I got exactly what I was looking for, thanks!. What a horrible design choice it is to have people perform separate t-tests after performing the ANOVA...
Thank you for the video. Can you perhaps cite a source for the procedure when box-M-test is signifikant? You suggest alternatively to take the statistics from Pillai-Trace.
Thank you Dr. Christiansen for this very helpful demo. I have a question for you (Or for anyone else on this page who may know the answer). Is this also the correct video if my study involves 2 independent variables and 1 dependent variable? (In my case, the 2 independent variables are condition and time intervals, and the dependent variable would be (for example) # of errors?
It doesn't matter. The intercept is the dv value when all ivs are at 0. Often it is pretty meaningless as it may be (in the real world) impossible to have iv values of 0. Its not reported.
I assume it's too late for you, but others may benefit from this answer. Basically, there are two types of assumptions, the design assumptions and the parametric assumptions. The design assumptions are: - The outcome (dependent) variable must be continuous. - All independent variables (factors) must be categorical. The parametric assumptions are: - There must be no outliers. - For every combination of categories (in this video 2x2, so 4 combinations of categories) separately, the observations must be distributed normally (except if N > 30). - The data should conform to the assumption of sfericity (as explained in the video). - The data should conform to the assumption of homogeneity of variance (as explained in the video).
@@floriscos452 For every combination of categories (in this video 2x2, so 4 combinations of categories) separately, the observations should be APPROXİMATELY distributed normally (except if N > 30). You can flex the distribution rule up to some amount.
@@floriscos452 I thought you might be able to answer my question here: Is this also the correct video if my study involves 2 independent variables and 1 dependent variable? (In my case, the 2 independent variables are condition and time intervals, and the dependent variable would be (for example) # of errors [that participants make when they redo the same writing task each time interval]?
Amazing video!! Thank you so much!
this was very very helpful! thanks!
That was really helpful. Thanks a lot.
thank you, this helped a lot!
Thank you very much!
Many thanks
I got exactly what I was looking for, thanks!. What a horrible design choice it is to have people perform separate t-tests after performing the ANOVA...
thanks!
Thank you for the video. Can you perhaps cite a source for the procedure when box-M-test is signifikant? You suggest alternatively to take the statistics from Pillai-Trace.
Thank you Dr. Christiansen for this very helpful demo. I have a question for you (Or for anyone else on this page who may know the answer). Is this also the correct video if my study involves 2 independent variables and 1 dependent variable? (In my case, the 2 independent variables are condition and time intervals, and the dependent variable would be (for example) # of errors?
Hello, thank you for this video. What if the "intercept" column in the between-subjects table is significant?
It doesn't matter. The intercept is the dv value when all ivs are at 0. Often it is pretty meaningless as it may be (in the real world) impossible to have iv values of 0. Its not reported.
What assumptions do I need to test before I can do this same analysis? I want to do a mixed anova just like the video.
I assume it's too late for you, but others may benefit from this answer. Basically, there are two types of assumptions, the design assumptions and the parametric assumptions.
The design assumptions are:
- The outcome (dependent) variable must be continuous.
- All independent variables (factors) must be categorical.
The parametric assumptions are:
- There must be no outliers.
- For every combination of categories (in this video 2x2, so 4 combinations of categories) separately, the observations must be distributed normally (except if N > 30).
- The data should conform to the assumption of sfericity (as explained in the video).
- The data should conform to the assumption of homogeneity of variance (as explained in the video).
@@floriscos452 For every combination of categories (in this video 2x2, so 4 combinations of categories) separately, the observations should be APPROXİMATELY distributed normally (except if N > 30).
You can flex the distribution rule up to some amount.
@@floriscos452 I thought you might be able to answer my question here: Is this also the correct video if my study involves 2 independent variables and 1 dependent variable? (In my case, the 2 independent variables are condition and time intervals, and the dependent variable would be (for example) # of errors [that participants make when they redo the same writing task each time interval]?