I am trying to conduct an analysis of complex survey data from The Survey of Health, Ageing and Retirement in Europe (SHARE) using either the R "survey" package or SPSS Complex Samples. The issue I am having is that the sampling design varied by country/subsample. Every main participant will have data for a sampling weight and at least one of the following: subsample, stratum1 or psu (depending on the sampling design applied in their area).
When I try to specify the survey design in R "survey" I get an error messages due to the presence of missing values (different survey designs). I was using the following code:
svydesign(ids= ~psu+ssu, strata = ~stratum1+stratum2, data = SHARE, weights = ~cciw). Have I done something wrong here?
If I use SPSS Complex Samples and specify the design as sampling with replacement (1-stage) with 2 clusters (psu, ssu) and 2 strata (stratum1, stratum2) the analysis will run despite the missing values. Not sure what the difference is here, or if this is appropriate.
Would it be a better approach to only adjust for the sampling weights? For example:
svydesign(id = ~mergeid, data = SHARE, weights = ~cciw).
The SHARE includes the following cross-sectional sample design variables
- mergeid: Unique identifier for each participant
- dw: Design weight
- cchw: Calibrated cross-sectional household-level weight [calibrated using Deville and Särndal (1992) approach]
- cciw: Calibrated cross-sectional individual-level weight [calibrated using Deville and Särndal (1992) approach]
- subsample: Indicator for country-specific subsample [E.g., different age groups when some form of oversampling has been applied]
- stratum1: Indicator for primary stratum
- stratum2: Indicator for secondary stratum
- psu: Primary sampling unit
- ssu: Secondary sampling unit