Collaborative coding in inductive content analysis: Why, when, and how to do it

Abstract

Inductive content analysis (ICA) is a useful method for analyzing qualitative data in genetic counseling research. It is particularly relevant when the goal is to examine and improve practices or develop recommendations. Although ICA can be undertaken by a single analyst, ideally there is involvement of multiple analysts (or co-coders). Co-coding can bring many benefits to qualitative analysis that sits within a constructivist paradigm, including developing a representation of the data that is not only understandable to more than one individual but also richer and more nuanced. It also provides an opportunity for mentoring more junior researchers and can be an efficient way to analyze large datasets. However, co-coding requires important planning and consideration, and there is currently a paucity of clear guidance. In this paper, we provide an outline of the small body of existing literature on this topic and propose six flexible step-by-step components of our approach to co-coding in ICA, based on our own work. We have utilized it to analyze reporting practices and perspectives for diagnostic genomic sequencing, informed consent for genetic testing, data sharing and storage, and genomic newborn screening, among other topics. To illustrate these components, we present some example vignettes to show how these procedures can be applied in different scenarios and with different analysts.