While finding group difference through One way ANOVA and again finding these through General Linear Model via Univariate Analysis, the F value and significant value changes a lot. However, the result of One Way ANOVA and GLM Univariate remains same if there is only one factor but it changes when there are two factors.
For understanding the question a file is attached please clear this phenomenon.
Somehow I can't follow your approach (btw which software did you use?). How can you use a one-way ANOVA procedure to process a two-factorial design?? As you mentioned, the results for one factor are the same for 'one-way' and 'GLM-univariate', as expected. With a one-way procedure you can only handle one(!) factor, so may it be possible that you did something wrong with your data handling?
Add: I also noticed in your headlines that you tested in your first model (first output on each page) that you had one factor "designation", but in your second model, in the one-way analysis you had only the factor "department" (not surprising since you can only analyze one factor with the one-way model) and the factors "Designation & Department" in the GLM model. May this be the problem?
Mr. Rainer thanks for interest in helping me. For more clearity of the question the file is again sent alongwith the question given at the end of the page. Please elaborate the difference.
Thanks.
I think it is really what I already tried to explain. You made it even clearer with your read accentuations.
In all four models you have one DV Role overload, fine!
In your first model you have one IV: Designation (One-way)
In your second model, you have one IV: Department (One-way)
In your third model you have one IV: Designation (GLM, what is basically the same as model 1)
In your fourth model you have two IV: Designation AND Department, so this is a two factorial model, which is NOT the same as model three, nor as model one or two (what you are trying to compare with)!!
Please run the GLM model again, only including Department as IV and it will have the same result as your model 2. The other way round: you can not include a second independent variable into the one-way structure (so you can not transfer model 2 into model 4!), because it is, as the name tells you, an analysis with only one IV. If you have two or more factors, the GLM is the correct approach (if you provided that you meet all other assumptions and so on)
I hope this helps to clarify you confusion.
Edit: one thing about the terminology: in your doc file you talk about "different values of F in Multivariate result ", you do not have a multivariate analysis! Typically, multivariate analysis have more than one dependent variabel. You have one DV but two IV, so it is a two factorial, but still univariate design.
Thanks Mr. Rainer. Now i have got the actual difference between factorial and multivariate analysis and that all you have cleared here in detail. Thanks again.
various subgroups in statistics use terms differently. in the biostat literature, multivariate usually means 'more than one predictor.' so maybe muhammad's use is ok in his 'world.'
i have not looked at the data, but if the effect for one of your IVs differs substantially between the univariate analysis (it is the only IV) and the multivariate analysis (both IVs), the implication is that the effects of the IVs on the DV are not independent of each other.
Dear Ellen,
please have a look at the data, it is just the case that he did not use the same model, although he thought he did, I think thats all.
How do you define "substantially"? If I have one between IV check the results and then do the same but include of a second IV, I am pretty sure thist will change the overall outcome and specifically the result for the first IV (the only case I can imagine when this wouldn't hold would be, if the second IV has perfect zero correlations with both, the DV and the other IV, which won't happen with real data), because in a complete between design, the error term is the same for both IVs . So if the second variable explains any variance in the DV, this will decrease the error variance, which in turn changes the F-value for the first IV (even if both IVs are completely uncorrelated). So, there will be always a difference in the results if you compare two different models. "Substantially" is up to your defintion.
About the terminology: maybe you are right that terms differ between subgroups, but I have never seen that a one or two factorial ANOVA with one DV has been labeled as "multivariate analysis". Maybe I have overlooked this or simly did not came across it, but please provide some references, I am really willing to learn something new. ;-)
rainer, my terminology is more like living in the regression (glm) world, than the anova world. but i would think of a 2 factor anova as a multivariate analysis. not such a big deal, as long as we (and our readers eventually) know what we are talking about.
as for 'substantially,' that's why there's still some 'art' left in this business.
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