Hi! I was wondering if anyone had any suggestions for me.

I'm working with two datasets. One contains a population of cases (n=25) from 2005, and the other contains a population of cases (27) from 2007. Each case has a score on each of six variables/indicators (v1, v2, v3...), each indicator has a different max value (v1's max= 30, v2's max= 22, v3's max= 20...); the sum of all six indicators will range from 0 to 100 for any given case.

The point of the variables was the construction of an additive scale (the 0-100 one), and I was hoping to test the validity of the scale. On the surface, weighting the variables and adding them this randomly seemed a bit problematic, but the idea of a single variable combining the other six makes sense. My question is how to combine the variables (or test how they've been weighted/combined).

I tried Principal Component Analysis and Factor Analysis, and both extracted one component/factor with a very high eigenvalue. This seems to indicate to me that on the surface, the single latent variable (through combination of the six variables) makes sense. However, when I had STATA predict scores on the factor, the predictions are very highly correlated with the additive scale (r2=.98). Is that to be expected, or is something fishy?

All I want is to confirm that the additive scale is useful OR to suggest more useful weightings for the variables. I recognise that I have a very small sample here, and so confirmatory factor analysis is probably out.

Can anyone suggest how I go about combining the six variables into an optimal scale, or assessing their additive combination?

(Also: since there are two sets of countries, data for 2005 and for 2007, I have been analysing each group separately. Should I combine the two and analyse the cases as country-years in order to double to population?)

Thanks a million!

I'm working with two datasets. One contains a population of cases (n=25) from 2005, and the other contains a population of cases (27) from 2007. Each case has a score on each of six variables/indicators (v1, v2, v3...), each indicator has a different max value (v1's max= 30, v2's max= 22, v3's max= 20...); the sum of all six indicators will range from 0 to 100 for any given case.

The point of the variables was the construction of an additive scale (the 0-100 one), and I was hoping to test the validity of the scale. On the surface, weighting the variables and adding them this randomly seemed a bit problematic, but the idea of a single variable combining the other six makes sense. My question is how to combine the variables (or test how they've been weighted/combined).

I tried Principal Component Analysis and Factor Analysis, and both extracted one component/factor with a very high eigenvalue. This seems to indicate to me that on the surface, the single latent variable (through combination of the six variables) makes sense. However, when I had STATA predict scores on the factor, the predictions are very highly correlated with the additive scale (r2=.98). Is that to be expected, or is something fishy?

All I want is to confirm that the additive scale is useful OR to suggest more useful weightings for the variables. I recognise that I have a very small sample here, and so confirmatory factor analysis is probably out.

Can anyone suggest how I go about combining the six variables into an optimal scale, or assessing their additive combination?

(Also: since there are two sets of countries, data for 2005 and for 2007, I have been analysing each group separately. Should I combine the two and analyse the cases as country-years in order to double to population?)

Thanks a million!

Last edited: