Code Together, Grow Together

This chapter provides doctoral students with a student-centered, collaborative model for engaging in qualitative data analysis. The purpose of this chapter is to demystify the processes of coding, codebook development, and theme generation by situating them within the lived experience of a dissertation-in-practice. Through examples drawn from data analysis, the chapter illustrates how first- and second-cycle coding, iterative refinement, and collaborative memoing foster deeper analytic understanding. Attention is given to the importance of reflexivity, positionality, and trustworthiness in guiding methodological choices and ensuring rigor. Practical strategies, such as KWL charts, reflective journaling, and integrating hand-coding with digital software, are presented as scaffolds to support doctoral students in building confidence and competence. By blending conceptual grounding with applied practice, this chapter provides a roadmap that guides students from uncertainty to agency in conducting meaningful qualitative research.