I am design a sampling strategy for a health survey in long-term care facilities (LTCFs). I refer to the article here and want to ask question about the cluster size.
I am thinking of doing a stratified cluster randomized sample (addition question, if I sample everyone in a single LTCF, is it still a 2-stage sampling? or just 1-stage? Is that I need to sample some LTCF, and then sample some resident in the sampled LTCF, then this can be called as 2-stage sampling? I am getting lost in the terminology jungle), something like this one I asked before. After reading a few books and articles, I think I need to use both stratification and clustering so to have better balance on inflating/deflating the standard error for my measurement.
I have assigned the size of a cluster to be 60 and then do a sample size estimation (approx. 3000), my question is as follow:
Say if I sampled a "small" LTCF, only 20 residents inside, I need 40 more, what should I do? To sample additional cluster?
In another way round, if I sampled a "large" LTCF, with 400 residents inside, I only need 60, should I only do a simple random sampling to pick up the 60 residents I need?
The "unequal cluster size" has been a nightmare to me for quite sometime, right now I have no clue how to process this kind of data appropriately (as I can't do it as if the data is collected by simple random sampling, right?).
I am reading Sharon L. Lor's Sampling - Design and Analysis; and Steven G. Herringa's Applied Survey Data Analysis. Can anyone give me some clue, perhaps in the 2 books, that can give me some insight on getting out of the nightmare? Recommendation of other books/articles is also welcomed. Thanks.