I am using the ‘Teaching Ratings’ data from the AER package. The data set was used in a study to determine if students were influenced by the instructor’s good looks or otherwise while evaluating the instructor’s teaching performance.Data on course evaluations, course characteristics, and professor characteristics for 463 courses for the academic years 2000 to 2002 at the University of Texas at Austin.
‘Teaching Ratings’ data frame contains 463 observations on 13 variables.
minority : Factor variable. Does the instructor belong to a minority ?
age : Age of the professor.
gender : Factor variable indicating instructor’s gender.
credits : Factor variable. 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 variable. Is the course an upper or lower division course? (Lower division courses are mainly large freshman and sophomore courses)?
native : Factor variable. Is the instructor a native English speaker?
tenure : Factor variable. 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 variable indicating instructor identifier.
I assume the null hypothesis that there is no influence of professor’s Looks On students’ teaching evaluations. Let’s test it.
library(ggplot2)
data(TeachingRatings, package="AER")
TR <- data.frame(x = TeachingRatings$eval,y = TeachingRatings$beauty,z = TeachingRatings$gender, z1=TeachingRatings$tenure, z2=TeachingRatings$minority)
plot(eval ~ beauty, data = TeachingRatings,col = TeachingRatings$gender,main = "Teacher Evaluation Vs Beauty", pch = 16)
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
Since the ‘p’ value is less than 0.01, there is a strong evidence against the null hypothesis. Therefore, we reject the null hypothesis.
The blue triangles represent women and the red dots represent male instructors. The Y-axis is a normalized measure of instructor’s looks and teaching evaluations are presented on the x-axis.
TeacherLooksVsEvaluations <- ggplot(data = TR, aes(x = x, y = y, colour = z, shape = z)) + geom_point() + scale_colour_brewer(palette="Set1") +facet_wrap(z1~z2) + xlab("eval") + ylab("beauty") + labs(colour = "gender", shape = "gender")
TeacherLooksVsEvaluations
Conclusions
1) The top right quadrant shows that non-tenured, visible minority faculty members happen to be exclusively males receiving low beauty evaluations, but higher teaching evaluations.
2) The bottom right quadrant however shows that most tenured visible minority instructors are females who receive a higher teaching and beauty evaluation.
3) Therefore, it can be concluded that there exist influence of professor’s looks On students’ teaching evaluations.