This case study centers on the issues associated with defining and coding multiple variables from a large set of special education due process hearing officer decision documents as part of an investigation into predictors of due process hearing timeliness. Legal studies of court or hearing officer decisions generally require document content analysis of the written decisions. This empirically styled study used deductive a priori coding to collect the data elements and then applied a quantitative analysis to the data set; as a result, the codes for each variable needed to align with the chosen statistical procedure of a linear multiple regression. Further, each state sets its own requirements for the format and content of hearing officer decisions, resulting in great variability among the states in terms of what variables are included in the decision and the degree of detail with which each variable is described. In this case study, readers will learn successful practices for defining and coding a priori variables, gaining intercoder reliability for the data collection process, and continually refining the variable definitions and codes based on the information available in the data set. Readers will also gain insight into the issues surrounding use of large data sets, specifically the pace of data collection and the variability of the documents comprising the data set. [This content is provided in the format of an e-book.]