Youth Risk Behavior Surveillance System (NLS)

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The Youth Risk Behavior Surveillance System is the high school edition of the Behavioral Risk Factor Surveillance System (BRFSS), a scientific study of good kids who do bad things.

  • One table with one row per sampled youth respondent.

  • A complex sample survey designed to generalize to all public and private school students in grades 9-12 in the United States.

  • Released biennially since 1993.

  • Administered by the Centers for Disease Control and Prevention.

Simplified Download and Importation

The R lodown package easily downloads and imports all available NLS microdata by simply specifying "nls" 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( "nls" , output_dir = file.path( path.expand( "~" ) , "NLS" ) )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the NLS 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 NLS microdata files
nls_cat <-
    get_catalog( "nls" ,
        output_dir = file.path( path.expand( "~" ) , "NLS" ) )

# 2015 only
nls_cat <- subset( nls_cat , year == 2015 )
# download the microdata to your local computer
lodown( "nls" , nls_cat )

Analysis Examples with the survey library

Construct a complex sample survey design:

library(survey)

nls_df <- readRDS( file.path( path.expand( "~" ) , "NLS" , "2015 main.rds" ) )

nls_design <- 
    svydesign( 
        ~ psu , 
        strata = ~ stratum , 
        data = nls_df , 
        weights = ~ weight , 
        nest = TRUE 
    )

Variable Recoding

Add new columns to the data set:

nls_design <- 
    update( 
        nls_design , 
        q2 = q2 ,
        never_rarely_wore_bike_helmet = as.numeric( qn8 == 1 ) ,
        ever_smoked_marijuana = as.numeric( qn47 == 1 ) ,
        ever_tried_to_quit_cigarettes = as.numeric( q36 > 2 ) ,
        smoked_cigarettes_past_year = as.numeric( q36 > 1 )
    )

Unweighted Counts

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

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

svyby( ~ one , ~ ever_smoked_marijuana , nls_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , nls_design )

svyby( ~ one , ~ ever_smoked_marijuana , nls_design , svytotal )

Descriptive Statistics

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

svymean( ~ bmipct , nls_design , na.rm = TRUE )

svyby( ~ bmipct , ~ ever_smoked_marijuana , nls_design , svymean , na.rm = TRUE )

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

svymean( ~ q2 , nls_design , na.rm = TRUE )

svyby( ~ q2 , ~ ever_smoked_marijuana , nls_design , svymean , na.rm = TRUE )

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

svytotal( ~ bmipct , nls_design , na.rm = TRUE )

svyby( ~ bmipct , ~ ever_smoked_marijuana , nls_design , svytotal , na.rm = TRUE )

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

svytotal( ~ q2 , nls_design , na.rm = TRUE )

svyby( ~ q2 , ~ ever_smoked_marijuana , nls_design , svytotal , na.rm = TRUE )

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

svyquantile( ~ bmipct , nls_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ bmipct , 
    ~ ever_smoked_marijuana , 
    nls_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE ,
    keep.var = TRUE ,
    na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ ever_tried_to_quit_cigarettes , 
    denominator = ~ smoked_cigarettes_past_year , 
    nls_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to youths who ever drank alcohol:

sub_nls_design <- subset( nls_design , qn41 == 1 )

Calculate the mean (average) of this subset:

svymean( ~ bmipct , sub_nls_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( ~ bmipct , nls_design , na.rm = TRUE )

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

grouped_result <-
    svyby( 
        ~ bmipct , 
        ~ ever_smoked_marijuana , 
        nls_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( nls_design )

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

svyvar( ~ bmipct , nls_design , na.rm = TRUE )

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

# SRS without replacement
svymean( ~ bmipct , nls_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ bmipct , nls_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( ~ never_rarely_wore_bike_helmet , nls_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( bmipct ~ never_rarely_wore_bike_helmet , nls_design )

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

svychisq( 
    ~ never_rarely_wore_bike_helmet + q2 , 
    nls_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        bmipct ~ never_rarely_wore_bike_helmet + q2 , 
        nls_design 
    )

summary( glm_result )

0.36 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 NLS users, this code replicates previously-presented examples:

library(srvyr)
nls_srvyr_design <- as_survey( nls_design )

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

nls_srvyr_design %>%
    summarize( mean = survey_mean( bmipct , na.rm = TRUE ) )

nls_srvyr_design %>%
    group_by( ever_smoked_marijuana ) %>%
    summarize( mean = survey_mean( bmipct , na.rm = TRUE ) )

Replication Example

This snippet replicates the “never/rarely wore bicycle helmet” row of PDF page 29 of this CDC analysis software document.

unwtd.count( ~ never_rarely_wore_bike_helmet , yrbss_design )

svytotal( ~ one , subset( yrbss_design , !is.na( never_rarely_wore_bike_helmet ) ) )
 
svymean( ~ never_rarely_wore_bike_helmet , yrbss_design , na.rm = TRUE )

svyciprop( ~ never_rarely_wore_bike_helmet , yrbss_design , na.rm = TRUE , method = "beta" )