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 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.
- Systematic sampling is a probability sampling method in which a random sample, with a fixed periodic interval, is selected from a larger population.
- The fixed periodic interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.
- 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.
Understanding Systematic 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.
There are several methods of sampling a population for statistical inference; systematic sampling is one form of random sampling.
Examples of Systematic Sampling
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 Vs. Cluster Sampling
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.
Limitations of 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.
Frequently Asked Questions
What Are the Advantages of Systematic Sampling?
Systematic sampling is to simple to conduct and easy to understand, which is why it's generally favored by researchers. The central assumption, that the results represent the majority of normal populations, guarantees the entire population is evenly sampled. Also, systematic sampling provides an increased degree of control when compared to other sampling methodologies because of its process. Systematic sampling also carries a low-risk factor because there is a low chance that the data can be contaminated.
What Are the Disadvantages of Systematic Sampling?
The main disadvantage of systematic sampling is that the size of the population is needed. Without knowing the specific number of participants in a population, systematic sampling does not work well. For example, if a statistician would like to examine the age of homeless people in a specific region but cannot accurately obtain how many homeless people there are, then they won't have a population size or a starting point. Another disadvantage is that the population needs to exhibit a natural amount of randomness to it else the risk of choosing similar instances is increased, defeating the purpose of the sample.
How Do Cluster and Systematic Sampling Differ?
Cluster sampling and systematic sampling differ in how they pull sample points from the population included in the sample. Cluster sampling divides the population into clusters and then takes a simple random sample from each cluster. 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 is susceptible to a larger sampling error than is systematic sampling though it may be a cheaper process.