How to Determine the Right Sample Size for Quantitative Research

Sample size is just as important as any other aspect of your project. We use market research as a tool to understand consumers’ attitudes and behaviors and base critical business decisions off its results. To do this, we use surveys and respondents who act as a representation of the larger population. That’s why it’s so important that your sample matches the population. We have to understand that the sample size needs to correlate to the actual population in order to produce statistically relevant results that can be attributed to actual consumers, helping you make the best decisions for your business. 

As providers of sample, we are often asked what market research sample size is needed for a study. The answer to this question is complicated—not only because the statistical formula to calculate sample size is complicated, but also because there are other factors that determine base size—primarily quotas and the level of risk of the study. 

Determining the sample size in a quantitative research study is challenging. There are a variety of factors and variables to consider, including population size, confidence level, the margin of error, standard deviation, etc.

Population Size:

How big is the actual population of the group you’re surveying? Generally, in market research, we deal with rather large population sizes and use a constant for this input. When using statistical probability, the same sample size can be used to represent the opinions of 100,000 people or many million. 

Our sample comes from actively managed online research panels, but we can also access other sources depending on the target group, and upon your request or approval. While our focus has been on double opt-in online panelists, we’ve enhanced our offering with river and social media sample so that we can deliver the most representative sample possible.

Confidence Level:

When it comes to the research behind your business decisions, you want to make sure the results are accurate. There are many ways to ensure confidence in your data, and there are also ways to measure it. Typically, the following confidence levels are utilized, although you can really pick any number:

• 99% = High Risk.  For example, if you are making a billion-dollar investment decision you may want to be extremely confident in the data.
• 95% = Medium-to-High Risk. When you want high confidence, but at a smaller base size.
• 90% = Medium-to-Low Risk. This is rather typical in most consumer research.
• 80% = Low Risk. If you only need to be reasonably confident in the research and your business decision is low risk, this is ideally the lowest confidence level you would ever choose.

For the formula, the confidence level corresponds to a z-score. Here are the z-scores for the above confidence levels:

• 99% = 2.326
• 95% = 1.96
• 90% = 1.645
• 80% = 1.282

The z-score is the number of standard deviations a given proportion is away from the mean.

Margin of Error: 

The margin of error is a percentage that tells you how much you can expect your survey results to reflect the views of the overall population. The smaller the margin of error, the closer you are to having the exact answer at a given confidence level.

Typical margins of error are:

• 2% = High Risk
• 5% = Normal or Low Risk

Standard Deviation:

Standard deviation measures how much variance you expect in the data. We typically use .5 as a constant. However, there are a few things that must be considered when calculating sample size. First, if you want a smaller margin of error, you must have a larger sample size given the same population. Second, the higher the sampling confidence level you want to have, the larger your sample size will need to be. Generally, the rule of thumb is that the larger the sample size, the more statistically significant it is—meaning there’s less of a chance that your results happened by coincidence. 

In market research, this is key. It’s vital to ensure that the sample provider you work with looks at research and data as a whole. A quality market research partner takes research into account beyond just that which was fielded.

How to Calculate Your Needed Sample Sample Size

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

Here is a quick cheat sheet for sample size (based on some basic assumptions):

Low Risk

• Margin of error:  5%
• Confidence level: 80%
• Recommended sample = 165

Low-to-Mid Risk

• Margin of error:  5%
• Confidence level: 90%
• Recommended sample = 271

Mid-to-High Risk

• Margin of error:  4%
• Confidence level: 95%
• Recommended sample = 600

High Risk

• Margin of error:  2%
• Confidence level: 99%
• Recommended sample = 4,130

As you can see, determining the correct sample size for a study is not an easy task. There are many factors and variables to consider, and each has a direct impact on the success of your study. However, it is a necessary aspect of market research and one that is crucial to the accuracy of the data we collect. We must ensure that the sample size correlates to the population to produce statistically relevant results. Here at EMI, we understand how to determine the correct sample size for each unique study we conduct, ensuring you get the highest quality data and the best results.

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