evals: Teaching evaluations at the UT Austin

Description Usage Format Source See Also Examples

Description

The data are gathered from end of semester student evaluations for a sample of 463 courses taught by 94 professors from the University of Texas at Austin. In addition, six students rate the professors' physical appearance. The result is a data frame where each row contains a different course and each column has information on either the course or the professor https://www.openintro.org/data/index.php?data=evals

Usage

1

Format

A data frame with 463 observations corresponding to courses on the following 13 variables.

ID

Identification variable for course.

prof_ID

Identification variable for professor. Many professors are included more than once in this dataset.

score

Average professor evaluation score: (1) very unsatisfactory - (5) excellent.

age

Age of professor.

bty_avg

Average beauty rating of professor.

gender

Gender of professor (collected as a binary variable at the time of the study): female, male.

ethnicity

Ethnicity of professor: not minority, minority.

language

Language of school where professor received education: English or non-English.

rank

Rank of professor: teaching, tenure track, tenured.

pic_outfit

Outfit of professor in picture: not formal, formal.

pic_color

Color of professor’s picture: color, black & white.

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.

Source

Çetinkaya-Rundel M, Morgan KL, Stangl D. 2013. Looking Good on Course Evaluations. CHANCE 26(2).

See Also

The data in 'evals' is a slight modification of evals.

Examples

1
2
library(dplyr)
glimpse(evals)

Example output

Attaching package:dplyrThe following objects are masked frompackage:stats:

    filter, lag

The following objects are masked frompackage:base:

    intersect, setdiff, setequal, union

Rows: 463
Columns: 14
$ ID           <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1$ prof_ID      <int> 1, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5,$ score        <dbl> 4.7, 4.1, 3.9, 4.8, 4.6, 4.3, 2.8, 4.1, 3.4, 4.5, 3.8, 4$ age          <int> 36, 36, 36, 36, 59, 59, 59, 51, 51, 40, 40, 40, 40, 40,$ bty_avg      <dbl> 5.000, 5.000, 5.000, 5.000, 3.000, 3.000, 3.000, 3.333,$ gender       <fct> female, female, female, female, male, male, male, male,$ ethnicity    <fct> minority, minority, minority, minority, not minority, no$ language     <fct> english, english, english, english, english, english, en$ rank         <fct> tenure track, tenure track, tenure track, tenure track,$ pic_outfit   <fct> not formal, not formal, not formal, not formal, not form$ pic_color    <fct> color, color, color, color, color, color, color, color,$ cls_did_eval <int> 24, 86, 76, 77, 17, 35, 39, 55, 111, 40, 24, 24, 17, 14,$ cls_students <int> 43, 125, 125, 123, 20, 40, 44, 55, 195, 46, 27, 25, 20,$ cls_level    <fct> upper, upper, upper, upper, upper, upper, upper, upper,

moderndive documentation built on Jan. 9, 2021, 1:34 a.m.