Please help...

You don't really have to choose between the two if you use multilevel modeling (also know hierarchical linear regression). This approach allows you analyze data at both the school-level and student-level.

I have a similar problem. I am trying to evaluate impact of an intervention that was implemented in very poor areas (more poor people, undeserved communities). In addition, the location of these areas were such that health services were limited because of various administrative reasons. Thus, the intervention areas had  two problems: (1) individuals residing in these areas were mostly poor, illiterate and belonged to undeserved communities; (2) the geographical location of the area was also contributing to their vulnerability (as people with similar profile but living elsewhere (non-intervention areas) had better access to services.

I have a cross sectional data about health service utilization from both types of areas at endline. There is no baseline data available for intervention and control.

I am willing to do two analyses: (1) intent to treat analysis: Here, I wish to compare the service utilization in "areas" (irrespective of whether the household in intervention area was exposed to the intervention). The aim is to see whether the intervention could bring some change at "area" (village) level. My question is: can I use Propensity Score Analysis for this? (by matching intervention "areas" with control "areas" on aggregated values of covariates obtained from survey and Census?). For example, matching intervention areas with non-intervention areas in terms of % of poor households, % of illiterate population, etc.

The second analysis is to examine the treatment effect: Here I am using Propensity score analysis at individual level (comparing those who were exposed in intervention areas with matched unexposed people from non-intervention areas). Is it right way of analysing data for my objective?

tank you all