I read a blog post entitled On Not Writing and it felt a little close to home. The author, an academic who is in a non-tenure position, writes,

The footnote read, “Of course, all of this writing presupposes that the stacks of papers get graded.” Ouch. Too close to home. I sent this on to some of my non-tenure track peers and Rob responded that I had tapped into his blog guilt. My blog guilt had been at an all time high already, and so I vowed that I would immediately post something to Citizen Statistician. Well, that was several weeks ago, but I am finally posting.

Fall semester I taught a PhD seminar on categorical data analysis that I had proposed the previous spring. As with many first-time offerings, the amount of work was staggering and intellectually wonderful. The course notes, assignments, etc. are all available at the course website (which also doubled as the syllabus).

The course, like so many advanced seminars, had very few students actually take the course for a grade, but had quite a few auditors. The course projects were a blast to read and resulted in at least two pre-dissertation papers, a written prelim paper, and so far, two articles that have been submitted to journals!

After some reflection, there are some things I will do differently when I teach this again (likely an every-other-year offering):

  • I would like to spend more time on the classification methods. Although we talked about them a little, the beginning modeling took waaaay more time than I anticipated and I need to re-think that a bit.

  • I would like to cover mixed-effects models for binary outcomes in the future. This wasn’t possible this semester since we only had a regression course as the pre-requisite. Now, there is a new pre-requisite which includes linear mixed-effects models with continuous outcomes, so at least students will have been exposed to those types of models. This course also includes a much more in-depth introduction to likelihood, so that should also open up some time.

  • I will not teach the ordinal models in the future. Yuck. Disaster.

  • I probably won’t use the Agresti book in the future. While it is quite technical and comprehensive, it is expensive and the students did not like it for the course. I don’t know what I will use instead. Agresti will remain on a resources list.

  • The propensity score methods (PSM) were a hit with the students and those will be included again. I will also probably put together an assignment based on those.

  • I would like to add in survival analysis.

There are a ton of other topics that could be cool, but with limited time they probably aren’t feasible. I think in general my thought was to spend the first half of the course on introducing and using the logistic and multinomial models and the second half of the course on advanced applications (PSM, classification, etc.)

If anyone has any great ideas or suggestions, please leave comments. Also, I am always on the lookout for some datasets that were used in journal articles or are particularly relevant.