What is 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 and 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.
BREAKING DOWN Systematic Sampling
Since simply random sampling 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.
For 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.
Defining a Population
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.
Risks Associated With Systematic Sampling
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.