Coding in Qualitative Research

In this chapter, Yaghi offers detailed suggestions on how to code qualitative data after they have been gathered. Based on his doctoral dissertation, this chapter explains that the logic behind coding qualitative data is to turn a significant amount of information into categories that can be used to explain a phenomenon, reveal a concept, or render the data comparable across different case studies. It also elaborates through examples from author’s fieldwork in Tunisia, Egypt, and Jordan on four potential problems that may face researchers in coding qualitative data. These are the questions of preparation, categorization, consistency, and saturation. The chapter concludes by asking researchers to be flexible, and open to the process of trial and error in coding, to confront the data with questions before categorization, and to gather sufficient data on their topics before running their qualitative surveys.

Categorization · Coding (social sciences) · Data science · Epistemology · Machine learning · Phenomenon · Qualitative property · Qualitative research · Social science · Sociology · Artificial Intelligence · Computer Science · Qualitative Research Methods and Ethics