Table 2

Methods associated with interpretative phenomenological analysis (IPA)

Methods	Application to IPA
Sample and recruiting participants	IPA focuses on small and homogeneous samples; the research question being addressed must be meaningful to participants who are purposively selected because they have experience of the phenomena. The number of participants in IPA studies is small (typically less than 10) to enable a detailed microlevel analysis of the participants’ accounts.3 Each participant offers a rich reflective account of their experience/s and represent their own perspective/s.3 Notions of generalisability are a contradiction in IPA because participants are recruited for their individual experience/s and perspective/s, rather than to represent perceptions of a wider population.3
Data collection	IPA has been undertaken using numerous qualitative data collection techniques that allow the participant to provide a rich account of their personal and lived experience including written accounts such as paper and online diaries, interviewing and focus groups. However, the in-depth semistructured interview is typically used to collect data in IPA. The aim of the interview in IPA is to facilitate participants to share the experiences that are important to them, while an interview topic guide may be used the participant leads the direction of the interview. The researcher’s role in the interview is to guide the discussion in a way that focuses on the lived experience of the phenomena of interest.
Data analysis	Analysis begins with the close examination of the first case, leading to the development of case themes and then consideration of themes across the data set. IPA analysis involves a step-by-step approach3 4: Reading and rereading: the researcher immerses themselves in the data or transcript of a single case. Initial noting: as the researcher reads the case, observations are recorded which are often noted in the margin of the transcript. Developing emergent themes: the researcher ‘chunks’ data relating to the observational ‘notes’ of the case. Searching for connections across emergent themes: the researcher clusters the ‘chunks of data’ and ‘notes’ together and considers how they relate. Moving to the next case: the themes derived from the previous case are ‘bracketed’ as the new case is considered with ‘open and fresh eyes’, again becoming immersed in the case. Steps 1–4 are undertaken for each case before progressing to the next stages of the analysis. Seeking patterns across cases: the researcher asks, are there any themes/qualities identifiable across cases?, these are highlighted making a note of any idiosyncratic differences. Moving the interpretation to a deeper level: reviewing the themes across the data set and by using metaphors and temporal referents the researcher aims to further elicit the meaning of the experience. In the final stage of analysis the researcher draws on existent theory/concepts to further explore the data. IPA findings are presented as a coherent analytical account including pertinent participant quotes and a detailed interpretative commentary.
Rigour, reflection and reflexivity	Four broad principles are used to judge the credibility of IPA: sensitivity to context; commitment and rigour in undertaking the analysis; transparency and coherence of the narrative produced and impact and importance.6 Strategies to establish trust and credibility in IPA include: Epoché (‘bracketing’): the researcher must make their assumptions explicit in an attempt to reduce researcher bias that could influence data collection and analysis processes. Peer critique: enhances the plausibility and acceptability of the findings by involving a peer group to critique each stage of the research process and comment on the descriptive validity and the transparency of the interpretation of the data and findings. Structure resonance: others with similar experiences are invited to comment on findings, focusing on whether the findings resonate with them. Participant verification: the participants are invited to comment on the researchers’ interpretation of the data. Triangulation: using different data collection methods or different conceptual frameworks can increase the validity of a study because the phenomena under investigation is approached from a range of perspectives. The researcher must offer detailed reflection and document decisions made at each stage of the research process.