Hi,
The issue of which effect size measure to use and how to interpret effect size measures is a quite complex issue, and lots of people seem to have quite different opinions on it. Here is my take, based on what you write in your post. I'll list up the points for convenience.
1) There is no established effect size measure that the field generally agrees with. Some researchers often use MI with collocates, while others might use a different value, or even a combination of values.
2) Each effect size measure will generate values on a different scale so they are not comparable. One may vary from 0 to 1, while another may vary from 0 to infinity.
3) I'm not sure if you are quoting Brezina (2018) accurately, but point 2) holds, so we cannot say 0.1 = small and 0.5 = large. It depends on the measure that you are using.
4) There is no general agreement on how to interpret effect size measures. You need to decide on a measure, understand what the scale is, and then interpret the value appropriately. Take Dice as an example. It varies from 0 (completely different) to 1 (exactly the same). So, if you are comparing groups of people, you would probably not consider a value of 0.5 to be 'very similar', but for two corpora, you might consider a value of 0.5 to suggest that the corpora have 'a lot of overlap' so the effect is 'strong'.
5) AntConc offers a range of effect sizes. So, you are free to choose the ones that you feel familiar with or ones that are intuitively more easy to interpret.
6) For keyword analysis, I don't recommend using an effect size measure to rank the keywords. This is why the default setting in AntConc is to rank by log-likelihood (a statistical test, which is not an effect size measure, but may have an effect size component to it).
7) As each effect size measure exaggerates/weights different types of values (some weight low frequencies higher and others weight high frequency values higher), the results will vary dramatically depending on your choice of effect size measure. This is one reason why I don't recommend using them for keywords (see point 6).
I hope that helps!
Laurence.
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Laurence ANTHONY, Ph.D.
Professor of Applied LinguisticsFaculty of Science and Engineering
Waseda University
3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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