39-nsch.Rmd

# National Survey of Children's Health (NSCH) {-}

[![Build Status](https://travis-ci.org/asdfree/nsch.svg?branch=master)](https://travis-ci.org/asdfree/nsch) [![Build status](https://ci.appveyor.com/api/projects/status/github/asdfree/nsch?svg=TRUE)](https://ci.appveyor.com/project/ajdamico/nsch)

*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.

* One row per sampled child under eighteen.

* A complex sample survey designed to generalize to non-institutionalized children in the United States at the state-level.

* Released every four or five years since 2003.

* Sponsored by the [Maternal and Child Health Bureau of the Health Resources and Services Administration](http://www.mchb.hrsa.gov/).

## 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.

```{r eval = FALSE }
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).

```{r eval = FALSE , results = "hide" }
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
nsch_cat <- lodown( "nsch" , nsch_cat )
```

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

Construct a multiply-imputed, complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
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:
```{r eval = FALSE }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
MIcombine( with( nsch_design ,
	svyratio( numerator = ~ k6q63 , denominator = ~ totkids4 )
) )
```

### Subsetting {-}

Restrict the survey design to only children:
```{r eval = FALSE , results = "hide" }
sub_nsch_design <- subset( nsch_design , agepos4 == 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
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:
```{r eval = FALSE , results = "hide" }
degf( nsch_design$designs[[1]] )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
MIcombine( with( nsch_design , svyvar( ~ ageyr_child ) ) )
```

Include the complex sample design effect in the result for a specific statistic:
```{r eval = FALSE , results = "hide" }
# 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:
```{r eval = FALSE , results = "hide" }
MIsvyciprop( ~ indicator_5_2 , nsch_design ,
	method = "likelihood" )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
MIsvyttest( ageyr_child ~ indicator_5_2 , nsch_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
MIsvychisq( ~ indicator_5_2 + povcat , nsch_design )
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	MIcombine( with( nsch_design ,
		svyglm( ageyr_child ~ indicator_5_2 + povcat )
	) )
	
summary( glm_result )
```

---

## Replication Example {-}

```{r eval = FALSE , results = "hide" }

```