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Systematic Sampling 101: Definition, Types and Examples

Parvathi Vijayamohan

25 February 2024

7 min read

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What is systematic sampling?

In statistics, a sampling method is systematic if it involves selecting individuals or items for a sample in such a way that every nth item is selected.

These intervals are known as skip or sampling intervals. This interval is calculated by dividing the population size by the desired sample size.

Calculations in Systematic Sampling
Calculations in Systematic Sampling

So every 5th item will be selected for the sample.

Differences between simple random sampling and systematic sampling


Simple Random Sampling

Systematic Sampling


Individually identify and select each item.

Use a sampling interval rule to select items.


Each item has an equal likelihood of being chosen.

Chooses an item after a predetermined interval.

Audience/Sample Size

Ideal for small audience sizes and sample numbers.

Better option as the audience/sample size increases.


Depends on the sample size.

Depends on how well the sample reflects the relevant characteristics of the audience.

Systematic sampling methods: The six types (with examples)

Each type of systematic sampling can be used for single or multi-phase surveys.

But before you go any further, you must consider a few things.

Sampling is not without its own challenges. There can be biases due to underlying patterns in the population or the need for an extensive, organized list. For this:

  • Stop guessing which customers represent your entire audience.
  • Start gathering representative feedback with the help of an Audience panel. And make sure you opt for a trusted panel. (We’ll come to this in a bit)

Now, it’s time to choose the right method.

1. Systematic Random Sampling

This is the most basic type. You just need to select from a random starting point but with a fixed, periodic sampling interval.

Example: Suppose a supermarket wants to study its customers’ buying habits.

With systematic random sampling, they can choose every 10th or 15th customer entering the supermarket. Then, they can conduct the study on this sample.

2. Stratified Systematic Sampling

Stratified sampling divides your audience into sub-groups called strata. Any characteristic can be the basis for this strata, like age, ethnicity, religion, etc. Then, using sampling intervals, you can choose sample members from each strata.

Example: Let’s say you’re researching the factors that influence consumer preferences towards bread.

Age does play a role here, so you would want to divide your audience into age groups like 18-25, 25-40 etc. From each strata, you can select individuals to study using sampling intervals.

3. Linear Systematic Sampling

This type treats the audience list as a fixed line divided at periodic sampling intervals. So once you reach the end of the line, you have exhausted your list and the sampling ends there.

This is a helpful sampling type if you require only a one-time sample and know exactly how many units are there in your audience.

Example: You are sampling for a work stress study within your organization between March-December, you can easily find out the current number of employees and apply the linear method.

From basic to disproportionate sampling, type of systematic sampling can be used for single or multi-phase surveys.

4. Circular Systematic Sampling

This type treats the audience as a circular list. Once you reach the end of the list, you can continue the selection from the beginning. You can visualize this as a clock, with the hour lines symbolizing intervals.

Example: What if you have a huge population to draw from? Or you need multiple sets of samples?

This type of sampling is the best option. Because assuming the total audience is N, you can potentially get an N number of samples to work with.

5. Proportionate Systematic Sampling

In proportionate sampling, the sample size from each strata is proportional to the strata size.

Example: When you’re doing a Teacher Feedback survey among three classes of 30 students each. To save time, you choose proportional samples of 10 students from each class.

6. Disproportionate Systematic Sampling

This is a sampling method in which the size of the sample from a strata is not proportional to the relative size of that strata.

Example: Let’s say you’re doing a study of pizza sales in your city.

One strata could be fast food chains. Though they only account for 20% of all the pizza shops in your city, they get 70% of customer footfalls. So they will be disproportionately represented in your final sample.

Don’t worry we have the right tool to help you.

Unlocking the Full Potential of Systematic Sampling with SurveySparrow

As we mentioned above, sampling comes with its own challenges and limitations- bias and error. With the platform’s Audience Panel, you can access a world-class pool of pre-screened and profiled respondents from diverse demographics across 149+ countries.

By tapping into the carefully curated audience, you ensure that each respondent represents a defined segment of your target population.

Also, the custom-profiled audience ensures that your surveys are tailored to reach the most relevant participants, elevating the quality and accuracy of your data.

That’s not it.

If you’re looking to maximize the potential of your systematic sampling efforts, the executive dashboard feature and Tableau integration are essential tools in your arsenal.

With a user-friendly executive dashboard, you can gain real-time insights into your survey data, monitor responses, and track trends effortlessly. Define specific demographics or attributes you wish to include or exclude from your sample. Share your survey with the right audience, improving the quality and impact of your data.

Systematic Sampling - Monitor and segment data with SurveySparrow's executive dashboard
SurveySparrow’s Executive Dashboard

When it comes to data visualization and analysis, the seamless integration with Tableau opens up a whole new world of possibilities. Unleash the power of data-driven decision-making with Surveysparrow and Tableau as you transform raw survey data into actionable insights that drive your business forward.

Draw statistically sound conclusions confidently! Sign up for free.

How to use systematic samples in 7 steps

Step 1: Select a Population and Determine Its Size

The first step in using systematic sampling is to identify the target population for your study. For instance, if you’re conducting a retail store study, your customers constitute the population of interest. To determine the population size, you’ll need a comprehensive list of every customer who has shopped at the store during the relevant time period.

Step 2: Divide the Population into Subgroups or Strata

Once you have the population size, the next step is to divide it into smaller subgroups or strata of approximately equal size. This stratification ensures that the systematic sampling process covers the entire population in a representative manner, allowing you to draw more accurate and insightful conclusions.

Step 3: Decide Your Sample Size and Sampling Interval

Determining the appropriate sample size and sampling interval is crucial for a successful systematic sampling approach. This step involves using a formula, as mentioned earlier, to establish the right sample size and interval for your survey.

Step 4: Record Data with Survey Software

Once your sampling plan is in place, it’s time to conduct your survey and gather the necessary data. Utilizing survey software simplifies the data collection process, providing a user-friendly platform for respondents to participate in the survey effortlessly.

Step 5: Analyze Your Data Using Real-time Analysis

With data collection complete, the next step is to analyze the obtained data. Real-time analysis allows you to gain immediate insights into response trends, patterns, and other crucial metrics, empowering you to make informed decisions promptly.

Step 6: Form Conclusions Based on Analysis

Using the insights gained from the data analysis, you can draw meaningful conclusions about your target population. Visualizing the data and identifying key trends will make it easier to interpret the results accurately and draw actionable conclusions.

Step 7: Repeat Steps 2 through 6 with Another Sample Subgroup

To further enhance the robustness of your conclusions and validate your findings, it’s recommended to repeat steps 2 through 6 with another sample subgroup. This iterative process ensures comprehensive coverage of the entire population and strengthens the reliability of your study.

Bonus Tip: Sample On-the-Spot with Observational Studies

Creating an exhaustive list of your audience can be time-consuming or even impractical in some cases. However, if you have the opportunity to physically observe your target audience, you can opt for on-the-spot sampling during the study. This approach allows you to adapt your systematic sampling strategy to suit your unique research needs.

Creating such a list can be time-consuming, if not impossible. So, if you are able to physically observe your audience, you can instead choose to sample them at the time of the study.


In the context of a retail store study, one effective way to implement systematic sampling is by surveying every nth customer as they exit the store. This method ensures that each customer has an equal chance of being included in the sample, providing a representative cross-section of the store’s overall customer base.

However, to achieve a truly representative sample, it’s essential to consider the variation in customer types and their shopping patterns. Different customer segments may shop at varying times of the day and week.

For instance, need-based shoppers might frequent the store during lunch hours or later in the evening, while casual browsers tend to visit on weekends. To account for these variations, it’s important to allocate enough time for data collection and cover different time periods to capture the diversity of customers.

Despite best efforts, sometimes the in-store sampling alone might not yield a sufficiently diverse and representative sample. In such cases, turning to online panels can be a valuable solution. Online panels provide access to a broader range of demographics, allowing you to target specific groups that may be underrepresented in the in-store sample. This approach ensures that you gather insights from the exact demographic you need, enhancing the overall quality and accuracy of your study.

By combining systematic sampling at the store with online panels, researchers can optimize their data collection process and achieve a more comprehensive understanding of their target audience. The integration of in-person and online sampling methods empowers businesses to make data-driven decisions that cater to the diverse needs and preferences of their customers.

FAQs on Systematic Sampling

When is it inappropriate to use systematic sampling?

Systematic sampling may not be suitable when the population exhibits a regular pattern or periodicity, leading to potential biases in the sample.

Why is systematic random sampling sometimes used in place of simple random sampling?

Systematic random sampling is chosen when efficiency is desired, as it requires a smaller sample size and is easier to implement than simple random sampling.

Why might a researcher choose purposive sampling over systematic sampling?

Researchers may opt for purposive sampling when they specifically target certain individuals or groups that possess unique characteristics or expertise.

When to use systematic sampling?

Appropriate for large populations with a known and organized structure, where equal representation of individuals is desired.

Wrapping up

That’s all, folks! Hopefully, after reading this article, you can determine the most appropriate method for your project.

So, let’s get started!

Perhaps SurveySparrow can help you. Whether you need an expert panel, data collection tool, feedback, and experience management platform, or if you wish to kickstart your survey creation journey, feel free to reach out!

Parvathi Vijayamohan

Growth Marketer at SurveySparrow

Fledgling growth marketer. Cloud watcher. Aunty to a naughty beagle.

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