model, minimizing the sum of the squared differences between observed and predicted values. Cheers, Björn.