Data runs the business world these days. It’s great to always use data to back everything from major business decisions to website tweaks. But data is only as good as the survey that pumped it out. If you put bad information into your survey, your data isn’t reliable to base your business decisions off of. How do you prevent this from happening? After all, you don’t want to have bad data at the helm of your decisions.
One way researchers try to ensure their information is great is to use random sampling, specifically systematic sampling. By adding the element of randomness, results are more representative of the population you’re trying to study. Handpicking participants gives you skewed data, but random sampling can give you a real look at your population. But there are a few different ways to go about sampling randomly. One way is to use systematic sampling. Read on to learn all about systematic sampling as a way to boost your survey methods.
What Is Systematic Sampling?
Systematic sampling is a type of probability sampling that uses a specific interval to select participants. Probability sampling is when every member of the population (the entire group you want to study) has an equal chance of being selected. It’s the foundation of good data collection. But with systematic sampling you choose a regular interval and select your participants that way.
Imagine you have a list of 100 people in your population, and you want to use systematic sampling to select your sample. You decide on an interval of five. The best way to use systematic sampling is then to choose a random place to start on your list. Maybe you start at the second name listed. From there, you would choose every fifth name to be a participant. That’s systematic sampling.
The key features of systematic sampling are that it’s probability based and that there’s a specific number interval used to select your sample.
Types of Systematic Sampling
The basics of systematic sampling are the same, but there are a few ways you can perform a systematic sample. Here are three types of systematic sampling and how each one works.
Systematic Random Sampling
Systematic random sampling is the classic way to use systematic sampling. It works exactly as described earlier: a particular interval is used to randomly select participants. But how do researchers choose effective intervals? Most use their population size and figure out how many they want in a sample. For example, if you have 100 people in your population, and you know you want to survey 20 of them, you know your interval will be 5.
Circular Systematic Sampling
Circular systematic sampling is most useful if you know you want to sample your entire population, but you still want the element of randomness in your sampling methods. Circular systematic sampling works the same as classic systematic sampling at first. But instead of stopping selection after you reach the end of the population list, you start again and keep selecting using your numeric interval until you’ve sampled everyone in your population.
For example, let’s return to our list of 100 people. You choose the interval of 5 for your sampling and randomly select starting on the fourth name. You sample every fourth name until you reach the end of the list. But instead of stopping there, circular systematic sampling has you keep sampling every fourth name until you’ve gone through the whole list. It’s a great way to continue to select randomly while sampling your whole population. Circular systematic sampling isn’t a great choice if you have a very large population, and you really only need a small sample and a way to whittle the list down.
Linear Systematic Sampling
Linear systematic sampling is another variation, but it’s different from circular sampling. Linear systematic sampling doesn’t repeat and continue until the whole population is sampled. Instead, linear sampling uses a form of skip logic to select. Skip logic is something you might use to send participants in a survey to a different spot in the survey based on their answers. The researcher uses skip logic to select where to start on the survey and the interval to use. If you think of the list of 100 population members, here you would use skip logic to determine where the sampling starts and who is chosen, and it doesn’t repeat at the end.
When Is Systematic Sampling Used?
When do researchers choose to use systematic sampling? Systematic sampling provides a unique way to use random sampling without having to have a lot of details on your population or on a tight budget or timeline. These are some of the scenarios when systematic sampling is commonly used:
- Budget or timing restrictions: If you have an upcoming deadline or a small budget for sampling, systematic sampling is simple to implement and quick for picking a sample under a time crunch. You can very quickly number a population list and randomly select an interval and starting point and choose participants quickly. This same process also requires few resources, and you don’t have to know much about your population to get started with systematic sampling.
- Simple outcomes: Systematic sampling is a simple way to choose a sample, so it’s best when used when the outcomes are simple. A complicated survey isn’t the place for a simple systematic sample.
- Absence of data patterns: Systematic sampling is used when the data aren’t arranged or have an obvious pattern or opportunity for data manipulation. That could lead to problems. But without that, systematic sampling is a great choice.
Advantages and Disadvantages of Systematic Sampling
As with most things in sampling, there are advantages and disadvantages to using systematic sampling. There are times when it’s particularly valuable to use systematic sampling, and there are weak points with the method that are important to watch for and mitigate as much as possible.
There are some of the major advantages of systematic sampling:
- Simple to understand: Not every method of sampling randomly is easy for researchers to understand—especially not with an extensive background in statistics. Systematic sampling is easy to grasp and easy to get started with.
- Easy to implement: Nothing’s worse than having extremely difficult sampling requirements to grapple with that take forever to actually get started. Systematic sampling avoids that. Instead, it’s easy to get started with quickly.
- Organized method of sampling: Organization with your data is key to making analysis simple and doable down the road. Systematic sampling is exactly what it sounds like: systematic. And systematic is organized and can help you keep track of what’s going on.
- Low risk for bias or contamination when done well: Data contamination and bias can leave you with bad results and bad data to base your decisions off of. Systematic sampling can be a way to combat that. So long as the population list can be ordered randomly, there’s a low risk for bias or data contamination when you use this sampling method.
There are also a few disadvantages that researchers have to plan for and make sure to avoid when using systematic sampling:
- Risk of bias: While there’s a low risk of bias, there’s still a risk that has to be managed. The list of population members must be ordered randomly, or there’s sampling bias. That’s easy when you have a list of names and can order that randomly but could be complicated for other populations.
- Risk of data manipulation: When you use systematic sampling, you’re setting up a system to use. There’s a risk that researchers might set up a system to intentionally give them the results they want, introducing a world of problems into their data.
- Requires population size: The other risks can be controlled for, but this disadvantage is an inherent one. To effectively use systematic sampling, you have to be able to number your population. That means you have to know your population size exactly before you can sample. It might be easy when you’re sampling the employees at your company, but it might be more difficult when you have a potentially massive population that you aren’t sure what it entails.
The Bottom Line
Overall, systematic sampling is a form of probability sampling and can be incredibly valuable on a tight timeline or budget with simple populations. The key is to make sure you’re using the right sampling method for your surveys. InMoment has special CX software that’s designed to help you make the most of your surveys—from sampling methods to analysis. Schedule a demo to see how InMoment can help you optimize the results from your CX surveys.