What Is Systematic Sampling?

Systematic sampling is a probability sampling method where samples from a larger population are selected according to a random startin🥂g point but with a fixed, periodic interval. This sampling interval is calculated by dividing the population size by the desired sample size.

Understanding Systematic Sampling

When carried out correctly on a large population of a defined size, systematic sampling can help researchers, including 澳洲幸运5开奖号码历史查询:marketing and sales professionals, obtain representatꦕive findings on a huge group of people without having💙 to reach out to each and every one of them.

Since 澳洲幸运5开奖号码历史查询:simple random sampling of a population can be inefficient and time-consuming, 澳洲幸运5开奖号码历史查询:statisticians turn to other methods, such as systemati𓂃c 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 leng🐼th 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 澳洲幸运5开奖号码历史查询:populations unless a random characteristic disproportionately exists with every nth data samp𓄧le (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.

When To Use Systematic 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 work well is when the population size is known and relatively large. Instead of having to list and randomly select i🧜ndividuals 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 spr♋ead 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.

Steps To Create a Systematic Sample

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?
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.
   

Examples of Systematic Sampling

As a hypothetical example of systematic sampling, assume that, in a population of 10,000 people, a statistician s🌊elects 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🎀.

Types of Systematic Sampling

Generally, there are three ways to generate a systematic sample: systematic random sampling, linear systematic sampling, and circular systematic sampling.

Systematic Random Sampling

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 1,000, they could use systematic random sampling by selecting every 10th student from a list sorted in r🍰andom order. This approach ensures that each member of the population has an equal chance of being selected, while still maintaining a systematic sampling pattern.

Linear Systematic Sampling

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 fifth member, then every seventh member, then every ninth 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.

Circular Systematic Sampling

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 vs. Cluster Sampling

Systematic sꦰampling and cluster sampling differ in how they pull sample points from the population included in the sample. Cluster sampling breaks the population down into , while systematic sampling uses fixed intervals from the larger population to create the sample.

Systematic sampling select🅺s 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 to be less precise t🐓han 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 e𒁏xample, 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 simpl♍e, manual process that can save time and money.

Mistakes To Avoid When Systematically Sampling

One common pitfall to watch out for when using systematic sampling is selecting an inappropriate sampling interval. Choosing a sampling interval that is toꦑo 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 populati🦋on. 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 perio👍dic pattern or trend in the population that aligns with the sampling interval, certain segments of the population may be systematically overrepresented or underrepresented. 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.

Limitations of Systematic Sampling

One risk that statisticians must consider when conducting systematic sampling involves how the list used wit♏h 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, say a company’s 澳洲幸运5开奖号码历史查询:human resources department wants to pick a sample of employees and ask how they feel about company policies. Employees a🔯re 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.

The Bottom Line

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 dataset without patterns between intervals, systematic sampling is capable of provid♎ing reliable samples at a relatively low 📖cost.