If you had told me a year or two ago that I would one day learn how to estimate a structural topic model (STM) -- an unsupervised machine learning based form of text analysis that incorporates “metadata” (i.e., a matrix of covariates) in the estimation of a generalized linear model of topical prevalence and content -- I would have either laughed until I cried or stared blankly at you wondering if either you or I had just had a stroke because I didn't understand a word of the gibberish that just came out of your mouth. And yet, here I am, taking my first steps into the wild world of text analysis. Unlike other, more traditional quantitative methods, topic models allow researchers to conduct statistical analysis on textual data -- open-ended survey questions, newspaper articles, blogs, transcripts, etc. -- where "topics" (i.e., sets of highly associated words) are identified, not by a person, but by an algorithm, allowing users to conduct statistical analysis on larg