Aka everything you always wanted to know about IPA sample sizes but were too afraid to ask
This article explores the thorny issue of optimal sample sizes for your Interpretative Phenomenological Analysis (IPA) research study. My aim with this piece is to help you understand the theoretical and practical rationales for the use of smaller sample sizes in IPA research. In addition, I would love to equip you with the ability to develop a clear argument or rationale that you can employ to robustly make the case for your teeny tiny IPA sample size.
This material is largely drawn from the references listed immediately below with some additional ideas from yours truly derived from my extensive knowledge of IPA accrued over years of teaching plus supervisory and examining experience.
What will this article cover?
We will start by examining general guiding principles on IPA sample size and will move onto strategies that you might employ to argue the case for a smaller sample size for your IPA if you are being pressured otherwise or need to defend the use of a smaller sample size at viva or when presenting your work.
*Side note: it is very rare that students report not having enough data or having a sample size that is too small (unless of course recruitment is for an unusual/rare participant group without easy access).
Students do often report having data that they are concerned lacks depth and/or is ‘not rich enough’ but this issue is usually related to poor interview technique. We address this common issue robustly in my Supercharge Your Semi-Structured Interviewing workshop and on the relevant ‘How do I…?’ page here on my website.
The importance of sample size in IPA
The issue of sample sizes in an Interpretative Phenomenological Analysis (IPA) research study is something that frequently rears its ugly head, regardless of the context or level of study (e.g., PhD>Prof Doc>M).
In my experience, students, supervisory panels, and research boards all struggle with the fundamental question of the ideal sample size for an IPA research study.
There are of course, numerous considerations for this question and I will attempt to outline some of these here for you today.
Firstly, to remind you all that for an IPA study, we recruit a closely defined group for whom the research question (RQ) will be meaningful and significant.
This is because we want to gather in-depth and detailed accounts of the phenomenon in question. So, we are looking for people that can speak to the topic in detail from personal experience and are therefore considered to be ‘experts’ in the experience of the area or phenomenon that you are researching.
In summary, we want to gather an in-depth account of the experience/phenomenon of interest from a small group of individuals who have had that experience.
To do this, we require sufficient participants or cases to enable us to examine the convergences and divergences in that experience or phenomenon BUT not too many that we are overwhelmed with data as this will make the in-depth IPA analytic process extremely demanding, if not impossible.
Remember IPA’s idiographic commitment: we analyse each participant’s account of their experience individually and to great depth and this requires time. Then we go cross-case (if we have more than one) and perform a fine-grained analysis of the group as a whole.
Doing all this to a sufficient level of depth and complexity is time-consuming and requires significant personal commitment to the process.
IPA studies thus tend to benefit the most from QUALITY rather than QUANTITY and DEPTH rather than BREADTH – in other words a concentrated focus on a small number of cases providing rich and in-depth data so that we can achieve sufficient interpretative depth in our analysis.
What follows on from all this is that sample sizes tend to be SMALL and targeted PURPOSIVELY – i.e., we choose a very specific population that has had the experience we are researching and can therefore provide detailed insight.
What is an optimal sample size for my IPA study?
There is no ‘correct’ number for sample size for your IPA, as much will depend upon the context of your research study:
The level of study for your degree. Of course, a PhD tends to be bigger in size and scope than a Prof Doc, which in turn tends to bigger in size and scope than a masters level study, which in turn tends to be bigger in size and scope than an undergraduate project – you get my drift here!
The expectations of your major stakeholders at an institutional level, such as your research degree board.
The expectations of stakeholders at a programme level, such as your supervisor(s) and examiner(s).
Guidance from ‘the dons’ on sample size in your IPA
Guidance has been supplied in both editions of the Smith, Flowers, and Larkin (2009/2022) texts and in the Smith and Nizza (2021) text. I paraphrase and summarise here but do check out the references listed at the start of this article for more detail and to get this information straight from the horse’s mouth, so to speak.
The first point worth considering is that IPA values the single case study and what it can offer – I believe that this gives us a clear indicator in itself that small is beautiful for our IPAs.
For an undergraduate project that would likely span a few months at most, the recommendation is n=3. This number of participants is argued to provide enough data to get to grips with the process of conducting an IPA PLUS allow for sufficient comparison across cases.
For a master’s level project, we would step it up a level and the recommended sample size is n=5.
For Prof Docs and PhDs, it is much more difficult to give a useful rule of thumb, mostly due to the varied nature of these designs.
For example, Prof Docs vary widely in scope, scale, and output length, depending on discipline. PhDs are even more variable in terms of design and may constitute several different studies or have more sophisticated and unusual designs (e.g., multi-perspectival or longitudinal).
All these factors point to a greater variability in sample sizes for these more advanced degrees and make it far more complicated to point to an actual number.
In these cases, it may be more useful to think of data points – in other words, data collection events or transcripts, rather than participants themselves.
For example, if you are working longitudinally, you might have only three participants, but might interview each of them at four different timepoints, giving rise to twelve data points.
The general guidance from the powers that be in the IPA world is that 10 to 12 data points is a good number for more advanced degrees.
What is the problem with having a sample size for my IPA that is too large?
The main issue in this case tends to be massive overwhelm with too much data which in turn leads to a superficial analysis that lacks sufficient synthesis and interpretative depth.
Needless to say, this is absolutely NOT what we are trying to achieve with an IPA study!
Larger sample sizes could also be argued to violate the underpinning principle of adopting an idiographic approach for IPA. A larger sample may lower the quality of the analysis produced, can limit the amount of idiographic detail that can be included, and thus negatively impact the findings.
A smaller sample can provide the opportunity for much more detailed single case analysis, which in turn translates into a more detailed cross-case group analysis, which in turn preserves the idiographic commitment of IPA. In other words, it will enhance your ability to privilege and foreground individual experiences throughout the group experience in the final written narrative account of your analysis.
Other factors that may be brought to bear on your IPA sample size
Any precedent in the extant literature:
What has been done before regarding sample size in your field/discipline may have some impact on what you decide to do or how hard you may need to argue for a smaller sample size for your study (if appropriate, obvs).
The nature of the experience you are examining:
If the phenomenon or experience is super-niche, or highly unusual/rare, or the population you wish to speak to are very difficult to access, recruitment may be problematic, and you may need to keep numbers very small for that reason alone.
The timeframe that you have for completion:
This is a very practical consideration and a no brainer really – the less time you have for recruitment, interviewing, transcription and then to execute the very lengthy analytic process for your IPA, the smaller your sample needs to be! Thus, the shorter your timeline, the smaller the sample so as to avoid sacrificing depth for breadth – less is more in IPA.
Your previous experience of conducting qualitative research and IPA:
if you are a first timer on either count, your IPA will take longer to carry out as you will be learning the ropes and getting into your own IPA groove, and this takes time.
Your institutional and/or programme requirements:
This varies enormously from institution to institution, and you will need to meet the standards and requirements of your main stakeholders in this respect. I hope this is where this article will come in very handy as we will move on in a moment to arguing the case for a small sample size for your IPA.
Side note: I have also written an article troubleshooting what to do if you have too big a sample and an excess of data to deal with – this is coming soon so watch this space
Your IPA study design:
If you are doing a multi-perspectival design (e.g., using dyads, triads, or even larger groups such as work-related teams, for example) or employing focus groups to gather your data, this may impact your sample size decisions and could lead to larger samples for your IPA.
How to argue for a small sample size for your IPA
To do this, you must develop and/or construct a carefully evidenced and well-argued case for a small sample size for your IPA.
Top tip #1 Find a precedent in the literature
Conduct a thorough literature search and find examples of IPA studies in your discipline or field (or in a closely related discipline/field if there are none directly related to your field: for example, if you are looking at depression and there are no studies, then perhaps you look for another mental health issue that is like depression). Cite these as examples of smaller sample sizes that have been published and have therefore gained academic credibility through meeting the requirements of a peer-review process.
Top tip #2 Find unpublished theses in your discipline or field that have used a small sample for an IPA
This is a no brainer really – find examples of unpublished work that has employed a sample size similar to that which you wish to argue for. You can download British doctoral theses for free from EThOS – see the resource repository of this very website to link directly to this wonderful service.
Top tip #3 Draw upon the methodological IPA literature (i.e., the main texts) to generate a robust and fully evidenced written argument for IPA sample sizes being small
This article may help, and there is a wealth of citable, supporting arguments in the two texts referenced above.
In conclusion, let us not forget that a single case study drawing upon good quality data in a multifaceted topic area can produce enormously fruitful and noteworthy IPA findings.
Plus, the process of conducting an IPA data analysis to a sufficient level of depth, coupled with the task of writing up into a decent, coherent, and compelling narrative for your analysis chapter is demanding, arduous and lengthy.
For these reasons we do not want to be overwhelmed with data: we choose small, purposively selected samples for an IPA and we argue robustly for this if we need to.
I hope this article has helped you appreciate two of the main mottos for IPA that are particularly relevant to sample size: small is beautiful and less is more.
Until next time!
To your research success, Elena
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