Analysis of complex survey data with more than one sampling design

Question

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

Answer 1

here's how i might stack two distinct survey designs?

options( survey.lonely.psu = 'adjust' )

library(survey)

# if you have these two small datasets
df1 <- data.frame( onepsu = c( 1 , 1 , 2 , 2 ) , onestrata = c( 'a' , 'b' , 'c' , 'd' ) , myweights = 1:4 , somedata = c( 0 , 1 , 0 , 1 ) )
df2 <- data.frame( twopsu = c( 'w' , 'x' , 'y' , 'z' ) , twostrata = c( 1 , 2 , 1 , 2 ) , myweights = 4:1 , somedata = c( 1 , 1 , 0 , 0 ) )

# with these two distinct survey designs (both linearized, so `svydesign()` not `svrepdesign()`)
design1 <- svydesign( ~ onepsu , strata = ~ onestrata , data = df1 , weights = ~ myweights , nest = TRUE )
design2 <- svydesign( ~ twopsu , strata = ~ twostrata , data = df2 , weights = ~ myweights , nest = TRUE )

# add unique prefixes to the psu & strata variables
# and also add a dataset name to distinguish them
stackable_df1 <- transform( df1 , combinedpsu = paste0( 'first' , onepsu ) , combinedstrata = paste0( 'first' , onestrata ) , dataset = 'df1' )
stackable_df2 <- transform( df2 , combinedpsu = paste0( 'second' , twopsu ) , combinedstrata = paste0( 'second' , twostrata ) , dataset = 'df2' )

# remove non-matching variables
stackable_df1[ c( 'onepsu' , 'onestrata' ) ] <- NULL
stackable_df2[ c( 'twopsu' , 'twostrata' ) ] <- NULL

# stack the two datasets
combined_df <- rbind( stackable_df1 , stackable_df2 )

combined_design <- svydesign( ~ combinedpsu , strata = ~ combinedstrata , data = combined_df , weights = ~ myweights , nest = TRUE )

# separate dataset SEs match combined
svymean( ~ somedata , design1 )
svymean( ~ somedata , design2 )
svyby( ~ somedata , ~ dataset , combined_design , svymean )

# total across both designs also works
svymean( ~ somedata , combined_design )