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Effects size statistic and how to interpret it

My question is how to interpret Effects Size statistics - eta squared - JASP application

Data Set very large >22,000 data observations

Am told in the literature that the p-values will be very small and useless.

I have looked at a dozen publications on Effect Sizes, why to use, why not to use this or that one

So I reran my AVOVA in JASP with eta squared, got some numbers and have NO idea how to interpret.

The first data set is a subset of the second dataset, BTW.

Here are two printouts. Any help or suggestions would be wonderful.

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  • Here is an interesting reveiw that may be helpful

    Breaugh, J. A. (2003). Effect size estimation: Factors to consider and mistakes to avoid. Journal of Management, 29(1), 79-97.   the free pdf

    Good luck


  • Hi JDeBeer,

    You've also corresponded about this issue on our GitHub page, right? In general, I'd say that it is always a good idea to plot the data -- perhaps even the raw data, and/or means with CIs.



  • thanks, yes I have asked the question multiple places. Sorry of that is wrong.

    Trying to understand eta squared, omega squared and cohen's d, and what to report.

    I now understand how to report Cohen's d and omega squared, but have NO idea of the importance of eta squared.

    The numbers for Cohen's d effect size seem to be well published but the only thing I have found for omega sq, is from page 184 of Keppel et al, "Intro to design and analysis" where they offer this, from Cohen 1988, a book. "a small effect is omega sqr 0.01, medium is 0.06, and large effect is 0.15 or greater. it is a real struggle to understand this, I am retired, and not in school so no professor to ask. And I am only reading free pdf papers.

    Not the place for a suggestion, but maybe it would be good to add these kind of interpretations to the raw statistic in JASP, which BTW I like very much. Very very helpful for a paper we are writing.



  • Hi JDB,

    Asking the same question on different fora is a good idea! I have never really looked into the interpretation of eta-squared. I know that some people like to convert all effect sizes to r; if that's legit and possible for eta-squared, it could help. But it seems that the more complicated design, the more options there exist for effect size. And I am not sure people really care about it (in addition to what the eye can pick up from a plot of the descriptive results).


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