fairml (version 0.6.1)

law.school.admissions: Law School Admission Council data

Description

Survey among students attending law school in the U.S. in 1991.

Usage

data(law.school.admissions)

Format

The data contains 20800 observations and the following variables:

• age, a continuous variable containing the student's age in years;

• decile1, a continuous variable containing the student's decile in the school given his grades in Year 1;

• decile3, a continuous variable containing the student's decile in the school given his grades in Year 3;

• fam_inc, a continuous variable containing student's family income bracket (from 1 to 5);

• lsat, a continuous variable containing the student's LSAT score;

• ugpa, a continuous variable containing the student's undergraduate GPA;

• gender, a factor with levels "female" and "male";

• race1, a factor with levels "asian", "black", "hisp", "other" and "white";

• cluster, a factor with levels "1", "2", "3", "4", "5" and "6" encoding the tiers of law school prestige;

• fulltime, a factor with levels "FALSE" and "TRUE", whether the student will work full-time or part-time;

• bar, a factor with levels "FALSE" and "TRUE", whether the student passed the bar exam on the first try.

References

Sander RH (2004). "A Systemic Analysis of Affirmative Action in American Law Schools". Stanford Law Review, 57:367--483.

Examples

# NOT RUN {
data(law.school.admissions)

# short-hand variable names.
ll = law.school.admissions
r = ll[, "ugpa"]
s = ll[, c("age", "race1")]
p = ll[, setdiff(names(ll), c("ugpa", "age", "race1"))]

m = nclm(response = r, sensitive = s, predictors = p, unfairness = 0.05)
summary(m)

m = frrm(response = r, sensitive = s, predictors = p, unfairness = 0.05)
summary(m)
# }