I'm having trouble creating a composite measure. I have 5 different non-normal variables (all different scales). I'd like to create one score that takes into account these 5 variables. I can manually weight the variables. How do i do this? I have JMP, so I have been trying to use that, but am not sure where to begin. All the of the variables have different scales and units.
In general, a simple weighted linear composite can be formed as follows:
newvar = w1 * x1 + w2 * x2 + ... + w5 *x5
where w1 to w5 are your five weights and x1 to x5 are your five variables.
The question is what weights should you use?
A common approach in my field (psychology) would be convert each variable to a z-score and then unit-weight the variables (i.e., take a simple sum of z-scores). This in some sense represents an equal weighting that controls for the fact that the variables are on different metrics.
Even if you want to weight some variables conceptually more (e.g., variable 1 is more important), it can still be useful to first convert the variables to z-scores and then apply differential weights based on your conceptual weighting.
In other cases, the variables are on roughly the same scale (i.e., very similar standard deviations). In which case, you can often skip the z-score step. You see this a lot with multi-item self-report scales (e.g., life satisfaction, personality, etc.).
Also, sometimes you have items that are negatively related to the construct and you need to reverse certain items. So in that cause, you could use a weight of -1 instead of 1 after z-score transformation.
I've received a few questions about this over the while (see here).
I've never used JMP. But most general purpose statistics sofwtare have tools to create new composites: A quick google suggested this might be useful:http://www.jmp.com/support/help/Formula_Editor.shtml