NLSY | R Documentation |

## National Longitudinal Survey of Youth Data

### Description

The dataset come from a small random sample of the U.S. National Longitudinal Survey of Youth.

### Format

A data frame with 243 observations on the following 6 variables.

`math`

Math achievement test score

`read`

Reading achievement test score

`antisoc`

score on a measure of child's antisocial behavior,

`0:6`

`hyperact`

score on a measure of child's hyperactive behavior,

`0:5`

`income`

yearly income of child's father

`educ`

years of education of child's father

### Details

In this dataset, `math`

and `read`

scores are taken at the outcome
variables. Among the remaining predictors, `income`

and `educ`

might be considered as background variables necessary to control for.
Interest might then be focused on whether the behavioural variables
`antisoc`

and `hyperact`

contribute beyond that.

### Source

This dataset was derived from a larger one used by Patrick Curran at the 1997 meeting of the Society for Research on Child Development (SRCD). A description now only exists on the WayBack Machine, http://web.archive.org/web/20050404145001/http://www.unc.edu/~curran/example.html.

More details are available at http://web.archive.org/web/20060830061414/http://www.unc.edu/~curran/srcd-docs/srcdmeth.pdf.

### Examples

```
library(car)
data(NLSY)
#examine the data
scatterplotMatrix(NLSY, smooth=FALSE)
# test control variables by themselves
# -------------------------------------
mod1 <- lm(cbind(read,math) ~ income+educ, data=NLSY)
Anova(mod1)
heplot(mod1, fill=TRUE)
# test of overall regression
coefs <- rownames(coef(mod1))[-1]
linearHypothesis(mod1, coefs)
heplot(mod1, fill=TRUE, hypotheses=list("Overall"=coefs))
# additional contribution of antisoc + hyperact over income + educ
# ----------------------------------------------------------------
mod2 <- lm(cbind(read,math) ~ antisoc + hyperact + income + educ, data=NLSY)
Anova(mod2)
coefs <- rownames(coef(mod2))[-1]
heplot(mod2, fill=TRUE, hypotheses=list("Overall"=coefs, "mod2|mod1"=coefs[1:2]))
linearHypothesis(mod2, coefs[1:2])
heplot(mod2, fill=TRUE, hypotheses=list("mod2|mod1"=coefs[1:2]))
```