European Social Survey (ESS)

Build Status Build status

Contributed by Dr. Daniel Oberski <daniel.oberski@gmail.com>

The European Social Survey measures political opinion and behavior across the continent.

  • One table per country with one row per sampled respondent.

  • A complex sample survey designed to generalize to residents aged 15 and older in participating nations.

  • Released biennially since 2002.

  • Headquartered at City, University of London and governed by a scientific team across Europe.

Simplified Download and Importation

The R lodown package easily downloads and imports all available ESS microdata by simply specifying "ess" with an output_dir = parameter in the lodown() function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

library(lodown)
lodown( "ess" , output_dir = file.path( path.expand( "~" ) , "ESS" ) , 
    your_email = "email@address.com" )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the ESS catalog, you could pass a subsetted catalog through the lodown() function in order to download and import specific extracts (rather than all available extracts).

library(lodown)
# examine all available ESS microdata files
ess_cat <-
    get_catalog( "ess" ,
        output_dir = file.path( path.expand( "~" ) , "ESS" ) , 
        your_email = "email@address.com" )

# 2014 only
ess_cat <- subset( ess_cat , year == 2014 )
# download the microdata to your local computer
lodown( "ess" , ess_cat , 
    your_email = "email@address.com" )

Analysis Examples with the survey library

Construct a complex sample survey design:

library(survey)

ess_be_df <- 
    readRDS( file.path( path.expand( "~" ) , "ESS" , "2014/ESS7BE.rds" ) )

ess_sddf_df <- 
    readRDS( file.path( path.expand( "~" ) , "ESS" , "2014/ESS7SDDFe01_1.rds" ) )

ess_df <-
    merge( 
        ess_be_df , 
        ess_sddf_df , 
        by = c( 'cntry' , 'idno' ) 
    )

stopifnot( nrow( ess_df ) == nrow( ess_be_df ) )

ess_design <- 
    svydesign(
        ids = ~psu ,
        strata = ~stratify ,
        probs = ~prob ,
        data = ess_df
    )

Variable Recoding

Add new columns to the data set:

ess_design <- 
    update( 
        ess_design , 
        
        one = 1 ,
        
        non_european_immigrants =
            factor( impcntr ,
                labels = c( 'Allow many to come and live here' , 
                'Allow some' , 'Allow a few' , 'Allow none' )
            ) ,
        
        sex = factor( icgndra , labels = c( 'male' , 'female' ) ) ,
            
        more_than_one_hour_tv_daily = as.numeric( tvtot >= 3 )
    )

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( ess_design , "sampling" ) != 0 )

svyby( ~ one , ~ non_european_immigrants , ess_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , ess_design )

svyby( ~ one , ~ non_european_immigrants , ess_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ ppltrst , ess_design )

svyby( ~ ppltrst , ~ non_european_immigrants , ess_design , svymean )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ sex , ess_design , na.rm = TRUE )

svyby( ~ sex , ~ non_european_immigrants , ess_design , svymean , na.rm = TRUE )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ ppltrst , ess_design )

svyby( ~ ppltrst , ~ non_european_immigrants , ess_design , svytotal )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ sex , ess_design , na.rm = TRUE )

svyby( ~ sex , ~ non_european_immigrants , ess_design , svytotal , na.rm = TRUE )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ ppltrst , ess_design , 0.5 )

svyby( 
    ~ ppltrst , 
    ~ non_european_immigrants , 
    ess_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE ,
    keep.var = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ ppltrst , 
    denominator = ~ pplfair , 
    ess_design 
)

Subsetting

Restrict the survey design to voters:

sub_ess_design <- subset( ess_design , vote == 1 )

Calculate the mean (average) of this subset:

svymean( ~ ppltrst , sub_ess_design )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ ppltrst , ess_design )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ ppltrst , 
        ~ non_european_immigrants , 
        ess_design , 
        svymean 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( ess_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ ppltrst , ess_design )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ ppltrst , ess_design , deff = TRUE )

# SRS with replacement
svymean( ~ ppltrst , ess_design , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ more_than_one_hour_tv_daily , ess_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( ppltrst ~ more_than_one_hour_tv_daily , ess_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ more_than_one_hour_tv_daily + sex , 
    ess_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        ppltrst ~ more_than_one_hour_tv_daily + sex , 
        ess_design 
    )

summary( glm_result )

Analysis Examples with srvyr

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for ESS users, this code replicates previously-presented examples:

library(srvyr)
ess_srvyr_design <- as_survey( ess_design )

Calculate the mean (average) of a linear variable, overall and by groups:

ess_srvyr_design %>%
    summarize( mean = survey_mean( ppltrst ) )

ess_srvyr_design %>%
    group_by( non_european_immigrants ) %>%
    summarize( mean = survey_mean( ppltrst ) )

Replication Example