25-gss.Rmd

# General Social Survey (GSS) {-}

[![Build Status](https://travis-ci.org/asdfree/gss.svg?branch=master)](https://travis-ci.org/asdfree/gss) [![Build status](https://ci.appveyor.com/api/projects/status/github/asdfree/gss?svg=TRUE)](https://ci.appveyor.com/project/ajdamico/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](http://www.nsf.gov/) and administered by the [National Opinion Research Center](http://www.norc.org/).

## 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.

```{r eval = FALSE }
library(lodown)
lodown( "gss" , output_dir = file.path( path.expand( "~" ) , "GSS" ) )
```

## Analysis Examples with the `survey` library \ {-}

Construct a complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
library(survey)

gss_files <-
	list.files(
		file.path( path.expand( "~" ) , "GSS" ) ,
		full.names = TRUE 
	)

gss_rds <-
	grep( 
		"cross sectional cumulative(.*)([0-9][0-9][0-9][0-9])\\.rds$" , 
		gss_files , 
		value = TRUE 
	)
	
gss_df <- readRDS( gss_rds )

# keep only the variables you need
keep_vars <- 
	c( "vpsu" , "vstrat" , "compwt" , "polviews" , 
		"born" , "adults" , "hompop" , "race" , "region" ,
		"age" , "sex" , "one" )
		
gss_df <- gss_df[ keep_vars ] ; gc()
# this step conserves RAM

# https://gssdataexplorer.norc.org/pages/show?page=gss%2Fstandard_error
gss_design <- 
	svydesign( 
		~ vpsu , 
		strata = ~ vstrat , 
		data = gss_df , 
		weights = ~ wtssall , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
svytotal( ~ one , gss_design )

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

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ adults_in_hh , 
	denominator = ~ persons_in_hh , 
	gss_design ,
	na.rm = TRUE
)
```

### Subsetting {-}

Restrict the survey design to females:
```{r eval = FALSE , results = "hide" }
sub_gss_design <- subset( gss_design , sex == 2 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
degf( gss_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ age , gss_design , na.rm = TRUE )
```

Include the complex sample design effect in the result for a specific statistic:
```{r eval = FALSE , results = "hide" }
# 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:
```{r eval = FALSE , results = "hide" }
svyciprop( ~ born_in_usa , gss_design ,
	method = "likelihood" , na.rm = TRUE )
```

### Regression Models and Tests of Association {-}

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( age ~ born_in_usa , gss_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
svychisq( 
	~ born_in_usa + race , 
	gss_design 
)
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
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](https://github.com/tidyverse/dplyr/)-like syntax. [srvyr](https://github.com/gergness/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](https://cran.r-project.org/web/packages/srvyr/vignettes/srvyr-vs-survey.html) details the available features. As a starting point for GSS users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
gss_srvyr_design <- as_survey( gss_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
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 ) )
```