Qualitative Coding: Strategies for Transparency and Reproducibility

Abstract

Coding -- the process of categorizing segments of data (whether text, audio, video, still photos, etc.) -- is an important aspect of qualitative data analysis. Beyond this general definition, however, coding can take multiple forms depending on the goal of the research, the researcher's broad theoretical and/or epistemological approach, the tools and strategies that are employed, and the specific purposes or aims of coding. This workshop will provide an overview of qualitative coding, focusing specifically on reproducibility and transparency in the coding process.

The first part of the workshop provides a general overview of qualitative data coding, a discussion of the purposes and types of coding, and a review of the tools and strategies used for coding (e.g., qualitative data analysis software). In the second part of the workshop, we will delve further into strategies and best practices for reproducibility and transparency in the coding of qualitative data. We will talk about developing and documenting codes, codebooks, and coding procedures; discuss approaches to evaluating the reliability and reproducibility of coding; and discuss strategies for making coding and analysis transparent to others, regardless of whether or not you are able to share your raw data.

This workshop will incorporate a mix of lecture, hands-on activities, and discussion. Attendees are encouraged to bring their own data to work with in individual exercises, and/or to bring a particular research project or research question to keep in mind during hands-on applications.