Is the best method to collect original data from a population that is too large to observe directly?

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.

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.

• 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.
• Advantages of this methodology include eliminating the phenomenon of clustered selection and a low probability of contaminating data.
• Disadvantages include overrepresentation or underrepresentation of particular patterns and a greater risk of data manipulation.
• There are three main types of systematic samples: Random systematic samples, linear systematic samples, and circular systematic samples.

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.

You can use the following steps to create a systematic sample:

1. Define your population: This is the group from which you are sampling.
2. Settle on a sample size: How many subjects do you want/need to sample from the population to get a reflective idea of it?
3. Assign every member of the population a number: If the group you’re looking at consists of, say, 10,000 people, start lining them up and giving them numbers.
4. Decide the sampling interval: This can be achieved by dividing the population size by the desired sample size.
5. Choose a starting point: This can be done by selecting a random number.
6. Identify members of your sample: If you have a starting point of 15 and a sample interval of 100, the first member of the sample would be 115, and so forth.
   

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 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.

To conduct systematic sampling, researchers must first know the size of the target population.

Generally, there are three ways to generate a systematic sample:

• Systematic random sampling: The classic form of systematic sampling where the subject is selected at a predetermined interval.
• Linear systematic sampling: Rather than randomly selecting the sampling interval, a skip pattern is created following a linear path.
• Circular systematic sampling: A sample starts again at the same point after ending.
  

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 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.

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.