# General Social Survey (GSS)

The General Social Survey (GSS) has captured political beliefs and social attitudes since 1972. In contrast to non-trendable tracking polls that capture newspaper headlines, the GSS has sustained a set of questions over four decades.

One table with one row per sampled respondent.

A complex sample survey designed to generalize to the non-institutional population of adults (18+) in the United States.

Updated biennially since 1972.

Funded by the National Science Foundation and administered by the National Opinion Research Center.

## Simplified Download and Importation

The R `lodown`

package easily downloads and imports all available GSS microdata by simply specifying `"gss"`

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( "gss" , output_dir = file.path( path.expand( "~" ) , "GSS" ) )
```

## Analysis Examples with the `survey`

library

Construct a complex sample survey design:

```
library(survey)
gss_df <-
readRDS( file.path( path.expand( "~" ) , "GSS" ,
"gss 1972 2016 cross sectional cumulative data release 2 september 29 2017.rds" ) )
gss_df <-
transform(
gss_df ,
# the calculation for compwt comes from
# http://sda.berkeley.edu/D3/GSS10/Doc/gs100195.htm#COMPWT
compwt = oversamp * formwt * wtssall ,
# the calculation for samplerc comes from
# http://sda.berkeley.edu/D3/GSS10/Doc/gs100195.htm#SAMPLERC
samplerc =
# if sample is a three or a four, samplerc should be a three
ifelse( sample %in% 3:4 , 3 ,
# if sample is a six or a seven, samplerc should be a six
ifelse( sample %in% 6:7 , 6 ,
# otherwise, samplerc should just be set to sample
sample ) )
)
# keep only the variables you need
keep_vars <-
c( "sampcode" , "samplerc" , "compwt" , "polviews" ,
"born" , "adults" , "hompop" , "race" , "region" ,
"age" , "sex" , "one" )
gss_df <- gss_df[ keep_vars ] ; gc()
# this step conserves RAM
gss_design <-
svydesign(
~sampcode ,
strata = ~samplerc ,
data = subset( gss_df , !is.na( sampcode ) ) ,
weights = ~compwt ,
nest = TRUE
)
```

### Variable Recoding

Add new columns to the data set:

```
gss_design <-
update(
gss_design ,
polviews =
factor( polviews ,
labels = c( "Extremely liberal" , "Liberal" ,
"Slightly liberal" , "Moderate, middle of the road" ,
"Slightly conservative" , "Conservative" ,
"Extremely conservative" )
) ,
born_in_usa = ifelse( born %in% 1:2 , as.numeric( born == 1 ) , NA ) ,
adults_in_hh = ifelse( adults > 8 , NA , adults ) ,
persons_in_hh = ifelse( hompop > 11 , NA , hompop ) ,
race = factor( race , labels = c( "white" , "black" , "other" ) ) ,
region =
factor( region ,
labels = c( "New England" , "Middle Atlantic" ,
"East North Central" , "West North Central" ,
"South Atlantic" , "East South Central" ,
"West South Central" , "Mountain" , "Pacific" )
)
)
```

### Unweighted Counts

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

```
sum( weights( gss_design , "sampling" ) != 0 )
svyby( ~ one , ~ region , gss_design , unwtd.count )
```

### Weighted Counts

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

```
svytotal( ~ one , gss_design )
svyby( ~ one , ~ region , gss_design , svytotal )
```

### Descriptive Statistics

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

```
svymean( ~ age , gss_design , na.rm = TRUE )
svyby( ~ age , ~ region , gss_design , svymean , na.rm = TRUE )
```

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

```
svymean( ~ race , gss_design , na.rm = TRUE )
svyby( ~ race , ~ region , gss_design , svymean , na.rm = TRUE )
```

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

```
svytotal( ~ age , gss_design , na.rm = TRUE )
svyby( ~ age , ~ region , gss_design , svytotal , na.rm = TRUE )
```

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

```
svytotal( ~ race , gss_design , na.rm = TRUE )
svyby( ~ race , ~ region , gss_design , svytotal , na.rm = TRUE )
```

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

```
svyquantile( ~ age , gss_design , 0.5 , na.rm = TRUE )
svyby(
~ age ,
~ region ,
gss_design ,
svyquantile ,
0.5 ,
ci = TRUE ,
keep.var = TRUE ,
na.rm = TRUE
)
```

Estimate a ratio:

```
svyratio(
numerator = ~ adults_in_hh ,
denominator = ~ persons_in_hh ,
gss_design ,
na.rm = TRUE
)
```

### Subsetting

Restrict the survey design to females:

`sub_gss_design <- subset( gss_design , sex == 2 )`

Calculate the mean (average) of this subset:

`svymean( ~ age , sub_gss_design , na.rm = TRUE )`

### 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( ~ age , gss_design , na.rm = TRUE )
coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )
grouped_result <-
svyby(
~ age ,
~ region ,
gss_design ,
svymean ,
na.rm = TRUE
)
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )
```

Calculate the degrees of freedom of any survey design object:

`degf( gss_design )`

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

`svyvar( ~ age , gss_design , na.rm = TRUE )`

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

```
# SRS without replacement
svymean( ~ age , gss_design , na.rm = TRUE , deff = TRUE )
# SRS with replacement
svymean( ~ age , gss_design , na.rm = TRUE , deff = "replace" )
```

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

for alternatives:

```
svyciprop( ~ born_in_usa , gss_design ,
method = "likelihood" , na.rm = TRUE )
```

### Regression Models and Tests of Association

Perform a design-based t-test:

`svyttest( age ~ born_in_usa , gss_design )`

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

```
svychisq(
~ born_in_usa + race ,
gss_design
)
```

Perform a survey-weighted generalized linear model:

```
glm_result <-
svyglm(
age ~ born_in_usa + race ,
gss_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 GSS users, this code replicates previously-presented examples:

```
library(srvyr)
gss_srvyr_design <- as_survey( gss_design )
```

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

```
gss_srvyr_design %>%
summarize( mean = survey_mean( age , na.rm = TRUE ) )
gss_srvyr_design %>%
group_by( region ) %>%
summarize( mean = survey_mean( age , na.rm = TRUE ) )
```