# InstEval

##### University Lecture/Instructor Evaluations by Students at ETH

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.

- Keywords
- datasets

##### 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.

##### 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. % ("d": \dQuote{Dozierende} in German)
`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).

##### Examples

```
# NOT RUN {
str(InstEval)
head(InstEval, 16)
xtabs(~ service + dept, InstEval)
# }
```

*Documentation reproduced from package lme4, version 1.1-21, License: GPL (>= 2)*