There is no formula. A systematic review of empirical tests found studies reached saturation within 9 to 17 interviews, or 4 to 8 focus group discussions, where the population was homogenous and the objectives narrow. Ten to fifteen participants is defensible for a Nigerian undergraduate project — provided you can say how you arrived at it.
Why can’t I use the Taro Yamane formula?
Because it answers a different question.
Taro Yamane estimates how many respondents you need in order to generalise a measurement from a known population within a stated margin of error. It requires a population figure, a numeric variable and a sampling frame, and it exists to control sampling error in a quantitative study.
A qualitative project is not estimating a population value. It is trying to understand how something is experienced, why it happens, or what it means to the people involved. There is no margin of error to control, because there is no estimate. Substituting a population into a formula and reporting the answer produces a number that looks rigorous and justifies nothing.
The formula and its worked computation belong in a quantitative Chapter Three, where they are covered in the guide to writing Chapter Three section by section. This article is about the other case.
What do I say to a panel that expects a formula?
This is the real problem, and it is worth naming plainly. Many Nigerian departments teach sample size almost entirely through Taro Yamane, Krejcie and Morgan, and Cochran. A panellist who has examined forty quantitative projects this session will ask you how you got your number, and “I did not calculate it” sounds like an admission.
It is not one, and the answer that works is a positive statement rather than a defence. It has three parts:
- Name the different goal. “My study seeks depth of meaning rather than generalisation to the population, so sample size was determined by data saturation rather than by a sampling formula.”
- Cite the evidence. Name a published empirical study rather than asserting the practice — the two below are the standard references.
- Show your record. Produce the saturation table described further down. This is the qualitative equivalent of showing your Yamane computation, and it does the same job.
A student who does all three is not conceding a weakness; they are demonstrating that they understand their own design. A student who mumbles “the supervisor said fifteen” is conceding one.
What is data saturation?
Saturation is the point at which collecting more data stops producing anything new — no new codes, no new themes, no new information relevant to your research questions.
It is a stopping rule, not a target, and that distinction has a hard practical consequence: you cannot observe saturation if you conduct all your interviews first and analyse them afterwards. Saturation only becomes visible if you transcribe and code as you go, so you can watch the new codes taper off.
A student who runs fifteen interviews in one week and codes them the following month has not reached saturation. They have run fifteen interviews and hope it was enough.
What does the published evidence actually say?
Two studies carry this question in the international literature, and one of them is unusually relevant to a Nigerian project.
Guest, Bunce and Johnson (2006) analysed 60 in-depth interviews with women in two West African countries, documenting interview by interview when new themes stopped appearing. They reported that saturation occurred within the first twelve interviews, although the basic elements for metathemes were present as early as six. That West African fieldwork base is worth mentioning in your Chapter Three — it is a methodological study conducted in a context closer to yours than most of the literature you will cite.
Hennink and Kaiser (2022) systematically reviewed studies that had empirically tested saturation, identifying 23 articles — 17 using empirical data and 6 using statistical modelling. Their finding was a narrow range: saturation within 9 to 17 interviews, or 4 to 8 focus group discussions, particularly in studies with relatively homogenous populations and narrowly defined objectives. Multi-country research and meta-theme analysis needed more.
| Source | Basis | Finding |
|---|---|---|
| Guest, Bunce & Johnson (2006), Field Methods 18(1) | 60 in-depth interviews, two West African countries | Saturation within 12 interviews; metatheme elements by 6 |
| Hennink & Kaiser (2022), Social Science & Medicine 292 | Systematic review of 23 empirical tests | 9–17 interviews or 4–8 focus group discussions |
A typical Nigerian undergraduate qualitative project — one department or organisation, one fairly homogenous group, three research questions — sits squarely inside those conditions. Ten to fifteen participants, with a stated intention to continue until saturation, is a planning figure you can defend.
Is saturation a settled idea?
No, and knowing that protects you against a well-read panellist.
Braun and Clarke, whose thematic analysis many Nigerian projects cite, published a direct challenge — “To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales” — arguing that saturation fits awkwardly with an interpretive approach where meaning is generated by the analyst rather than sitting in the data in a fixed quantity.
You do not need to settle that debate in an undergraduate project. What you must do is be internally consistent. If your Chapter Three says you used reflexive thematic analysis, do not also claim the data were exhausted. Say instead that recruitment continued until additional interviews were no longer generating material relevant to your research questions.
How do I write the justification into Chapter Three?
Four sentences. A filled-in version is more useful than a rule, so here is one you can adapt:
Fifteen participants were selected through purposive sampling using the criteria stated above. Sample size was determined by data saturation rather than by a statistical formula, following empirical guidance that studies with homogenous populations and narrowly defined objectives commonly reach saturation between nine and seventeen interviews (Hennink & Kaiser, 2022). Transcription and coding were carried out concurrently with data collection so that the emergence of new themes could be monitored. No new themes were identified after the thirteenth interview; two further interviews were conducted to confirm this, and recruitment then closed.
Those four sentences name the sampling method, state the basis for the size, explain how saturation was observable, and give the point at which it occurred plus the confirmation interviews. A bare “fifteen respondents were purposively selected” does none of that.
How do I actually track saturation?
Keep a log. It costs a few minutes per interview and it is the most convincing single exhibit you can bring to a defence.
- Transcribe each interview before the next one, or within a day at the latest.
- Code it and list every theme. For interview one, every theme is new.
- Record two numbers per interview: total themes so far, and new themes from this interview.
- Watch the second column. It falls steeply, flattens, then reaches zero.
- Run two more interviews after your first zero. A single zero can just be a similar participant.
Put the completed table in your appendix and refer to it by number in Chapter Three. When a panellist asks how you knew you had enough, you turn to it — the same move as turning to a Yamane computation.
Does the number change with my design?
Yes, and one sentence about this in Chapter Three shows the panel you know which design you are running.
- Phenomenology works with small, deeply engaged samples. Depth of each account is the point, not the count.
- Case study is bounded by the case. The unit is the case, and you may interview several people about one of them.
- Focus group designs count discussions, not people — the empirical range is 4 to 8 discussions. A group of eight participants is one discussion.
- Grounded theory uses theoretical sampling: who you recruit next is decided by what the analysis so far suggests you still need, which makes the final number genuinely unknowable in advance.
A project claiming grounded theory while recruiting a fixed sample in a single week has a mismatch, and it is the kind a methodologically minded lecturer finds quickly.
What if my supervisor insists on a specific number?
Use it, and justify it anyway.
Departments and supervisors often set an expected range for undergraduate qualitative work. Your department governs you, and arguing methodology over a number is a poor use of the weeks you have left.
What you can always do is write the justification regardless: “Fifteen participants were selected in line with departmental guidance; coding was conducted concurrently and no new themes emerged after the thirteenth interview.” The number came from the department, the defensibility came from you, and nobody is contradicted.
What will the panel ask?
Four questions, reliably. The wider question bank for a Nigerian defence is in the article on what questions are asked during a project defence.
- “Why fifteen?” Your stopping rule and your log, not a citation on its own.
- “How did you know you reached saturation?” This is what the log exists for.
- “How were the respondents selected?” Name the technique and the inclusion criteria. Never call purposive sampling random.
- “Can your findings be generalised?” No — say so plainly. Qualitative findings are transferable to similar contexts, not generalisable to a population, and claiming otherwise loses the room.
Write the justification, not just the number
Tesify structures your project chapter by chapter in the format your department expects and keeps every citation attached to a source you actually opened, so a methodology section that has to justify a sample reads like a decision rather than an assertion. Over 9,000 students have used it to write more than 15,000 chapters, and 100 per cent of the work is still written by you.
Frequently asked questions
How many respondents do you need for a qualitative final year project?
There is no formula. Published empirical tests converge on 9 to 17 interviews or 4 to 8 focus group discussions for studies with homogenous populations and narrow objectives. Ten to fifteen participants is defensible for a Nigerian undergraduate project provided you state how you determined it.
Can I use Taro Yamane for a qualitative study?
No. Yamane estimates the sample needed to generalise a measurement within a margin of error, which is a quantitative goal. A qualitative project seeks depth and meaning, so the formula answers a question your study is not asking.
What do I write in Chapter Three instead of a computation?
A saturation justification: the sampling technique, the basis for the size with a citation, the fact that coding ran concurrently with collection, and the interview at which new themes stopped appearing plus your confirmation interviews.
How many focus groups do I need?
Four to eight discussions on the systematic review evidence. Count discussions rather than participants — one group of eight people is one focus group discussion.
How few is too few?
Below about eight becomes hard to defend for an interview study with more than one research question, because saturation has not been given a chance to appear. If circumstances force a smaller sample, declare it as a limitation rather than leaving the panel to find it.
Do I have to reach saturation to pass?
Not necessarily. Many undergraduate timelines make true saturation impractical. A project that states honestly that recruitment closed at the timetable rather than at saturation, and lists that as a limitation, is stronger than one claiming a saturation it cannot evidence.
Can I combine interviews and focus groups?
Yes, and it is common. Treat them as two data sources with two stopping rules and explain how they relate. Do not add ten interviews to four focus groups and report fourteen of anything.
Does a qualitative project still need Chapter Four?
Yes. It presents themes with supporting quotations rather than tables of frequencies, but the chapter is still where your data is reported and interpreted. The five-chapter structure itself is set out in the answer to how many chapters a Nigerian final year project has.
Should my sample size appear in the abstract?
Yes, and it must match Chapter Three, Chapter Four and any table that counts participants. A count that drifts between sections is among the most frequently caught defects in a project document.
Are these studies relevant to Nigeria?
Methodological guidance is cited for its method rather than its locale, and both are standard international references. The Guest and colleagues study is also directly relevant on its own terms, having been conducted on interviews in two West African countries. Pair them with any Nigerian study whose design resembles yours, using the synthesis method in the guide to writing Chapter Two.
Where does the sample justification go in the proposal?
In the methodology, written in the future tense — “recruitment will continue until saturation” — and converted to past tense once collection is complete. The full proposal structure is in the guide to writing a research proposal for a Nigerian final year project.
Does my population figure still matter?
You should still describe the population you drew participants from, because inclusion criteria have to make sense against it. What changes is that you do not convert that figure into a sample size arithmetically. The population description belongs in Chapter One’s scope as well, which is covered in the guide to writing Chapter One.