## University Lecture/Instructor Evaluations by Students at ETH

### 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.

### Format

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

`s`

a factor with levels `1:2972`

denoting
individual students.

`d`

a factor with 1128 levels from `1:2160`

, denoting
individual professors or lecturers.

`studage`

an ordered factor with levels `2`

<
`4`

< `6`

< `8`

, denoting student's “age”
measured in the *semester* number the student has been enrolled.

`lectage`

an ordered factor with 6 levels, `1`

<
`2`

< ... < `6`

, measuring how many semesters back the
lecture rated had taken place.

`service`

a binary factor with levels `0`

and
`1`

; a lecture is a “service”, if held for a
different department than the lecturer's main one.

`dept`

a factor with 14 levels from `1:15`

, using a
random code for the department of the lecture.

`y`

a numeric vector of *ratings* of lectures by
the students, using the discrete scale `1:5`

, with meanings
of ‘poor’ to ‘very good’.

Each observation is one student's rating for a specific lecture
(of one lecturer, during one semester in the past).

### Details

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.

### Examples

str(InstEval)
head(InstEval, 16)
xtabs(~ service + dept, InstEval)