My study is on the impact of human factor on regional development.
My “subjects” are regions of the European Union. 276 exist in total under NUTS 2 classification. The idea is to use a sample of 16-20, so that I can also do a small case study on each of them and approach them qualitatively as well.
My independent variables are demographic statistics (e.g. % of people with university degrees, % regional GDP spent on R&D and so on). I have around 20 different potential IVs or predictors, although I suppose I can group them or skip some unnecessary ones and narrow them down to around 8 if necessary.
I have a single dependent variable which will be taken from some widely-accepted scale showing the level of regional development so that doesn’t need tampering with.
Obviously, the goal is to check the impact of the IVs (both separately and as a complete model) on the DV. A regression seems to be the right way to go.
- Is it incorrect to attempt to check the impact of 20 predictors on such a small sample (16-20 regions)?
- If so, will things improve if I narrow down the predictors to 8 or so?
- What is the best way to narrow down or group the predictors into few important ones? Common sense? Factor analysis? Cronbach’s Alpha? A stepwise regression?
- If that is still not enough, should I also increase the sample size to, say, 50 or 100 regions (out of a total “population” of 276) and skip the qualitative part?
- Is a regression the best choice for the “main” test?
- Somebody mentioned an “artificial neural network” to me!? Isn’t that way too complicated for a relatively straightforward study like this one?