TeachingRatings dataset

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")

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

StockWatson2007

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)