Axial coding is about linking codes into broader categories and themes by mapping how different concepts relate to each other. After the initial pass of breaking the data into concepts (open coding), axial coding reassembles those pieces by examining relationships among subcategories and connecting them around a central phenomenon. It looks at the conditions that influence things, the context in which they occur, the actions or interactions people take, and the consequences that follow. This creates a coherent structure that turns disparate codes into a clearer, more organized understanding or theory. For example, if you first identify codes like “fear of stigma,” “seeking support,” and “trust in professionals,” axial coding would group these into a broader category such as “help-seeking in stigmatized contexts,” outlining how context and interactions shape outcomes. Other options don’t fit because identifying initial concepts occurs in open coding, not axial; testing hypotheses statistically is a quantitative method; and validating measurement instruments is about instrument validity, not about organizing qualitative codes into themes.