# 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
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 ) )
```