How to read these examples
Qualitative coding examples are most useful when you can see the passage and the coding decision together. A code claims that some part of the passage matters for the analysis. That claim can be close to the participant's words, as in open coding or in vivo coding, or it can start to organize relationships between conditions, actions, and consequences, as in axial coding. Thematic coding goes further by connecting evidence to a broader interpretive pattern.
The examples on this page are synthetic teaching material. They are written to feel like plausible interview excerpts, but they are not real participant data. That makes them safe for practice, classroom discussion, and tool testing. When you use them, focus on the link between each excerpt and the coding result. Ask what the code captures, what it leaves out, and whether a different researcher could reasonably code the same passage another way.
Open coding example
Open coding is often the first analytic pass. You read a passage and mark meaningful pieces without forcing them into a final hierarchy. In the discussion board example, the student expected the online task to be easy, found it difficult, missed immediate feedback, kept checking for replies, and struggled to interpret silence. Each of those observations can become an open code because each points to a distinct feature of the experience.
The value of open coding is that it slows the researcher down. A weak summary might say, "The student disliked online discussion." Open codes show more texture: the passage also contains delayed feedback, uncertainty about how comments are received, and anxiety caused by silence. Those distinctions may matter later. If several students describe silence as ambiguous, that code could support a theme about social feedback in asynchronous learning.
Axial coding example
Axial coding asks how codes relate to one another. In the weekly checklist example, the checklist provides structure, the job schedule disrupts rhythm, the student still completes the tasks, and the group conversation has moved on by the time they arrive. The coding result identifies a condition, context, action, consequence, and category. That structure helps the researcher explain a process rather than simply list topics.
The category "time flexibility with social cost" captures a tension. The online course gives the student enough flexibility to finish the work despite schedule changes, but that same asynchronous structure can separate the student from the active group moment. If later interviews show a similar pattern, the researcher may refine the category, compare it across students with different work schedules, or turn it into a candidate theme.
In vivo coding example
In vivo coding uses the participant's own words as the code. The phrase "ghost student" works because it carries more meaning than a researcher-made label like "low engagement." The participant is not absent. Their name appears, assignments are submitted, and quizzes are passed. The issue is that participation feels socially invisible. Keeping the phrase preserves voice and helps prevent the researcher from flattening the account too quickly.
In vivo codes are especially helpful when participants use vivid, repeated, or culturally meaningful language. They are not automatically better than researcher-generated codes, but they can keep the analysis close to how participants frame their own experience. Later, an in vivo code may become a theme name, a subtheme, or a memorable quote supporting a broader claim.
Thematic coding example
Thematic coding links excerpts to a broader interpretive pattern. In the remote work example, the participant says quiet improves their writing, but remote onboarding weakens opportunities to observe senior judgment. A possible theme is: productivity can increase while apprenticeship weakens. That theme is stronger than a topic label such as "remote work" because it states a relationship between two parts of the account.
A thematic code should still be tested against evidence. Does the passage clearly support both productivity and weakened apprenticeship? Are there other excerpts that confirm, complicate, or contradict the pattern? Would the theme hold for people who joined the company before remote work? These questions keep thematic coding tied to support rather than surface polish.
Using software and AI with examples
If you are learning a new QDA tool, import these examples and recreate the coding decisions. Highlight the sentence that supports "ambiguity of silence." Attach "ghost student" as an in vivo code. Write a memo explaining why "time flexibility with social cost" is an axial category rather than a simple topic. Then export the coded excerpts and check whether the evidence still makes sense outside the tool.
AI can help by proposing initial codes or suggesting that two excerpts may support a similar theme. The key is review. A suggestion should remain provisional until a researcher confirms it against the text. This is the principle behind OpenVerbatim's suggested and confirmed states: the assistant can speed up the first pass, but the final coding record should show what was accepted, edited, or rejected.
A useful practice exercise is to code the same excerpt twice. First, code it manually with no software. Second, let a tool or assistant suggest labels, then compare the two sets of decisions. The differences are often more educational than the matches because they force you to explain what the evidence supports, where the wording is too broad, and which interpretation you are willing to carry into the codebook.
Download and keep practicing
Download the qualitative coding examples pack and use it alongside the sample interview transcript and sample codebook. For step-by-step practice, start with how to code interview transcripts, then read how to do thematic analysis. If you are choosing tools, compare AI qualitative data analysis, open source qualitative research software, and the open source QDA software pillar, then try the sandbox.