Hello,
I’m working with a data set with 1600 observations from a total population of about 3200. The variables are mostly categorical, and mostly dichotomous. The data are made up of two strata, one of which is an entire population and the other
a random sample. The first stratum makes up about 80% of the data and 36% of the entire population. The random sample stratum contains four components which potentially could have been strata as well, but weren’t sampled that way. The sampling plan was determined
externally. Some findings will have to compare the five components (the stratum with the entire population and the four components within the other stratum). Others will be for the entire sample.
I first thought that the way handle the data was to weight, and I used the formula given by Maletta (2007, available on line), which divides the proportion of the entire population made up by each stratum by the proportion of the sample
made up by that stratum. This reduces the influence of the first stratum while expanding the influence of the second. When I examine the differences among the five components using the SPSS Complex Samples procedure, the results come with acceptable margins
of error, less than 5%.
Is this the best way to look at the data, given that I have a complete population for one stratum and relatively small samples from the four components that make up the other one? That is, should I honor the completeness of the data in
the first stratum?
The write-up will be a report, rather than an article, and the audience is professional, but not necessarily research minded.
Many thanks,
Henry
Henry Ilian, Ph.D.
ACS Office of Quality Improvement
150 William Street, 17th Fl
New York, NY 10038
(212) 227-5414
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