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Qualitative Sample size Calculator

What is a good sample size for a qualitative research study? 

Our sample size calculator will work out the answer based on your project's scope, participant characteristics, researcher expertise, and methodology.

Just answer 4 quick questions to get a super actionable, data-backed recommendation for your next study.

Get your results in your inbox!

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How the calculator works

The User Interviews Qualitative Sample Size Calculator helps you answer the question: “How many participants do I need for qualitative research?”

Our approach to answering this question is based on the concept of saturation in qualitative research. Saturation can be defined as:

“A point where themes emerging from the research are fleshed out enough such that conducting more [research] won’t provide new insights that would alter those themes.” (Maria Rosala, 2021)

Essentially, we worked out a formula for estimating the number of participants you need to reach a point of saturation, depending on the unique characteristics of your study. 

Our formula takes into account 4 variables, as well as no-show rate:

  1. Scope — what are the boundaries and goals of your study? 
  2. Diversity — how similar will qualifying participants be to each other, after screening criteria (e.g. filters for job title, demographics, behaviors) is applied?
  3. Methodology — which research method are you using?
  4. Researcher expertise — how experienced is your team with this type of research?
  5. No-show rate — how many “extra” participants should you recruit?

The value of each variable depends on the responses you give to the questions above. No-show rate (discussed below) is fixed at 1.10.

The recommendations you’ll receive are informed by dozens of journal articles, empirical studies, and industry advice.  You can read a summary of  our methodology and sources below.

Formula for calculating qualitative sample size

An earlier formula, proposed by researcher Victor Yocco (2017), served as the starting point for our own calculations. The variables and values we use differ from his proposals—in some cases more than others—but we are indebted to Yocco for providing the initial inspiration.

Our formula (the one which powers this calculator) is as follows:

P = No-show rate x ((scope x diversity x method)/expertise)

Calculator variables (inputs)

There are many factors that can influence the number of participants you need for any given study. We built our calculations around 4 of the big ones. 

1. Scope

Research scope refers to the boundaries and goals of your study. It describes—in general terms—what you’re hoping to get out of your research.

In our formula, the value for scope is determined by your answer to question 1: “How would you define the scope and goals of your research?

  • Narrow (fewest unknowns, tactical, formative): Scope = 1
  • Focused (some unknowns, tactical or strategic, formative): Scope = 1.25
  • Broad (many unknowns, strategic, foundational): Scope = 1.5

For our framework and definitions of scope, we drew from those offered by sources like Hennick, et al. (2017), Macefield (2009), Schiessel (2023), Slater Berry (2023), and Spillers (2021). Some of these sources discuss scope in terms of tactical vs. strategic research. Others break things down along different lines, such as by summative vs. formative vs. foundational research.

We synthesized these differing definitions to find common ground between them, making it easier for you to identify your study type among our list, regardless of which framework you usually use to talk about research objectives.

2. Diversity 

Earlier attempts to create a formula for calculating qualitative study size (Yocco, 2017; Blink UX) require you to know the number of user types/personas in your sample and to plug that number in for this variable. 

We were skeptical that researchers would always know this number beforehand (especially when it comes to broad or exploratory research). So we took a different approach.

In our formula, the value for diversity is determined by your answer to question 2: “How similar or different are your participants to each other? Put another way, how specific are your recruiting criteria?

  • Very similar: Diversity = 1
  • Somewhat similar: Diversity = 1.3
  • Somewhat different: Diversity = 1.5
  • Very different: Diversity = 1.7 

Diversity in this context is a neutral term, and is inversely related to how niche your screening criteria is. Oftentimes, the more screening criteria you use, the less diverse your population is likely to be. 

For this reason, B2B studies are likely to be more homogenous than general consumer research studies, by which we mean participants in B2B research tend to be more similar to each other in terms of job titles, education, software habits, etc.

👉 Find out more about the niche consumer and professional segments you can recruit from our pool of 4.1 million quality participants in our panel report.

3. Method

In our calculator, the value for method is determined by your answer to question 3: “Which qualitative method are you planning to use?

Below, you can see the value for each UX research method included in our calculator: Card sorting, focus groups, interviews, diary studies, usability testing, co-design (participatory design), and concept testing.

These numbers represent baseline recommendations for reaching saturation in homogenous studies (i.e. in studies where participants are very similar to each other), according to an extensive review of existing literature. In the right hand column, we’ve listed the source(s) that we relied on to set each baseline value. 

Table with recommended qualitative sample sized by method. Card sorting (20), focus groups (18), interviews (12), diary studies (10), co-design (10), usability testing (9),  concept testing (5)
Note: An earlier version of this table contained an error (the co-design multiplier was shown as 8).

In general, we chose a baseline on the lower end of recommended sample size ranges, since the method value gets multiplied by the other variables in our formula. Note that for focus groups, 18 = 6 participants x 3 groups.

If you’re curious about the basis for our suggestions—for instance, why we start from a baseline of 9 participants for usability testing, instead of the 5 users famously recommended by Jakob Nielsen (2000)—stay tuned and subscribe for an upcoming even deeper dive into qualitative sampling based on our literature review.

Need a sample size recommendation for a survey? You may want to check out this calculator from SurveyMonkey for solid, statistically backed recommendations for survey sampling. If your team needs to uncover a specific percent of total problems for your usability testing, we'd recommend you use this calculator from MeasuringU.

👉 For a complete list of sample size recommendations for each method, as well as our sources, please see Appendix III in Sample Sizes for Qualitative Research: A Definitive Guide.

4. Expertise 

Skilled researchers can often uncover insights with fewer participants. 

Onwuegbuzie, et al. (2010), Adiabat & Le Navenec (2018), Bonde (2013), and others discuss the impact of researcher expertise on saturation, with the latter concluding that: 

“The more expertise the researcher has, and the more familiar they are with the phenomena under investigation, the more effectively they can perform as a research instrument.”

In his formula, Yocco (2017) suggests that this variable should start at 1 for beginner UXRs and increase by 0.10 for every 5 years of experience.

But we found that increasing the expertise denominator by 0.10 every 5 years produced too dramatic of an effect in our own formula. We increased this value by 0.10 between 0-4 and 5-9 years of experience, but reduced the step up to 0.05 for every 5-year interval after that.

Therefore our formula, expertise is a value between 1 - 1.3, depending on your answer to the question: “How many years of experience (on average) do the researchers involved in this project have with this type of research?”

  • 0-4 years. Expertise = 1
  • 5-9 years. Expertise = 1.1
  • 10-14 years. Expertise = 1.15
  • 15-19 years. Expertise = 1.2
  • 20-24 years. Expertise = 1.25
  • 25+ years. Expertise = 1.3

No-show rate

No-shows are participants who accept a study invite but never show up to the session. We won’t hold it against them—sh*t happens. But we do need to be prepared to fill their spot with another qualified participant.

User Interviews’ no-show rate is under 8%, which is lower than the industry average—around 10-11%, though some sources report no-show rates as high as 20%! (Nielsen, 2003; Sauro, 2018; Burnam, 2023). 

We stuck with a rate of 10%—meaning for every 10 participants you need, you should recruit 11, since it’s a tidy number and not everyone recruits with User Interviews.

Therefore in our calculator, this value is fixed and no-show rate = 1.10.

Why should you trust these recommendations?

We should be clear about something here: Researchers have spent untold hours trying to answer the question “how many participants do you need for X qualitative study?”

They’ve yet to develop a consensus around the best way to answer this question.

So... why should you trust our recommendations? 

Well, for one thing these sample size estimates are based on a careful and considerate review of peer-reviewed studies, and have been made with UX research in mind. 

For another thing, we’re not telling you that you will always need exactly this many folks for a study—you and I both know that’s not how qual research works. We know sometimes, for instance, tight budgets or tight timelines may mean you have to recruit fewer participants or do less rounds of testing than you’d like. Or, conversely, your team may need more statistical power, which will increase your sample size.

At the same time, we know that offering broad ranges (e.g. “recruit 5-40 participants”) is not particularly helpful when you’re trying to budget time and resources for a study.

That is why our calculator gives you a single, easy to understand number that you can run with—or, in some cases, benchmark against.

We hope that this decisiveness on our part, as well as the transparent explanation of our formula (above) helps remove some of the considerable ambiguity around this topic, and makes it easier to gut check and validate your study designs with confidence.

What we mean when we talk about the "right" qualitative sample size

Now, when we ask “how many participants do I need for qualitative research?,” in most cases, what we are really asking is: 

“How many participants do I need to reach saturation?” 

Put another way, we’re asking: 

“How many participants do I need to feel confident in the insights I present to stakeholders or clients?” 

Saturation and sample sizes in qualitative research is a big, complex topic, and we’ve mostly skipped over mentions of it above. But if you want to learn more about what the  literature and UX industry pros have to say about saturation in different types of qualitative studies—and how we applied this data to our calculator—you can read all about that our definitive guide to qualitative sampling.

Further reading

Aldiabat, K. M., & Le Navenec, C. (2018). "Data Saturation: The Mysterious Step In Grounded Theory Method." The Qualitative Report, 23(1), 245-261.

Anderson, Nikki (n.d.). “How Many Participants Do You Need? Choosing the ‘Right’ Sample Size.” Dscout.

Blink UX (n.d.). “Usability Sample Size Calculator.” Blink UX

Bonde, Donna (2013). Qualitative Market Research: When Enough Is Enough. Research by Design.

Burnam, Lizzy (2023). “Top 5 Takeaways from the 2023 User Interviews Research Panel Report.” User Interviews.

Eisenhauer, Karen (n.d.). “Sample Study Designs: Concept Testing Three Ways.” Dscout.

Faulkner, Laura (2003). “Beyond the Five-User Assumption: Benefits of Increased Sample Sizes in Usability Testing.” Behavior Research Methods, Instruments, & Computers, vol. 35, no. 3, pp. 379–83.

Fusch, Patricia, and Lawrence Ness (2015). “Are We There Yet? Data Saturation in Qualitative Research.” The Qualitative Report, vol. 20, no. 9..

Guest, Greg, et al. (2016). “How Many Focus Groups Are Enough? Building an Evidence Base for Nonprobability Sample Sizes.” Field Methods, vol. 29, no. 1, pp. 3–22.

Hennink, Monique M., et al. (2017). “Code Saturation versus Meaning Saturation: How Many Interviews Are Enough?Qualitative Health Research, vol. 27, no. 4, pp. 591–608.

Hennink, Monique, and Bonnie N. Kaiser (2022). “Sample Sizes for Saturation in Qualitative Research: A Systematic Review of Empirical Tests.” Social Science & Medicine, vol. 292, no. 1.

Lasch, Kathryn Eilene, et al. (2010). “PRO Development: Rigorous Qualitative Research as the Crucial Foundation.” Quality of Life Research, vol. 19, no. 8, pp. 1087–96, 

Macefield, Ritch (2009). “How to Specify the Participant Group Size for Usability Studies: A Practitioner’s Guide.” Journal of Usability Studies.

Maze (n.d.). “Diary Research: Understanding UX in Context with Diary Studies.” Maze.

Merkel, Sebastian, and Alexander Kucharski (2018). “Participatory Design in Gerontechnology: A Systematic Literature Review.” The Gerontologist, vol. 59, no. 1, pp. e16–25.

Murphy, Eugene (2022). “Tips for How to Plan and Conduct a Diary Study Research Project (Guide).” Indeemo.

Namey, Emily, et al. (2016). “Evaluating Bang for the Buck: A Cost-Effectiveness Comparison between Individual Interviews and Focus Groups Based on Thematic Saturation Levels.” American Journal of Evaluation, vol. 37, no. 3, pp. 425–40.

Nielsen, Jakob (2003). “Recruiting Test Participants for Usability Studies.” Nielsen Norman Group.

––– (2000). “Why You Only Need to Test with 5 Users.” Nielsen Norman Group.

Onwuegbuzie, Anthony, et al. (2014). “Innovative Data Collection Strategies in Qualitative Research.” The Qualitative Report, vol. 15, no. 3.

Rosala, Maria (2021). “How Many Participants for a UX Interview?” Nielsen Norman Group.

Sauro, Jeff (2018). “8 Ways to Minimize No Shows in UX Research.” MeasuringU.

––– (2011). “How Many Customers Should You Observe?.” MeasuringU.

––– (2015). “How to Find the Sample Size for 8 Common Research Designs.” MeasuringU.

Schiessel, Brittany (2023). “How Many Research Participants Do I Need for Sound Study Results?Blink UX.

Six, Janet M., and Ritch Macefield (2016). “How to Determine the Right Number of Participants for Usability Studies.” UX Matters.

Slater Berry, Ray (2023). “User Testing: How Many Users Do You Need?Maze.

Spillers, Frank (2019). “The 5 User Sample Size Myth: How Many Users Should You Really Test Your UX With?Experience Dynamics – Medium.

Tullis, Thomas, and Larry Wood (2004). “How Many Users Are Enough for a Card-Sorting Study?” Presented at the Annual Meeting of the Usability Professionals Association.

Turner, Carl, et al (2006). “Determining Usability Test Sample Size.” International Encyclopedia of Ergonomics and Human Factors, 2nd Edition., CRC Press, pp. 3076–80.

Yocco, Victor (2017). “Filling up Your Tank, or How to Justify User Research Sample Size and Data.” Smashing Magazine.

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