Great job. Thank you. I used the same data in this example; I found them not normally distributed and did not have equal variance. They violated the assumptions of the ANOVA test!
Hi, just a correction. When you report the results from ANOVA, you need to use df of main effect and df of error df(main,error). In your case, sex = F(1,24) = 75.... and group = F(1,24) ....
Hi! If I am planning to compare the effect of two independent variable (say: type of sweetener and daily intake frequency) on one dependent variable (antioxidant capacity), do I still need to have a negative control group? Thank you so much!
Yes it is good to have a group where there is no sweetener a control group.. you can easily compare your treatment results …and you can say that everything being equal the changes observed is due to the sweetener .. if you donot have a control group you can’t conclude so.
Thank you so much! I was in a crazy condition and your video helped me so much! Thanks again....
Thanks, helped me a lot. Great job.
This was really helpful, great job. thank you!
This was very helpful. Thank you
Great job. Thank you. I used the same data in this example; I found them not normally distributed and did not have equal variance. They violated the assumptions of the ANOVA test!
Than u can apply ANOVA?
Thanks a lot!
Hi, just a correction. When you report the results from ANOVA, you need to use df of main effect and df of error df(main,error). In your case, sex = F(1,24) = 75.... and group = F(1,24) ....
Very nice demonstration.
Great.. Really helpful
thanks!
very great explanation
Thank you so much
If you are testing for repeated measures, why pick the univariate linear model instead of repeated measures model?
Excellent
In my results the f-value and the significance don't show anything, they're empty. Any suggestions?
Hi! If I am planning to compare the effect of two independent variable (say: type of sweetener and daily intake frequency) on one dependent variable (antioxidant capacity), do I still need to have a negative control group? Thank you so much!
Yes it is good to have a group where there is no sweetener a control group.. you can easily compare your treatment results …and you can say that everything being equal the changes observed is due to the sweetener .. if you donot have a control group you can’t conclude so.
can you run follow up tests to make more concrete conclusions based on the interaction?
How can interaction effect be determined in descriptive statistics
Hi. How to you interpret a "cross" interaction?
did you find out?
So does each participant need 2 data points for a 2x2 independent ANOVA, or would it be 4? I'm confused 🤔
Isn't this a mixed-design ANOVA? Cause you have 2 groups and also 3 repeated measures per group?
Two way anova with replication
So, does the result mean women can hold their drink better than men?