American National Election Study (ANES)

The American National Election Study (ANES) collects information on political belief and behavior from eligible voters in the United States.

• Most tables contain one row per sampled eligible voter.

• A complex sample survey designed to generalize to eligible voters in the United States.

• Time series studies released biennially.

• Administered by Stanford University and the University of Michigan and funded by the National Science Foundation.

Simplified Download and Importation

The R lodown package easily downloads and imports all available ANES microdata by simply specifying "anes" 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( "anes" , output_dir = file.path( path.expand( "~" ) , "ANES" ) , 
    your_email = "email@address.com" )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the ANES 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 ANES microdata files
anes_cat <-
    get_catalog( "anes" ,
        output_dir = file.path( path.expand( "~" ) , "ANES" ) , 
        your_email = "email@address.com" )

# 2016 only
anes_cat <- subset( anes_cat , directory == "2016 Time Series Study" )
# download the microdata to your local computer
anes_cat <- lodown( "anes" , anes_cat , 
    your_email = "email@address.com" )

Analysis Examples with the survey library  

Construct a complex sample survey design:

library(survey)

anes_df <- 
    readRDS( 
        file.path( path.expand( "~" ) , "ANES" , 
            "2016 Time Series Study/anes_timeseries_2016_.rds" )
    )

anes_design <-
    svydesign( 
        ~v160202 , 
        strata = ~v160201 , 
        data = anes_df , 
        weights = ~v160102 , 
        nest = TRUE 
    )

Variable Recoding

Add new columns to the data set:

anes_design <- 
    update( 
        anes_design , 
        
        one = 1 ,
        
        pope_francis_score = ifelse( v162094 %in% 0:100 , v162094 , NA ) ,

        christian_fundamentalist_score = ifelse( v162095 %in% 0:100 , v162095 , NA ) ,
        
        primary_voter = ifelse( v161021 %in% 1:2 , as.numeric( v161021 == 1 ) , NA ) ,

        think_gov_spend =
            factor( v161514 , levels = 1:4 , labels =
                c( 'foreign aid' , 'medicare' , 'national defense' , 'social security' )
            ) ,
        
        undoc_kids =
            factor( v161195x , levels = 1:6 , labels =
                c( 'should sent back - favor a great deal' ,
                    'should sent back - favor a moderate amount' ,
                    'should sent back - favor a little' ,
                    'should allow to stay - favor a little' ,
                    'should allow to stay - favor a moderate amount' ,
                    'should allow to stay - favor a great deal' )
            )

    )

Unweighted Counts

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

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

svyby( ~ one , ~ undoc_kids , anes_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , anes_design )

svyby( ~ one , ~ undoc_kids , anes_design , svytotal )

Descriptive Statistics

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

svymean( ~ pope_francis_score , anes_design , na.rm = TRUE )

svyby( ~ pope_francis_score , ~ undoc_kids , anes_design , svymean , na.rm = TRUE )

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

svymean( ~ think_gov_spend , anes_design , na.rm = TRUE )

svyby( ~ think_gov_spend , ~ undoc_kids , anes_design , svymean , na.rm = TRUE )

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

svytotal( ~ pope_francis_score , anes_design , na.rm = TRUE )

svyby( ~ pope_francis_score , ~ undoc_kids , anes_design , svytotal , na.rm = TRUE )

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

svytotal( ~ think_gov_spend , anes_design , na.rm = TRUE )

svyby( ~ think_gov_spend , ~ undoc_kids , anes_design , svytotal , na.rm = TRUE )

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

svyquantile( ~ pope_francis_score , anes_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ pope_francis_score , 
    ~ undoc_kids , 
    anes_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE ,
    keep.var = TRUE ,
    na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ christian_fundamentalist_score , 
    denominator = ~ pope_francis_score , 
    anes_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to party id: independent:

sub_anes_design <- subset( anes_design , v161158x == 4 )

Calculate the mean (average) of this subset:

svymean( ~ pope_francis_score , sub_anes_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( ~ pope_francis_score , anes_design , na.rm = TRUE )

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

grouped_result <-
    svyby( 
        ~ pope_francis_score , 
        ~ undoc_kids , 
        anes_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( anes_design )

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

svyvar( ~ pope_francis_score , anes_design , na.rm = TRUE )

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

# SRS without replacement
svymean( ~ pope_francis_score , anes_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ pope_francis_score , anes_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( ~ primary_voter , anes_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( pope_francis_score ~ primary_voter , anes_design )

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

svychisq( 
    ~ primary_voter + think_gov_spend , 
    anes_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        pope_francis_score ~ primary_voter + think_gov_spend , 
        anes_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 ANES users, this code replicates previously-presented examples:

library(srvyr)
anes_srvyr_design <- as_survey( anes_design )

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

anes_srvyr_design %>%
    summarize( mean = survey_mean( pope_francis_score , na.rm = TRUE ) )

anes_srvyr_design %>%
    group_by( undoc_kids ) %>%
    summarize( mean = survey_mean( pope_francis_score , na.rm = TRUE ) )

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Replication Example