So far, I’ve attacked my data to get my most important variable by generating first-dimension ideology scores using the CJR model for the 110th House through this morning’s roll calls; pscl makes this incredibly easy, although a tweak to allow the readKH function to support the CSV files from Jeff Lewis’ site in addition to the traditional Poole-Rosenthal data dictionaries would be nice (if I get bored later in the week, I may hack something together).

The drudge work to come: marrying these data with some member and constituency demographics so I can slap together some models (some based on real data, some fake based on a known data generating process) with the ideology scores as independent and dependent variables to see if incorporating error really helps anything or not. If not, this may turn out to be the dullest paper in methods meeting history.

With a little Python scripting (to pull down a few constituency statistics for every House member from the online Almanac of American Politics) and about an hour of grunt work (coding the race and gender of 435 House members), I now have some covariates to play with for my methods meeting paper. About halfway through it occurred to me that I could have just recycled the 105th Congress data I compiled for the Damn Impeachment Paper™ and used it, but maybe this will be fresher and more interesting even though it’s all just illustrative anyway.

Next steps: hack together some R code to run some simulations and start thinking about how to get this down on paper, in both prose and poster format.