Hello
I conducted one-way ANOVA to compare the average of a variable across 3 groups. The reviewer of my paper asked for Cohen's d test for measuring the effect size. How can I get this measure in JMP? I am using JMP pro V17
Cohen's d is not directly available in JMP 17. However, it is in JMP 18 under
Analyze=>Fit Y by X=>Means/Anova/Pooled t
In JMP 17 it has to be calculated using JSL. Here is an example script that generates Cohen's d for all pairs of groups
or
Names Default To Here( 1 ); dt = open("$SAMPLE_DATA/big class.jmp"); var = "height"; grp = "sex"; Eval( Parse( "Summarize( dt, bygp = by( :" || grp || " ), stds = Std Dev( :" || var || " ), ns = Count( :" || var || " ), means = Mean( :" || var || " ) );" ) ); nw = New Window( "Cohen's d", vlb = V List Box() ); For( i = 1, i <= N Items( bygp ) - 1, i++, For( k = i + 1, k <= N Items( bygp ), k++, pooled = Sqrt( (stds[i] ^ 2 * (ns[i] - 1) + stds[k] ^ 2 * (ns[k] - 1)) / (ns[i] + ns[k] - 2) ); dif = Abs( means[i] - means[k] ); cohen = (dif / pooled); stats = Matrix( dif ) |/ Matrix( pooled ) |/ Matrix( cohen ); compare = {}; insert into( compare, bygp[i] || " vs. " || bygp[k]); ob = Outline Box( grp || "=" || bygp[i] || " vs. " || grp || "=" || bygp[k], Table Box( String Col Box( "", compare ), String Col Box( "", {"Difference", "Pooled Std Dev", "Cohen's d"} ), Number Col Box( "", stats ) ) ); vlb << append( ob ); ) );
Hi @Samira : FWIW, for 2 or more groups in the ANOVA, the RMSE (Root Mean Squared Error) is the pooled SD.
Thanks
It was helpful. I wonder if there is a way to get it in JMP 18 for 3 groups, not 2. pooled t is only available when comparing means in 2 groups, however, in my case, I conducted one-way ANOVA to compare means of 3 groups
Hi @Samira,
Outside of 2 groups, this option is not possible with JMP 18 natively. You can have a look at the Wish list, this need has already been raised and acknowledged : Cohen's d for "Fit Y by X"
Let's hope for a release in a future 18.X version or JMP 19.
This helped me a lot to calculate Cohen's d manually. For more clarification, I found RMSE in the summary of fit table.