"However [political parties] may now and then answer popular ends, they are likely in the course of time and things, to become potent engines, by which cunning, ambitious, and unprincipled men will be enabled to subvert the power of the people and to usurp for themselves the reins of government, destroying afterwards the very engines which have lifted them to unjust dominion."<p>- GEORGE WASHINGTON, Farewell Address, Sep. 17, 1796<p>Our democracy has evolved into what Washington had prophesied in his farewell address ... a system where diversity of thought and political compromise are rejected in favor of the strength of a unified political party and the power that brings with it.<p>When political positions consolidate into just two choices, so do the proposed solutions to the world's problems. These solutions are artificially constrained for the sake of political loyalty. Ultimately, party loyalty requires individual politicians to sacrifice their personal beliefs on individual issues ... and once those values have been compromised, the only thing worth clinging to is control.
Op here, this is part of my PhD research. Please bare in mind that I'm French and that my knowledge of U.S politics is limited to what I can read online.<p>If you find any errors in my analysis or have any tips or other interesting pieces of information I'd be really happy to discuss with you.
Would be interesting to see PCA and CA* applied to the same data.<p>On a quick google trip I found<p>[1] Principal Component Analysis of Senate Voting Patterns (<a href="http://escholarship.org/uc/item/3xm9z62w#page-1" rel="nofollow">http://escholarship.org/uc/item/3xm9z62w#page-1</a>)<p>[2] Shlomo S. Sawilowsky, Real data analysis; Chapter 28, Principal Component Analysis of Senate Voting Patterns (on Google Books)<p>* Principal Component and Correspondence Analyses respectively -- <a href="http://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow">http://en.wikipedia.org/wiki/Principal_component_analysis</a> and <a href="http://en.wikipedia.org/wiki/Correspondence_analysis" rel="nofollow">http://en.wikipedia.org/wiki/Correspondence_analysis</a>
It does look nice... and it does raise interesting questions. I'm particularly intrigued as to John Kerry, John Edwards, and Joe Lieberman's presences in that anomalous 108th Congress. Can you tell us any more about how you define an "agreement group"?<p>Visually, the result reminds me of [<a href="http://xkcd.com/657/" rel="nofollow">http://xkcd.com/657/</a>] - is this sort of diagram a recognised concept in data presentation, or a new idea?<p>[edit - hmm... <a href="http://en.wikipedia.org/wiki/108th_Congress" rel="nofollow">http://en.wikipedia.org/wiki/108th_Congress</a>]
Looks really great and seems like a useful tool. My only real complaint, is that I use a high sensitivity on my mouse and I found it difficult to highlight a line. If you could make the lines slightly thicker, it would be easier to select them.
For what it's worth, I thought the gray background bars were far too light - it took me a while to find them. I also couldn't intuit what the branching lines were all about.<p>Also, Chrome had some difficulty with typesetting, though it's possible that's a localized issue.<p>*edit: I figured out why the boxes seem so light - it's because I view my screen at a slight angle. From a straight angle they show up fine.
Simon Jackman calculates ideal point estimates based on non-unanimous roll calls: <a href="http://jackman.stanford.edu/ideal/currentSenate/x1.pdf" rel="nofollow">http://jackman.stanford.edu/ideal/currentSenate/x1.pdf</a><p>I think his R source is available too.
Good stuff. Can you talk more about the algorithm?<p>Can you use this to detect issue clusters rather than voting clusters?<p>I have tried to build subgroup-in-graph detectors with only partial success.