instructor_evaluations: University Lecture/Instructor Evaluations by Students at ETH

In m-clark/noiris: Data sets

Description

University lecture evaluations by students at ETH Zurich, anonymized for privacy protection. This is an interesting “medium” sized example of a partially nested mixed effect model.

Usage

instructor_evaluations

Format

A data frame with 73421 observations on the following 7 variables.

student_id

a factor with levels 1:2972 denoting individual students.

instructor_id

a factor with 1128 levels from 1:2160, denoting individual professors or lecturers.

student_age

an ordered factor with levels 2 < 4 < 6 < 8, denoting student's 'age' measured in the semester number the student has been enrolled.

lecture_age

an ordered factor with 6 levels, 1 < 2 < ... < 6, measuring how many semesters back the lecture rated had taken place.

lecture_age_num

Same but as numeric.

service

a binary factor with levels 'main' and 'service'; a lecture is a 'service', if held for a different department than the lecturer's main one.

department

a factor with 14 levels from 1:15, using a random code for the department of the lecture.

rating

a numeric vector of ratings of lectures by the students, using the discrete scale 1:5, with meanings of 'poor' to 'very good'.

Details

This data is directly imported from the lme4 package. Column names have been changed to be more discernable, and possibly altered to have meaningful labels. Each observation is one student's rating for a specific lecture (of one lecturer, during one semester in the past). The main goal of the survey is to find 'the best liked prof', according to the lectures given. Statistical analysis of such data has been the basis for a (student) jury selecting the final winners.

The present data set has been anonymized and slightly simplified on purpose.

See Also

InstEval

Examples

library(noiris)
str(instructor_evaluations)

head(instructor_evaluations, 16)

m-clark/noiris documentation built on Sept. 9, 2019, 9:08 a.m.