National Longitudinal Survey of Youth Data

Description

The dataset 'NLSY' comes from a small part of the National Longitudinal Survey of Youth, which is a series of annual surveys conducted by the U.S. Department of Labor to examine the transition of young people into the labor force. This particular subset gives measures of 243 children on mathematics and reading achievement and also measures of behavioral problems (antisocial, hyperactivity). Also available are the yearly income and education of the child's father.

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

For the examples using 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 behavioral variables antisoc and hyperact contribute beyond that.

The distribution of father's income is very highly skewed in the positive direction. Linear model analysis should probably use log(income), but this is omitted for simplicity.

The dataset also contains a few unusual observations for you to discover.

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
# -------------------------------------
NLSY.mod1 <- lm(cbind(read, math) ~ income + educ, data=NLSY)
Anova(NLSY.mod1)
heplot(NLSY.mod1, fill=TRUE)

# test of overall regression
coefs <- rownames(coef(NLSY.mod1))[-1]
linearHypothesis(NLSY.mod1, coefs)
heplot(NLSY.mod1, fill=TRUE, hypotheses=list("Overall"=coefs))

# coefficient plot
coefplot(NLSY.mod1, fill = TRUE,
         col = c("darkgreen", "brown"),
         lwd = 2,
         ylim = c(-0.5, 3),
         main = "Bivariate coefficient plot for reading and math\nwith 95% confidence ellipses")

 
# additional contribution of antisoc + hyperact over income + educ
# ----------------------------------------------------------------
NLSY.mod2 <- lm(cbind(read,math) ~ antisoc + hyperact + income + educ, data=NLSY)
Anova(NLSY.mod2)

coefs <- rownames(coef(NLSY.mod2))[-1]
heplot(NLSY.mod2, fill=TRUE, hypotheses=list("Overall"=coefs, "mod2|mod1"=coefs[1:2]))
linearHypothesis(NLSY.mod2, coefs[1:2])

heplot(NLSY.mod2, fill=TRUE, hypotheses=list("mod2|mod1"=coefs[1:2]))

# check for outliers
idx <- cqplot(NLSY.mod2, id.n = 5)
idx