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https://www.medicaljournals.se/jrm/content/html/10.2340/16501977-2210

I understand the difference between statistical significance and effect size, but I am having trouble interpreting the data on Table III in the article linked above. I am evaluating the WOMAC scores. My first question is can effect size confidence intervals cross zero and still be valid? My second question is about the negative and positive values of the Hedges'g. The first study scores the WOMAC in a way where the higher the score, the better the outcome. The second two studies score the WOMAC in a way where the lower the score, the better the outcome. Does this mean that in the first study a positive Hedges'g indicate larger effect for the treatment group and a negative Hedges' g for the other two studies indicate larger effect for the treatment group?

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    $\begingroup$ Hi and welcome to the site. Please reproduce the relevant part of the paper here so we can understand what you are asking. $\endgroup$
    – terdon
    Sep 12 '17 at 8:31
  • $\begingroup$ 1. higher score = higher function Hedges'g g of -0.63 CI95% (-23.9, 5.9) $\endgroup$
    – CharlieCal
    Sep 13 '17 at 23:00
  • $\begingroup$ 2. lower score = higher function. Hedges' g of 0.18 & CI95% (-2.9, 4.3) $\endgroup$
    – CharlieCal
    Sep 13 '17 at 23:01
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Hedge's g measures the difference between two groups here experimental and control in terms of combined (weighted) standard deviation. A higher g implies greater difference and lower g means lesser difference between two groups. It depends on the study how the scoring of questions is proceeded with : the effect-size will be same. For example, answer to question item may be scored 1 to 10 depending on whether question item is written in positive sense (the apple is good for health) or negative sense (the apple is not good for health). Positive Hedge's g does not indicate larger effect for the treatment group. THE negative Hedges' g for the other two studies indicates positive effect for the treatment group if the difference has been computed by deducting experimental group mean from the control group mean (which is less than exp. Group Mean). Overall, I find that a knowledge of questionnaire scaling and scoring of scales and question items could be very helpful in general and in doing a meta-analysis effectively.

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