I have data from several (22?) samples, which vary considerably in sample size. I understand that parameters are estimated better with larger samples, but that is not the issue I want to address here. It is that I would like to both graph distributions, and estimate parameters, as if the sample sizes didn't differ across the groups/samples. This means that large samples will need to be de-weighted and small samples up-weighted.
I tried to do this in "Fit Y by X," as follows: I computed a new variable, WEIGHT, as the sample size of the Group divided by the total sample size. So let's say that I have four groups:
Group 1: N1=100
Group 2: N1=500
Group 3: N1=200
Group 4: N1=400
Also Groups 1 and 2 are Type 1 and Groups 3 and 4 are Type 2
WEIGHT will be:
Group 1: 100/1200
Group 2: 500/1200
Group 3: 200/1200
Group 4: 400/1200
If I do "Fit Y by X" where Y is the DV and X is "Type" and weight by WEIGHT, then use "Compare Densities" to get a plot of overlapping densities, it looks right. However, if I continue and get the standard deviations for the DV, these are much larger than the standard deviations for any of the groups. This makes me think that I am not understanding what I'm doing.
Help?
