How Many People Do I Need To Survey To Get Meaningful Answers?

A central decision for anyone considering a survey – or any other quantitative research – is figuring out how big the survey sample needs to be in order to produce meaningful answers to the research questions. Researchers focus on sample size because it ties together three core aspects of any research effort:

• Cost – the bigger the sample, the more it will cost to collect, process and analyze the data
• Speed – the bigger the sample, the longer it will take to collect it (big samples can sometimes be collected quickly, but usually only by further raising costs!)
• Accuracy – the bigger the sample, the more certain we can be that we have correctly captured the perceptions/opinions/behavior/beliefs/feelings of the population we are interested in (the technical term for this is statistical reliability)

As we see from these three bullets, the decision about sample size essentially boils down to a trade-off between cost, speed and accuracy. So when we pick a sample size we are making a decision about how much accuracy we are going to purchase, within the framework of our budget and timing.

Fortunately for researchers, quantitative samples do not have to be enormous to provide findings that are accurate enough to answer most market research questions. Any unbiased sample (we’ll talk about sample bias in another blog entry) of 50 or more stands a halfway decent chance of giving you a reasonable view of the population it is drawn from and, as we increase the sample size, our confidence that we have the correct answer increases. We can show this effect by looking at the margin of error – the plus or minus number – for some common sample sizes. To keep it simple, all of these are calculated using the assumption that the sample is drawn from a large population (20,000 or more) and that we are using the 95% confidence level of statistical reliability (the most typical standard for statistical reliability used in market research). If we are looking at percentages:

• A sample of 100 has a margin of error of ± 9.8%
• A sample of 250 has a margin of error of ± 6.2%
• A sample of 500 has a margin of error of ± 4.4%
• A sample of 1,000 has a margin of error of ± 3.0%
• A sample of 2,000 has a margin of error of ± 2.1%

Looking at these numbers you can see why national surveys, such as the big public opinion polls shown on TV or in newspapers, often have samples around 1,000 or so. Samples in that size range have small margins of error, and doubling the sample size wouldn’t make the margin of error much smaller – there’s no reason to spend money making the sample bigger for such a small gain in accuracy.

These numbers also show why we often urge clients to spend a bit to make a small sample bigger, but not too big! The gains in accuracy are all at the beginning – moving from a sample of 100 to something larger is almost always a good idea, while adding anything over 1,000 usually is not. So the rule of thumb is: 100 is probably too small and 1,000 is probably too big.

Of course, in real life it can be more complicated. We may need to examine sub-groups (age or income groups, political parties, geographic regions, etc.) within the population we are looking at. If a sub-group is small, we may need a bigger overall sample to capture enough of each of the sub-groups in order to provide an accurate picture of their views. So we have a rule of thumb about sub-groups, as well – don’t make decisions about any sub-group smaller than 30. For example, if we do a survey of households in a large urban area and we want to compare households by income level, we need to make our sample big enough to have at least 30 households in each of the income categories we want to compare. Assuming this is a normal city, there will be fewer households at the high end of the income distribution than at the low end, so we need to think about how to get enough of the high-end households to be able to make how comparison. So, if we want to be able to look at households with income over $100K, and 15% of the population has an income of $100K more, we need to have a sample of at least 200 households to ensure that 30 of the households would be in that category.

Using these rules of thumb, you can form an idea about how big your sample needs to be to answer your research questions - without spending more than you can afford.

The “Dirty Dozen” – The Most Common Things That Cause Market Research To Go Wrong

Clients who are new to market research often worry that they will spend time and money on a market research project only to later realize that errors in the design or execution of the study have rendered their investment much less valuable than they had hoped. And they are right to worry – there are plenty of cases of market research blunders and even examples where market research findings have led to product design or marketing decisions that were worse than if there had been no research at all!
There are lots of individual events or mistakes that can knock market research projects off track and a short blog entry could never list them all. But there are a few types of errors that account for most of the problems. We think of these as the “dirty dozen,” – the common mistakes that we see over and over again and that rob market research studies of their potential value to decision makers. The tables below list these problems by the stage of the research project where they typically occur, along with the consequences the problem brings and – most importantly – ways to avoid them.
At the research design stage:

Problem	Consequences	Solution
Poorly formulated objectives or research questions	The data collected will not address the real issues, resulting in findings that are vague, inconclusive or even misleading.	Write out your objectives and research questions and think about what kind of data would count as an answer to each one. Don’t skip this step or assume that everybody on the project has a shared understanding of what the research is supposed to accomplish.
Poor choice of data collection method(s)	Lack of insight when needed depth or breadth (or sometimes both!) is missing from the collected data.	Make the method(s) suit the objectives and research questions. Don’t get locked into “standard” approaches or doing what is easy rather than what is best for the study.
Sample design issues	Asking questions of the wrong people can produce misleading answers in qualitative studies or statistically invalidate a quantitative project.	Be explicit about the sample parameters. Know who you are going to talk to and exactly what larger population the sample is supposed to represent.
Poorly designed/untested research instrument	Garbage in – garbage out. Failing to ask well-though-out questions, whether in a survey, a focus group or an in-depth interview, will produce poor quality results.	Every question you ask should have a purpose that relates back to the research objectives. Don’t neglect review and testing of the questionnaire or interview guide.

At the data collection stage:

Problem	Consequences	Solution
Inadequately trained or prepared data collectors	Data that is inconsistently gathered can produce gaps, validity problems and lack of depth.	Use professionals who know their jobs and have proven track records. Even experienced survey data collectors, interviewers and focus group moderators need to practice with the research instrument.
Failure to meet the sample specifications	If the sample you get is not the sample you intended, you may have data that is not pertinent or that misrepresents the views of the target population.	Have good quality control on the sample. If adjustments have to be made, be very sure you are not giving up the validity of your sample in order to fill your groups or meet numerical quotas.
Quality control issues	Poor quality control can result in errors in the data files, data that is missing or is mis- categorized.	Have a plan to check incoming data as it is collected. Don’t wait until data collection is over to begin the process of checking for errors or problems.
Loss of data	You can’t analyze data that has disappeared.	Have back-ups (and more-back-ups). Never let data reside longer than necessary in a single location or file. Have security and back-up procedures for all data storage media.

At the data analysis/interpretation stage:

Problem	Consequences	Solution
Lazy/incomplete review of the raw data	Important insights can be missed.	Have a data analysis plan that sets out how the raw data will be handled. Allow enough time for data review and processing. Don’t rely on human memory or quick skimming to capture all the meaning that the data holds.
Inappropriate data reduction techniques	Each time data is reduced, whether through coding of qualitative data or numerical consolidation of quantitative data, there is some potential loss of information or important details.	Make sure your chosen data reduction techniques capture the themes, ideas and categories that will answer the research questions. Don’t be afraid to recode or re-analyze if new issues emerge while data reduction is in progress. Remember recoding means you have learned something from your data – it’s a step forward not a step back!
Over-reliance on statistics	Interpretation that is guided only by statistical testing runs the risk of missing insights that didn’t quite pass the test criteria or finding “insights” that are really just an artifact of the statistical method.	Know the strengths and weaknesses of the statistics you use. Use them as guidelines and tools, not as the word of the research gods. Statistics are not a substitute for common sense of familiarity with your data and your research topic.
Canned answers	Having a bias toward a particular answer or type of interpretation can blind you to new themes and ideas that emerge from your data.	Keep an open mind. Let the data speak to you. Think about what would count as disproof of your preferred interpretation and make sure that there’s a way for that evidence to emerge.

As you can see, there are many potential pitfalls and room for error in market research projects of any type. Careful planning, design, oversight and analysis are absolutely key to getting the best value from the money you spend on market research!

How Do Qualitative and Quantitative Research Differ?

For market researchers, the distinctions between qualitative and quantitative research are very clear, even to the point where many researchers specialize in one or the other of these two types. If the difference is so clear in our minds, then why is it so difficult to write a clear, straightforward, practical and meaningful explanation of the difference?
Most explanations of the qualitative/quantitative distinction tend to focus on listing the typical methods used for each. But this puts the emphasis on the means – the way the data is collected. The more important issue in thinking about the qualitative/quantitative distinction is on the ends. It is the type of research questions you are trying to answer, and more importantly, what type of information would count as a meaningful answer, that determines whether your approach should be qualitative, quantitative or a mix of both.
For example, if your research question is about how some behavior or opinion is distributed across different groups of people, geographic areas or across time, you need numbers – usually percentages – to make these comparisons. This sort of numerical comparison implies a quantitative approach and all the methodological trappings that go with it, including:

• Closed-end survey-type questions
• Relatively large numbers of research participants or direct observations
• A sampling approach that ensures that you have the statistical validity to project the findings from your sample onto the population you are interested in

In contrast, if your research questions are less about comparisons and more about understanding chains of logic, decision processes, group norms, reactions to ideas or sensory stimuli, or cultural domains, you probably need a qualitative approach. Qualitative research focuses on understanding the human experience in context by either:

• Directly embedding the research in the scene or cultural setting (ethnography)
• Using group dynamics to explore norms, attitudes, reactions and beliefs (focus groups)
• Employing inductive questioning to probe reasoning, causation and psychology (in-depth interviews)

In short, it is knowing both the research question and what counts as an answer to the question that determines which “toolkit” the researcher will turn to for a specific project.
That said, the stage of our knowledge about a topic is often an important factor. When we first begin learning about a new topic we are usually in an exploratory mode. We may still be developing our vocabulary about the topic and couldn’t write a good survey about it if we tried – we don’t know enough to write good closed-end questions! Qualitative research is often used at this stage. The very “hands-on” qualitative techniques enable us to learn the lay of the land – how consumers think and speak about the topic. When we see research objectives using verbs like explore or identify – we are usually at this stage of knowledge and lean toward qualitative techniques. We may also use qualitative research at a later stage of knowledge to help interpret quantitative data. In these cases we use interviews or focus groups to understand the correlations that appear in survey data.
Quantitative studies are usually done only in areas where we already have the appropriate knowledge and vocabulary to design good survey questions. We have to know enough about what to ask about, how to ask it, and who to ask it of, to meaningfully quantify the responses. Research objectives that include words such as test, measure or evaluate are more typical of this stage of knowledge.
In short, thinking about qualitative and quantitative research is not about choosing methods or techniques – it is about understanding the nature of your research questions and having a good sense of what type of data – percentages, narratives, images, word counts, observations, maps of logic chains or decision processes would provide useful answers. If you are clear about what you are asking and what you need to know, the choice of methods will largely take care of itself.