Omega squared (ω2) is wrong.

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by Martin » Fri May 25, 2018 5:01 pm

Hi,
When I do the exact same analysis, a repeated measures ANOVA in Jamovi and in JASP I get the exact same results in everything except for ω2. Here it is a big difference. Either Jamovi or JASP is wrong, or both.

Thanks for a great program!
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by Ravi » Fri May 25, 2018 6:11 pm

So it looks like we are using the regular calculation of omega squared, while JASP is doing a generalized version of omega squared. I think we should probably use the generalized version as well, because the regular one is not completely appropriate for repeated measures designs. I'll fix this quickly and the change will be added to the next release. Thanks for reporting!
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by Martin » Fri May 25, 2018 6:46 pm

Thanks Ravi for the quick response! Yes that makes sense. See below:

Olejnik, S., & Algina, J. (2003). Generalized Eta and Omega Squared Statistics: Measures of Effect Size for Some Common Research Designs. Psychological Methods, 8, 434-447. http://dx.doi.org/10.1037/1082-989X.8.4.434
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by gabrielarantest » Sat Mar 30, 2019 9:06 pm

Hi. I have a similar problem with the eta squared when conducting a mixed design ANOVA. When conducting a mixed design ANOVA (2 within and 1 between factors) JASP and JAMOVI give me the same p values and the same partial eta squared. However, JAMOVI and JASP give me different eta squared. While JASP gives me a n² number very close to the np², JAMOVI gives a n² very different from the np². (e.g. np² = .138, JAMOVI gives me n²=.21 while JASP n²=.136).

I know that devs here are not responsible for JASP outputs. But I would like to ask if someone can tell me why these eta squared are so different.

Cheers.
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by Ravi » Mon Apr 01, 2019 11:19 am

So I think this has to do with how JASP calculates the total sum of squares. We used to calculate the total sum of squares in the same way as JASP (i.e., add the SS of all terms belonging to an individual error term together + the SS of the residual), but somebody sent us an email telling us that this was wrong in case of a RM/Mixed model so we corrected it. To calculate the total sum of squares, we now add all the sums of squares together and to get the eta squared you divide the SS of the term by the total SS.
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by gabrielarantest » Wed Apr 24, 2019 7:11 pm

Thanks for the reply Ravi!

Cheers.
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