School_data: Simulated data bearing on school vouchers
In ProjectMOSAIC/mosaicModel: Create, Visualize, and Predict with Models

Description Usage Format Details Examples

In the US, there have been controversial proposals to provide vouchers to students in failing public schools. The vouchers would allow the students to attend private schools. There are arguments pro and con that are often rooted in political philosophy (free choice!) and politics. The presumption behind the pro arguments is that attending private schools would create better outcomes for students.

1	data(School_data)

A data frame with 500 cases, each of which is a simulated student, with observations on the following variables.

test_score a simulated test score for the student
school whether the student attended public or private school
lottery whether the student was entered into a lottery for a private-school voucher
group the racial/ethnic group of the student
acad_motivation the overall level of involvement and concern of the student's parents for the student's academic performance
relig_motivation the overall level of interest motivated by religion. This is potentially an issue because a large majority of urban private schools are Catholic.

A reasonable way to test this presumption is to compare test scores for students in public and private schools. One famous analysis (Howell and Peterson, 2003, "The Education Gap: Vouchers and Urban Schools") found that voucher schools are most helpful for African-American students, and not so much for white or Hispanic students.

The School_data data frame comes from a simulation designed by the package author to replicate the overall results but supporting a very different policy recommendation. WARNING: This is just a simulation, reflecting one hypothesis about how the world might work. Don't be tempted to draw conclusions about the actual factors involved in school performance from such simulated data.

lm(test_score ~ school, data = School_data)
# the simulation mechanism itself:
nstudents <- 500
acad_motivation <- rnorm(nstudents)
group <- sample(c("black", "hispanic", "white"), replace = TRUE, size = nstudents)
relig_motivation <- ifelse( group == "black", -1, ifelse(group == "white", 0, 1))
relig_motivation <- rnorm(nstudents, mean = relig_motivation)
lottery <- (acad_motivation + relig_motivation) > 0
school <- ifelse( (runif(nstudents) + .8* lottery ) > 1, "private", "public")
test_score <- rnorm(nstudents, mean = 100 - 5 * (school == "private") + 20 * acad_motivation)
School_data <- data.frame(test_score, acad_motivation, group, relig_motivation, lottery, school)