Guide to systematic sampling

Systematic sampling is a probability sampling method where you select every nth member of a population after a random starting point. You set a fixed interval, pick a random start, and sample at that interval until you have enough participants.

Studying an entire population is often impractical—too costly, too slow, or too resource-intensive. Surveying a sample lets the happen anyway. For the right type of population, systematic sampling is a simple and effective way to get a representative sample.

What is systematic sampling?

In statistical analysis, researchers can use various methods to select samples. Systematic sampling entails choosing every nth item after a random start. Each subsequent item is picked at a regular, predetermined interval, which makes the selection systematic.

Systematic sampling is particularly advantageous when the sample population is large and well organized.

Systematic vs. standard sampling

Standard sampling is also known as simple random sampling. This method is entirely random, with each person in the sample population having an equal chance of being selected. Systematic sampling provides a simpler way of generating a representative sample from an evenly distributed population.

Systematic vs. stratified sampling

When the data is heterogeneous, stratified sampling can help organize it. Stratified sampling involves dividing the population into distinct subgroups or strata based on characteristics important to the study.

If a school wants to measure the performance of all grades, for example, it might first break the students up by grade level and then gather samples from each grade to represent the whole school. Systematic sampling may lead to the overrepresentation of one grade compared to others.

These two methods aren’t mutually exclusive. The students could be stratified by grade and then systematically sampled from each grade.

Systematic vs. cluster sampling

involves dividing the population into groups and selecting entire groups to be part of the sample. These groups should be representative of the entire population. For instance, a school might select specific classes from a particular grade to assess that grade’s academic performance. This differs from systematic sampling, where students would be picked individually.

Similarly, the methods can be combined. The population can be clustered, and then the clusters can be chosen systematically.

Types of systematic sampling

Systematic sampling uses several methods. The first thing to understand is the difference between linear and circular sampling, as their difference impacts the other choices you make in the sampling process.

Linear systematic sampling

This type of sampling treats the population as a line, selecting every fixed nth interval sample until the end is reached and then stopping.

Circular systematic sampling

This type of sampling treats the population like a circle. Once it reaches the end, it loops back around and selects every nth sample until the desired number of samples is reached.

The most basic type of systematic sampling involves selecting a value for n, randomly selecting from the first n items, and then selecting every nth item afterward. For example, let’s say n is 10. A random number between 1 and 10 will be chosen. If the number turns out to be 8, the 8th item would be the first sample, then the 18th, 28th, 38th, and so on.

When using circular systematic sampling, the first sample doesn’t need to come from the first n samples. For example, given a population of 5,000, you can choose the first sample randomly from anywhere in that range. If the random number is 4,995, the 4,995th item would be the first sample.

Because circular sampling is used, moving 10 forward would loop around to the 5th item. Next would come the 15th, 25th, and so on.

If the data allows for it, you can modify the above methods by shuffling the data before beginning the sampling process. Depending on how you arrange the data, this might produce a more representative sample of the population and remove any bias in the default ordering.

When to use systematic sampling

Systematic sampling is a useful method in several situations. Its simplicity is especially valuable when researchers need to sample from a large population with high sampling costs, or when they need to perform the sampling task quickly.

To help you decide when you should and shouldn’t use systematic sampling, here’s a set of pros and cons for the method.

Advantages of using systematic sampling

Efficient

Systematic sampling requires less time and fewer resources to obtain a sample than many other methods. Researchers can get the samples they need cost-effectively and under tighter time constraints than might otherwise be possible.

Less variability

Systematic sampling often results in less variability than other sampling methods. When the data is relatively homogeneous, the nature of the sampling ensures an even representation of the population.

Easy to implement

Systematic sampling is easy to use and doesn’t require specialized knowledge or software. This makes the method accessible and can reduce the chance of error.

Disadvantages of using systematic sampling

Biased samples

There are several instances where systematic sampling won’t provide a representative sample. If the data is periodic in nature, the sampling interval may align with that period, leave some important items out, and introduce bias to your results. Similarly, a sampling interval that’s too large could skip over important data.

Limited flexibility

Systematic sampling can be less flexible than other sampling methods. It relies heavily on a homogeneous and evenly distributed population, which may be an issue with populations that have underlying patterns or characteristics. For more precise control over sample selection, researchers should use other methods.

Sampling frame

Systematic sampling relies on a sampling frame—a list of all people or items available for sampling—which may not always be available or complete. For certain research types, building one is impractical.

Steps to create a systematic sample

The examples so far cover the basics of creating a systematic sample. Here’s a more detailed look at how the process works.

You can break the development of the sample down into five steps:

1. Define the population

Decide what type of population will best meet the needs of your research question.

2. Determine the sample size

Then, determine the desired sample size. Pick a sample size that’s large enough to provide a representative population sample but not so large it becomes cost-prohibitive or otherwise impractical.

You can calculate the ideal sample size from the required level of precision and confidence interval.

3. Define the sampling interval

With the sample size determined, calculate the sampling interval. This is the value of n in the earlier examples.

For linear sampling, this number is a function of the sample size and the population size. To calculate it, divide the total population size by the sample size. For example, a population size of 1,000 and a sample size of 100 would give a sampling interval of 10. Because it loops around, circular sampling allows you to choose a larger interval if you’d like.

4. Select the first unit at random

Next, select the first unit in the sample at random. You can use any of the methods mentioned earlier. The right one depends on personal preference and the structure of the data. When choosing a method, consider how bias may impact the selection and try to account for it.

5. Select subsequent units

After selecting the first unit, select the remaining units systematically by taking every nth unit in the population, where n is the sampling interval. For example, if the sampling interval is 15 and your random starting point is 7, you’d select the 7th item, then the 22nd, 37th, 52nd, and so on.

When using linear sampling, you stop when you reach the end of the data. For circular sampling, loop around and continue until you get the required number of samples.

Examples of systematic sampling

Let’s close out with some examples of how researchers in different fields might use systematic sampling. These will help you understand how it may apply to your research situation.

Education

A researcher wants to survey students at a large university on their opinions of the campus dining services. They get a list of all enrolled students and choose a random starting point. From there, they select every tenth student on the list to participate in the survey.

Marketing

A company decides to conduct a to improve its products. They generate a list of all customers who purchased in the past month and choose a random starting point. From there, they select every fifth customer on the list to participate in the survey.

Healthcare

A researcher wants to study how common a particular illness is in a large city. They divide the city into regions and obtain a list of all households in each region. From there, they choose a random starting point in each region and select every tenth household on the list to participate in the study.