UC San Diego

Measurement is foundational to political science research. Theories are only testable to theextent that their abstract concepts can be connected to empirical reality. Political science is a field where many important questions deal with concepts whose measurement is not immediately obvious. Does democracy reduce corruption in a country? Does ideological extremism impact the electoral success of politicians? To answer these questions, valid measurement of the key variables is an essential first step. In this dissertation I propose three projects which help improve measurement in political science. The first is a new method for measuring race/ethnicity when this data is missing. My model uses Bayes’ theorem to predict the posterior probability that an individual identifies with a particular race or ethnicity, given other known attributes. I validate these predictions against voter registration data, and I show that my model is far more accurate compared to previous methods. I also develop an R package to provide easy implementation of my method. The second project is a new model for measuring the political ideology of actors under extreme missing data conditions. Models of political ideology usually use observed actions, such as taking positions on legislation, to infer an actor’s latent ideology. But I show that in contexts where most actors fail to take explicit positions on most pieces of legislation, the measurements from traditional models can quickly degrade. The model I develop directly accounts for these missing signals, thereby generating more accurate measurements of political ideology. I apply the model to data on federal interest group lobbying. The final project is a method for incorporating measurement model uncertainty into an empirical theory testing model. Estimates of ideology from the model in project #2, as well as from any other statistical measurement model, produce more than a single value of the latent variable—they also produce some measure of the error/uncertainty for the true value. I show that failing to account for this measurement error in the theory testing stage can lead to misleading or biased conclusions. The method I propose in this project fixes this source of bias.