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]))