When to Use Systematic Sampling Instead of Random Sampling

What is Systematic Sampling?

Systematic sampling is when researchers select items from an ordered population using a skip or sampling interval.

For example, if researchers are interested in the population that attends a particular restaurant on a given day, they could set up shop at the restaurant and ask every tenth person to enter to be a part of their sample. 

They could also elect to ask the twentieth person, the thirtieth, or any other sample interval that suits the requirements of their research study.

Systematic sampling differs from simple random sampling, because in simple random sampling a sample of items is chosen at random from a population, and each item has a perfectly equal probability of being chosen. 

Simple random sampling leverages tables of random numbers or an electronic number generator to determine a sample, whereas these components are not necessary to perform systematic sampling.

When to Use Systematic Sampling Over Simple Random Sampling

Researchers should use systematic sampling instead of simple random sampling when a project is on a tight budget, or requires a short timeline.

Systematic sampling is also preferred over random sampling when the relevant data does not exhibit patterns, and the researchers are at low risk of data manipulation that will result in poor data quality.

Systematic sampling is simple to execute.

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. 

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