Impact of Beauty on Instructor's Teaching Ratings
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.
data("TeachingRatings")A data frame containing 463 observations on 13 variables.
factor. Does the instructor belong to a minority (non-Caucasian)?
the professor's age.
factor indicating instructor's gender.
factor. Is the course a single-credit elective (e.g., yoga, aerobics, dance)?
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.
course overall teaching evaluation score, on a scale of 1 (very unsatisfactory) to 5 (excellent).
factor. Is the course an upper or lower division course? (Lower division courses are mainly large freshman and sophomore courses)?
factor. Is the instructor a native English speaker?
factor. Is the instructor on tenure track?
number of students that participated in the evaluation.
number of students enrolled in the course.
factor indicating instructor identifier.
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.
The data were provided by Prof. Hamermesh. The first 8 variables are also available in the online complements to Stock and Watson (2007) at
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.