I have 21 socio-economic and attitudinal macro-level variables (such as percentage of mothers aged 24-54 not employed, percentage of children aged 3-5 in nursery schools and so on). I also have data on the proportions of grandparents which provided intensive childcare. Most of the socio-economic variables which I selected are highly correlated with childcare provision (for instance, there is a negative correlation between proportion of mothers employed part-time and provision of grandparental childcare).
Ideally, I would like to create a typology of different kinds of countries. My hope would be to use some kind of dimension reduction technique whose components or factors would make some intuitive sense (e.g. attitudes towards family and gender, labour market structure, family policies). Or, alternatively, assess which of the 21 macro-level indicators best explain the variability in childcare provision across countries.
My main problem is that I only have 12 European countries. I reckon that PCA and factor analyses are not appropriate techniques with so few cases. Am I correct? I was told to try use qualitative comparative analysis or multiple correspondence analysis, although to my understanding the latter techniques are more appropriate for binary (or categorical) macro-level indicators (whereas mine are percentages or continuous variables).