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

http://wps.aw.com/aw_stock_ie_2/

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/TeachingRatings.Rd_%03d_medium.png", width=480, height=480)
> ### Name: TeachingRatings
> ### Title: Impact of Beauty on Instructor's Teaching Ratings
> ### Aliases: TeachingRatings
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data("TeachingRatings")
> 
> ## evaluation score vs. beauty
> plot(eval ~ beauty, data = TeachingRatings)
> fm <- lm(eval ~ beauty, data = TeachingRatings)
> abline(fm)
> summary(fm)

Call:
lm(formula = eval ~ beauty, data = TeachingRatings)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.80015 -0.36304  0.07254  0.40207  1.10373 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.99827    0.02535 157.727  < 2e-16 ***
beauty       0.13300    0.03218   4.133 4.25e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5455 on 461 degrees of freedom
Multiple R-squared:  0.03574,	Adjusted R-squared:  0.03364 
F-statistic: 17.08 on 1 and 461 DF,  p-value: 4.247e-05

> 
> ## 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))
  Watson    Stock 
3.998272 4.103163 
> 
> ## Hamermesh and Parker, 2005, Table 3
> fmw <- lm(eval ~ beauty + gender + minority + native + tenure + division + credits,
+   weights = students, data = TeachingRatings)
> coeftest(fmw, vcov = sandwich)

t test of coefficients:

               Estimate Std. Error t value  Pr(>|t|)    
(Intercept)    4.223142   0.063947 66.0417 < 2.2e-16 ***
beauty         0.274805   0.034761  7.9056 2.033e-14 ***
genderfemale  -0.238993   0.056402 -4.2373 2.740e-05 ***
minorityyes   -0.248937   0.089177 -2.7915  0.005467 ** 
nativeno      -0.252713   0.098061 -2.5771  0.010277 *  
tenureyes     -0.135923   0.060122 -2.2608  0.024245 *  
divisionlower -0.045895   0.059307 -0.7739  0.439421    
creditssingle  0.686507   0.114675  5.9866 4.351e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> ## (same coefficients but with different covariances)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>