Systematic Sampling Method: Definition and 7 Pros & Cons

Organizations worldwide use surveys to gain insight into how their business is perceived by others and how it’s performing overall. The United States Census Bureau alone conducts more than 130 surveys a year to ensure communities are receiving the resources and services they need to thrive. When gathering feedback, sampling can simplify the process by providing data on a subset of your audience. This allows you to make inferences about the population as a whole. Systematic sampling is an effective way to gain input for market research, clinical studies, political polling, and more.

What’s the Definition of the Systematic Sampling Method?

Systematic sampling is a survey methodology in which elements are chosen sequentially from an ordered population. Founded on probability, in this method samples are chosen from a larger group based on a random starting point with a periodic, fixed interval. Researchers then use these samplings to gather feedback and make determinations about the entire population. For instance, if 45% of the sample shared one response, it is assumed that 45% of all the subjects would share that same response.

There are essentially three kinds of systematic sampling:

  • Random: Researchers use the fixed interval to choose individuals until they reach their optimal sample size.
  • Linear: Samples are selected from a finite list that eventually reaches an end.
  • Circular: Sample selection restarts from its ending point and cycles through the list again.

Regardless of which kind of sampling is implemented in data collection, systematic sampling is a highly effective way to choose a subset of survey participants and get the most out of your questionnaires.

How Do You Do Systematic Sampling?

Systematic sampling can be completed in these four simple steps:

  • Determine population size: Whether you have confirmed numbers or can only make an educated guess, every sampling begins by determining your overall population size.
  • Choose sample size: Figure out what your sample size should be. While this number is typically smaller than the total population, data volume needs to be high enough to gather significant statistics. For a quick and easy way to work this out, use our sample size calculator.
  • Decide interval frequency: Divide the entire population by the desired subset to decide how frequently to choose a subject.
  • Figure out your starting point: Decide which individual to start with, and begin counting off intervals from that point to select your subjects. Choosing the first person in your population doesn’t allow for random selection, so it’s best to start further down your list.

A systematic sampling example situation could be a study consisting of 100 people. The researcher randomly decides to begin with the fifth person in the population. Then, they choose every third person after that. So the sample consists of person 5, 8, 11, 14, 17, and so forth. These are the subjects chosen for the survey, and their responses will serve as a representation of the overall population.

When to Use Systematic Sampling

There are multiple methods for selecting samples for surveys, from random and cluster to stratified and multistage sampling. While each has their pros and cons, there are several conditions during which systematic sampling is ideal. These include:

  • Any time a project is on a restricted budget: When projects involve a large population, creating multiple samples can be expensive and time-consuming. Systematic sampling simplifies this process and makes it more affordable.
  • If results are needed in a short timeframe: Since systematic sampling only deals with a small portion of the overall population, it can be completed much quicker than other methods of survey sampling.
  • If the overall population is a high number: It would be extremely time-consuming to number participants in a population of hundreds (or thousands). Systematic sampling lets you easily number a smaller subset of the overall participant pool.
  • When data doesn’t reflect patterns: Patterns can lead to samples that have similar characteristics, which can negatively impact data quality. Systematic sampling helps minimize biased samples and poor survey results.
  • If there’s a low risk for manipulation of data: If researchers reconfigure a data set, data validity can be jeopardized. When there’s little chance of data manipulation, systematic sampling is an ideal method for surveys.

When used under the right circumstances, systematic sampling is an affordable, time saving method that can produce highly effective results.

Systematic Sampling Advantages

Like any sampling method, there are systematic sampling advantages and disadvantages. By understanding the pros and cons of this popular input-gathering technique, you can determine whether it’s the right tool for you. Here are three advantages to this sampling method.

Pro #1: It’s Simple and Quick to Implement

The structure of systematic sampling enables researchers to build, assess, and manage samples easily. Because the formula to choose sample subsets is predetermined, the only random aspect of the study is choosing the initial subject. From there, the selection process follows a fixed pattern until the desired sample group is complete.

Additionally, since systematic sampling builds representative data for the overall group, researchers don’t need to number each subject. This means sample selection and data analysis are quick and easy.

Pro #2: Less Opportunity for Manipulated Data

In some sampling methods, there’s a distinct possibility that certain individuals will be sampled more than others. This can affect data and produce inaccurate results. With systematic sampling, each participant is a fixed distance from the next. This means that samples are clearly separated and helps minimize the chance for bias.

Also, by using a fixed interval, researchers have no influence over which individuals are chosen for sampling. Samples are more precise, which helps protect data collection from favoritism, minimizes the risk of error, and reduces the chance of data manipulation. With systematic sampling, you can trust the data you gather.

Pro #3: Samples are Evenly Distributed

In some random selection processes, subjects are located too close together. This can contaminate data and result in inaccurate findings. Systematic sampling is highly structured, resulting in a more authentic representation of the overall population. No matter how diverse the group is, this selection process produces an evenly distributed collection of subjects. This makes their results easier to compare, execute, and analyze.

Systematic Sampling Disadvantages

Systematic sampling isn’t necessarily a perfect system. There are also disadvantages to using this method for sample selection. Here are three cons to systematic sampling.

Con #1: Success Relies on Population Count

The effectiveness of systematic sampling depends on the initial count of the population. After all, that’s the number that is divided by the desired sample size to determine the fixed interval for sample selection. When the population isn’t measurable or available, researchers have to be able to make a close approximation. If the population is estimated to be smaller or larger than its actual number, this can affect the samples and produce inaccurate results.

Con #2: Patterns Can Be Predicted

When a smaller population is being surveyed, the fixed integer pattern used to select samples can be predicted. This can cause bias among the participants, and some could provide erroneous responses to increase the chance that the study will end with a specific outcome. Rather than organic results, the study could result in an undetected bias. This would result in a compromised study with false, untrustworthy results.

Con #3: Unequal Selection is Possible

Because systematic sampling makes inferences from a representative subset of a population, there is a chance that results won’t be completely accurate. Systematic sampling relies on a numbering system to choose sample participants. Especially in circular systematic sampling, it’s possible for individuals to be overlooked due to their position in the periodic interval. The perspectives of these people won’t be included in the responses, so the results can’t be complete. This means researchers will always miss feedback that could lead to a new discovery.

CheckMarket Helps You Manage Systematic Sampling

CheckMarket’s online survey software is easy to use and offers great depth. We understand how important sample size is to delivering effective results. We help you identify population size, navigate the margin of error, and establish confidence in each questionnaire and the results they provide. Let us help you manage systematic sampling to gather the insights you need to grow, improve, and achieve your goals.

Ready to implement systematic sampling into your surveys? Sign up for free and learn more about creating subset samples that deliver helpful, representative insight.

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