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Systematic Sample

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In a systematic sample, the elements of the population are put into a list and then every kth element in the list is chosen (systematically) for inclusion in the sample. For example, if the population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students, the students would be put into list form and then every 20th student would be selected for inclusion in the sample. To ensure against any possible human bias in this method, the researcher should select the first individual at random. This is technically called a 'systematic sample with a random start'.

The procedure for selecting a systematic random sample is very easy and can be done manually. This process is much like an arithmetic progression. First, the researcher selects a number that is less than the total number of individuals in the population. This number will correspond to the first subject chosen for the sample. Next, the researcher chooses the sampling interval, which is the standard distance between elements selected in the sample. The sampling interval is calculated by dividing the population size by the sample size. For example, if the population size is 10,000 and you wanted a sample of 1,000, you would select every tenth element for you sample.

Advantages of Systematic Sampling

The main advantage of using systematic sampling is its simplicity. It allows the researcher to add a systematic element into the random selection of subjects, yet it is very easy to do.

Another advantage of systematic sampling is that the researcher is guaranteed that the population will be evenly sampled. In simple random sampling, there exists a chance that subjects are selected in clusters. This is systematically eliminated in systematic sampling because the sample elements are equal distances apart in the population.

Disadvantages of Systematic Sampling

The biggest disadvantage of systematic sampling is that the process of selecting the sample can interact with a hidden periodic trait within the population. In an extreme example, let’s say every tenth person in the population was Hispanic and the sampling technique coincided with the periodicity of that trait. The selected sample would then be mostly (or all) Hispanic, which would overrepresent Hispanics in the final sample. This means the sampling technique is no longer random and the representativeness of the sample is compromised.

Example

A researcher wants to select a systematic random sample of 10 people from a population of 100. If he or she has a list of all 100 people, he would assign each person a number from 1 to 100. The researcher then picks a random number, 6, as the starting number. He or she would then select every tenth person for the sample (because the sampling interval = 100/10 = 10). The final sample would contain those individuals who were assigned the following numbers: 6, 16, 26, 36, 46, 56, 66, 76, 86, 96.

References

Babbie, E. (2001). The Practice of Social Research: 9th Edition. Belmont, CA: Wadsworth Thomson.

Castillo, J.J. (2009). Systematic Sampling. Retrieved March 2012 from: http://www.experiment-resources.com/systematic-sampling.html

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