Tag Archives: statistics

The Language of Theory, or, How to Escape the Humanities Ghetto

This morning I read an article by Patrick Thaddeus Jackson and Daniel Nexon, titled “Paradigmatic Faults in International-Relations Theory.” This piece originally appeared in a 2009 edition of Internaionl Studies Quartlerly.

I like when people agree with me, so when I saw my words echoed across time (it’s as if Jackson and Nexon read my post, built a time machine, and told their former selves what a great idea they read on tdaxp). Yesterday, I said it was riduclous to describe the International Relations cliques of “Realism,” “Liberalism,” and such as paradigms. I wrote:

The highlighted passage, originally by Daniel Maliniak simply means that empirical research is increasing, and that non-empirical research is declining, within political science. But Maliniak, and thus Walt and Mearsheimer, bizarrely use “paradigmatic” to refer to less paradigmatic (that is, less capable of progress) fields, and “non-paradigmatic” to more to more paradigmatic (that is, more capable of progress) fields.

Political science has been in the fever swamp for so long that the notion of progress as an outcome of normal science has almost entirely been lost. If Walt and Mearsheimer had their way, it might be lost, and the field simply divided into a stationary oligarchy of old boys network.

As Jackson and Nexon write:

The terminology of ‘‘paradigms’’ and ‘‘research programmes’’ produces a num-ber of deleterious effects in the field. It implies that we need to appeal to criteria of the kind found in MSRP in order to adjudicate disputes that require no such procedures. In order to do so, we spend a great deal of time specifying the ‘‘boundaries’’ of putative research programmes and, in effect, unfairly and misleadingly holding scholars accountable for the status of theories they often view as rivals to their own.

Perhaps the most well-known instance of this kind of boundary-demarcation occurs in the debates surrounding ‘‘realism’’ in international relations theory. The proliferation of countless lists of the ‘‘core commitments’’ of a realist ‘‘paradigm’’—by adherents and critics alike—shifts the focus of scholarship away from any actual investigation of whether these commitments give us meaningful leverage on the phenomenal world, and instead promotes endless border skirmishes about who is and is not a realist (Legro and Moravcsik 1999), whether predictions of balancing are central to the ‘‘realist paradigm’’ (Vasquez 1998:261–65), and so forth. Such debates and demarcations not only distract us from the actual study of world politics, but also harm disputes over international relations theory by solidifying stances that ought to remain open to debate and discussion.

So I enjoyed Jackson’s and Nexon’s takedown of the so-called “paradigms” in International Relations.

But they don’t go far enough.

Their piece ends with an appeal to Max Weber (how non-progressive can you get?!?) and an unfalsifiable taxonomy that I won’t go into

ideal_typical_taxonomy

A more useful conclusion to the paper would have been to recognize that statistics is the language of theory, the language of modeling. Instead of inviting international relations scholars to chase their own tale and bow to Max Weber and the dead, how much more useful would a positive theory of research programs in International Relations have been? For instance, consider a citation indexing method, such as PageRank [pdf] to determine if they are “clusters” PageRank sets in which certain articles were influential (exemplars?) and others were not. Did Jackson and Nexon really have no one availability to sketch even a proposed methodology for testing their claim?

The answer is probably “no.” My purpose isn’t to pick on Jackson and Nexon, but to point out the weakness of International Relations as a whole. In a related post by Patrick Musgrave, titled “The Crass Argument for Teaching More Math In Poli Sci Courses“, the following diagram showing is shown:

wages_employment_majors_md

Which clearly displays a “humanities ghetto,” that includes political science.

wages_employment_majors_humanities_ghetto_md

How can this be, if International Relations is the disciplined extraction of meaning from data, which is the same focus as the high-paying, well-employed fields?

The obvious answer is that International Relations does not teach actually useful methods for the disciplined extraction of data. It does not teach critical thinking or logical reasoning. It teaches something that apes these skills, a rhetorical ability that impresses old scholars and does not help society.

International Relations is a non-progressive field where, by and large, it sucks to be young.

ways_of_knowing_3

In an evocative comment that ties the article and the blog post together, Patrick Thaddeus Jackson states:

I don’t think that it is our job as university faculty to increase students’ future earning potential. Nor do I think that it is our job in teaching PoliSci undergrads to make sure that they can read APSR in the 1980s and 1990s. Our job is to teach students to think critically about politics, and while I am perfectly fine with the suggestion that some statistical literacy can be useful to that end, I am not prepared to give that higher pride of place than things like reading closely, writing cogently, and disagreeing with one another civilly.

The dichotomy that Jackson notes is entirely false. In his own piece, he was not able to express a constructive critical thought about paradigms — the original Nexon and Jackson article is devoid of the model specification or operationalization that would needed to turn his criticisms and taxonomy into something capable of progress. Any competent graduate from the humanities ghetto can read “closely” or write “cogently.” That’s needed is to think usefully, and for this statistical literary is required.

The R Statistics Language

R (also called GNU R, or even GNU S) is the open-source version of the S Programming Language, a language which fulfills the same statistical needs as SAS and SPSS. While SAS is a macro language designed for statistics, and SPSS is a macro language designed for statistics with a very nice graphical front-end, R looks like dialects ot C, acts like a dialect of LISP, and function as nifty alternative to SPSS and SAS. As I come from a programming background, R is beautiful in concept.

R’s learning curve is steep. If perl tries to make ‘impossible things hard and hard things easy,’ then R’s philosophy seems to be ‘make hard things easy and easy things hard.’ Some procedures that are complex and tedious in SPSS and R, such as taking the inverse of a matrix by the loadings of its correlation matrix as determined bya one-factor Principal Component Analysis, or PCA (in that case, it would be solve(ad.data.cor) %*% as.matrix(principal(ad.data.cor,nfactor=1)$loadings). Other tasks are requirer a deepper understanding of the material, however. For example, in SPSS creating a ‘Component Score Coefficient Matrix’ after a PCA is as simple as ticking a check box, or adding a simple request in the macro code. In R, you need to realize that the Component Score Coefficient Matrix is actually just the inverse of a matrix multiplied by the loadings of the matrix after running it through PCA: so you’d enter the line solve(ad.data.cor) %*% as.matrix(principal(ad.data.cor,nfactor=1)$loadings).

By far the coolest part of R and PCA is learning what unknown unknowns you forgot to solve for. For instance, a bundle of seemingly meaningless data can be examined through a ‘scree plot,’ to see which things you forgot to measure for (‘latent variables’) mattered, and which did not.

Unknown Unknowns? That’s the R

Avandia has a moderate-to-very-large practical effect on heart failure

Home, P.D., et al. Rosiglitazone evaluated for cardiovascular outcomes — an interim analysis. The New England Journal of Medicine. 5 June 2007. Available online:http://content.nejm.org/cgi/content/full/NEJMoa073394 (via Medical News Today).

Avandia is a drug designed to treat Type II Diabetes. Type 2 Diabetes leads to heart attack, death, and a lot of other bad things. A safe drug that treats it would be very good. Many people think that Avandia (rosiglitazone maleate) is that drug. However, a recent article in The New England Journal of Medicine reported that Avandia has a large-to-very-large effect on patient death. Because this is important news, a new article was rushed to the New England Journal that reported results-so-far of a study that’s not completed.

The results section is statistics-y:

Because the mean follow-up was only 3.75 years, our interim analysis had limited statistical power to detect treatment differences. A total of 217 patients in the rosiglitazone group and 202 patients in the control group had the adjudicated primary end point (hazard ratio, 1.08; 95% confidence interval [CI], 0.89 to 1.31). After the inclusion of end points pending adjudication, the hazard ratio was 1.11 (95% CI, 0.93 to 1.32). There were no statistically significant differences between the rosiglitazone group and the control group regarding myocardial infarction and death from cardiovascular causes or any cause. There were more patients with heart failure in the rosiglitazone group than in the control group (hazard ratio, 2.15; 95% CI, 1.30 to 3.57).

Several results are reported here. The most important to consider are practical signifiance and statistical significance . From my statistics notes:

Statistical significance is concerned with whether an observed mean difference could likely be due to sampling error
Practical significance is concerned with whether an observed effect is large enough to be useful in the real world

For instance, imagine that you wish to be more productive, so you buy a new computer . You notice that you get twice as much done in an hour with the computer than without it. The practical significance would be very large (double!). However, you didn’t look at enough people to reject the notion that maybe it was just a fluke. So there would not be statistical significance.

A similar thing happened in this study. The last part of the quoted paragraph (“hazard ratio, 2.15”) means that, practically speaking, for every heart attack for diabetes type 2 patients who aren’t taking Avandia, patients taking Avandia have 2.15 heart attacks. However, the study did not meet statistical significance — the new research did not look at enough people to say whether or not this very large practical effect was due to chance or not.

A problem with the study — that the authors note — is that they are reporting their results too soon. (They are doing this because there is talk of forcing Avandia off the market, which would effect all patients who currently take Avandia and obviously hurt GlaxoSmithKline, the company that makes it.) I have heard anecdotes that one of the side-effects of Avandia is “preamature-aging.” If this is true, the negative effects of Avandia would get worse and worse over time. Thus, future research may go from the current two (where all find practical significance, but only one finds statistical significance) to a situation where all find statistical significance.