# National Longitudinal Study of Adolescent to Adult Health (ADDHEALTH)

The National Longitudinal Study of Adolescent to Adult Health follows a cohort of teenagers from the 1990s into adulthood.

Many tables, most with one row per sampled youth respondent.

A complex sample survey designed to generalize to adolescents in grades 7-12 in the United States during the 1994-95 school year.

Released at irregular intervals, with 1994-1995, 1996, 2001-2002, and 2008-2009 available and 2016-2018 forthcoming.

Administered by the Carolina Population Center and funded by a consortium.

## Simplified Download and Importation

The R `lodown`

package easily downloads and imports all available ADDHEALTH microdata by simply specifying `"addhealth"`

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( "addhealth" , output_dir = file.path( path.expand( "~" ) , "ADDHEALTH" ) ,
your_email = "email@address.com" ,
your_password = "password" )
```

`lodown`

also provides a catalog of available microdata extracts with the `get_catalog()`

function. After requesting the ADDHEALTH 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 ADDHEALTH microdata files
addhealth_cat <-
get_catalog( "addhealth" ,
output_dir = file.path( path.expand( "~" ) , "ADDHEALTH" ) ,
your_email = "email@address.com" ,
your_password = "password" )
# wave i only
addhealth_cat <- subset( addhealth_cat , wave == "wave i" )
# download the microdata to your local computer
addhealth_cat <- lodown( "addhealth" , addhealth_cat ,
your_email = "email@address.com" ,
your_password = "password" )
```

## Analysis Examples with the `survey`

library

Construct a complex sample survey design:

```
options( survey.lonely.psu = "adjust" )
library(survey)
addhealth_df <-
readRDS(
file.path( path.expand( "~" ) , "ADDHEALTH" ,
"wave i consolidated.rds" )
)
addhealth_design <-
svydesign(
id = ~cluster2 ,
data = addhealth_df ,
weights = ~ gswgt1 ,
nest = TRUE
)
```

### Variable Recoding

Add new columns to the data set:

```
addhealth_design <-
update(
addhealth_design ,
one = 1 ,
male = as.numeric( as.numeric( bio_sex ) == 1 ) ,
how_many_hours_of_computer_games = ifelse( h1da10 > 99 , NA , h1da10 ) ,
how_many_hours_of_television = ifelse( h1da8 > 99 , NA , h1da8 )
)
```

### Unweighted Counts

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

```
sum( weights( addhealth_design , "sampling" ) != 0 )
svyby( ~ one , ~ h1gh25 , addhealth_design , unwtd.count )
```

### Weighted Counts

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

```
svytotal( ~ one , addhealth_design )
svyby( ~ one , ~ h1gh25 , addhealth_design , svytotal )
```

### Descriptive Statistics

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

```
svymean( ~ how_many_hours_of_computer_games , addhealth_design , na.rm = TRUE )
svyby( ~ how_many_hours_of_computer_games , ~ h1gh25 , addhealth_design , svymean , na.rm = TRUE )
```

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

```
svymean( ~ h1gh24 , addhealth_design , na.rm = TRUE )
svyby( ~ h1gh24 , ~ h1gh25 , addhealth_design , svymean , na.rm = TRUE )
```

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

```
svytotal( ~ how_many_hours_of_computer_games , addhealth_design , na.rm = TRUE )
svyby( ~ how_many_hours_of_computer_games , ~ h1gh25 , addhealth_design , svytotal , na.rm = TRUE )
```

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

```
svytotal( ~ h1gh24 , addhealth_design , na.rm = TRUE )
svyby( ~ h1gh24 , ~ h1gh25 , addhealth_design , svytotal , na.rm = TRUE )
```

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

```
svyquantile( ~ how_many_hours_of_computer_games , addhealth_design , 0.5 , na.rm = TRUE )
svyby(
~ how_many_hours_of_computer_games ,
~ h1gh25 ,
addhealth_design ,
svyquantile ,
0.5 ,
ci = TRUE ,
keep.var = TRUE ,
na.rm = TRUE
)
```

Estimate a ratio:

```
svyratio(
numerator = ~ how_many_hours_of_computer_games ,
denominator = ~ how_many_hours_of_television ,
addhealth_design ,
na.rm = TRUE
)
```

### Subsetting

Restrict the survey design to self-reported fair or poor health:

`sub_addhealth_design <- subset( addhealth_design , as.numeric( h1gh1 ) %in% c( 4 , 5 ) )`

Calculate the mean (average) of this subset:

`svymean( ~ how_many_hours_of_computer_games , sub_addhealth_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( ~ how_many_hours_of_computer_games , addhealth_design , na.rm = TRUE )
coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )
grouped_result <-
svyby(
~ how_many_hours_of_computer_games ,
~ h1gh25 ,
addhealth_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( addhealth_design )`

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

`svyvar( ~ how_many_hours_of_computer_games , addhealth_design , na.rm = TRUE )`

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

```
# SRS without replacement
svymean( ~ how_many_hours_of_computer_games , addhealth_design , na.rm = TRUE , deff = TRUE )
# SRS with replacement
svymean( ~ how_many_hours_of_computer_games , addhealth_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( ~ male , addhealth_design ,
method = "likelihood" )
```

### Regression Models and Tests of Association

Perform a design-based t-test:

`svyttest( how_many_hours_of_computer_games ~ male , addhealth_design )`

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

```
svychisq(
~ male + h1gh24 ,
addhealth_design
)
```

Perform a survey-weighted generalized linear model:

```
glm_result <-
svyglm(
how_many_hours_of_computer_games ~ male + h1gh24 ,
addhealth_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 ADDHEALTH users, this code replicates previously-presented examples:

```
library(srvyr)
addhealth_srvyr_design <- as_survey( addhealth_design )
```

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

```
addhealth_srvyr_design %>%
summarize( mean = survey_mean( how_many_hours_of_computer_games , na.rm = TRUE ) )
addhealth_srvyr_design %>%
group_by( h1gh25 ) %>%
summarize( mean = survey_mean( how_many_hours_of_computer_games , na.rm = TRUE ) )
```