Discovering the Correct Survey Sampling Size for Your Market Research Study
Discovering the Correct Survey Sampling Size for Your Market Research Study
Determining the correct survey sampling size is necessary for establishing a statistically valid survey study. A well-founded survey study must be correctly designed and feature accurate conclusions, those that properly reflect the views of its target audience.
Although it is impossible to survey every member of a target population, your survey findings should be an accurate representation of the studied population. That’s where determining the correct survey sampling size comes into play.
However, even with the proper sampling size in tow, all surveys will undergo some degree of the sampling error. This is because surveys don’t extract the thoughts of every member of a population. Therefore, some findings will be inaccurate.
This article explains how to find the correct survey sampling size, its importance and the sampling size in relation to the margin of error.
Understanding the Survey Sampling Size
The survey sampling size refers to the number of respondents that take part in a survey study, which is also called the sampling pool. Used in market research and statistics, this size is usually determined with regards to the estimated size of the entire population under study.
This sampling pool is composed of a random sample of respondents who represent the studied population at large, as opposed to making up the entire population being studied, as it is impossible to survey every single member in a targeted population.
Market researchers need to determine the proper sampling size for all kinds of survey sampling methods, including probability or random sampling and non-probability or non-random sampling.
Aside from being calculated to find the size most appropriate for representing a large population, the survey sampling size is also useful for reducing the four types of sampling errors.
To determine the correct sampling size for your study, you’ll need to calculate it using several variables. (More on this in a later section).
The Importance of the Survey Sampling Size
The size of your sampling pool should never be random or made at a whim. Otherwise, you’re risking the quality and accuracy of your survey study. The survey sampling size is crucial on a number of fronts.
It is critical to market research campaigns, in that the sample size influences two statistical properties: the precision of our estimates and the power of the study to draw conclusions.
The survey study you run will always have some margin of error in the study results. This influences the precision of the survey results and their use to make predictions. Although this error is inevitable, it can be reduced with the correct sample size.
Larger samples are usually associated with a smaller margin of error. This makes sense when you consider the thousands and even millions of consumers that can fit under one segment of a target market, which can be the target audience of a survey campaign. As such, the larger your sample size, the more insights you gain that represent this audience.
However, there is a point when increasing the survey sample size has no impact on the sampling error. There are also plenty of instances when failing to use an appropriate sampling size undermines the entire study.
You should therefore be careful when calculating your survey sampling error, as both a sample size that is too large and too small will have substandard results.
What Happens When a Sample Size is Too Small
When a sample size is too small, its results will be inconclusive and, in many cases, such as in experimental research, unethical. Thus, in these cases, you won’t be able to make accurate predictions or take actions that require testing, as they may be dangerous. For example, you can’t expose humans or lab animals to possible risks associated with research.
Additionally, the data from this kind of sampling size will yield a disproportionate number of respondents with anomalous answers. These will skew the results, so you won’t retrieve an accurate understanding of the entire targeted population.
What Happens When a Sample Size is Too Large
Using a sample size that is too large is wasteful on both time and resources. It relies on more respondents than is necessary to carry out the survey campaign for accurate findings. With surplus data, the study is not representative of its target population.
This kind of sampling size will also incur more expenses to run the study, which could and should have been avoided.
All in all, an appropriate determination of the survey sampling size for a survey study is an essential aspect of the design of this kind of study.
How to Find the Correct Survey Sampling Size
As aforementioned, determining the correct survey sampling size involves using four variables. These are the population size, margin of error or confidence interval and sampling confidence level.
Once you’ve gathered the information you’ll need to determine the value of each, you can then proceed to the survey sampling size formula and simply plug them in.
Population Size
This variable refers to the total number of people in the population that you seek to survey. When dealing with broad populations, this size will likely be an estimate. For example, the population size of New York City is about 8.419 million people. A much smaller population, such as that of employees, is going to be definite.
Margin of Error
Also called the confidence interval, this is a percentage that relays how much you can expect your survey results to reflect the views of the overall population you’re surveying. Market researchers ought to aim for a smaller margin of error, as the smaller it is, the closer you’ll be to the exact answer at a given confidence level.
Essentially, it answers how much error you’ll have, which dictates how far a percentage will stray. For example, if 70% of respondents said yes to a particular question, with a margin of error of +/- 5%, then the correct percentage is straying by 5% points in either direction. As such, the true percentage is between 65 and 75%.
If you need to determine your margin of error, use the Pollfish Margin of Error Calculator.
Sampling Confidence Level
Although it has a similar name to the previous variable, this is a separate input, dealing with how confident you can be that the population would select an answer within a certain range. For example, a 95% confidence level means that you can be 95% sure the results lie between two certain numbers. The three most common confidence intervals are 90% confident, 95% confident, and 99% confident.
Standard Deviation
This variable Is an estimate of how much your responses will vary from each other and from the mean number. It studies how much individual sample data points deviate from the average population.
A low standard deviation shows that all the values will be situated around the mean number. A high standard deviation means the values are spread out on a much wider range and have very small and very large outlying figures.
Use the standard deviation of 0.5 to make sure that your sampling pool is large enough.
The Next Steps
When you have determined the value of each variable, fill them into the calculation in the section below. The following is a breakdown of the variables:
N = population size
E = Margin of error (percentage in decimal form)
Z = z-score
P = standard deviation
The z-score is the number of standard deviations that a given proportion is away from the mean. To find your proper z-score, use the table below as a reference:
| Desired confidence level | Z-score |
| 80% | 1.28 |
| 85% | 1.44 |
| 90% | 1.65 |
| 95% | 1.96 |
| 99% | 2.58 |
How to Calculate the Survey Sampling Size
Now that you understand each variable used to determine your ideal sampling size, use the following formula to calculate the correct survey sampling size for your particular survey study.
The formula is:
Sample Size = (Z² x P (1 - P) / E²) ÷ 1 + ( (Z² x P (1 - P)) / E² N))
Please refer to the following image for a more visual representation:
If you prefer a more efficient way to determine your sampling size, refer to the Pollfish sample size calculator.
Survey Sampling Size Tips
Calculating your sample size can be a complex process for those not familiar with statistical calculations or who don’t perform mathematics in their daily lives. There are a few key considerations that will help you make sense of the confusion.
Firstly, if you want a smaller margin of error, you will need to have a larger sample size given the same population. Next, if you seek a high sampling confidence level, you’ll need a larger sample size.
If your survey sample size becomes too big to handle, you can adjust the results by doing the following:
- decreasing your confidence level
- increasing your margin of error
Doing so will increase the chance for error in your sampling pool. However, it can also significantly decrease the number of responses you’ll need.
Optimizing Your Market Research with the Right Sampling Size
To optimize the findings of your survey study, you’ll need to use the correct survey sampling size. The correct sampling size will solidify the accuracy of your data and posit you as more of an authority figure in any of the six main types of research.
Additionally, it will give you peace of mind that the results you’ve extracted are accurate and can be used to take action, whether it is for a business or a research study in other fields.
In addition to an accurate sampling size, you’ll need a strong online survey platform to carry out all your survey campaigns.
We suggest using a strong online survey platform, one that offers agile market research and engages respondents in their natural digital environments via random device engagement (RDE) sampling. It should also be designed via a mobile-first platform, as mobile devices dominate the digital space.
When market researchers use an online survey platform with these capabilities, they are setting themselves up for success in all their survey campaign needs.
Mastering Survey Sampling Methods for Consumer Intelligence
Mastering Survey Sampling Methods for Consumer Intelligence
Survey sampling methods are a crucial part of the survey research process, as the aspect of sampling is more than just a data collection practice.
This is because in order to glean any valuable insight from surveying, the respondents must be as representative of the study’s target population as can be. The correct survey sampling method can make this possible.
When conducting survey research, there are several sampling methods researchers can leverage. In order to apply the correct method, there are certain things you need to establish. This article delves into survey sampling methods, including the considerations to take before settling on a sampling method for your research needs.
Defining Survey Sampling Methods
Survey sampling methods denote the types of techniques used to select participants from a target market (or any target population) to take part in a survey sampling pool.
In survey research, the sampling pool is the group, or “pool” of targeted respondents who participate in a survey study. This sampling pool must accurately represent the targeted subject population.
It is important to have a group of people who will participate in the survey and be able to represent the whole target population. This group is called a “sample".
Settling on the proper sampling pool is known as sampling, which is critical to surveys, as it makes up the foundation of the survey campaign.
Why Survey Sampling Methods are Necessary
The main goal of surveys is to gather accurate information about a particular population. As such, they would be futile if they insufficiently accounted for the participants that they’re set on studying.
Survey sampling is necessary, as sampling provides a potent means of extracting and analyzing a targeted subset of a population. Even when researchers zero in on a subset, it is still virtually impossible to study the entire population of a targeted group.
The reason is twofold and fairly straightforward: not all members of a particular population will be exposed to a survey, and out of those that do, most will not be willing to spend time filling out a survey.
As such, researchers turn to survey sampling methods, so that their sampling pool best represents the population of researchers’ interest. With the right method, researchers can make well-informed inferences about their targeted population.
Sampling reduces the sampled respondents, which lessens both the workload and costs associated with a particular survey study. However, researchers have to find the correct balance of participant involvement to accurately ascertain associations between variables.
Determining Your Target Population: The Precursor to Survey Sampling
Effective survey sampling occurs when the researchers have established the population subset which they intend to study. As such, you should begin any survey sampling campaign by defining your targeted population.
If you need to conduct surveys for a business, you should always aim your studies on your target market (when not observing your competitors). This is because the target market is the group of individuals most interested in your sector and most likely to buy from you.
In this regard, it is also important to conduct market segmentation of your target market, as your target market is made up of several consumer segments. Surveys are a powerful tool for segmenting your target market.
But again, you must properly sample your population before conducting any survey research. There are several ways to approach survey sampling.
Probability (Random) Sampling Vs. Non-Probability Sampling
There are various kinds of survey sampling methods, which fall under two main classifications: probability and non-probability sampling. Businesses, governments and other entities can apply either one or both of these methods for their research needs.
Before navigating the multitude of survey sampling methods, it is key to be able to differentiate the two main categories of sampling. This will put the subcategories, i.e., the specific sampling methods into sharper perspective.
The following explains the core aspects of the main types of sampling methods.
Probability Sampling:
Also called random sampling, this category initiates with a full sampling of all the individuals qualified to be in your sample. This main method grants all eligible participants the chance to be used in the sample. In this way, your sample will be able to allow you to make generalizations from your survey results.
The methods that fall under probability sampling can be more expensive and take up more time than their non-probability sampling counterparts.
The main advantage of using probability, or random sampling is that the chosen sample is more representative of the target population. As such, this kind of sampling fosters credible statistical conclusions.
There are five main types of probability sampling methods: simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling.
- Simple Random Sampling: The most common form of probability sampling, random device sampling involves each member of the population receiving an equal chance of being selected in the sample pool. True to its name, the respondent is chosen by chance. This method reduces selection bias and allows you to calculate the sampling error.
- Stratified Sampling: This method involves dividing the population into subgroups. Known as strata, these groups are divided based on a shared characteristic. This method is used when there is reason to believe the variables will differ between each subgroup. Populations can be stratified by gender, age, location, interests, habits, etc. The study sample is acquired by taking either equal or unequal sample sizes from each stratum. This method enables all categories within the population to be represented in the sample.
- Cluster Sampling: This form of sampling assigns every member of the population to a single group called a cluster. Then, a sample of clusters is chosen, typically via simple random sampling. Contrary to stratified sampling, which includes elements from each stratum in the sample, cluster sampling uses a sample with elements only from the sampled clusters. As such, it is more exclusive. This method can be efficient when it comes to studying a wide geographical area, as it’s easier to contact many members of one area than a few members of various regions. The disadvantage includes an increased risk bias when chosen respondents are not representative of a population, which yields sampling errors.
- Multistage Sampling: This technique relies on selecting a sample by way of combining different sampling methods. As such, this method involves different stages, wherein Stage 1 may use random sampling, while Stage 2 may use stratified sampling. This method allows researchers to merge different styles of sampling, as a means to study various variables and draw conclusions through different focuses.
- Systematic Random Sampling: This method is used when a given population is logically homogenous. It involves enumerating all members of the given population on a list. When all members of the list are compiled, the researchers select the first sample element from the first several elements on the list at regular intervals. The advantage in using this method is its relative ease of use, in comparison to simple random sampling. Also, since simple random sampling may involve clusters, systematic random sampling offers a contrast: evenly sampling the population.
Non-Probability Sampling:
Also referred to as non-random sampling, this chief method does not start with a complete sampling pool, as some participants will not have a chance to be selected in the sample. Instead, it relies on the researcher's judgment.
As such, researchers can’t assess the effect of the sampling error. Additionally, there is a higher risk of using an unrepresentative sample, which harms the chances of reaping generalized results.
On the other hand, non-random sampling methods are less costly and are easier to conduct, making them conducive for exploratory research and formulating hypotheses.
There are four main types of non-probability sampling methods: convenience sampling, quota sampling, judgment (purposeful) sampling and snowball sampling.
- Convenience sampling: Known as the simpler non-random sampling method, convenience sampling selects respondents based on their own availability and willingness to participate in the sample. Although researchers can amass valuable information, this method carries a greater risk of volunteer bias, as those who wish to take part may be significantly different from those who don’t. Thus, the sample may not be representative of certain characteristics, such as habits, age or geographical location.
- Quota sampling: The most pertinent non-probability sampling method for market research, as respondents are chosen based on quotas. For example, a survey study may require 100 adult men, 100 adult women and 200 children. The quotas used would need to represent the characteristics of the studied population. The benefit in this method is the potential to be highly representative. However, respondents may not be representative of characteristics that were not considered, which is one of the general drawbacks in non-random sampling.
- Judgment Sampling: Also referred to as purposeful, selective or subjective sampling, this method involves exercising the researcher’s own judgment when choosing sample participants. Therefore, they may decide on a representative sample, one that exhibits certain characteristics. Oftentimes, media outlets use this method when surveying the public on qualitative research.
- Snowball sampling: This method is named based on the analogy its methodology puts into practice. Typically used in surveying groups that are difficult to reach, respondents are tasked with calling on more respondents (the ones they know) to take part in the sample. This is how the sample of an otherwise hard-to-recruit group increases, or snowballs, in size. This method is productive for bringing on individuals that can be difficult to study, but it risks selection bias, as is the case when choosing a large group of people with similar traits to the original respondent).
Survey Sampling Size, Bias & Other Considerations
When undertaking survey research, aside from understanding your target population and what kind of data to derive from them for your survey research, researchers need to decide on a sample size. This does not necessarily need to occur before deciding on the best survey sampling methods for an investigation.
Instead, it is apt to start with an approximate number of respondents in your sample, while identifying an exact size after you settled on a sampling method. This is because researchers may come upon factors that change the proper sample size for their studies. Additionally, facets such as budget and availability come into play.
Researchers also ought to note that both probability and non-probability sampling methods run the risk of developing a survey bias. These biases take place in various situations. These include omitting respondents from hard-to-recruit groups, straying from sampling rules, replacing already opted-in individuals with others, low response rates and others.
Another critical issue to consider is that your survey vehicle, the tool you will use to design and deploy your survey. A strong online survey platform can help you avoid biases and will offer a modern survey sampling method. One of the latest and most potent techniques is RDE (Random Device Engagement), which can reach a massive sample and incorporate several of the methods listed above.
Businesses and researchers in other industries should therefore consider using this survey sampling method.
Frequently asked questions
What is a survey sampling method?
The survey sampling method refers to the technique used to choose individuals to participate in a survey.
Why are survey sampling methods necessary?
In order to gain accurate information about a population, researchers must select participants who provide a good representation of that population. Survey sampling methods provide a way of selecting participants that will best represent the targeted population.
What is a target population?
A target population is a narrowly defined group of people that will be studied in order to draw conclusions about a wider population.
What is probability sampling?
Also called random sampling, probability sampling is a sampling technique in which participants are chosen at random from a larger population.
What is a survey sample size?
A survey sample size is the number of individuals who have been chosen from the target population to participate in a survey.
