| TeachingRatings {AER} | R Documentation |
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.
Source
The data were provided by Prof. Hamermesh. The first 8 variables are also available in the online complements to Stock and Watson (2007) at
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
Examples
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)