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I've run into a bit of a problem that I'll try to explain with a hypothetical scenario.
I have 100 records that I would like to place into 20 groups of 5 based on 3 factors. The factors are gender (M/F), program (5 programs) and activity level (3 levels). I’d like each of the 20 groups to be as similar as possible across those factors. I guess I’m not really grouping the factors but distributing them as evenly as possible across the different groups.
If all of those factors had equal numbers (50 males/50 females, 20 from each program and ~33 at each activity level) each group would have one person from each of the 5 programs, 2 or 3 males and 1 or 2 from each activity level. In reality the data won’t be evenly distributed though.
Does anyone have any suggestions as to what tools I could use to assign people to groups to get as even a spread as possible across the three factors?
This is a sample of 360 students. Would it be possible to create 60 groups of 6 students with each group being as similar as possible based on Gender, Program and Activity Level. There isn't an even distribution in any of those categories which complicates things a little e.g. there are only 37 female users so 23 groups will be all male if the female users were distributed evenly.
It might be necessary to stratify the factors to decide which is more important e.g. will I place a student in one group because it satisfies a need based on gender or another group to satisfy a need for a student with a high activity level. If that's the case lets put Gender as most important, then Program and lastly Activity Level.
That is awesome, I'll definitely use that method. I also like your point about weighting which is something that I hadn't really considered but will have a go at if needed. Thanks so much for taking the time to help me on this. I'd been getting a bit bogged down and you've really moved things along for me.