Why systematic sampling is good

Internally, an organization could simply choose not to manipulate the data. However, because these data sets could be manipulated by ill-intentioned researchers, this particular sampling method can generate scrutiny—particularly in industries that are prone to statistical manipulations, such as politics. There are several reasons why SurveyMonkey Audience is ideal for systematic sampling. With an audience consisting of more than 50 million nearly-randomized people, it is easy to conduct a variety of different types of surveys and generate statistically significant results.

Furthermore, SurveyMonkey makes it easier to find a random population, assign the population specific numbers, and then systematically generate a subpopulation. With limited time and resources, organizations of all kinds can gain greater insights and adjust their own behaviors in response.

Systematic sampling is a sampling methodology that samples a larger population via fixed, repeated intervals. When all else is equal, it is a sampling methodology that is simple, straightforward, and easy to conduct. While there are indeed some drawbacks, such as strict pre-requisites and clustering issues, this type of sampling is still incredibly popular among researchers around the world.

Collect market research data by sending your survey to a representative sample. Get help with your market research project by working with our expert research team. Test creative or product concepts using an automated approach to analysis and reporting. Products Surveys. Specialized products.

View all products. Survey Types. People Powered Data for business. Solutions for teams. Explore more survey types. Curiosity at Work. Help Center. Watch a demo. Solutions Services More Resources. What is systematic sampling? Get started. How systematic sampling works. When to use systematic sampling.

Optimize your research sample. Get insights from your target audience with SurveyMonkey Audience. Learn more. Steps to form a sample using systematic sampling. Step 1: Defining the population. Step 2: Identifying the population and sample size.

Step 3: Assigning a number. Step 4: Determining the interval. Step 5: Selecting the sample. Want to learn more about sampling best practices? Discover examples and resources in our Ultimate Guide to Market Research. Read more. Types of systematic sampling. Systematic random sampling. Linear systematic sampling. Circular systematic sampling. Advantages of systematic sampling. Random probability.

Limitations of systematic sampling. Strict prerequisites. In order to perform simple random sampling, each element of the population of interest must be separately identified and selected.

With systematic sampling, a sampling interval is used to select the individuals that will comprise the sample. If researchers are working with a small population, random sampling will provide the best results. However, if the size of the size of the sample that is required to perform the study increases, and researchers find themselves needing to create multiple samples from the population, these processes end up being extremely time-consuming and expensive.

When there is no pattern in the data, systematic sampling is more effective than simple random sampling. But in circumstances where the population is not random, researchers are at risk of selecting individuals to comprise their sample that possess the same characteristics, which in turn has a negative effect on data quality.

For example, if a farm that grows oranges has a sorting machine that is on the fritz, and every tenth orange that passes the sorting test is damaged, researchers are more likely to select a damaged orange to be a part of their sample if they use systematic sampling than if they were to use simple random sampling. This would result in a biased sample, and inherently poor data quality.

Systematic sampling is the preferred method over simple random sampling when a study maintains a low risk of data manipulation. Data manipulation is when researchers reorder or restructure a data set, which can result in a decrease in the validity of the data. If the risk of data manipulation is high, and the sampling interval that comes with systematic sampling has the potential to alter the data being collected, then a simple random sampling method is more appropriate and effective.

The first step of performing systematic sampling is to estimate the size of the population that visits the restaurant on a given day. The researchers would be unable to know the exact size of the population, since they cannot be percent confident who will visit the restaurant during the day, but they can make some informed estimate as to how many restaurant patrons they can expect to see.

Depending on the estimate derived from step one, the size of the sample could be ten, one hundred, or even more than that — it all depends on the desired data volume target for the research to be statistically significant. In this step, the researchers would take the estimated population size from step one , and divide it by the number of people that need to be in the sample from step two.

For instance, suppose researchers want to study the size of rats in a given area. If they don't have any idea how many rats there are, they cannot systematically select a starting point or interval size. A population needs to exhibit a natural degree of randomness along the chosen metric. If the population has a type of standardized pattern, the risk of accidentally choosing very common cases is more apparent. For a simple hypothetical situation, consider a list of favorite dog breeds where intentionally or by accident every evenly numbered dog on the list was small and every odd dog was large.

If the systematic sampler began with the fourth dog and chose an interval of six, the survey would skip the large dogs. There is a greater risk of data manipulation with systematic sampling because researchers might be able to construct their systems to increase the likelihood of achieving a targeted outcome rather than letting the random data produce a representative answer.

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We and our partners process data to: Actively scan device characteristics for identification. I Accept Show Purposes. Your Money. Personal Finance. Your Practice. Popular Courses. Key Takeaways Because of its simplicity, systematic sampling is popular with researchers. Other advantages of this methodology include eliminating the phenomenon of clustered selection and a low probability of contaminating data.

Disadvantages include over- or under-representation of particular patterns and a greater risk of data manipulation. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation.

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