Grading the professor

Grading the professor

  • Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously.

  • However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical appearance of the instructor.

  • The article titled, “Beauty in theclassroom: instructors’ pulchritude and putative pedagogical productivity” (Hamermesh and Parker, 2005) found that instructors who are viewed to be better looking receive higher instructional ratings. Today we’ll use data from this study.

Daniel S. Hamermesh, Amy Parker, Beauty in the classroom: instructors pulchritude and putative pedagogical productivity, Economics of Education Review, Volume 24, Issue 4, August 2005, Pages 369-376, ISSN 0272-7757, 10.1016/j.econedurev.2004.07.013.

The data

  • Gathered from end of semester student evaluations for a large sample of professors from the University of Texas at Austin.

  • In addition, six students rated the professors’ physical appearance.

  • The result is a data frame where each row contains a different course and columns represent variables about the courses and professors.


This is a slightly modified version of the original data set that was released as part of the replication data for Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman and Hill, 2007).

The data

variable description
score average professor evaluation score: (1) very unsatisfactory - (5) excellent.
rank rank of professor: teaching, tenure track, tenured.
ethnicity ethnicity of professor: not minority, minority.
gender gender of professor: female, male.
language language of school where professor received education: english or non-english.
age age of professor.
cls_perc_eval percent of students in class who completed evaluation.
cls_did_eval number of students in class who completed evaluation.
cls_students total number of students in class.
cls_level class level: lower, upper.

The data

variable description
cls_profs number of professors teaching sections in course in sample: single, multiple.
cls_credits number of credits of class: one credit (lab, PE, etc.), multi credit.
bty_f1lower beauty rating of professor from lower level female: (1) lowest - (10) highest.
bty_f1upper beauty rating of professor from upper level female: (1) lowest - (10) highest.
bty_f2upper beauty rating of professor from second upper level female: (1) lowest - (10) highest.
bty_m1lower beauty rating of professor from lower level male: (1) lowest - (10) highest.
bty_m1upper beauty rating of professor from upper level male: (1) lowest - (10) highest.
bty_m2upper beauty rating of professor from second upper level male: (1) lowest - (10) highest.
bty_avg average beauty rating of professor.
pic_outfit outfit of professor in picture: not formal, formal.
pic_color color of professor’s picture: color, black & white.

Exploring the data

Is this an observational study or an experiment? The original research question posed in the paper is whether beauty leads directly to the differences in course evaluations. Given the study design, is it possible to answer this question as it is phrased? If not, rephrase the question.

More exploration of the data

Describe the distribution of score. Is the distribution skewed? What does that tell you about how students rate courses? Is this what you expected to see? Why, or why not?
qplot(score, data = evals)

plot of chunk unnamed-chunk-3

Simple linear regression

Beauty and evaluation score

The fundamental phenomenon suggested by the study is that better looking teachers are evaluated more favorably. Let’s create a scatterplot to see if this appears to be the case:

qplot(bty_avg, score, data = evals, geom = "jitter")

plot of chunk unnamed-chunk-4

Simple linear regression

Fit a linear model called m_bty to predict average professor score from average beauty rating. Is beauty score a statistically or practically significant predictor?
m_bty = lm(score ~ bty_avg, data = evals)
## Call:
## lm(formula = score ~ bty_avg, data = evals)
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.925 -0.369  0.142  0.398  0.931 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.8803     0.0761   50.96  < 2e-16 ***
## bty_avg       0.0666     0.0163    4.09  5.1e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 0.535 on 461 degrees of freedom
## Multiple R-squared:  0.035,  Adjusted R-squared:  0.0329 
## F-statistic: 16.7 on 1 and 461 DF,  p-value: 5.08e-05

Multiple linear regression

Beauty scores

The data set contains several variables on the beauty score of the professor: individual ratings from each of the six students who were asked to score the physical appearance of the professors and the average of these six scores.

Would we expect these variables to be dependent or independent?

ggpairs(evals[,13:19], alpha=0.7)

plot of chunk unnamed-chunk-6


  • These variables are collinear (correlated), and adding more than one of these variables to the model would not add much value to the model.

  • In this application and with these highly-correlated predictors, it is reasonable to use the average beauty score as the single representative of these variables.

Controlling for gender - visually

Create a visualization that displays the relationship of score and beauty, while controlling for gender.
qplot(bty_avg, score, data = evals, geom = "jitter", color = gender)