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
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
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,
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
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,
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
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
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
(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
(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
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
