National Survey of Children’s Health (NSCH)

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Contributed by Emily Wiegand <erowewiegand@gmail.com>

The National Survey of Children’s Health (NSCH) offers state-level estimates of children’s health care and the family environment.

Simplified Download and Importation

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

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

# 2012 only
nsch_cat <- subset( nsch_cat , year == 2012 )
# download the microdata to your local computer
lodown( "nsch" , nsch_cat )

Analysis Examples with the survey library

Construct a multiply-imputed, complex sample survey design:

library(survey)
library(mitools)

nsch_imp <- readRDS( file.path( path.expand( "~" ) , "NSCH" , "2012 main.rds" ) )

nsch_design <- 
    svydesign( 
        id = ~ 1 , 
        strata = ~ state + sample , 
        weights = ~ nschwt , 
        data = imputationList( nsch_imp )
    )

Variable Recoding

Add new columns to the data set:

nsch_design <-
    update(
        nsch_design ,
        
        indicator_1_3 = ifelse( k6q40 > 1 , NA , k6q40 ) ,

        indicator_5_2 =
            ifelse( k7q05r %in% 1:5 , 1 ,
            ifelse( k7q05r %in% 0 , 0 , NA ) ) ,
            
        indicator_5_3 =
            ifelse( k7q30 == 1 | k7q31 == 1 | k7q32 == 1 , 1 ,
            ifelse( k7q30 == 0 | k7q31 == 0 | k7q32 == 0 , 0 , NA ) ) ,
            
        povcat = 
            factor( 
                findInterval( povlevel_i , c( 1 , 2 , 6 , 8 ) ) ,
                labels = 
                    c( "below poverty" , "100-199% fpl" , "200-399% fpl" , "400%+ fpl" )
            ) ,
        
        sex = factor( ifelse( sex %in% 1:2 , sex , NA ) , labels = c( "male" , "female" ) )
        
    )

Unweighted Counts

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

MIcombine( with( nsch_design , svyby( ~ one , ~ one , unwtd.count ) ) )

MIcombine( with( nsch_design , svyby( ~ one , ~ state , unwtd.count ) ) )

Weighted Counts

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

MIcombine( with( nsch_design , svytotal( ~ one ) ) )

MIcombine( with( nsch_design ,
    svyby( ~ one , ~ state , svytotal )
) )

Descriptive Statistics

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

MIcombine( with( nsch_design , svymean( ~ ageyr_child ) ) )

MIcombine( with( nsch_design ,
    svyby( ~ ageyr_child , ~ state , svymean )
) )

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

MIcombine( with( nsch_design , svymean( ~ povcat ) ) )

MIcombine( with( nsch_design ,
    svyby( ~ povcat , ~ state , svymean )
) )

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

MIcombine( with( nsch_design , svytotal( ~ ageyr_child ) ) )

MIcombine( with( nsch_design ,
    svyby( ~ ageyr_child , ~ state , svytotal )
) )

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

MIcombine( with( nsch_design , svytotal( ~ povcat ) ) )

MIcombine( with( nsch_design ,
    svyby( ~ povcat , ~ state , svytotal )
) )

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

MIcombine( with( nsch_design , svyquantile( ~ ageyr_child , 0.5 , se = TRUE ) ) )

MIcombine( with( nsch_design ,
    svyby( 
        ~ ageyr_child , ~ state , svyquantile , 0.5 ,
        se = TRUE , keep.var = TRUE , ci = TRUE 
) ) )

Estimate a ratio:

MIcombine( with( nsch_design ,
    svyratio( numerator = ~ k6q63 , denominator = ~ totkids4 )
) )

Subsetting

Restrict the survey design to only children:

sub_nsch_design <- subset( nsch_design , agepos4 == 1 )

Calculate the mean (average) of this subset:

MIcombine( with( sub_nsch_design , svymean( ~ ageyr_child ) ) )

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 <-
    MIcombine( with( nsch_design ,
        svymean( ~ ageyr_child )
    ) )

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

grouped_result <-
    MIcombine( with( nsch_design ,
        svyby( ~ ageyr_child , ~ state , svymean )
    ) )

coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( nsch_design$designs[[1]] )

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

MIcombine( with( nsch_design , svyvar( ~ ageyr_child ) ) )

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

# SRS without replacement
MIcombine( with( nsch_design ,
    svymean( ~ ageyr_child , deff = TRUE )
) )

# SRS with replacement
MIcombine( with( nsch_design ,
    svymean( ~ ageyr_child , deff = "replace" )
) )

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

lodown:::MIsvyciprop( ~ indicator_5_2 , nsch_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

lodown:::MIsvyttest( ageyr_child ~ indicator_5_2 , nsch_design )

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

lodown:::MIsvychisq( ~ indicator_5_2 + povcat , nsch_design )

Perform a survey-weighted generalized linear model:

glm_result <- 
    MIcombine( with( nsch_design ,
        svyglm( ageyr_child ~ indicator_5_2 + povcat )
    ) )
    
summary( glm_result )

Replication Example