Writing center studies has sought to move towards research methods that are replicable, aggregable, and data-supported (RAD) as a means to scholarly legitimacy. While a number of RAD research methods have been identified (surveys, qualitative analysis, observation, case studies, experimentation, discourse analysis, teacher research, action research, and ethnography), one important source of information has been largely overlooked: the scheduling metadata that writing centers routinely collect in the course of normal operations. The present research seeks to demonstrate the validity of metadata-driven research by interrogating an area of writing center scholarship that has been predominantly studied through theoretical or small group means: the impact of gender on writing consultations. It investigates whether the gender of the writing consultant significantly affects a student’s choice in scheduling appointments.

This research note focuses on how corpus analysis tools can help researchers make sense of the data writing centers collect. Writing centers function, in many ways, like large data repositories; however, this data is under-analyzed. One example of data collected by writing centers is session notes, often collected after each consultation. The four institutions featured in this noteâ€"Michigan State University, the University of Michigan, Texas A&M University, and The Ohio State Universityâ€"have analyzed a subset of their session notes, over 44,000 session notes comprising around 2,000,000 words. By analyzing the session notes using tools such as Voyant, a web-based application for performing text analysis, writing center researchers can begin to explore critically their large data repositories to understand and establish evidence-based practice, as well as to shape external messaging about writing center laborâ€"separate from and in addition to impact on student writersâ€"to institutional administrators, state legislators, and other stakeholders.