I’ll pitch a couple of items from the Harvard Social Science Statistics Blog worth mentioning.

First, Sebastian Bauhoff plugs a number of summer quantitative methods programs. My overall review of ICPSR would be more positive than his, but as he mentions much depends on the courses you choose: Charles Franklin’s MLE class is generally a subject of rave reviews, and I can personally vouch for Bill Jacoby’s class in scaling and Doug Baer’s class in latent variable structural equation modeling (LISREL models). I’ve also heard that the advanced MLE course has vastly improved since I took it in 2001 (when it batted around .500 while rotating four instructors). Other advanced classes that seem to get good reviews include Jeff Gill’s Bayesian class and the simultaneous equations class. Historically I know time series and categorical data analysis were somewhat hit-and-miss; the latter was regarded as excellent when taught by Jeremy Freese, but I’m told it has gone downhill since.

Second, James Griener expresses concern that people may start applying statistical models willy-nilly to explaining lower-court decision-making, on the basis that decisions are not iid but instead controlled largely by precedent. Certainly sticking circuit court opinions in as the dependent variable in a logit would be stupid without paying some serious attention to the error structure. But that hardly forecloses interesting analysis.

Also, my vague applied notion of the ideal-point model is that items (decisions) are not actually believed to be iid (there is at least one latent variable explaining them, so by definition they are not truly independent of each other), so I don’t think using an item-response theory model would be problematic—however, you’d certainly end up recovering a “respect for stare decisis” dimension in addition to the ideology dimension(s) you recover from the Supreme Court, which might actually help contribute to interesting substantive debates.