How Systematic Sampling Works

What It Is and How to Do It

Systematic sampling
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Systematic sampling is a technique for creating a random probability sample in which each piece of data is chosen at a fixed interval for inclusion in the sample. For example, if a researcher wanted to create a systematic sample of 1,000 students at a university with an enrolled population of 10,000, he or she would choose every tenth person from a list of all students.

How to Create a Systematic Sample

Creating a systematic sample is rather easy. The researcher must first decide how many people out of the total population to include in the sample, keeping in mind that the larger the sample size, the more accurate, valid, and applicable the results will be. Then, the researcher will decide what the interval for sampling is, which will be the standard distance between each sampled element. This should be decided by dividing the total population by the desired sample size. In the example given above, the sampling interval is 10 because it is the result of dividing 10,000 (the total population) by 1,000 (the desired sample size). Finally, the researcher chooses an element from the list that falls below the interval, which in this case would be one of the first 10 elements within the sample, and then proceeds to select every tenth element.

Advantages of Systematic Sampling

Researchers like systematic sampling because it is a simple and easy technique that produces a random sample that is free from bias. It can happen that, with simple random sampling, the sample population may have clusters of elements that create bias. Systematic sampling eliminates this possibility because it ensures that each sampled element is a fixed distance apart from those that surround it.

Disadvantages of Systematic Sampling

When creating a systematic sample, the researcher must take care to ensure that the interval of selection does not create bias by selecting elements that share a trait. For example, it could be possible that every tenth person in a racially diverse population could be Hispanic. In such a case, the systematic sample would be biased because it would be composed of mostly (or all) Hispanic people, rather than reflecting the racial diversity of the total population.

Applying Systematic Sampling

Say you want to create a systematic random sample of 1,000 people from a population of 10,000. Using a list of the total population, number each person from 1 to 10,000. Then, randomly choose a number, like 4, as the number to start with. This means that the person numbered "4" would be your first selection, and then every tenth person from then on would be included in your sample. Your sample, then, would be composed of persons numbered 14, 24, 34, 44, 54, and so on down the line until you reach the person numbered 9,994.