Must I code every line?

Not always. The level of detail depends on the research question, material and methodological approach. Close line-by-line work is one option, not a universal requirement.

Can I start with prior knowledge?

Researchers inevitably bring knowledge and assumptions. The important task is to make their influence visible and remain responsive to evidence rather than pretending to have none.

Is agreement between coders always required?

Practices vary across qualitative approaches. If a team uses agreement procedures, explain their purpose and method rather than treating one numerical threshold as universal proof of quality.

AI Glossary / Data science and analytics / Intermediate

Open coding

Definition

Open coding is an exploratory qualitative-analysis process in which a researcher examines data closely and develops initial labels for concepts, actions or meanings found in it. It is strongly associated with grounded theory, although related exploratory coding is used in other qualitative approaches.

What it means

The data might be interview transcripts, observations, documents or other qualitative material. Instead of assigning every passage to a fixed category list from the outset, the analyst remains open to concepts that the material suggests.

A code is a short analytical label, not computer programming. One passage may receive several codes when it contains distinct ideas. Codes are provisional: comparison with other passages may lead the analyst to rename, split, combine or abandon them.

Different grounded-theory traditions describe their coding processes differently. Open, axial and selective coding are associated with particular approaches; they are not a compulsory sequence for every qualitative study. The method should be explained in relation to the actual research question and chosen methodology.

How it works

Read the material in context, then examine manageable segments. Ask what is happening, how participants describe it and which conditions or actions seem relevant. Attach concise labels linked to the original excerpts.

Compare new segments with earlier ones and with the developing codes. Similar wording may express different meanings, while different wording may describe a related process. Write memos explaining interpretations, uncertainties and possible relationships rather than keeping only a list of labels.

As analysis develops, inspect variation and counterexamples. Discuss code meanings where the research design uses a team, while retaining the interpretive reasoning behind decisions. Software can organize excerpts and labels, and AI can suggest candidates, but neither supplies a neutral or automatically correct reading of participants' experiences.

Three practical examples

1. Product research: analysts explore interviews about why users abandon an onboarding process, allowing unexpected explanations to emerge before imposing a final classification.

2. Workplace change: researchers examine staff descriptions of adopting an AI assistant, distinguishing practical checking strategies from broad positive or negative sentiment.

3. Service improvement: a team studies open-ended customer comments to identify different kinds of uncertainty, effort or support-seeking behavior, while retaining the context behind each label.

Think of it this way

Think of placing tentative notes beside passages while reading a collection of stories. The notes help you compare what is happening without deciding the final chapter headings immediately. The analogy has limits: open coding is a systematic analytical practice, with documented questions and comparisons, not merely highlighting interesting sentences or sorting text by instinct.

Worked example

Consider this invented interview excerpt: “I use the suggested reply when the queue is long, but I check every price because it sometimes uses last week's offer.”

Possible initial codes include using automation under workload pressure, checking price-sensitive content and encountering outdated suggestions. These are analytical choices, not the only valid labels. The analyst records which words support each interpretation.

A second invented excerpt says: “Even on quiet days I check prices; customers remember the number we promise.” This comparison suggests that checking may relate to accountability as well as workload. A memo records that possibility and identifies evidence needed to explore it.

The analyst should not jump from these two excerpts to a population claim such as most staff distrust AI. Nor should a broad sentiment label erase the concrete actions and reasons present in the text.

Illustrative case study

Illustrative fictional case: Larch Support asks an AI tool to code employee interviews. Its first output labels most critical comments resistance to change. A human researcher returns to the excerpts and finds several descriptions of verifying source dates, protecting customer commitments and working around missing system access.

The team replaces the overly broad label with more grounded initial codes and documents where the AI suggestions influenced its interpretation. It compares examples that do and do not fit the emerging concepts before developing broader categories.

The fictional case illustrates interpretive accountability. It does not establish a measured accuracy rate for AI coding or suggest that a human's first reading is automatically free from assumptions.

Common misconceptions and mistakes

Do not confuse open coding with open-source programming or automatically generating a final thematic report. Do not force every excerpt into familiar business categories while claiming an entirely data-led analysis.

Avoid treating code frequency as direct evidence of importance or prevalence in a population. Interview prompts, sample selection and how much a participant speaks affect counts. Do not detach quotations from their context. Also avoid claiming that coding has reached theoretical saturation merely because a software tool stops suggesting new labels.

Business use

Use open coding when the purpose is to explore meanings or processes in qualitative material and the approach fits the study design. Define the unit of analysis, retain links to evidence and document how codes evolve.

For AI-assisted analysis, keep suggested codes distinguishable from accepted interpretations. Review sensitive or ambiguous passages in context, and use data-handling arrangements appropriate to the material and participant commitments. The useful business output is a defensible account of what the evidence suggests, including variation and limits, rather than a polished list of themes unsupported by traceable excerpts.

Check your understanding

An AI tool labels an excerpt negative sentiment, but the passage describes a specific workaround for checking source dates. Is the sentiment label necessarily sufficient for open coding?

Answer: No. It may miss the action and meaning relevant to the research question. The analyst should inspect the excerpt and consider more informative, evidence-linked codes.

Related terms

Codebook
Qualitative analysis
grounded theory
initial coding
constant comparison
memo writing
axial coding
thematic analysis
reflexivity

Learn next

Practice coding two short invented interview excerpts and write a memo explaining the difference between your labels. Then study the grounded-theory tradition or other qualitative methodology you intend to use, including how it moves from initial codes toward categories and explanatory claims.

Sources

Corbin and Strauss, Basics of Qualitative Research, publisher-provided chapter on analytical tools and open coding: https://us.sagepub.com/sites/default/files/upm-binaries/17551_Chapter_3.pdf

Makri and Neely, Grounded Theory: A Guide for Exploratory Studies in Management Research, methodological approaches and coding stages: https://journals.sagepub.com/doi/10.1177/16094069211013654

Timonen, Foley and Conlon, Challenges When Using Grounded Theory, differences across approaches: https://journals.sagepub.com/doi/10.1177/1609406918758086

All interview excerpts and the support-team case above are invented teaching examples, not participant data.

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