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Hello all, this is my first posting, but it's a good one.
I'm analyzing a dataset which is both longitudinal and follows a complex
sampling methodology. For cross-sectional descriptions the complex
sampling module is used, while any analyses are carried using the GENLIN
procedure. I'm currently deciding on whether to use Multiple Imputation
(probably via AMELIA) to correct for the large numbers of missing
responses. My questions are:
(1) Is this even a good idea, perhaps I should use some other missing
value analysis;
(2) Once I have multiple imputed datasets, (a) how would I combine the
estimates derived from the complex sampling frequency procedure for each
imputed dataset and (b) similarly for the estimates from the GENLIN
procedure. i.e. are Rubins rules appropriate (particularly for nominal
data)
More detail if required:
833 Households nested within 161 Primary Sampling Units (PSU), which are
in turn nested within 51 Strata, and across 4 sampling waves (taken
every 3 years).
weight =
Number of PSUs in strata x
Number of PSUs selected
Number of eligible dwellings in PSU _ x
Number of dwellings selected from PSU
Enumerated dwellings x
Screened dwellings
Eligible dwellings selected _
Eligible dwellings responding
Thanks in advance,
Brendan Stevenson
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