Systematic Sampling

Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

Despite the sample population being selected in advance, systematic sampling is still thought of as being random if the periodic interval is determined beforehand and the starting point is random.

[Important: there are several methods of sampling a population for statistical inference. Systematic sampling is one form of random sampling.]

Since 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. Once a fixed starting point has been identified, a constant interval is selected to facilitate participant selection. Systematic sampling is preferable to simple random sampling when there is a low risk of data manipulation. If such a risk is high when a researcher can manipulate the interval length to obtain desired results, a simple random sampling technique would be more appropriate.

Systematic sampling is popular with researchers and analysts because of its simplicity. Researchers generally assume the results are representative of most normal populations unless a random characteristic disproportionately exists with every "nth" data sample (which is unlikely). In other words, 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.

Within systematic sampling, as with other sampling methods, a target population must be selected prior to selecting participants. A population can be identified based on any number of desired characteristics that suit the purpose of the study being conducted. Some selection criteria may include age, gender, race, location, education level and/or profession.

• Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval (the sampling interval).
• 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.

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

As another example, if you wanted to select a random group of 1,000 people from a population of 50,000 using systematic sampling, all the potential participants must be placed in a list and a starting point would be selected. Once the list is formed, every 50th person on 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 on the list would be chosen followed by the 120th, and so on. Once the end of the list was reached and if additional participants are required, the count loops to the beginning of the list to finish the count.

Systematic sampling and cluster sampling differ in how they pull sample points from the population included in the sample. Cluster sampling breaks the population down 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 depending on 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 methods of sampling. However, it may save costs on obtaining a sample. Cluster sampling is a two-step sampling procedure. It may be used when completing a list of the entire population is difficult. For example, it could be difficult to construct the entire population of the customers of a grocery store to interview. However, a 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.

One risk that statisticians must consider when conducting systematic sampling involves how the list used with the sampling interval is organized. If the population placed on the list is organized in a cyclical pattern that matches the sampling interval, the selected sample may be biased. For example, a company's human resources department wants to pick a sample of employees and ask how they feel about company policies. Employees are grouped in teams of 20, with each team headed by a manager. If the list used to pick the sample size is organized with teams clustered together, the statistician risks picking only managers (or no managers at all) depending on the sampling interval.