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These "standards" in psychology are an unfortunate consequence of poor statistics training. Don't look for such standards in an entire field but only within a particular subject matter. Cohen never intended these to be standards and just suggested them as a starting point for interpretation based on a prior analysis of social science effect sizes and ...

Cohen’s d is not directly available in Stata, and you have to resort on external macros, e.g. sizefx (ssc install sizefx). It works fine if you have to series of values, but I found it less handy when you work with a full data set because there's no possibility to pass options to this command (e.g. by()). Anyway, you can still use the original formula (with ...

Both Cohen's d and Hedges' g pool variances on the assumption of equal population variances, but g pools using n - 1 for each sample instead of n, which provides a better estimate, especially the smaller the sample sizes. Both d and g are somewhat positively biased, but only negligibly for moderate or larger sample sizes. The bias is reduced using g*. The ...

Yes, what you are suggesting is exactly what has been suggested in the literature. See, for example: Morris, S. B. (2008). Estimating effect sizes from pretest-posttest-control group designs. Organizational Research Methods, 11(2), 364-386 (link, but unfortunately, no free access). The article also describes different methods for estimating this effect size ...

To my understanding, Hedges's g is a somewhat more accurate version of Cohen's d (with pooled SD) in that we add a correction factor for small sample. Both measures generally agree when the homoscedasticity assumption is not violated, but we may found situations where this is not the case, see e.g. McGrath & Meyer, Psychological Methods 2006, 11(4): ...

The SD used in the t calculation is that of the effect, not of the individual groups. In the paired case the SD to use in Cohen's d and t are the same. But, the SD used in the d calculation for independent samples is the pooled SD of the individual groups, not the theoretical SD of the effect. Under the assumptions of 0 correlation and equal variance (the ...

Given the current issues with the user-written sizefx command for Stata that chl and I have uncovered, here's an alternative way of doing things in Stata using the user-written metan command. This is designed for meta-analysis of study-level summary data so you need to enter the means, sds and ns (which i've taken from chl's answer) in one row, then run a ...

For SPSS, look at : www.mathkb.com/Uwe/Forum.aspx/stat-consult/1201/Effect-Size-in-SPSS www.spsstools.net/Syntax/T-Test/StandardizedEffectsSize.txt (maybe better organized) For Stata, I used SIZEFX: Stata module to compute effect size correlations (findit sizefx at Stata command prompt), but metan as suggested by @onestop is probably more featured.

In Stata: 1) Download the user-written package -metan- from the SSC software library: . ssc install metan 2) Assuming you have numbers, means and SDs for two groups, the syntax is of the form: . metan n1 mean1 sd1 n0 mean0 sd0 (Cohen's d is the default method for continuous data) This gives a fixed-effect meta-analysis. Add the 'random' option for a ...

I believe that generalized eta-square (Olejnik & Algena, 2003; Bakeman, 2005) provides a reasonable solution to the quantification of effect size that generalizes across between-Ss and within-Ss designs. If I read those references correctly, generalized eta-square should also generalize across sample sizes. Generalized eta-square is automatically ...

According to p238 of standard text on meta-analysis in social science The Handbook of Research Synthesis, the variance of Cohen's $d$ is $$\left( \frac{n_1 + n_2}{n_1 n_2} + \frac{d^2}{2(n_1+n_2-2)}\right) \left(\frac{n_1 + n_2}{n_1+n_2-2} \right), $$ where $n_1$ and $n_2$ are the sample sizes of the two groups being compared and $d$ is Cohen's $d$. Taking ...

You can translate pretty much any of the effect sizes to Cohen's d, so start with omega and work your way over. Equations are available for them all online... the wikipedia page for effect size is good for this. In your particular case though you can get a numerator for cohen's d as (a1 - b1) - (a2 - b2). That's the simple effect of a 2x2 interaction. ...

It means that the change in the observed difference between the groups on your manipulation check measure is smaller than the change in the observed difference on the main outcome variable, relative to (an estimate of) their respective within-group variances. At least three things can contribute to produce such a result: The effect is simply smaller, in ...

Updated: I came across an excellent post by @JeromyAnglim, and he makes a good point that standardized mean differences will be more intuitive for people, so I have made a couple of changes marked * below. I don't think this is a valid approach. The most appropriate strategy is to fit the full model that you are interested in and stick with that. If some ...

Normally you always find ways to convert effect size. For example you can calculate $r$ from $d$ and back. So you surely will be able to converts odds ratio to Cohen's $d$. One book I usually found a good resource for stuff like that is the one by Rosenthal & Rosnow. I think it was this one: Rosenthal & Rosnow - Essentials of Behavioral Research: ...