Systematic sampling is one of the methods you can use to select your sample size in a population. If you are looking for another method of sample selection other than random sampling, then you should consider using systematic sampling. 

In this article, we’ll be explaining the concept of systematic sampling, the types, and its uses.

What is Systematic Sampling

Systematic Sampling is a type of probability sampling method where random starting points with fixed intervals are used to select members from a larger population.  

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.

Unlike simple random sampling and other types of population sampling methods, systematic sampling can be done quickly and it also has a low data manipulation risk.

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To carry out systematic sampling, you are required to identify and fix a starting point. After which you’d select a constant interval to facilitate the selection of the respondents.

Systematic sampling is preferable to simple random sampling when there is a low risk of data manipulation. 

Because of its simplicity, systematic sampling has gained popularity among researchers and analysts. So, they target and select a population before selecting the participants. This targeted population can be identified by the presence of any amount of desired characteristics such as age, location, gender, race, education, and other traits suitable for the purpose of the study. 

Types of Systematic Sampling

There are three types of systematic sampling, namely:

1. Systematic random sampling: Systematic random sampling is a method used by researchers or statisticians to select samples at an already determined interval. The researcher will select a random starting point between 1 and the sampling interval. Follow the below-listed steps to set up a systematic random sample:

• To begin your systematic sampling, you must first calculate and preset the sampling interval. To calculate this, divide the number of subjects in the population by the number of subjects needed for the sample.
• After the calculation, select a random starting point between 1 and the sampling interval.
• Continue to repeat the sampling interval to choose the next subjects.

2. Linear systematic sampling: Linear systematic sampling is a method in systematic sampling where you cannot repeat samples at the end. In this type of systematic sampling, the ‘n’ units are selected as part of a sample having ‘N’ population units. A researcher does not have to select these ‘n’ units randomly, rather, the researcher can simply do the selection using skip logic. The units usually follow a linear path and stop at the end of a specific population.

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This is calculated as sample interval (k) = N/n where N= total population units and n = sample size.

Here is the linear systematic sample selection process.

• Use a classified sequence to arrange the entire population.
• Select your sample size (n)
• Calculate the sample interval (k) = N/n
• Then select a number randomly between 1 to k. You can also select the  (k)
• Add the sampling interval (k) and the randomly selected number so as to add the next subject to the sample. Keep repeating this procedure to add the remaining subjects to the sample.
• If (k) isn’t an integer, select the closest integer to N/n.

3. Circular systematic sampling: circular systematic sampling, starts to sample again from the same point it once ended; thus, the name circular systematic sampling. Let us consider this example, if N = 7 and n =2 k= 3.5. Only two probable ways to form a sampling exist. It is either from a-b. Therefore,

• If k=3, you can form samples from a-d, b-e, c-a, d-b, and e-c.
• If k=4, you can form samples from – a-e, b-a, c-b, d-c, and e-d.

To select a circular systematic sample follow these steps;

• Calculate the sample interval (k) = N/n. (If N = 13 and n = 2, then k will be 6)
• Start the sampling randomly between 1 to N
• Skip through (k) units to create samples until you select all the subjects of the entire population.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Difference between Systematic Random, and Circular Systematic Sampling

There are some distinct differences between random and circular systematic sampling and they are:

While Circular Systematic Sampling allows you to create samples = N (total population), you will have to select your sample subjects from an interval in systematic random sampling.

In circular systematic sampling, your selection will restart from the start point once you have considered the entire population. However, in systematic random sampling, your random starting point must be between 1 and the sample interval. 

Also, in circular systematic sampling, your subjects will be arranged in a circular manner. While in systematic random sampling, your subjects are already determined and do not require any special arrangements.

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When to Use Systematic Sampling

• As a researcher, you can use of systematic sampling when your project is on a tight budget or requires to be completed within a short time.
• Use of systematic sampling when there are no patterns in your data.
• If you are certain that there’s a low risk of data manipulation in your research, you can use systematic sampling. It will also reduce the risk of poor data quality.
• Use systematic sampling if the size of your population sample increases and you need to develop multiple samples.

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How to Conduct Systematic Sampling

Here are the steps to follow when conducting a systematic sample:

Step one: Create a well-structured and defined audience that can start working on the sampling.

Step two: You will have to figure out what size will be suitable or ideal for the sample, i.e., how many people from the entire population choose to be a part of the sample.

Step three: When you are done deciding on the sample size, you can assign a number to each participant or subject in the sample.

Step four: Have a defined interval for the sample. The interval you decide will be the standard distance between the subjects.

For example, your sample interval will be 20, if your population size (N) of 10,000 is divided by the sample size (n) of 500.

Step five: Now choose the people who fit the criteria which is from 1 in 20 individuals on the list.

Step six: Select the starting subject (r ) randomly from the sample and add the interval to the random number. Keep adding people in the sample till you fill your entire population. So, start with r, r+i, r+2i, etc.

Read: What is Stratified Sampling? Definition, Examples, Types

Advantages of Systematic Sampling 

Here are some of the advantages of systematic sampling.

1. Systematic sampling is an extremely simple and convenient sampling method. It allows researchers to develop, conduct, and analyze samples from a targeted population easily.
2. Systematic sampling is a faster choice of population sampling representative. This is because there won’t be a need to number each member of a sample.
3. The systematic sampling samples are created based on precision in the subject selection and not from favoritism.
4. Systematic sampling is characterized by its minimal or lower risk factor.
5. It uses an even distribution method to delegate the members to form a sample group. This commonly happens when there are diverse subjects or members of a population.

Read: Snowball Sampling Guide: Types, Examples, Pros & Cons

Disadvantages of Systematic Sampling

There are some disadvantages of using systematic sampling and they include:

1. You cannot conduct systematic sampling without a population and you must also have a specific number or size. 
2. Another disadvantage of systematic sampling is that there has to be an indication of natural random selection because you stand the risk of selecting similar instances from the population.
3. A researcher is also at risk of biased research if the selected population is in a cynical pattern that is a match to the sampling interval. Hence the researcher must consider how the list is organized with the sampling interval. 

Explore: Sampling Bias: Definition, Types + [Examples]

Examples of Systematic Sampling 

Let us consider a hypothetical example of systematic sampling. Assuming that in a population of 20,000 people, a researcher selects every 200th person to partake in the sampling. The researcher can also set the sampling intervals to be systematic, for example, the researcher can choose a new sample to draw from every 6 hours.

For another example of systematic sampling, if a researcher wants to use systematic sampling to select a random group of 2,000 people from a population of 100,000, all the likely participants have to be placed in a list. Afterward, the researcher will select a  starting point. 

So once the list has been formed, the participants would be chosen from every 50th person on the list (The count must start from the chosen starting point). Also, note that the focus would be on every 50th person on the list since 100,000/2,000 = 50.

Therefore, if your starting point is from 30, the 80th person on the list would be selected as a participant followed by the 130th, and so on. Once you have reached the end of the list and you need additional participants, the count loops to the beginning of the list so that you can conclude your count.

This is why it is very important for any researcher that wants to conduct systematic sampling, to first familiarize themselves with the size of the target population.

Conclusion

Consider the size of your population sample and what method is the most appropriate before deciding on the sampling technique to use in your study.

If you want an evenly distributed sample population in your research and you are conscious of time, you should consider making use of systematic sampling.