Posts Tagged ‘ Polisci ’

Musings on Correlation (or yet another reason I fear for those non-methodologically inclined students in my cohort)

August 12, 2011
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Musings on Correlation (or yet another reason I fear for those non-methodologically inclined students in my cohort)

I’ve been thinking a lot about what it means for two variables to be correlated.  Scientists throw around the term like it’s uniformly understood, but I fear that an understanding of the concept is elusive to substantive researchers who aren’t interested in empirical methods, except as a means by which we can demonstrate that our

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Clustering U.S. Senators using roll call voting data

July 22, 2011
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Clustering U.S. Senators  using roll call voting data

For our forthcoming book on machine learning for hackers, John Myles White and I will discuss clustering, and various methods for doing so. One common method for clustering observations

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Measuring the EIU Democracy Index (with Polity IV)

July 12, 2011
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Measuring the EIU Democracy Index (with Polity IV)

Yet again, I have conjured up an (academically) unusual dataset on democracy! This time it’s the Economist Intelligence Unit’s Democracy Index, a weird little gem.  The dataset is the basis for a paper the Economist publishes every two years.  Because of this biannuality, there is data estimating the “Democratic-ness” of the world’s countries for 2006,

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More fun with the Failed States Index (and the State Fragility Index)

July 9, 2011
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More fun with the Failed States Index (and the State Fragility Index)

So the other day’s experiment with the Failed States Index and the Polity Data didn’t yield the linear trend I had originally expected.  After all, the two measure fundamentally distinct things.  But perhaps there’s another dataset which will match linearly.  The same people who made polity also put out a dataset called the State Fragility

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Analyzing the Failed States Index (with Polity IV)

July 7, 2011
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Analyzing the Failed States Index (with Polity IV)

So, I decided to sit down and have a little fun with that Failed States Index data I put together. To start, I expect that the dataset will be pretty linearly correlated with the polity IV data. This makes sense–true democracies aren’t failed states, and failed states tend not to be democratic. To test this,

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Language used by Academics with the Protection of Anonymity

March 14, 2011
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Language used by Academics with the Protection of Anonymity

Those in the political science discipline probably remember their first encounter with poliscijobrumors.com. For those outside, you have probably never heard of this particular message board, and you would have no reason to. As the URL suggests, the board specializes in rumor, gossip, back-bitting, mudslinging, and the occasional lucid thread on the political science

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Visualizing the Language Used by Academics when Protected by Anonymity

March 7, 2011
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Those in the political science discipline probably remember their first encounter with poliscijobrumors.com. For those outside, you have probably never heard of this particular message board, and you would have no reason to. As the URL suggests, the board specializes in rumor, gossip, back-bitting, mudslinging, and the occasional lucid thread on the political science

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Dynamic Modeling 3: When the first-order difference model doesn’t cut it

June 12, 2010
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Dynamic Modeling 3: When the first-order difference model doesn’t cut it

Data must be selected carefully.  The predictive usefulness of the model is grossly diminished if outliers taint the available data.  Figure 1, for instance, shows the Defense spending (as a fraction of the national budget) between 1948 and 1968. Note how the trend curve (as defined by our linear difference model from the last post: see

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Dynamic Modeling 2: Our First Substantive Model

May 30, 2010
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Dynamic Modeling 2: Our First Substantive Model

(This is the second of a series of ongoing posts on using Graph Algebra in the Social Sciences.) First-order linear difference equations are powerful, yet simple modeling tools.  They can provide access to useful substantive insights to real-world phenomena.  They can have powerful predictive ability when used appropriately.  Additionally, they may be classified in any number

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Dynamic Modeling 1: Linear Difference Equations

May 28, 2010
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Dynamic Modeling 1: Linear Difference Equations

(This is the first in a series on the use of Graph Algebraic models for Social Science.) Linear Difference models are a hugely important first step in learning Graph Algebraic modeling.  That said, linear difference equations are a completely independent thing from Graph Algebra.  I’ll get into the Graph algebra stuff in the next post or

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