Describe the process of axial coding in grounded theory research.

Axial coding in grounded theory research is the process of relating categories to their subcategories to form more precise and complete explanations.

In grounded theory research, axial coding is a crucial step that follows open coding. Open coding is the initial phase where data is broken down into discrete parts, closely examined, and compared for similarities and differences. Once these categories have been identified, axial coding comes into play. It involves taking the categories identified during open coding and linking them to their subcategories. This is done based on the properties and dimensions of the category.

The term 'axial' refers to the axis of a wheel, symbolising the process of linking categories around a central idea or theme. This process helps to organise the data in a way that makes it easier to understand the relationships between different categories. It's like piecing together a puzzle, where each category and subcategory is a piece that fits into the larger picture.

The aim of axial coding is to refine and clarify the categories identified during open coding. It helps to develop a more detailed and nuanced understanding of the phenomena being studied. This is achieved by exploring how categories are related to each other, and how they are influenced by various conditions and consequences.

In axial coding, researchers use a coding paradigm to guide their analysis. This paradigm typically includes conditions, context, action/interactional strategies, and consequences. Conditions refer to the specific situations or events that lead to the occurrence of a phenomenon. Context refers to the specific set of circumstances or conditions in which the phenomenon is embedded. Action/interactional strategies are the specific actions or interactions that are taken in response to the phenomenon. Consequences are the outcomes or results of these actions or interactions.

Overall, axial coding is a systematic and rigorous process that helps to deepen our understanding of the data and the phenomena it represents. It is a key step in grounded theory research, helping to build a rich and detailed theory that is grounded in the data.