Video summary

The BEST Way to Write Your IPA Findings Chapter (Step-by-Step)

Main summary

Key takeaways

Educational

Main ideas and lessons (IPA Chapter 4: presenting qualitative findings)

  • In Interpretative Phenomenological Analysis (IPA), you are not just a “reporter” of participants’ accounts—you are also an interpreter, presenting how you make sense of the data.
  • IPA involves a double hermeneutic:
    • Participants make sense of their own experience.
    • The researcher then makes sense of what participants said.
  • When writing Chapter Four (Findings), structure your presentation so readers can track:
    1. what the study aimed to find,
    2. the context of the research,
    3. who the participants are,
    4. how the analysis was carried out (to build credibility),
    5. what each participant experienced (recommended in IPA),
    6. the overarching themes across cases (“main themes” / “master themes”),
    7. quotations and interpretation that connect themes to the evidence,
    8. a concluding recap.

Step-by-step methodology / structure for presenting IPA findings

1) Start the findings section with an Overview

  • Remind the audience of the study purpose/objective
    • Re-state the research objective (what you were trying to find out).
    • Help readers understand whether you accomplished what you set out to do.
  • Preview what will be covered in the findings section.
  • Example outline (as described):
    • Purpose of the study (e.g., explore how healthcare physicians make sense of their experiences)
    • Brief background about the research setting
    • Demographics and brief participant info
    • Presentation of themes
      • “final theme,” including “master themes” that address the research question

2) Include a Research setting (optional, but recommended)

  • Not mandatory if it overlaps heavily with data collection procedures, but recommended.
  • Provide background that helps readers understand:
    • how data collection happened from your perspective,
    • how it felt collecting data,
    • reflexive notes (briefly).
  • Example scenario:
    • You visited participants in a private clinic after hours
    • Participants were available and shared experiences during a private conversation

3) Present Demographic information (important)

  • Demographics help explain how participants’:
    • backgrounds and identity
    • may influence how they interpret their experience.
  • Include details that help readers understand quotations in context, such as:
    • gender
    • age
    • years of experience
    • where they work (e.g., urban/rural)
  • Avoid including information that makes participants easily identifiable.
  • Choose individual vs collective demographic reporting based on sample size:
    • If many participants (e.g., 30–35): summarize collectively
      • e.g., most participants were female; about half had 12+ years’ experience
    • If fewer participants (e.g., 10–12 to 15 or “up to maybe 10–12 or 15”):
      • provide individual participant descriptions
      • IPA emphasizes how background/preconceptions affect sense-making

4) Explain data analysis in a credible, step-by-step way (critical)

Data analysis should be framed as:

  • reducing data into something understandable to the audience,
  • while still showing how you reduced it, so readers can trust your results.

Include:

  • detailed steps
  • outcomes of each step (e.g., how many codes/comments/themes resulted)
  • (ideally) citation of a book/article/model for the process, referencing tested methods from prior researchers.

IPA: the “six steps” for analysis (as described)

  1. Read and reread
    • Outcome example: researcher becomes immersed in Participant PT1 transcript and understands the participant experience.
  2. Initial noting
    • Do line-by-line attention to participant statements.
    • Take notes about:
      • meaning of each statement
      • what label/code might be used later
    • Document the process and outcome:
      • Outcome example: “about sixty plus exploratory comment” for PT1.
  3. Develop emergent themes
    • Go through the data again to identify significant information.
    • Create themes as short phrases (2–5 words) capturing understanding.
    • Outcome example:
      • PT1 produced 14 candidate/emergent themes.
  4. Develop and cluster themes into higher-order themes
    • Explore characteristics of each emergent theme.
    • Cluster based on commonalities.
    • Outcome example for PT1:
      • 14 themes → 4 groups → 4 superordinate themes.
  5. Repeat for each participant
    • The same process is applied to each transcript/participant.
    • Individual cases lead to each participant’s superordinate themes.
  6. Create master themes (across cases)
    • Compare all participants’ superordinate themes.
    • Categorize/synthesize to form the broader main themes / master themes.
    • Outcome example described:
      • “three main theme(s)” across all participants (in their example)

5) Present individual participant experience before cross-case themes (recommended)

  • After you have superordinate themes for each participant (steps 1–4), present:
    • each participant’s unique experience and how you make sense of it.
  • Recommended approach depends on sample size:
    • If up to ~12 participants: describe individual experiences first.
    • If more than ~12–15: focus on main themes across cases, but still refer to individual unique experience when describing each theme.
  • Flexibility goal:
    • don’t overwhelm the audience
    • don’t lose the richness of the data

6) For each participant’s case, include the minimum theme-to-question logic

  • Provide a brief participant-specific background (don’t repeat demographics, but add context relevant to their experience).
  • Ensure the account connects to the research question:
    • “technically you are answering the research question” by describing that participant’s sense-making.

7) Present main themes across cases using a consistent theme template

For each main theme:

  • Define the theme
    • What it represents, grounded in the evidence and aligned with the research question.
  • Provide quotations as evidence
    • Best practice given: 2–4 quotes supporting the theme (may be fewer/more depending on the example later in the talk).
  • Interpret the quotations
    • Quotations are not enough; explain what they mean.
    • Clarify that the participant quote provides evidence, but your interpretation shows the analytical contribution.
  • Give characteristics/uniqueness of the theme
    • Explain what makes the theme distinctive and how it fits the evidence.
  • Identify who said what
    • Indicate participant ID (e.g., “Participant P1”) and optionally brief context (e.g., years of experience, gender) when helpful.

Flow tip:

  • When possible, link interpretations across multiple quotes so the narrative doesn’t feel like isolated evidence.

Example interpretive logic mentioned:

  • Theme about “hidden erosion of professional identity”
  • Quote suggests competence remains while identity dissolves
  • Interpretation explains the separation of “doing the job” from “knowing who I am”

8) End with a Conclusion / recap

  • Briefly summarize:
    • what was covered,
    • the main issues/themes discussed.
  • Serve as closure to the findings section (typically a paragraph or so).

Speakers / sources featured

  • Speaker/creator: The video narrator/presenter (no name given in the subtitles).
  • Sources mentioned: prior researchers’ tested IPA analysis steps (no specific author/book named in the subtitles).

Original video