Check out these introductory slides and R code for great tips on spatial statistics, including information on how to create a figure like the one shown above and much, much more!

Health reform is more popular in some of these states than in others. Where it's popular, Democratic candidates don't have too much of a problem, but where it's unpopular--and that includes most states--the Democratic Senate candidates are fighting an uphill battle. Support for health reform varies in these 11 states from a low of 33% in North Dakota to a high of 48% in Nevada. Democrats trail Republicans in six of the states; three are toss-ups; and in two, Democrats have a solid lead.

Despite the general tone of the piece ("The culprit is the unpopularity of health reform...") , I believe the authors are making a different argument. Namely, that voters are responding to their senator's vote on health care. Based on their evidence, however, I think this is a flawed argument as well.

Many Eyes lets you use textual data, so I just tried it out using the majority and dissenting opinions from Citizens United v. FEC, today's Supreme Court's decision striking down existing campaign finance law. (Note: Let's just say it's not a bad idea to use publicly available, non-copyrighted data.)

The resulting visualizations are just terrific, and they actually go far in illustrating the substantive differences between the conservative and liberal Justices on the campaign finance issue.

Mad Men, Season 1 Disc 1:

Paul Blart, Mall Cop:

The other thing is that there doesn't seem to be any obvious "vote 1″ update for ideal points. That is, there is no simple mapping from the ideal point estimate based on m roll call to ideal point estimates based on m+1 roll calls. You have to start the fitting algorithm from scratch each time (and hence the appeal of exploiting multiple cores etc), although the results from the previous run giving pretty good start values.

I recently read a paper by David Jensen and coauthors from the UMass Knowledge Discovery Laboratory that proposes a systematic way of uncovering causal relationships from databases. Their approach (which they call AIQ -- "Automated Identification of Quasi-experiments") is not to mine the joint density of variables for independencies that can produce a causal graph (as discussed in Jamie Robins' talk last March), but rather to produce a list of feasible quasi-experiments based on a standard database schema that has been augmented with some causal information (e.g. A might cause B, C does not cause A or B) and some temporal information (i.e. ordering and frequency of events). In the paper, the authors provide an overview of the approach as applied to three commonly-used databases, including some candidate quasi-experiments that the algorithm suggests.

My impression after reading the paper was that AIQ's discovery potential is pretty limited (at least at this stage), because most users who could provide the inputs AIQ needs could very likely think up the quasi-experimental design themselves. Any valid quasi-experiment design that AIQ can discover at this point appears to come from the user specifying that the treatment and outcome have no common cause or confounding factors, which is a very unusual situation that is either quite obvious (e.g. because there is a lottery or other explicit randomization) or requires significant substantive knowledge. I wonder how commonly a researcher would a) have in mind a causal model that is sufficiently restrictive to produce plausible quasi-experimental designs through AIQ, and b) not have already thought of those designs.

The example of causal discovery the authors provide comes from a combined IMDB/Netflix movie database; they assert that winning an Oscar improves the reviews a movie receives on Netflix. In order for AIQ to suggest this quasi-experiment, the authors had to specify in advance that the Oscar-winning film is chosen from among nominees at random. One can of course criticize that assumption, but the point is that once you make that assumption it should be quite obvious that you have a quasi-experiment with which to study the effect of winning the Oscar on various outcomes; any film-specific, post-awards ceremony outcome should do. AIQ may provide a structured way to go through that exercise, but I'm not convinced there are many circumstances in which it would be useful to a researcher.

What makes the paper especially notable around IQSS is that our own Arthur Spirling is cited in the first sentence:

The political scientist Spirling and others have correctly warned that finding common statistical distributions (for example, power laws) in sociological data is not the same as understanding their origin.

The citation is to Arthur's unpublished paper The Next Big Thing: Scale Invariance in Political Science, which provides a breezy overview of scale invariance as a concept and documents a few previously unremarked examples from political science.

Part of the point of Arthur's paper is that political science (and social science more broadly) has mostly ignored research in natural sciences that, like the Nature article, examines emergent patterns in social phenomena. As he points out, it's not how we "do business." The hard scientists chasing power laws attempt to explain an underlying random process starting from the distribution of outcomes; we're more accustomed to starting from the joint density of outcomes and covariates.

In a way, the fact that Arthur's paper was cited at all highlights the lack of interest in this style of work in social science. The authors of the Nature piece wanted to cite social science work on power laws, and they ended up with Arthur's piece, which is, for all its merits, several years old and unpublished.

I admit I've been a bit of a power-law curmudgeon, like other social scientists, but lately I've come to better understand the value of this approach. I don't expect that I'll be focusing on this kind of work myself, but, like Arthur, I believe it is a growth industry.

I was amused to read about the hoopla involving this online "study" in the BMJ entitled "Santa Claus: A Public Health Pariah." The tongue-in-cheek article, written by Australian epidiomiologist Nathan J. Grills, contends that the corpulent Mr. Claus and his "rotund sedentary image" set a bad example for kids and adults alike.