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.

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).

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.

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

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