What is systematic sampling?

Systematic sampling is a kind of probabilistic sampling method in which sample members from a larger population are selected at a random starting point but with a fixed and periodic interval. That interval, called sampling interval, is calculated by dividing the population size by the desired sample size.

Despite the sample population having been selected in advance, systematic sampling is still considered random if the periodic interval is determined in advance and the starting point is random.

How systematic sampling works

As a simple random sampling of a population can be inefficient and time-consuming, statisticians turn to other methods, such as systematic sampling. Choosing a sample size through a systematic approach can be done quickly. When a fixed starting point is identified, a constant interval is selected to facilitate the selection of participants.

Systematic random sampling is recommended over simple random sampling when there is a low risk of data manipulation. If this risk is high when a researcher can manipulate the length of the interval to obtain the desired results, a simple random sampling technique would be more appropriate.

This kind of sampling is popular among researchers and analysts because of its simplicity. Researchers generally assume that the results are representative of most normal populations unless there is a disproportionate random feature with each ‘ninth’ data sample (which is unlikely). In other words, a population must show a natural degree of randomness along with the chosen metric. If the population has a standardized model type, the risk of accidentally choosing very common cases is more evident.

In systematic sampling, as with other sampling methods, a target population must be selected before selecting participants. A population can be identified on the basis of any number of desired characteristics that fit the purpose of the current study. Some selection criteria may include age, gender, race, race, location, level of education and/or occupation.

Examples

As a hypothetical example of systematic sampling, assume that in a population of 10,000 people, one statistician selects every 100th person for sampling. Sampling intervals can also be systematic, such as choosing a new sample to be taken every 12 hours.

As another example, if you wanted to select a random group of 1,000 people out of a population of 50,000 using systematic sampling, all potential participants would have to be included in a list and a starting point would be selected. Once the list is formed, every 50th person in the list (starting the count at the selected starting point) would be chosen as a participant, since 50,000/1,000 = 50.

For example, if the selected starting point was 20, the 70th person in the list would be chosen, followed by the 120th and so on. Once the end of the list is reached and if more participants are needed, the count moves to the beginning of the list to finish the count.

Systematic sampling against cluster sampling

Systematic sampling and cluster sampling differ in the way they extract the sampling points from the population included in the sample. Cluster sampling divides the population into clusters, while systematic sampling uses fixed intervals from the larger population to create the sample.

Systematic sampling selects a random starting point from the population, and then a sample is taken from regular fixed intervals of the population according to its size. Cluster sampling divides the population into clusters and then takes a simple random sample from each cluster.

Cluster sampling is considered less precise than other sampling methods. However, it can save costs to obtain a sample. Cluster sampling is a two-step sampling procedure. It can be used when it is difficult to complete a list of the entire population. For example, it may be difficult to build the entire population of customers of a grocery store to be interviewed.

Examples of systematic sampling

As a hypothetical example of systematic sampling, assume that in a population of 10,000 people, one statistician selects every 100th person for sampling. Sampling intervals can also be systematic, such as choosing a new sample to be taken every 12 hours.

As another example, if you wanted to select a random group of 1,000 people out of a population of 50,000 using systematic sampling, all potential participants would have to be included in a list and a starting point would be selected. Once the list is formed, every 50th person in the list (starting the count at the selected starting point) would be chosen as a participant, since 50,000/1,000 = 50.

For example, if the selected starting point was 20, the 70th person in the list would be chosen, followed by the 120th and so on. Once the end of the list is reached and if more participants are needed, the count moves to the beginning of the list to finish the count.

Systematic sampling against cluster sampling

Systematic sampling and cluster sampling differ in the way they extract the sampling points from the population included in the sample. Cluster sampling divides the population into clusters, while systematic sampling uses fixed intervals from the larger population to create the sample.

Systematic sampling selects a random starting point from the population, and then a sample is taken from regular fixed intervals of the population according to its size. Cluster sampling divides the population into clusters and then takes a simple random sample from each cluster.

Cluster sampling is considered less precise than other sampling methods. However, it can save costs to obtain a sample. Cluster sampling is a two-step sampling procedure. It can be used when it is difficult to complete a list of the entire population. For example, it may be difficult to build the entire population of customers of a grocery store to be interviewed.

However, one person could create a random subset of stores, which is the first step in the process. The second step is to interview a random sample of the customers of those stores. This is a simple manual process that can save time and money.

Limitations of systematic sampling

One risk to be considered by statisticians when conducting systematic sampling concerns the organisation of the list used with the sampling interval. If the population included in the list is organised in a cyclic pattern that corresponds to the sampling interval, the selected sample may be distorted.

For example, the human resources department of a company wants to choose a sample of employees and ask them what they think of the company’s policies. Employees are grouped into teams of 20, with each team led by a manager. If the list used to choose the sample size is organized with the teams grouped together, the statistician risks choosing only managers (or no managers) depending on the sampling interval.