AER (version 1.2-9)

TeachingRatings: Impact of Beauty on Instructor's Teaching Ratings

Description

Data on course evaluations, course characteristics, and professor characteristics for 463 courses for the academic years 2000--2002 at the University of Texas at Austin.

Usage

data("TeachingRatings")

Format

A data frame containing 463 observations on 13 variables.

minority

factor. Does the instructor belong to a minority (non-Caucasian)?

age

the professor's age.

gender

factor indicating instructor's gender.

credits

factor. Is the course a single-credit elective (e.g., yoga, aerobics, dance)?

beauty

rating of the instructor's physical appearance by a panel of six students, averaged across the six panelists, shifted to have a mean of zero.

eval

course overall teaching evaluation score, on a scale of 1 (very unsatisfactory) to 5 (excellent).

division

factor. Is the course an upper or lower division course? (Lower division courses are mainly large freshman and sophomore courses)?

native

factor. Is the instructor a native English speaker?

tenure

factor. Is the instructor on tenure track?

students

number of students that participated in the evaluation.

allstudents

number of students enrolled in the course.

prof

factor indicating instructor identifier.

Details

A sample of student instructional ratings for a group of university teachers along with beauty rating (average from six independent judges) and a number of other characteristics.

References

Hamermesh, D.S., and Parker, A. (2005). Beauty in the Classroom: Instructors' Pulchritude and Putative Pedagogical Productivity. Economics of Education Review, 24, 369--376.

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

See Also

StockWatson2007

Examples

# NOT RUN {
data("TeachingRatings", package = "AER")

## evaluation score vs. beauty
plot(eval ~ beauty, data = TeachingRatings)
fm <- lm(eval ~ beauty, data = TeachingRatings)
abline(fm)
summary(fm)

## prediction of Stock & Watson's evaluation score
sw <- with(TeachingRatings, mean(beauty) + c(0, 1) * sd(beauty))
names(sw) <- c("Watson", "Stock")
predict(fm, newdata = data.frame(beauty = sw))

## Hamermesh and Parker, 2005, Table 3
fmw <- lm(eval ~ beauty + gender + minority + native + tenure + division + credits,
  weights = students, data = TeachingRatings)
coeftest(fmw, vcov = vcovCL, cluster = TeachingRatings$prof)
# }