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

`StockWatson2007`

### Examples

    data("TeachingRatings")

    ## 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 = sandwich)
    ## (same coefficients but with different covariances)

[Package AER version 1.2-5 Index]