Over the past few decades, the study of psychology has been undergoing a methodological transformation. The increasing availability and quantity of real-world big data have prompted researchers to look for new ways to use these data to gain psychological insights. In this case study, I trace the process of developing a new method for testing psychological theories using corpora. Instead of bringing participants to the lab, I analyze statistical patterns of co-occurrence in naturally occurring texts obtained from a variety of sources, including political speeches, literature, and the Internet. The basic assumption I make is that these patterns reflect the representations and cognitive processes of their author. I present three different applications of the method and use them to describe how such data can be analyzed and used to answer a range of questions in psychology and social science. The first application examines the linguistic question of the relationship between word form and meaning. The second application identifies cognitive frames as they are found in text. The final application uses texts to measure the style of moral reasoning individuals apply in particular contexts. This case study provides insight into the process of developing new methodologies for hypothesis testing, as well as demonstrating how to formulate hypotheses that can be tested using corpora. In addition, several key pitfalls in the process of adapting statistical methods to new uses are identified and discussed.