evaluations.csv

The data in evaluations.csv come from Hamermesh & Parker (2005) and were made available by Gelman & Hill (2007). This data were collected from student evaluations of instructors’ beauty and teaching quality for several courses at the University of Texas. The teaching evaluations were conducted at the end of the semester, and the beauty judgments were made later, by six students who had not attended the classes and were not aware of the course evaluations. The variables are:

• prof_id: Professor ID number
• avg_eval: Average course rating
• num_courses: Number of courses for which the professor has evaluations
• num_students: Number of students enrolled in the professor’s courses
• perc_evaluating: Average percentage of enrolled students who completed an evaluation
• beauty: Measure of the professor’s beauty composed of the average score on six standardized beauty ratings
• tenured: Is the professor tenured? (0 = non-tenured; 1 = tenured)
• native_english: Is the professor a native English speaker? (0 = non-native English speaker; 1 = native English speaker)
• age: Professor’s age (in years)
• female: Is the professor female? (0 = not female; 1 = female)

Preview

# Import Data
evaluations = readr::read_csv(file = "https://raw.githubusercontent.com/zief0002/rustic-penguin/main/data/evaluations.csv")

# View data
evaluations

# A tibble: 94 × 10
   prof_id avg_eval num_courses num_students perc_evaluating  beauty tenured
     <dbl>    <dbl>       <dbl>        <dbl>           <dbl>   <dbl>   <dbl>
 1       1     4              4          416            62.0  0.202        0
 2       2     3.53           3          104            87.0 -0.826        1
 3       3     3.45           2          250            78.5 -0.660        1
 4       4     4.01           8          223            84.3 -0.766        1
 5       5     4.35           6          331            81.8  1.42         0
 6       6     4.44           7         1849            59.8  0.500        1
 7       7     3.84           5          191            81.8 -0.214        0
 8       8     4.03           7          174            84.6 -0.347        1
 9       9     4.26           7          303            73.0  0.0613       0
10      10     4.56          10          191            82.1  0.453        0
# ℹ 84 more rows
# ℹ 3 more variables: native_english <dbl>, age <dbl>, female <dbl>

References

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.

Hamermesh, D. S., & Parker, A. M. (2005). Beauty in the classroom: Instructors’ pulchritude and putative pedagogical productivity. Economics of Education Review, 24, 369–376.