9 Scaling Methods

With a dictionary, we aimed to classify our texts into different categories based on the words they contain. While practical, there is no real way to compare these categories: one category is no better or worse than the other. If we do want to compare texts, we have to place them on some sort of scale. Here, we will look at three ways in which we can do so: Wordscores (Laver et al., 2003), Wordfish (Slapin & Proksch, 2008), and Correspondence Analysis. The first two methods used to be part of the main quanteda package, but have now moved to the quanteda.textmodels package, while we find CA in the FactoMineR package.

References

Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. The American Political Science Review, 97(2), 311–331. https://doi.org/10.1017/S0003055403000698

Slapin, J. B., & Proksch, S.-O. (2008). A scaling model for estimating time-series party positions from texts. American Journal of Political Science, 52(3), 705–722. https://doi.org/10.1111/j.1540-5907.2008.00338.x