what is case study sample size

Sample size for qualitative research

Qualitative Market Research

ISSN : 1352-2752

Article publication date: 12 September 2016

Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research.

Design/methodology/approach

The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years.

In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population.

Practical implications

Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research.

Originality/value

Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

• Qualitative research
• Qualitative methodology
• Case studies
• Sample size

Boddy, C.R. (2016), "Sample size for qualitative research", Qualitative Market Research , Vol. 19 No. 4, pp. 426-432. https://doi.org/10.1108/QMR-06-2016-0053

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

Introduction

This current article considers the seldom-written-about-but-much-questioned issue of sample size in qualitative research. This paper is inspired and informed by the author’s experiences in commercial marketing research, academic management research and as an editor for qualitative academic papers. Further, as an author of such papers and in the role of editor, the views of many reviewers have been read over the past 31 years in research, and these have also inspired this current paper. Reviewers clearly need guidance in this area, and researchers could also benefit from this discussion as they struggle to design qualitative research in terms of sample size.

Furthermore, qualitative research has recently come under criticism for its lack of rigour in terms of there being little or no justifications given for the sample sizes that are actually used in research ( Marshall et al. , 2013 ). Marshall, Cardon, Poddar and Fontenot considered 81 qualitative studies and concluded that scant attention was paid to estimating or justifying sample sizes.

The question of what sample size is needed for qualitative research is frequently asked by individual researchers ( Dworkin, 2012 ) but not frequently discussed in the literature ( Onwuegbuzie and Leech, 2005 ). Few studies approach this issue, and as much qualitative research does not involve the making of statistical generalisations, many qualitative researchers report that sample size is not an issue in qualitative research ( Onwuegbuzie and Leech, 2005 ). However, for reviewers it clearly is an issue as described below.

Furthermore, the related issue of what sample size is needed for qualitative research findings to have some validity is also one which many paper reviewers are concerned about enough for them to mention in their reviews. Reviewers typically, nonetheless, do not definitively answer their own questions regarding what size a sample should be. Comments from reviewers are usually to do with the sample size (whatever size it is), being too small, and they commonly state that this should be noted in the limitations sections of an academic research paper. This current paper reviews some of the sparse literature on this subject, investigates a case study from the physical sciences and one from management and comes to some tentative conclusions.

The concept of data saturation, which is the point at which no new information or themes are observed in the data from the completion of additional interviews or cases, ( Guest et al. , 2006 ) is a useful one in terms of discussing sample size in qualitative research. This approach implies that a single case study or interview is never enough, because data saturation can only be known after at least two cases, and usually more, are examined. This idea of sampling until data saturation is reached can be used as a justification for the use of a particular sample size in any qualitative research which is guided by this idea.

However, in practical terms, although the idea of saturation is very helpful at the conceptual level, it provides little guidance for estimating actual sample sizes, prior to data collection ( Guest et al. , 2006 ). For example, it is difficult to give cost and timing estimates for research where the sample size has not been pre-determined. This impracticality may be a reason why the data saturation approach does not appear to be used in practice, even in academic research.

For example, in a meta-analysis of 560 academic qualitative studies, the distribution of sample sizes used was found to be non-random, with a statistically significant proportion of studies, presenting sample sizes that were multiples of ten ( Mason, 2010 ). This strongly suggests that a premeditated approach to sample size determination was used, and this is not wholly congruent with some of the principles of qualitative research ( Mason, 2010 ). Clearly, there is confusion and a gap between theoretical expectations and practice.

This is corroborated by the investigation of 81 qualitative studies mentioned earlier ( Marshall et al. , 2013 ). This investigation found that those qualitative researchers who used data/theoretical saturation as an indicator that their sample size was sufficiently large did not explain this in sufficient detail, or in a way that was persuasive or entailed the presentation of any evidence to support the claim for data saturation ( Marshall et al. , 2013 ).

The idea underlying data saturation as a guide to sample size is the idea that once saturation is reached, the results must be capable of some degree of generalisation. Generalisation is traditionally seen as a central aim of science, as a process of theory formulation for further applications ( Mayring, 2007 ). However, as Mayring notes, the concept of generalisation has been criticised, for example because of the context specificity of all scientific findings.

Despite the apparent limitations of samples which involve a single case or single research participant as discussed above, it has nevertheless been noted that individual (single sample) case studies can provide reliable indications for the directions in which future research can go. Individual cases can also provide a new, deep and nuanced understanding of previously unexplored phenomena. Furthermore, qualitative researchers have noted that often a researcher can (unknowingly) have all the data they need from their first piece of data collection ( Sandelowski, 1995 ). It is also argued that case studies have been undervalued in terms of their ability to generate theoretical generalisations ( Tsang, 2014 ). This is demonstrated below from the discussion of two examples, one from the physical sciences and one from management research.

First, in medicine, it has been noted that findings from single case studies can have findings which can be generalised from and implications which are global in importance. The discovery of penicillin is a case in point. Alexander Fleming noticed an accidental case where mould was growing as a contaminant on the jelly in one of his culture plates (like Petri dishes). The mould appeared to have an inhibitory effect on the surrounding growth of bacteria. He called the mould Penicillin notatum ( American Chemical Society, 1999 ). Publishing his findings in 1929 in the British Journal of Experimental Pathology , he wrote that the broth from the mould had marked inhibitory, bactericidal and bacteriolytic properties to many of the more common pathogenic bacteria ( Fleming, 1929 ). His work was taken up by Howard Flory and Ernst Chain at Oxford University who developed penicillin as a medicine, with the eventual help of US drug companies.

Penicillin was so apparently successful and generally applicable that it did not initially undergo full randomised trails prior to use in humans. Nonetheless, the development of penicillin is noted as being one of the greatest breakthroughs in modern medicine ( American Chemical Society, 1999 ).

In management research, the longitudinal examination of an individual CEO who was highly psychopathic is also a plausible example of such a single-case approach being ground breaking and informative ( Boddy, 2015 ). This is particularly so because corporate psychopaths appear to have a common modus operandi and to be relatively stable personalities over time ( Boddy et al. , 2015 ) (just as penicillin has stable properties). The study of one corporate psychopath CEO, it was compellingly argued, can therefore inform how other psychopathic CEO’s will likely behave.

More theoretically, the research philosophy or paradigm adopted and discussions of an appropriate sample size are related ( Onwuegbuzie and Leech, 2005 ; Boddy, 2005b ). Some researchers associate size considerations with an approach to science based on positivism, which is an approach to scientific inquiry which many qualitative researchers reject ( Lincoln and Guba, 2000 ). However, it should be noted that some researchers do use a qualitative element of research to set the parameters for a further, positivist quantification. This usually means that they apply a positivist approach to qualitative research ( Boddy, 2005a , 2005b ) and, under this approach, a criticism of sample size because of smallness may well be justified. This is because the qualitative sample size has to be representative of the population under consideration as a breadth of inquiry is anticipated.

This is the approach recommended (pp. 25-28) by qualitative market researchers who suggest that researchers draw up a grid (such as sex by brand usage) to make sure that each segment of the population is covered by the research ( Gordon and Langmaid, 1990 ). Academic researchers also suggest this grid or matrix type approach to qualitative sample size determination ( Stake, 2000 ).

Commentators suggest that qualitative sample sizes of ten may be adequate for sampling among a homogenous population ( Sandelowski, 1995 ). Others state that qualitative sample sizes of 20-30 are typically (pp. 56) conducted by researchers to establish data saturation using a grounded theory approach to qualitative inquiry ( Creswell, 1998 ). However, no evidence is presented as the basis for this latter sample size claim. Marshall and colleagues refer to a sample size of 20 as being small for a grounded theory-type approach to qualitative research and to 40 being a large sample size for the same type of study. This gives a range of what sample size they would consider appropriate, and later in the same paper, they recommend a range of 20-30 interviews for grounded research and 15-30 interviews for case studies.

Bearing in mind their North American background, such a recommended range would certainly be smaller in number at both ends of the spectrum, e.g. from UK qualitative researchers. US qualitative researchers tend to adopt larger sample sizes than other qualitative researchers ( Marshall et al. , 2013 ).

In terms of the upper limits to sample size, Sandelowski is one of the few commentators on sample size in qualitative research to note that a sample can be too large. A sample which is very large does not permit the deep, case-oriented analysis that is the raison-d’etre of qualitative inquiry ( Sandelowski, 1995 ), at least in constructivist or in-depth approaches to scientific research. In terms of how large is too large, few have ventured an opinion. Sandelowski suggests that 50 interviews is a large sample for a qualitative study. Boddy (2005b , 2005a) mentions once being asked, as a commercial marketing researcher to conduct 1,000 in-depth interviews by a US positivist researcher. Upon learning that, given resources available, this would take over a year and cost about US$1m. The US researcher re-evaluated what was meant by “in-depth”. However, such a sample size would undoubtedly be “too large”, because the sheer volume of data would inhibit meaningful, timely, qualitative analysis. This current author’s view is that any qualitative sample size over 30 (per market/geography) becomes too unwieldy to administer and analyse.

Therefore, in a single market/country or relatively homogeneous population, any qualitative sample size at or over 12 focus groups or more than 30 in-depth interviews could be considered large and would require justification. Corresponding with this viewpoint, in one of the few studies investigating actual theoretical saturation, the authors found data saturation starting to become evident at six in-depth interviews and definitely evident at 12 in-depth interviews among a sample of women in two countries ( Guest et al. , 2006 ). This suggests that multiples of 12 in-depth interviews may be more appropriate than the multiples of 10 that were commonly found in a meta-analysis ( Mason, 2010 ) of qualitative research in practice.

In a review of sample sizes in qualitative studies in the information systems discipline, the authors note that (North) American studies tend to have larger sample sizes than those from other countries ( Marshall et al. , 2013 ). They state that they cannot account for this difference. However, in a discussion of the different, USA versus UK, approaches to qualitative research using focus group discussions (UK)/focus group interviews (USA), the author notes that US researchers tend to implicitly follow a positivist epistemology ( Boddy, 2005a , 2005b ). This logically results in their favouring larger sample sizes. On the contrary, for UK researchers, the concern is more about gathering in-depth information rather than quasi-measurement and so smaller sample sizes are intuitively more appealing.

In making a justification for an adopted sample size, qualitative researchers should make reference to the scope of the study and nature of the topic ( Morse, 2000 ), the contact time to be spent on each individual research participant (respondent) ( Marshall et al. , 2013 ) and the homogeneity of the population under consideration ( Trotter, 2012 ). In practical terms, attempts should be made to make sure that the sample is as representative of the population as possible ( Bock and Sergeant, 2002 ), albeit that it may be a very tightly defined or unusual population.

Conclusions

Qualitative research often concerns developing a depth of understanding rather than a breadth, particularly when undertaken under a non-positivist paradigm, such as that involving depth psychology or a constructivist approach to research. As such, we must conclude that in these cases a single case study involving a single research participant can be of importance and can generate great insight. This logically means that the smallest acceptable sample size in these types of qualitative research is a sample of one. In many cases, therefore, the observation that many reviewers would be tempted to make, that such a sample is too small or cannot be generalised from, is not a valid criticism, particularly if the researcher has justified the sample size. One case can produce an in-depth understanding that furthers knowledge as in the case of a psychopathic CEO. Furthermore, as the example of the discovery of penicillin demonstrates, a single case can also have findings, which do validly apply across many areas.

Exceptions to this guide to sample size may be where the qualitative research is being undertaken under a positivist approach to research, for example with a view to developing a quantitative measurement instrument such as a questionnaire. In this example, it would be useful to have a more representative understanding of likely incidence rates so that questions can be prioritised in terms of inclusion in any questionnaire or other instrument. This would necessitate sampling a greater number of respondents and, in general, at least one representative of each segment of the population under consideration in the wider research should be sampled in the qualitative research.

Thus, the issue of what constitutes an appropriate sample size in qualitative research is only really answerable within the context and scientific paradigm of the research being conducted. In constructivist or in-depth qualitative research, a single example can be highly instructive.

In positivist qualitative research, a representative sample is arguably needed, involving representatives of each of the sub-segments of the total population to be researched. Researchers and reviewers may take these arguments into consideration when respectively deciding what sample sizes to use and in deciding whether to criticise the sample size used in any qualitative research that is being evaluated.

American Chemical Society ( 1999 ), The Discovery and Development of Penicillin 1928-1945 , The Alexander Fleming Laboratory Museum , London .

Bock , T. and Sergeant , J. ( 2002 ), “ Small sample market research ”, International Journal of Market Research , Vol. 44 No. 2 , p. 235 .

Boddy , C.R. ( 2005a ), “ Groups in focus: the distinctive difference between focus group discussions and focus group interviews ”, Australasian Journal of Market and Social Research , Vol. 13 No. 2 , pp. 29 - 38 .

Boddy , C.R. ( 2005b ), “ A rose by any other name may smell as sweet but ‘group discussion’ is not another name for a ‘focus group’ nor should it be ”, Qualitative Market Research: An International Journal , Vol. 8 No. 3 , pp. 248 - 255 .

Boddy , C.R. ( 2015 ), “ Psychopathic leadership: a case study of a corporate psychopath CEO ”, Journal of Business Ethics , Vol. 1 No. 1 , pp. 1 - 16 .

Boddy , C.R. , Miles , D. , Sanyal , C. and Hartog , M. ( 2015 ), “ Extreme managers, extreme workplaces: capitalism, organisations and corporate psychopaths ”, Organization , Vol. 2 No. 4 , pp. 530 - 551 .

Creswell , J.W. ( 1998 ), Qualitative Inquiry and Research Design: Choosing Among Five Traditions , Sage , Thousand Oaks, CA .

Dworkin , S. ( 2012 ), “ Sample size policy for qualitative studies using in-depth interviews ”, Archives of Sexual Behavior , Vol. 41 No. 6 , pp. 1319 - 1320 .

Fleming , A. ( 1929 ), “ On the antibacterial action of cultures of a penicillium, with special reference to their use in the isolation of B. influenzae ”, British Journal of Experimental Pathology , Vol. 10 No. 3 , p. 226 .

Gordon , W. and Langmaid , R. ( 1990 ), Qualitative Market Research: A Practitioner’s and Buyer’s Guide , Gower , Aldershot .

Guest , G. , Bunce , A. and Johnson , L. ( 2006 ), “ How many interviews are enough? An experiment with data saturation and variability ”, Field Methods , Vol. 18 No. 1 , pp. 59 - 82 .

Lincoln , Y.S. and Guba , E.G. ( 2000 ), “ Paradigm controversies, contradictions and emerging influences ”, in Denzin , N.K. and Lincoln , Y.S. (Eds), Handbook of Qualitative Research , Sage , Thousand Oaks, CA , pp. 163 - 188 .

Marshall , B. , Cardon , P. , Poddar , A. and Fontenot , R. ( 2013 ), “ Does sample size matter in qualitative research?: a review of qualitative interviews in IS research ”, Journal of Computer Information Systems , Vol. 54 No. 1 , pp. 11 - 22 .

Mason , M. ( 2010 ), “ Sample size and saturation in PhD studies using qualitative interviews ”, Forum Qualitative Sozialforschung/Forum: Qualitative Social Research , Vol. 11 No. 3 .

Mayring , P. ( 2007 ), “ On generalization in qualitatively oriented research ”, Forum Qualitative Sozialforschung/Forum: Qualitative Social Research , Vol. 8 No. 3 .

Morse , J.M. ( 2000 ), “ Determining sample size ”, Qualitative Health Research , Vol. 10 No. 1 , pp. 3 - 5 .

Onwuegbuzie , A.J. and Leech , N.L. ( 2005 ), “ The role of sampling in qualitative research ”, Academic Exchange Quarterly , Vol. 9 No. 3 , p. 280 .

Sandelowski , M. ( 1995 ), “ Sample size in qualitative research ”, Research in nursing & health , Vol. 18 No. 2 , pp. 179 - 183 .

Stake , R.E. ( 2000 ), “ Case Studies ”, in Denzin , N.K. and Lincoln , Y.S. (Eds), Handbook of Qualitative Research , Sage , London , pp. 425 - 454 .

Trotter , R.T. ( 2012 ), “ Qualitative research sample design and sample size: resolving and unresolved issues and inferential imperatives ”, Preventive Medicine , Vol. 55 No. 5 , pp. 398 - 400 .

Tsang , E.W.K. ( 2014 ), “ Generalizing from research findings: the merits of case studies ”, International Journal of Management Reviews , Vol. 16 No. 4 , pp. 369 - 383 .

Corresponding author

About the author.

Clive Roland Boddy is Professor of Leadership and Organisation Behaviour at Middlesex University where he was previously Associate Professor of Marketing. He is also co-chief examiner for the Diploma of the Market Research Society. Prior to academia, Clive ran marketing research companies in Taiwan, Hong Kong, South Korea and the UK in the 1980s and 1990s. His current research concerns workplace ethical outcomes under corporate psychopaths and toxic leaders. He is a Fellow of the Market Research Society, the Australian Institute of Management, the Chartered Institute of Marketing and the Association for Tertiary Education Management.

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What Is Sample Size?

Learn what sample size is and why having the correct sample size is important in statistical research.

Sample size is the number of observations or individuals included in a study or experiment. It is the number of individuals, items, or data points selected from a larger population to represent it statistically. The sample size is a crucial consideration in research because it directly impacts the reliability and extent to which you can generalize those findings to the larger population.

A larger sample size can potentially enhance the precision of estimates, leading to a narrower margin of error . In other words, the results from a larger sample will likely be closer to the true population parameter. A larger sample size can also increase the power of a statistical test. This means that with a larger sample, you are less likely to find results that are not actually true.

However, having a sample size that is too large can cost unnecessary resources and time. A good study will be able to find the most accurate results with the least amount of subjects.

How do you determine sample size?

Determining the appropriate sample size for a study will usually involve considering the purpose of the study, population size, risk of committing an error, and available resources. When determining the appropriate sample size, you should consider factors such as the following:

Your study design

Different types of studies might require different sample sizes. For example, a study aiming to understand a rare disease might need a larger sample size to ensure it captures enough cases for analysis. Whether you choose to conduct an observational study, cohort study, case-control study, or experimental study will affect the sample size you need.

Your population

In general, with a smaller population, you will need a higher sampling ratio than in a larger population. If you are conducting a survey, you may also need to factor in your estimated response rate to ensure you are sampling enough people to get the number of responses you need. If you have a high degree of variability in your target population, you may also need to increase your sample size to increase the likelihood that your sample represents the population of interest.

Your statistical methods

You can use several statistical methods to calculate an appropriate sample size, such as power analysis. This method considers the effect size, significance level, and power to calculate the sample size. Essentially, you will need to determine what level of confidence you want to have in your results, and how much error you are willing to accept.

Your available resources

Lastly, practical considerations like time, money, and availability of subjects can affect the chosen sample size. While a larger sample size may offer more accurate results, it can also require more resources to collect and analyze.

Related terms

Margin of error

Unstructured data

Statistical modeling  

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Understanding what sample size is and its significance can help you perform many analytical tasks. The appropriate sample size is not only pivotal for the validity and reliability of the study's findings but also for ensuring that the resources used in conducting the research are efficiently utilized. To expand on your analytical skills and build a job-ready portfolio, consider the Google Data Analytics Professional Certificate on Coursera.

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• How To Determine Sample Size

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How to determine sample size.

12 min read Sample size can make or break your research project. Here’s how to master the delicate art of choosing the right sample size.

Author:  Will Webster

Sample size is the beating heart of any research project. It’s the invisible force that gives life to your data, making your findings robust, reliable and believable.

Sample size is what determines if you see a broad view or a focus on minute details; the art and science of correctly determining it involves a careful balancing act. Finding an appropriate sample size demands a clear understanding of the level of detail you wish to see in your data and the constraints you might encounter along the way.

Remember, whether you’re studying a small group or an entire population, your findings are only ever as good as the sample you choose.

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Let’s delve into the world of sampling and uncover the best practices for determining sample size for your research.

“How much sample do we need?” is one of the most commonly-asked questions and stumbling points in the early stages of research design . Finding the right answer to it requires first understanding and answering two other questions:

How important is statistical significance to you and your stakeholders?

What are your real-world constraints.

At the heart of this question is the goal to confidently differentiate between groups, by describing meaningful differences as statistically significant. Statistical significance isn’t a difficult concept, but it needs to be considered within the unique context of your research and your measures.

First, you should consider when you deem a difference to be meaningful in your area of research. While the standards for statistical significance are universal, the standards for “meaningful difference” are highly contextual.

For example, a 10% difference between groups might not be enough to merit a change in a marketing campaign for a breakfast cereal, but a 10% difference in efficacy of breast cancer treatments might quite literally be the difference between life and death for hundreds of patients. The exact same magnitude of difference has very little meaning in one context, but has extraordinary meaning in another. You ultimately need to determine the level of precision that will help you make your decision.

Within sampling, the lowest amount of magnification – or smallest sample size – could make the most sense, given the level of precision needed, as well as timeline and budgetary constraints.

If you’re able to detect statistical significance at a difference of 10%, and 10% is a meaningful difference, there is no need for a larger sample size, or higher magnification. However, if the study will only be useful if a significant difference is detected for smaller differences – say, a difference of 5% — the sample size must be larger to accommodate this needed precision. Similarly, if 5% is enough, and 3% is unnecessary, there is no need for a larger statistically significant sample size.

You should also consider how much you expect your responses to vary. When there isn’t a lot of variability in response, it takes a lot more sample to be confident that there are statistically significant differences between groups.

For instance, it will take a lot more sample to find statistically significant differences between groups if you are asking, “What month do you think Christmas is in?” than if you are asking, “How many miles are there between the Earth and the moon?”. In the former, nearly everybody is going to give the exact same answer, while the latter will give a lot of variation in responses. Simply put, when your variables do not have a lot of variance, larger sample sizes make sense.

Statistical significance

The likelihood that the results of a study or experiment did not occur randomly or by chance, but are meaningful and indicate a genuine effect or relationship between variables.

Magnitude of difference

The size or extent of the difference between two or more groups or variables, providing a measure of the effect size or practical significance of the results.

Actionable insights

Valuable findings or conclusions drawn from data analysis that can be directly applied or implemented in decision-making processes or strategies to achieve a particular goal or outcome.

It’s crucial to understand the differences between the concepts of “statistical significance”, “magnitude of difference” and “actionable insights” – and how they can influence each other:

• Even if there is a statistically significant difference, it doesn’t mean the magnitude of the difference is large: with a large enough sample, a 3% difference could be statistically significant
• Even if the magnitude of the difference is large, it doesn’t guarantee that this difference is statistically significant: with a small enough sample, an 18% difference might not be statistically significant
• Even if there is a large, statistically significant difference, it doesn’t mean there is a story, or that there are actionable insights

There is no way to guarantee statistically significant differences at the outset of a study – and that is a good thing.

Even with a sample size of a million, there simply may not be any differences – at least, any that could be described as statistically significant. And there are times when a lack of significance is positive.

Imagine if your main competitor ran a multi-million dollar ad campaign in a major city and a huge pre-post study to detect campaign effects, only to discover that there were no statistically significant differences in brand awareness . This may be terrible news for your competitor, but it would be great news for you.

With Stats iQ™ you can analyze your research results and conduct significance testing

As you determine your sample size, you should consider the real-world constraints to your research.

Factors revolving around timings, budget and target population are among the most common constraints, impacting virtually every study. But by understanding and acknowledging them, you can definitely navigate the practical constraints of your research when pulling together your sample.

Timeline constraints

Gathering a larger sample size naturally requires more time. This is particularly true for elusive audiences, those hard-to-reach groups that require special effort to engage. Your timeline could become an obstacle if it is particularly tight, causing you to rethink your sample size to meet your deadline.

Budgetary constraints

Every sample, whether large or small, inexpensive or costly, signifies a portion of your budget. Samples could be like an open market; some are inexpensive, others are pricey, but all have a price tag attached to them.

Population constraints

Sometimes the individuals or groups you’re interested in are difficult to reach; other times, they’re a part of an extremely small population. These factors can limit your sample size even further.

What’s a good sample size?

A good sample size really depends on the context and goals of the research. In general, a good sample size is one that accurately represents the population and allows for reliable statistical analysis.

Larger sample sizes are typically better because they reduce the likelihood of sampling errors and provide a more accurate representation of the population. However, larger sample sizes often increase the impact of practical considerations, like time, budget and the availability of your audience. Ultimately, you should be aiming for a sample size that provides a balance between statistical validity and practical feasibility.

4 tips for choosing the right sample size

Choosing the right sample size is an intricate balancing act, but following these four tips can take away a lot of the complexity.

1) Start with your goal

The foundation of your research is a clearly defined goal. You need to determine what you’re trying to understand or discover, and use your goal to guide your research methods – including your sample size.

If your aim is to get a broad overview of a topic, a larger, more diverse sample may be appropriate. However, if your goal is to explore a niche aspect of your subject, a smaller, more targeted sample might serve you better. You should always align your sample size with the objectives of your research.

2) Know that you can’t predict everything

Research is a journey into the unknown. While you may have hypotheses and predictions, it’s important to remember that you can’t foresee every outcome – and this uncertainty should be considered when choosing your sample size.

A larger sample size can help to mitigate some of the risks of unpredictability, providing a more diverse range of data and potentially more accurate results. However, you shouldn’t let the fear of the unknown push you into choosing an impractically large sample size.

3) Plan for a sample that meets your needs and considers your real-life constraints

Every research project operates within certain boundaries – commonly budget, timeline and the nature of the sample itself. When deciding on your sample size, these factors need to be taken into consideration.

Be realistic about what you can achieve with your available resources and time, and always tailor your sample size to fit your constraints – not the other way around.

4) Use best practice guidelines to calculate sample size

There are many established guidelines and formulas that can help you in determining the right sample size.

The easiest way to define your sample size is using a sample size calculator , or you can use a manual sample size calculation if you want to test your math skills. Cochran’s formula is perhaps the most well known equation for calculating sample size, and widely used when the population is large or unknown.

Beyond the formula, it’s vital to consider the confidence interval, which plays a significant role in determining the appropriate sample size – especially when working with a random sample – and the sample proportion. This represents the expected ratio of the target population that has the characteristic or response you’re interested in, and therefore has a big impact on your correct sample size.

If your population is small, or its variance is unknown, there are steps you can still take to determine the right sample size. Common approaches here include conducting a small pilot study to gain initial estimates of the population variance, and taking a conservative approach by assuming a larger variance to ensure a more representative sample size.

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Sample Size Determination: Definition, Formula, and Example

Are you ready to survey your research target? Research surveys help you gain insights from your target audience. The data you collect gives you insights to meet customer needs, leading to increased sales and customer loyalty. Sample size calculation and determination are imperative to the researcher to determine the right number of respondents, keeping in mind the research study’s quality.

So, how should you do the sample size determination? How do you know who should get your survey? How do you decide on the number of the target audience?

Sending out too many surveys can be expensive without giving you a definitive advantage over a smaller sample. But if you send out too few, you won’t have enough data to draw accurate conclusions. 

Knowing how to calculate and determine the appropriate sample size accurately can give you an edge over your competitors. Let’s take a look at what a good sample includes. Also, let’s look at the sample size calculation formula so you can determine the perfect sample size for your next survey.

What is Sample Size?

‘Sample size’ is a market research term used for defining the number of individuals included in conducting research. Researchers choose their sample based on demographics, such as age, gender questions , or physical location. It can be vague or specific. 

For example, you may want to know what people within the 18-25 age range think of your product. Or, you may only require your sample to live in the United States, giving you a wide population range. The total number of individuals in a particular sample is the sample size.

What is sample size determination?

Sample size determination is the process of choosing the right number of observations or people from a larger group to use in a sample. The goal of figuring out the sample size is to ensure that the sample is big enough to give statistically valid results and accurate estimates of population parameters but small enough to be manageable and cost-effective.

In many research studies, getting information from every member of the population of interest is not possible or useful. Instead, researchers choose a sample of people or events that is representative of the whole to study. How accurate and precise the results are can depend a lot on the size of the sample.

Choosing the statistically significant sample size depends on a number of things, such as the size of the population, how precise you want your estimates to be, how confident you want to be in the results, how different the population is likely to be, and how much money and time you have for the study. Statistics are often used to figure out how big a sample should be for a certain type of study and research question.

Figuring out the sample size is important in ensuring that research findings and conclusions are valid and reliable.

Why do you need to determine the sample size?

Let’s say you are a market researcher in the US and want to send out a survey or questionnaire . The survey aims to understand your audience’s feelings toward a new cell phone you are about to launch. You want to know what people in the US think about the new product to predict the phone’s success or failure before launch.

Hypothetically, you choose the population of New York, which is 8.49 million. You use a sample size determination formula to select a sample of 500 individuals that fit into the consumer panel requirement. You can use the responses to help you determine how your audience will react to the new product.

However, determining a sample size requires more than just throwing your survey at as many people as possible. If your estimated sample sizes are too big, it could waste resources, time, and money. A sample size that’s too small doesn’t allow you to gain maximum insights, leading to inconclusive results.

LEARN ABOUT: Survey Sample Sizes

What are the terms used around the sample size?

Before we jump into sample size determination, let’s take a look at the terms you should know:

1. Population size: 

Population size is how many people fit your demographic. For example, you want to get information on doctors residing in North America. Your population size is the total number of doctors in North America. 

Don’t worry! Your population size doesn’t always have to be that big. Smaller population sizes can still give you accurate results as long as you know who you’re trying to represent.

2. Confidence level: 

The confidence level tells you how sure you can be that your data is accurate. It is expressed as a percentage and aligned to the confidence interval. For example, if your confidence level is 90%, your results will most likely be 90% accurate.

3. The margin of error (confidence interval): 

There’s no way to be 100% accurate when it comes to surveys. Confidence intervals tell you how far off from the population means you’re willing to allow your data to fall. 

A margin of error describes how close you can reasonably expect a survey result to fall relative to the real population value. Remember, if you need help with this information, use our margin of error calculator .

4. Standard deviation: 

Standard deviation is the measure of the dispersion of a data set from its mean. It measures the absolute variability of a distribution. The higher the dispersion or variability, the greater the standard deviation and the greater the magnitude of the deviation. 

For example, you have already sent out your survey. How much variance do you expect in your responses? That variation in response is the standard deviation.

Sample size calculation formula – sample size determination

With all the necessary terms defined, it’s time to learn how to determine sample size using a sample calculation formula.

Your confidence level corresponds to a Z-score. This is a constant value needed for this equation. Here are the z-scores for the most common confidence levels:

90% – Z Score = 1.645

95% – Z Score = 1.96

99% – Z Score = 2.576

If you choose a different confidence level, various online tools can help you find your score.

Necessary Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Here is an example of how the math works, assuming you chose a 90% confidence level, .6 standard deviation, and a margin of error (confidence interval) of +/- 4%.

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603 respondents are needed, and that becomes your sample size.

Free Sample Size Calculator

How is a sample size determined?

Determining the right sample size for your survey is one of the most common questions researchers ask when they begin a market research study. Luckily, sample size determination isn’t as hard to calculate as you might remember from an old high school statistics class.

Before calculating your sample size, ensure you have these things in place:

Goals and objectives: 

What do you hope to do with the survey? Are you planning on projecting the results onto a whole demographic or population? Do you want to see what a specific group thinks? Are you trying to make a big decision or just setting a direction? 

Calculating sample size is critical if you’re projecting your survey results on a larger population. You’ll want to make sure that it’s balanced and reflects the community as a whole. The sample size isn’t as critical if you’re trying to get a feel for preferences. 

For example, you’re surveying homeowners across the US on the cost of cooling their homes in the summer. A homeowner in the South probably spends much more money cooling their home in the humid heat than someone in Denver, where the climate is dry and cool. 

For the most accurate results, you’ll need to get responses from people in all US areas and environments. If you only collect responses from one extreme, such as the warm South, your results will be skewed.

Precision level: 

How close do you want the survey results to mimic the true value if everyone responded? Again, if this survey determines how you’re going to spend millions of dollars, then your sample size determination should be exact. 

The more accurate you need to be, the larger the sample you want to have, and the more your sample will have to represent the overall population. If your population is small, say, 200 people, you may want to survey the entire population rather than cut it down with a sample.

Confidence level: 

Think of confidence from the perspective of risk. How much risk are you willing to take on? This is where your Confidence Interval numbers become important. How confident do you want to be — 98% confident, 95% confident? 

Understand that the confidence percentage you choose greatly impacts the number of completions you’ll need for accuracy. This can increase the survey’s length and how many responses you need, which means increased costs for your survey. 

Knowing the actual numbers and amounts behind percentages can help make more sense of your correct sample size needs vs. survey costs. 

For example, you want to be 99% confident. After using the sample size determination formula, you find you need to collect an additional 1000 respondents. 

This, in turn, means you’ll be paying for samples or keeping your survey running for an extra week or two. You have to determine if the increased accuracy is more important than the cost.

Population variability: 

What variability exists in your population? In other words, how similar or different is the population?

If you are surveying consumers on a broad topic, you may have lots of variations. You’ll need a larger sample size to get the most accurate picture of the population. 

However, if you’re surveying a population with similar characteristics, your variability will be less, and you can sample fewer people. More variability equals more samples, and less variability equals fewer samples. If you’re not sure, you can start with 50% variability.

Response rate: 

You want everyone to respond to your survey. Unfortunately, every survey comes with targeted respondents who either never open the study or drop out halfway. Your response rate will depend on your population’s engagement with your product, service organization, or brand. 

The higher the response rate, the higher your population’s engagement level. Your base sample size is the number of responses you must get for a successful survey.

Consider your audience: 

Besides the variability within your population, you need to ensure your sample doesn’t include people who won’t benefit from the results. One of the biggest mistakes you can make in sample size determination is forgetting to consider your actual audience. 

For example, you don’t want to send a survey asking about the quality of local apartment amenities to a group of homeowners.

Select your respondents

Focus on your survey’s objectives: 

You may start with general demographics and characteristics, but can you narrow those characteristics down even more? Narrowing down your audience makes getting a more accurate result from a small sample size easier. 

For example, you want to know how people will react to new automobile technology. Your current population includes anyone who owns a car in a particular market. 

However, you know your target audience is people who drive cars that are less than five years old. You can remove anyone with an older vehicle from your sample because they’re unlikely to purchase your product.

Once you know what you hope to gain from your survey and what variables exist within your population, you can decide how to calculate sample size. Using the formula for determining sample size is a great starting point to get accurate results. 

After calculating the sample size, you’ll want to find reliable customer survey software to help you accurately collect survey responses and turn them into analyzed reports.

LEARN MORE: Population vs Sample

In sample size determination, statistical analysis plan needs careful consideration of the level of significance, effect size, and sample size. 

Researchers must reconcile statistical significance with practical and ethical factors like practicality and cost. A well-designed study with a sufficient sample size can improve the odds of obtaining statistically significant results.

To meet the goal of your survey, you may have to try a few methods to increase the response rate, such as:

• Increase the list of people who receive the survey.
• To reach a wider audience, use multiple distribution channels, such as SMS, website, and email surveys.
• Send reminders to survey participants to complete the survey.
• Offer incentives for completing the survey, such as an entry into a prize drawing or a discount on the respondent’s next order.
• Consider your survey structure and find ways to simplify your questions. The less work someone has to do to complete the survey, the more likely they will finish it. 
• Longer surveys tend to have lower response rates due to the length of time it takes to complete the survey. In this case, you can reduce the number of questions in your survey to increase responses.  

QuestionPro’s sample size calculator makes it easy to find the right sample size for your research based on your desired level of confidence, your margin of error, and the size of the population.

FREE TRIAL         LEARN MORE

Frequently Asked Questions (FAQ)

The four ways to determine sample size are: 1. Power analysis 2. Convenience sampling, 3. Random sampling , 4. Stratified sampling

The three factors that determine sample size are: 1. Effect size, 2. Level of significance 3. Power

Using statistical techniques like power analysis, the minimal detectable effect size, or the sample size formula while taking into account the study’s goals and practical limitations is the best way to calculate the sample size.

The sample size is important because it affects how precise and accurate the results of a study are and how well researchers can spot real effects or relationships between variables.

The sample size is the number of observations or study participants chosen to be representative of a larger group

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• An Bras Dermatol
• v.89(4); Jul-Aug 2014

Sample size: how many participants do I need in my research? *

Jeovany martínez-mesa.

1 Latin American Cooperative Oncology Group - Porto Alegre (RS), Brazil.

David Alejandro González-Chica

2 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (SC), Brazil.

João Luiz Bastos

Renan rangel bonamigo.

3 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

Rodrigo Pereira Duquia

The importance of estimating sample sizes is rarely understood by researchers, when planning a study. This paper aims to highlight the centrality of sample size estimations in health research. Examples that help in understanding the basic concepts involved in their calculation are presented. The scenarios covered are based more on the epidemiological reasoning and less on mathematical formulae. Proper calculation of the number of participants in a study diminishes the likelihood of errors, which are often associated with adverse consequences in terms of economic, ethical and health aspects.

INTRODUCTION

Investigations in the health field are oriented by research problems or questions, which should be clearly defined in the study project. Sample size calculation is an essential item to be included in the project to reduce the probability of error, respect ethical standards, define the logistics of the study and, last but not least, improve its success rates, when evaluated by funding agencies.

Let us imagine that a group of investigators decides to study the frequency of sunscreen use and how the use of this product is distributed in the "population". In order to carry out this task, the authors define two research questions, each of which involving a distinct sample size calculation: 1) What is the proportion of people that use sunscreen in the population?; and, 2) Are there differences in the use of sunscreen between men and women, or between individuals that are white or of another skin color group, or between the wealthiest and the poorest, or between people with more and less years of schooling? Before doing the calculations, it will be necessary to review a few fundamental concepts and identify which are the required parameters to determine them.

WHAT DO WE MEAN, WHEN WE TALK ABOUT POPULATIONS?

First of all, we must define what is a population . Population is the group of individuals restricted to a geographical region (neighborhood, city, state, country, continent etc.), or certain institutions (hospitals, schools, health centers etc.), that is, a set of individuals that have at least one characteristic in common. The target population corresponds to a portion of the previously mentioned population, about which one intends to draw conclusions, that is to say, it is a part of the population whose characteristics are an object of interest of the investigator. Finally, study population is that which will actually be part of the study, which will be evaluated and will allow conclusions to be drawn about the target population, as long as it is representative of the latter. Figure 1 demonstrates how these concepts are interrelated.

Graphic representation of the concepts of population, target population and study population

We will now separately consider the required parameters for sample size calculation in studies that aim at estimating the frequency of events (prevalence of health outcomes or behaviors, for example), to test associations between risk/protective factors and dichotomous health conditions (yes/no), as well as with health outcomes measured in numerical scales. 1 The formulas used for these calculations may be obtained from different sources - we recommend using the free online software OpenEpi ( www.openepi.com ). 2

WHICH PARAMETERS DOES SAMPLE SIZE CALCULATION DEPEND UPON FOR A STUDY THAT AIMS AT ESTIMATING THE FREQUENCY OF HEALTH OUTCOMES, BEHAVIORS OR CONDITIONS?

When approaching the first research question defined at the beginning of this article (What is the proportion of people that use sunscreen?), the investigators need to conduct a prevalence study. In order to do this, some parameters must be defined to calculate the sample size, as demonstrated in chart 1 .

Description of different parameters to be considered in the calculation of sample size for a study aiming at estimating the frequency of health ouctomes, behaviors or conditions

Chart 2 presents some sample size simulations, according to the outcome prevalence, sample error and the type of target population investigated. The same basic question was used in this table (prevalence of sunscreen use), but considering three different situations (at work, while doing sports or at the beach), as in the study by Duquia et al. conducted in the city of Pelotas, state of Rio Grande do Sul, in 2005. 3

Sample size calculation to estimate the frequency (prevalence) of sunscreen use in the population, considering different scenarios but keeping the significance level (95%) and the design effect (1.0) constant

p.p.= percentage points

The calculations show that, by holding the sample error and the significance level constant, the higher the expected prevalence, the larger will be the required sample size. However, when the expected prevalence surpasses 50%, the required sample size progressively diminishes - the sample size for an expected prevalence of 10% is the same as that for an expected prevalence of 90%.

The investigator should also define beforehand the precision level to be accepted for the investigated event (sample error) and the confidence level of this result (usually 95%). Chart 2 demonstrates that, holding the expected prevalence constant, the higher the precision (smaller sample error) and the higher the confidence level (in this case, 95% was considered for all calculations), the larger also will be the required sample size.

Chart 2 also demonstrates that there is a direct relationship between the target population size and the number of individuals to be included in the sample. Nevertheless, when the target population size is sufficiently large, that is, surpasses an arbitrary value (for example, one million individuals), the resulting sample size tends to stabilize. The smaller the target population, the larger the sample will be; in some cases, the sample may even correspond to the total number of individuals from the target population - in these cases, it may be more convenient to study the entire target population, carrying out a census survey, rather than a study based on a sample of the population.

SAMPLE CALCULATION TO TEST THE ASSOCIATION BETWEEN TWO VARIABLES: HYPOTHESES AND TYPES OF ERROR

When the study objective is to investigate whether there are differences in sunscreen use according to sociodemographic characteristics (such as, for example, between men and women), the existence of association between explanatory variables (exposure or independent variables, in this case sociodemographic variables) and a dependent or outcome variable (use of sunscreen) is what is under consideration.

In these cases, we need first to understand what the hypotheses are, as well as the types of error that may result from their acceptance or refutation. A hypothesis is a "supposition arrived at from observation or reflection, that leads to refutable predictions". 4 In other words, it is a statement that may be questioned or tested and that may be falsified in scientific studies.

In scientific studies, there are two types of hypothesis: the null hypothesis (H 0 ) or original supposition that we assume to be true for a given situation, and the alternative hypothesis (H A ) or additional explanation for the same situation, which we believe may replace the original supposition. In the health field, H 0 is frequently defined as the equality or absence of difference in the outcome of interest between the studied groups (for example, sunscreen use is equal in men and women). On the other hand, H A assumes the existence of difference between groups. H A is called two-tailed when it is expected that the difference between the groups will occur in any direction (men using more sunscreen than women or vice-versa). However, if the investigator expects to find that a specific group uses more sunscreen than the other, he will be testing a one-tailed H A .

In the sample investigated by Duquia et al., the frequency of sunscreen use at the beach was greater in men (32.7%) than in women (26.2%).3 Although this what was observed in the sample, that is, men do wear more sunscreen than women, the investigators must decide whether they refute or accept H 0 in the target population (which contends that there is no difference in sunscreen use according to sex). Given that the entire target population is hardly ever investigated to confirm or refute the difference observed in the sample, the authors have to be aware that, independently from their decision (accepting or refuting H 0 ), their conclusion may be wrong, as can be seen in figure 2 .

Types of possible results when performing a hypothesis test

In case the investigators conclude that both in the target population and in the sample sunscreen use is also different between men and women (rejecting H 0 ), they may be making a type I or Alpha error, which is the probability of rejecting H 0 based on sample results when, in the target population, H 0 is true (the difference between men and women regarding sunscreen use found in the sample is not observed in the target population). If the authors conclude that there are no differences between the groups (accepting H 0 ), the investigators may be making a type II or Beta error, which is the probability of accepting H 0 when, in the target population, H 0 is false (that is, H A is true) or, in other words, the probability of stating that the frequency of sunscreen use is equal between the sexes, when it is different in the same groups of the target population.

In order to accept or refute H 0 , the investigators need to previously define which is the maximum probability of type I and II errors that they are willing to incorporate into their results. In general, the type I error is fixed at a maximum value of 5% (0.05 or confidence level of 95%), since the consequences originated from this type of error are considered more harmful. For example, to state that an exposure/intervention affects a health condition, when this does not happen in the target population may bring about behaviors or actions (therapeutic changes, implementation of intervention programs etc.) with adverse consequences in ethical, economic and health terms. In the study conducted by Duquia et al., when the authors contend that the use of sunscreen was different according to sex, the p value presented (<0.001) indicates that the probability of not observing such difference in the target population is less that 0.1% (confidence level >99.9%). 3

Although the type II or Beta error is less harmful, it should also be avoided, since if a study contends that a given exposure/intervention does not affect the outcome, when this effect actually exists in the target population, the consequence may be that a new medication with better therapeutic effects is not administered or that some aspects related to the etiology of the damage are not considered. This is the reason why the value of the type II error is usually fixed at a maximum value of 20% (or 0.20). In publications, this value tends to be mentioned as the power of the study, which is the ability of the test to detect a difference, when in fact it exists in the target population (usually fixed at 80%, as a result of the 1-Beta calculation).

SAMPLE CALCULATION FOR STUDIES THAT AIM AT TESTING THE ASSOCIATION BETWEEN A RISK/PROTECTIVE FACTOR AND AN OUTCOME, EVALUATED DICHOTOMOUSLY

In cases where the exposure variables are dichotomous (intervention/control, man/woman, rich/poor etc.) and so is the outcome (negative/positive outcome, to use sunscreen or not), the required parameters to calculate sample size are those described in chart 3 . According to the previously mentioned example, it would be interesting to know whether sex, skin color, schooling level and income are associated with the use of sunscreen at work, while doing sports and at the beach. Thus, when the four exposure variables are crossed with the three outcomes, there would be 12 different questions to be answered and consequently an equal number of sample size calculations to be performed. Using the information in the article by Duquia et al. 3 for the prevalence of exposures and outcomes, a simulation of sample size calculations was used for each one of these situations ( Chart 4 ).

Ho - null hypothesis; Ha - alternative hypothesis

E=exposed group; NE=non-exposed group; r=NE/E relationship; PONE=prevalence of outcome in the non-exposed group (percentage of positives in non-exposed group), estimated based on formula from chart 3 , considering an PR of 1.50; PR=prevalence ratio/incidence or expected relative risk; n= minimum necessary sample size; ND=value could not be determined, as prevalence of outcome in the exposed would be above 100%, according to specified parameters.

Estimates show that studies with more power or that intend to find a difference of a lower magnitude in the frequency of the outcome (in this case, the prevalence rates) between exposed and non-exposed groups require larger sample sizes. For these reasons, in sample size calculations, an effect measure between 1.5 and 2.0 (for risk factors) or between 0.50 and 0.75 (for protective factors), and an 80% power are frequently used.

Considering the values in each column of chart 3 , we may conclude also that, when the nonexposed/exposed relationship moves away from one (similar proportions of exposed and non-exposed individuals in the sample), the sample size increases. For this reason, intervention studies usually work with the same proportion of individuals in the intervention and control groups. Upon analysis of the values on each line, it can be concluded that there is an inverse relationship between the prevalence of the outcome and the required sample size.

Based on these estimates, assuming that the authors intended to test all of these associations, it would be necessary to choose the largest estimated sample size (2,630 subjects). In case the required sample size is larger than the target population, the investigators may decide to perform a multicenter study, lengthen the period for data collection, modify the research question or face the possibility of not having sufficient power to draw valid conclusions.

Additional aspects need to be considered in the previous estimates to arrive at the final sample size, which may include the possibility of refusals and/or losses in the study (an additional 10-15%), the need for adjustments for confounding factors (an additional 10-20%, applicable to observational studies), the possibility of effect modification (which implies an analysis of subgroups and the need to duplicate or triplicate the sample size), as well as the existence of design effects (multiplication of sample size by 1.5 to 2.0) in case of cluster sampling.

SAMPLE CALCULATIONS FOR STUDIES THAT AIM AT TESTING THE ASSOCIATION BETWEEN A DICHOTOMOUS EXPOSURE AND A NUMERICAL OUTCOME

Suppose that the investigators intend to evaluate whether the daily quantity of sunscreen used (in grams), the time of daily exposure to sunlight (in minutes) or a laboratory parameter (such as vitamin D levels) differ according to the socio-demographic variables mentioned. In all of these cases, the outcomes are numerical variables (discrete or continuous) 1 , and the objective is to answer whether the mean outcome in the exposed/intervention group is different from the non-exposed/control group.

In this case, the first three parameters from chart 4 (alpha error, power of the study and relationship between non-exposed/exposed groups) are required, and the conclusions about their influences on the final sample size are also applicable. In addition to defining the expected outcome means in each group or the expected mean difference between nonexposed/exposed groups (usually at least 15% of the mean value in non-exposed group), they also need to define the standard deviation value for each group. There is a direct relationship between the standard deviation value and the sample size, the reason why in case of asymmetric variables the sample size would be overestimated. In such cases, the option may be to estimate sample sizes based on specific calculations for asymmetric variables, or the investigators may choose to use a percentage of the median value (for example, 25%) as a substitute for the standard deviation.

SAMPLE SIZE CALCULATIONS FOR OTHER TYPES OF STUDY

There are also specific calculations for some other quantitative studies, such as those aiming to assess correlations (exposure and outcome are numerical variables), time until the event (death, cure, relapse etc.) or the validity of diagnostic tests, but they are not described in this article, given that they were discussed elsewhere. 5

Sample size calculation is always an essential step during the planning of scientific studies. An insufficient or small sample size may not be able to demonstrate the desired difference, or estimate the frequency of the event of interest with acceptable precision. A very large sample may add to the complexity of the study, and its associated costs, rendering it unfeasible. Both situations are ethically unacceptable and should be avoided by the investigator.

Conflict of Interest: None

Financial Support: None

* Work carried out at the Latin American Cooperative Oncology Group (LACOG), Universidade Federal de Santa Catarina (UFSC), and Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Brazil.

Como citar este artigo: Martínez-Mesa J, González-Chica DA, Bastos JL, Bonamigo RR, Duquia RP. Sample size: how many participants do I need in my research? An Bras Dermatol. 2014;89(4):609-15.