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Systematic sampling is a 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 sampling interval is calculated by dividing the population size by the desired sample size.
When carried out correctly on a large population of a defined size, systematic sampling can help researchers, including marketing and sales professionals, obtain representative findings on a huge group of people without having to reach out to each and every one of them.
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, then 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 with the chosen metric. If the population has a type of standardized pattern, then 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, or profession.
There are several methods of sampling a population for statistical inference. Systematic sampling is one form of random sampling.
One situation where systematic sampling may be best suited is when the population being studied exhibits a degree of order or regularity. For example, if you're surveying customers entering a store, systematic sampling allows you to systematically select every nth customer, ensuring representation across different times of the day or week. This approach helps to avoid bias that may arise from selecting only customers who arrive during specific periods.
Another scenario where systematic sampling can be good is when the population size is known and relatively large. Instead of having to list and randomly select individuals from the entire population, systematic sampling simplifies the process by selecting samples at a set cadence. This is particularly useful in large-scale studies where time and resources are limited, meaning you don't need to spend a large amount of energy planning out the sample.
Systematic sampling can be used when researchers want to ensure that the sample is evenly spread across the entire population. For example, a company could select every nth person from the company directory filtered by last name. Other forms of sampling may accidentally cluster similar populations (i.e. too many people from finance are selected based on how the sample is aggregated).
Additionally, systematic sampling offers the advantage of simplicity and ease of implementation compared to other sampling methods. It requires minimal computation and can be easily executed using simple algorithms, especially if the target sample size and total population size are known.
You can use the following steps to create a systematic sample:
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 on 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 60th person on the list would be chosen followed by the 120th, and so on. Once the end of the list is reached and if additional participants are required, the count loops to the beginning of the list to finish the count.
To conduct systematic sampling, researchers must first know the size of the target population.
Generally, there are three ways to generate a systematic sample:
This is the classic form of systematic sampling where the subject is selected at a predetermined interval. For example, if a researcher wants to select a sample of 100 students from a population of 1000, they could use systematic random sampling by selecting every 10th student from a list sorted in random order. This approach ensures that each member of the population has an equal chance of being selected, while still maintaining a systematic sampling pattern.
Rather than randomly selecting the sampling interval, this is when a skip pattern is created following a linear path. This means that instead of selecting every nth member from the population, the selection process follows a predetermined sequence, such as selecting every 5th member, then every 7th member, then every 9th member, and so on. Linear systematic sampling can be useful in situations where there is a specific order or sequence to the population, such as geographical locations along a linear path.
This is when a sample starts again at the same point after ending. This means that once the sampling interval reaches the last member of the population, it wraps around to the beginning and continues the selection process. Circular systematic sampling is often used in situations where the population exhibits cyclical patterns or where there is no clear starting or ending point. For example, if researchers are studying tree growth in a forest, they could use circular systematic sampling by selecting trees at regular intervals along a circular path, ensuring comprehensive coverage of the forest area.
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, 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 common pitfall to watch out for when using systematic sampling is selecting an inappropriate sampling interval. Choosing a sampling interval that is too small may lead to oversampling and increased sampling error, while selecting an interval that is too large may result in undersampling and decreased representativeness of the sample. This error can be avoided by fully understanding the full scope of the population before you begin sampling.
Another mistake to avoid is failing to account for potential biases introduced by the sampling frame. If the sampling frame is not representative of the population of interest, systematic sampling may lead to biased results. For example, if the sampling frame only includes individuals from certain demographic groups or geographic locations, the sample won't reflect the diversity of the entire population. This type of error is present in all forms of sampling.
Another tip to be mindful of is to account for the presence of systematic patterns or cycles in the population. If there is a periodic pattern or trend in the population that aligns with the sampling interval, certain segments of the population may be systematically over- or under-represented. For example, imagine selecting random players from baseball rosters. If those rosters are listed in order by position, you may end up selecting players from the same positions across teams because the population has a cyclical pattern.
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.
To conduct systematic sampling, first, determine the total size of the population you want to sample from. Then, select a random starting point and choose every nth member from the population according to a predetermined sampling interval.
You should use systematic sampling when you need a simple and efficient method to select a representative sample from a large population with a known and evenly distributed structure, and when randomization is not feasible or necessary for your research objectives.
Systematic sampling is 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 that the entire population is evenly sampled.
Also, systematic sampling provides an increased degree of control 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.
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 or else the risk of choosing similar instances is increased, defeating the purpose of the sample.
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, 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 systematic sampling, though it may be a cheaper process.
Sampling can be an effective way to draw conclusions about a broad group of people, items, or something else of interest. Systematic sampling is one of the most popular ways to go about this, as it is cheaper and less time-consuming than other options. Yes, it isn’t flawless. However, if you have a large data set without patterns between intervals, systematic sampling is capable of providing reliable samples at a relatively low cost.
