Qualitative Coding for beginners - How to name codes?

Code naming anxiety is a common trap in qualitative analysis, and it wastes time that should go into understanding the data well enough to build themes. The core message is straightforward: coding is a set of working tools for making sense of interview or text material, and the names of those tools matter far less than whether the codes actually make sense to the researcher during analysis.

Coding’s purpose is to turn raw material into an organized “table of contents.” A list of codes helps the researcher see what the dataset is about, and that organized view then supports the development of themes—interpretive summaries that represent what the data covers. Because themes are the final output that must communicate clearly to readers, attention should shift from perfect code labels to the clarity and coherence of the thematic framework. At the coding stage, the priority is internal usefulness: codes should be understandable and meaningful to the person doing the analysis, since those codes are what later enable theme-building.

The transcript also criticizes the way methodological guidance can make coding feel like a rigid science, with competing rules about “correct” naming conventions, including references to different coding approaches and varying expectations about short versus long forms. That abundance of guidelines can leave students stuck searching for a formula—whether a code name is “right” or “wrong”—instead of focusing on analysis. The practical takeaway is to stop treating code names as a correctness test and start treating them as functional labels.

Two concrete strategies are offered to reduce friction while still keeping coding organized. First, codes can function as notes to the researcher—especially when an extract feels important but the meaning isn’t fully clear yet. Examples include labels like “something potentially interesting that I do not understand,” created after an interviewer’s reaction signaled significance that the coder almost missed while tired. Another example, “he says he doesn’t know,” is used as a reminder of a potential challenge—such as a participant’s lack of awareness about workplace policies—rather than as a literal description of what was said. Similar note-like codes include reflections on organizational size (“challenge size in a big organization…”) and cultural awkwardness (“never asked to have a day off… thinks this may have to do with the culture”). These labels are intentionally not polished interpretations; they are memory aids that help the researcher return to relevant extracts later.

Second, the transcript recommends using keywords in code names when helpful, particularly in software-based workflows where autocomplete suggestions can reduce effort but also create duplication. With large code lists (sometimes hundreds), it becomes easy to forget whether an idea was labeled “suggestions” or “recommendations.” Using multiple keywords in a single code name helps the researcher recognize existing codes while scrolling and typing, reducing the need to merge near-duplicate codes later. The overall message is pragmatic: don’t overthink naming—start coding, keep codes useful to the analyst, and let themes carry the burden of clear communication to readers.