European Social Survey (ESS)
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
ess_cat <- 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 ) )