Meta analysis provides one step analysis and generalization of scattered but related studies. Can it also be useful for policy analysis and prescription in the interest of the vulnerable, isolated, and discreet people and populations?
Survey design is very important especially sampling framework. Cluster layers should identified first and method of selection of respondents should be carefully designed. After quantitative data collection, this specific group should be focused to be followed by qualitative research. Thus you do not miss any important info from the particular group of people.
As Win said, I think you'd need to be very careful when designing your analysis. You may find patterns that may be indicative but it would be difficult to extrapolate the effect of a policy or for prescribing a policy approach if you couldn't be certain that the correlations showed causation. If you knew the other studies very well and knew how the data were formulated, it might be possible. Best of luck!
I agree with Win. The balance has to be achieved with qualitative and quantitative research. As a practitioner, however, I've found that it's usually the qualitative research that yields the best results with vulnerable and isolated communities and individuals. You can sometimes find that vulnerable and isolated people have other, complex problems which may prevent them from participating in research. These can be related to a number of issues such as health, IT and communications (not all have or want a computer) logistics and sheer geography. This is why I have often defaulted straight to qualitative methods, sometimes on the back of local support groups and community volunteers. Hope it goes well.
Would you read the " Marc Orlitzky, Frank L. Schmidt, Sara L. Rynes. (2003). Corporate Social and Financial Performance: A Meta-analysis. Business Week, Organization Studies 24(3), 403-441 " The processes of a meta-analysis are reasonably clear.
A method that could be useful is to use Integrative Propositional Analysis to integrate the theories from related studies into a larger, more cohesive theory. This approach can be useful in situations involving many inter-related parts, like policy for vulnerable populations. It can show where theories agree, where there are gaps in the theory and what parts of the theory are better understood. See for example, http://sgo.sagepub.com/content/5/3/2158244015604190
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