Summary
Highlights
A code is a label describing content. Qualitative coding is the process of creating and assigning these codes to categorize data extracts, which are later used for deriving themes and patterns in qualitative analysis. Coding helps ensure data validity and transparency in research.
Deductive coding uses pre-established codes, often derived from research questions or literature reviews. Inductive coding develops codes directly from the data itself, useful for exploring new concepts. A hybrid approach combines both, starting with predetermined codes and adding new ones as they emerge from the data, offering the best of both worlds.
The coding process consists of two stages: initial coding and line-by-line coding. Initial coding involves getting a general overview of the data and developing initial, broad codes. Line-by-line coding delves deeper, refining and expanding upon these codes with more detail and specificity, paying close attention to each line of data. It's an iterative process requiring careful definition and consistent application of codes.
Five common methods for initial coding include: in vivo coding (using participants' exact words), process coding (using action-based codes like gerunds), descriptive coding (summarizing extracts with a single word), structural coding (labelling structural attributes like who, what, where, how), and values coding (focusing on participants' worldviews, attitudes, and beliefs). These methods can be used individually or combined depending on research aims.
After initial and line-by-line coding, the next step is qualitative analysis. This involves code categorization, bundling similar codes into broader categories to organize data effectively and reveal new connections. Following this is theme identification, where insights from coding and categorization are synthesized to develop overarching themes and produce a narrative, guided by research aims and questions.
Key tips include: planning steps and methods before starting, using a codebook for deductive approaches, tracking code meanings, keeping research aims and questions in mind to prevent directional drift, and ensuring consistent training for multiple coders. These practices lead to a high-quality, organized, and analyzable dataset.