Systematic Sampling

Systematic sampling is more or less a method that involves selecting various elements ordered from a sampling frame. Taking this statistical procedure starts from the random selection of elements that belongs to a list. Then every sampling interval from the frame is selected.

It is a probability sampling method by randomly selecting sample members from the mass population at a fixed interval. This periodic interval is termed the sampling interval. It can be calculated by ascertaining the required sample size and dividing it by population size.

• Systematic sampling is a method that involves selecting elements from a sampling frame in a systematic order.
• The process begins by randomly choosing an initial element from the frame, and then subsequent elements are selected at fixed intervals.
• Systematic sampling can be used effectively when the population is homogeneous, meaning there is a consistent pattern or order to the population elements.
• It is a probability sampling method where sample members are selected from the population at regular intervals, known as the sampling interval.

Systematic sampling method can only be applied if the given population is homogeneous, as these sample units are distributed systematically over the population.

It enables analysts and researchers to take a small sample from a larger population. This selection can be based on age, gender, location, etc. Statistical sampling is mostly used in the field of sociology and economics. It can be of two types- linear and circular systematic sampling.

It could be really easy. It also gives researchers and analysts a better degree of control. It can even help in the elimination of cluster selection. This type of statistical method has a very low probability of error and data manipulation. It is simple, and thus, it is why the systematic sampling method is really popular and preferred by most statisticians.

Statistically, statisticians can use systematic sampling technique if they want to save time or are dissatisfied with the results obtained from the simple random sampling method. After identifying a fixed starting point, the statisticians select a constant interval to facilitate the participant’s selection.

This method must initially select the target population before selecting participants. There are various characteristics based on which identify the population and conduct the study. These desired characteristics could be age, race, gender, location, profession, and/or education level.

Let us look at the types of systematic sampling technique.

#1 - Linear

• It terms linear since it follows a very linear path and tends to stop at the end concerning a particular population. In this type of sampling, any sample is unrepeated in the end.
• Also, ‘n’ units are chosen to form a part of the sample that has ‘N’ units of population. The analysts and researchers can take skip logic into use for the selection of ‘n’ units instead of randomly selecting these ‘n’ units from a given sample.
• A linear systematic sampling technique selects arranging the total population and classifying the same in a sequence, selecting the ‘n’ or the sample size, calculating the sampling interval (K= N/n), randomly selecting a number from 1 to K, adding ‘K’ (sampling interval) to the randomly chosen number for adding the next member to the sample and repeating this process for adding the remaining members from the sample.

#2 - Circular

• In this type of sampling, it is seen that the sample starts from a point where it has ended. It means the sample restarts from the point where it has ended. This type of statistical sampling method arranges the elements circularly.
• There are two ways to form a sample in this statistical sampling method. If K= 3, the samples will be the ad, be, ca, db and ec. Whereas if K=4, the samples are ae, ba, cb, dc, and ed.

For example, a researcher wants to choose 2,000 people from a population of 10,000 with the help of systematic sampling. He must enlist all the potential participants. Accordingly, they will select a starting point. As soon as it forms the list, every 5th person from the list will get selected as a participant, as 10,000/2,000 = 5.

Some advantages of systematic sampling research are given below:

#1 - Quick

It is a quick method; i.e., it can save statisticians a lot of their time. It becomes easy for researchers and analysts to choose a sample size with the help of this approach since it is really quick. There is little need to number every member from the sample. It also helps in the faster and simpler representation of a particular population.

#2 - Appropriateness and Efficiency

The results obtained from systematic sampling are appropriate as well. As compared to other statistical methods, the results derived from the statistical method are highly efficient and appropriate.

#3 - Low Risk of Data Manipulation

The data manipulation probabilities are really low compared to other statistical methods.

#4 - Simplicity

This method is really simple. It is one of the main reasons why analysts and researchers prefer to go for this method instead of any other method. The simplicity of this method has made it quite popular amongst analysts and researchers.

#5 - Minimal Risks

The risk involved in the systematic sampling method is the bare minimum.

Some disadvantages of systematic sampling research are given below:

It becomes difficult when one cannot estimate the population size. It even compromises the effectiveness of systematic sampling in various areas, such as field research on animals. There is also a possibility of data manipulation and business since the researcher gets to choose the sampling interval.

• The former depends on the rule of sampling interval to select individuals, whereas random sampling identifies each population separately.
• Random sampling gives equal opportunity to all for selection but systematic sample does not.
• The systematic sample method is better than the random sample in case data has no fixed pattern.
• Simple random sampling has lower manipulation opportunity then the systematic sample method.
• The former is better than the latter if the sample size is very large.

This article is a guide to what is Systematic Sampling. We explain it with example, advantages, disadvantages, method & types. You can learn more about from the following articles: -

• Stratified Sampling
• Cluster Sampling
• Random vs Systematic Error
• Formula of Sampling Error
• Point Estimators