[R-sig-ME] Providing data for a course project

Reinhold Kliegl reinhold.kliegl at gmail.com
Wed Apr 6 21:26:40 CEST 2011

You may want to point students to the Potsdam Mind Research Repository


Here we list preprint PDFs of peer-review papers along with R scripts
and R dataframes that were used for analyses and figures.
The best approach is to read the "About" page. The rest should be
self-explanatory. (For some older papers, analyses were based on

Under the first entry of  "R Playground", there is also a function
that allows students to generate simulated data for designs with
arbitrary between- and within-subject factors with prespecified tables
of means, standard deviations and correlations for within-subject


On Wed, Apr 6, 2011 at 10:55 AM, Douglas Bates <bates at stat.wisc.edu> wrote:
> I am currently teaching an advanced graduate course on mixed-effects
> models, anova and all that.  Students in the course are required to
> complete a data analysis project, which typically would use linear or
> generalized linear mixed-effects models.  If you have data that you
> would like to have analyzed with such methods please consider
> contacting the class at the email address
> <stat850-1-s11 at lists.wisc.edu> with a short description of your data
> and what you hope to learn from an analysis of it.
> Requests for data confidentiality will be honored.
> I place a heavy emphasis on data plotting and on plotting the results
> of model fits and I have a "no ugly graphics" policy for this project.
>  That is, the students know that if they want to get a good grade they
> better consider the graphics carefully.  I'll be grading the project
> reports in mid-May and can provide some feedback, although probably
> not an intensive analysis of each data set.
> I also welcome suggestions of data sets with some background
> description that you know of on the web.  I have pointed students to
> some of the data available at the Center for Multilevel Modeling, the
> U.C. Irvine machine learning data archive, and the UCLA archive of
> data sets from textbooks.  If you know of other sources we would
> appreciate learning of them.
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