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Systematic Sampling – A Comprehensive Guide With Examples

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In any research, selecting a sample is crucial for drawing a valid conclusion. If you want a sample evenly distributed throughout the whole population selected for your study, then the systematic sampling method is the best choice. Choosing samples systematically after a specified interval is better than choosing samples randomly because it can help you get quality data and avoid biased samples. You can save a lot of time and money by using this technique of selecting samples for your study. As per its importance, this article aims to discuss different aspects of systematic sampling in detail.

What is Meant by Systematic Sampling?

It is a statistical sampling method in which you select people at regular intervals. If the order of the population is random, then this method of sample collection can help you have a good sample to draw a strong conclusion for the research. This method can help you avoid poor results from surveys and biased samples.

Examples

Take an example of a local NGO that wants a systemic sample of five hundred people from an area with a population of five thousand people. For this, you can take every 10th individual from the population to make a systematic sample.

Take the example of another research project for 20,000 people. To do systematic sampling, you will select every 200th individuals from the population. You can also use time intervals to select samples. For instance, you could choose a new sample after every 10 hours.

Understanding Systematic Sampling

It can be very time consuming and inefficient to select samples randomly. Therefore, researchers have to come up with new methods to select samples. You can easily save time by choosing samples systematically. Once you have identified a fixed point for starting, you can select an interval to facilitate the selection of participants. Also, this method is better than simply doing random sampling, especially when there is a risk of data manipulation. In case of a high risk of data manipulation, a simple method of random sampling will be a better choice.

When to Use Systematic Sampling?

  • Researchers can use systematic sampling for a project with a low budget or a short deadline.
  • It is best to use this sampling method when there are no patterns in the data.
  • This method is also appropriate for projects with a very low risk where researchers can manipulate the data. This can also help decrease the risk of getting data of poor quality.
  • If the population you are using is very large, you can make several samples, so this method of collecting the sample is appropriate.

How Systematic Sampling Works?

It is important to make sure that you represent your population correctly while doing sampling. It is a statistical sampling method in which you select people from a place at a regular interval. This kind of sampling can help you avoid bias and get data of better quality. To understand the working of this sampling method, take an example of a dance class where the teacher will ask all the students to create a line. Then he will choose every third person from that line to participate in a dance performance. This case shows the teacher how to represent the class accurately.

Advantages

Following are the advantages of systematic sampling:

  • It is a simple and convenient method of sampling.
  • It takes less time to choose a sample from a large population.
  • It takes précised samples free from any bias.
  • There is a very low risk of data manipulation in this case.

Disadvantages

Following are the disadvantages of systematic sampling:

  • You must need to have a specific size or number of population for this method.
  • It is a kind of random selecting method, as there is a risk of collecting similar instances.
  • It can be a biased sample if the population is arranged in a pattern that matches the interval.

Mention the Different Types of SS

Following are the important types of systematic sampling:

Random sampling of Data

This type of sampling involves selecting samples at a specific pre-determined interval. You select a starting point randomly and then select persons after a specified interval. The following examples show how to do random sampling:

First, determine an interval for sampling. To calculate the interval, you can divide the number of individuals in the population from the number of people required for the sample.

  • After this, you need to choose starting point randomly.
  • Start selecting people at each specified interval.

Linear Sampling of Data

This type of systemic sampling is a technique where you do not repeat the end. Consider that there is an ‘’N’’ number of people in your selected population, and you need to choose the “n’’ number of people from it for the sample. In this method, the research will follow a linear path and then stops by applying “skip logic.”

K (skip interval) = N/n

The following steps show how you select samples in this method:

  • The first step is to arrange the whole population in a good pattern.
  • Find out the total population
  • Choose the size of your sample
  • Calculate skip interval

Circular Sampling of Data

Circular systematic sampling is a method where you start selecting a sample from a point and then again selecting a sample from that same point. This way, you follow a circulation path to select elements for your sample. The mean of the sample, in this method, is an unbiased estimator of the mean of whole population. In case of any query, you can consult with masters dissertation help.

Conclusion

With the help of systematic sampling, you can easily study large populations in a better way. This method has great scope as many businesses and researchers are using this technique to study a large number of people. It is a simple method of selecting a sample that will take much less time and money. This article has given proper guidance regarding this method and has explained how this method works. By reading this article, you can easily decide if this sampling technique suits your study goals and objectives.

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