What is systematic sampling?

Let’s say your company or nonprofit organization serves a large population, and you want to gather insights and feedback from this group to improve marketing, sales, fundraising, donor retention, or other key performance indicators. It’s usually not possible to contact every single member of the population, so how do you collect the data you need?

One way is by using systematic sampling. Systematic sampling is a research methodology in which researchers identify a representative subset of a larger population. They can then survey this population sample to collect the appropriate data.

In this article, we’ll define systematic sampling, discuss how to conduct it, review its advantages and disadvantages, discuss when to use this research method, compare it with other research methods, and share a few examples of it.

What is systematic sampling?

Systematic sampling is a probability sampling method in which researchers select respondents from a larger population at a fixed interval — for example, every 10^th person — beginning at a random starting point. These intervals, known as sampling intervals, are determined by dividing the population size by the sample size desired. For example, if the population size is 5,000 and the desired sample size is 500, 5,000 divided by 500 is 10, meaning every 10^th person from the larger population group will be selected for the sample.

This method helps researchers gather data that’s representative of the larger population without having to contact every single person in that group. Selection parameters for the starting population can include gender, age, education level, race, or other criteria.

How to conduct systematic sampling

To perform systematic sampling, you must define your population, determine your sample size, calculate the sampling interval by dividing the population by the sample size, then choose every nth member for the sample.

Let’s look at the steps of performing systematic sampling in more detail:

1. Define your population. Identify the overall population, such as all the residents of a city, all employees in a certain company location, all visitors to a website, all customers of a business, etc.
2. Determine your sample size. This will be based on your research objectives and resources like time and budget, and it should be a statistically significant sample of the larger population. For example, you might choose to survey 10 percent of 3,000 customers, or 300.
3. Decide on the sampling interval. Divide the total population by the desired sample size. If you have 3,000 people in the total population and you want a sample of 300, the sampling interval is 10 (3,000/300). You’ll survey every 10^th person.
4. Choose a random starting point from the overall population. You can do this by using a random number generator or similar method.
5. Select members of the sample systematically. Start at random and use the sampling interval you chose above — in this case, every 10^th person — until you attain your desired sample size. If you chose to start randomly at no. 7, then you’d choose 17, 27, 37, 47, and so on, until you reach the appropriate sample size.
6. Document how you selected your sample. This makes it possible to validate your process.
7. Conduct your survey. Gather the data using your survey software of choice.
8. Analyze the data. With the information you gather, you can make conclusions about your target population.

What is an example of systematic sampling?

A simple example of systematic sampling is a situation in which a researcher selects every 10^th person for sampling out of an overall population of 5,000, as in our definition above. For example, let’s say a company wants to understand employee satisfaction, and there are 5,000 people in the overall employee population group. The researcher determines a sample size of 500 will provide a representative sample of employee attitudes, and, using a list of the 5,000 employees, the researcher selects every 10^th person from the list to survey, for a total sample size of 500.

What are the advantages of systematic sampling?

Researchers favor systematic sampling for a few reasons:

• It’s simple to understand. You can find sampling intervals through the basic mathematical formula of dividing the target population by the desired sample size.
• It’s easy to implement. Systematic sampling doesn’t require specialized knowledge to conduct, and the resulting samples are simple to compare and understand.
• It’s efficient and cost-effective. Sample selection is simple and straightforward, requiring less time and resources than other sampling methods.
• There’s less risk of bias compared to other methods. The way you find sampling intervals reduces the risk of bias in the data set and provides more precise, dependable results.

What are the disadvantages of systematic sampling?

The main disadvantage of systematic sampling is the need to know the size of the overall population at the outset, which isn’t always possible. In our example above, we know we have a list of 5,000 employees, making systematic sampling easy and straightforward. But if a retail store wants to survey shoppers on a given day about customer service, they don’t know the number of people who will visit the store that day.

When researchers don’t know the population or it isn’t measurable, they must approximate the population count, which can impact the sample size and lead to imprecise results.

There’s also a small risk of bias if members of the population aren’t in random order, and there’s a risk of data manipulation if researchers set up a sampling system to intentionally generate the results they want.

When to use systematic sampling, with examples

It’s best to use systematic sampling when researchers know the overall population at the outset, that population is relatively large, and researchers have access to a complete list of members of the population, as in our employee satisfaction survey example from above.

Let’s look at some real-world examples of when systematic sampling would be appropriate.

Environmental science

An organization wants to study contamination in a body of water, such as a lake. Because researchers can’t analyze all the water in the lake, they instead divide the water surface into a grid and collect portions of the water at intervals and in a regular pattern. For an air pollution study, they could take an air sample at fixed intervals of every two hours. To investigate sand composition along a beach, they could collect a sand sample at a predetermined pattern of every 10 yards.

Market research

A lawn care company wants to improve its services. Using the company’s customer database, the marketing team generates a list of every customer who purchased lawn care services in the past six months, which amounts to 300 customers. They use a random number generator to choose a starting point and select every fifth person from the list to participate in a survey asking about desired service improvements, creating a sample of 60 participants total.

Quality control

The maker of pens and markers wants to test its fine-tip marker to ensure they’re free of defects. They produce 10,000 of these markers per day and want to check a sample size of five percent, or 500 markers daily. They divide the total output by the sample size of 500 to arrive at the sampling interval of 20. Using a random number generator to choose a starting point, the quality control team then tests every 20^th marker.

Healthcare

A researcher wants to investigate how many patients adopt a healthy diet after a diabetes diagnosis from their primary care physician. They gather a list of 300 patients from 10 physicians in a select city. They choose a random starting point on the list and survey every 15^th patient on the list, for a total sample size of 20.

Political polling

A pollster wants to examine how likely the residents of a certain county are to vote in an upcoming election. The pollster then selects every 10^th person from a list of registered voters in that county to participate in a survey.

How does systematic sampling compare to other sampling methods?

Here’s an overview of how systematic sampling differs from other popular methods.

Systematic sampling vs random sampling

Simple random sampling, also referred to as standard sampling, is, as the name implies, totally random. This means each person in the sample population has an equal chance of being selected. Systematic sampling is easier to conduct than random sampling, and because data points are chosen at predetermined intervals, it’s more effective at producing a representative sample from an evenly distributed population.

Systematic sampling vs stratified sampling

In stratified sampling, the overall population is divided into subgroups, called strata, with similar attributes like gender, income level, education level, and so on. This approach ensures that each segment of a population is properly represented within the sample. Systematic sampling can be more efficient and cost-effective than stratified sampling because it only requires a limited selection of the total population being sampled.

Systematic sampling vs cluster sampling

Cluster sampling is when a population is divided into groups, known as clusters, with entire groups as part of the sample. For example, a cluster might be made up of all 12th grade math classes in a high school. With systematic sampling, researchers would choose students individually, according to fixed intervals. Systematic sampling is more useful when researchers know the entire population from the outset, while cluster sampling is more effective when there are different subsets within a specific population.

How can Jotform help with systematic sampling?

Systematic sampling is one of several data collection sampling methods. Using our employee satisfaction example from earlier, let’s say you’ve determined your desired sample size is 500 out of a total of 5,000 employees. To gather employee satisfaction feedback from this sample, you select every 10^th person from the original list of 5,000 to send your survey to, which will net you a representative sample of employee attitudes.

With Jotform, you can easily build engaging surveys from scratch or use one of our premade survey templates. With over 1,000 premade survey templates to choose from, you can easily find one that fits your specific needs. Once you choose your survey template, use the Jotform builder to design, format, and customize a professional, user-friendly survey for your audience, completely code-free.

You’ll also be able to share, collect, and analyze your survey results with our free online survey maker. Use the drag-and-drop functionality, add your own questions, set up conditional logic, and share your custom survey with your selected sample to start collecting responses instantly. All the responses are automatically recorded in Jotform Tables, our spreadsheet-database tool, for easy analysis.

Systematic sampling can be more efficient and cost-effective than other methods, and it’s the preferred way to gather accurate data when you have a large population. Jotform’s free resources make it easy to get started today.

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