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Systematic Sampling
The techniques of inferential statistics are used to make educated guesses about the unknown characteristics of a population based on the known properties of a sample. There are many different kinds of samples, such as simple random samples, stratified random samples, multistage cluster samples, quota samples, and convenience samples. Another kind of sample is a systematic sample. Such samples differ from each other in terms of quality, availability, and popularity.
Systematic sampling refers to the process used to extract a sample from the population. From an ordered list of the population's N members (people, animals, or things), every kth member is selected to be included in the sample, where k is the interval between selected members of the list. The value of k is selected by the researcher to create a sample of a desired size, n. To determine k, N is divided by n.
A simple example might help to clarify how one creates a systematic sample. First, suppose a researcher has a population of 200 members and wants to have a sample of size 25. In this case, N = 200, n = 25, and k = 
200/5 = 8. In this situation, every 8th member on the list of the 200 elements of the population would be included in the sample. The 25 members of the sample would hold positions 8, 16, 24, and 200 in the list (presuming that the first sampled position on the list is position 8).
This entry describes the process of selecting a systematic sample and discusses the advantages and disadvantages of such a sample.
Selection Process
Need for a Sampling Frame
To create a systematic sample, there first must be a list of every member of the population. This list is called a sampling frame. Examples of sampling frames include the list of all admitted students who enroll in a given university as freshman during the fall of a particular year, the list of all gorillas housed in metropolitan zoos on a particular date in a given country, and the list of all new homes in a particular county that are sold during a given month. In each of these examples, the list of the population's members is the sampling frame.
In some studies, a sample is created by identifying every kth person who walks by a researcher who is stationed at a particular spot in a city. After these individuals are identified (and questioned on some topic of interest), with the value of n perhaps predetermined, the responses from the n people are treated as sample data. This kind of sample is not a systematic sample for one simple reason: No sampling frame was created or used. Systematic samples, by definition, require sampling frames.
The Starting Point
Because most lists that define sampling frames are created in an alphabetical, geographical, or time-based manner, it is important to have a randomly determined starting point among the initial k positions on the list. If this is not done, no member of the population has a chance to be included if its position in the sampling frame is smaller than k. Clearly, a systematic sample cannot be considered to be random if a portion of the population is prevented from entering the sample.
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