How many types are included in probability sampling?

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probability sampling

Introduction

Have you ever performed research on a topic? If yes, then you must know what sampling is and how to do it. But if you have not done it before, then this article is here to guide you about sampling techniques and types. More specifically, today’s article is all about probability sampling. Know that sampling is an important part of a research, and the whole of your research will depend on the sampling. Many students fail to collect samples that are representative of the whole population. Hence, they fail to conduct effective research. It is why today’s article will talk about probability sampling. It will discuss its types, methods and pros and cons. So, let’s start our discussion with the following very basic question.

What is probability sampling, and why is it important to do sampling?

Sampling is the choice of samples from a larger population. To collect the samples, researchers use some kind of method. Now, if the researcher applies the probability method to take the samples, it is called probability sampling. This type of sampling uses a random selection technique to collect the samples. Each element in the sample has a non-zero probability. In simple words, you can say that every element of the population has an equal chance of participation.

Let’s explain it with the help of an example. Let’s say that you have a population of 100 people to choose your samples from. Now, probability sampling says that each person in the population has a non-zero probability, i.e., 1. A rule of thumb for this sampling type is that the population must be more than 30. If it is less than 30, you might be asked to use the non-probability sampling technique.

Importance

Sampling is important to draw conclusions about a population. This type of sampling leads to higher quality findings. It happens because there is no bias element, and the researcher randomly chooses the samples. This method is particularly useful when the population is diverse and scattered. It is because it helps researchers create samples that represent the whole population.

What are the types of probability sampling?

From the discussion above, you must have known a lot about this sampling type, its definition and its importance. Now, it is time to discuss its types and the pros and cons of those types. Generally, there are four types of this sampling technique. Hence, a brief description of all the types is as below:

1.      Simple random sampling

It is the first type of probability sampling. As the name suggests, it is the most basic form of this sampling. This sampling involves selecting the participants spontaneously from the population. The researcher assigns different numbers to the samples. After assigning those numbers, he chooses any number randomly and selects that sample. The procedure of this sampling includes two steps. In the first step, you make a list of all population members. The second includes the selection of the members on a random basis.

Advantages

  • This sampling type is very easy to implement, requiring no specific skills.
  • Any research performed on this sampling type has high internal and external validation.
  • It is free from the bias of the researcher.

Disadvantages

  • You cannot not use it for a larger population as it will be difficult to list members and then choose from them.
  • It is time-consuming due to list-making and other tasks.
  • It requires the processing of a large amount of data.

2.      Systematic sampling

This particular type of probability sampling moves in accordance with a pre-defined system. The researcher uses a random starting point and selects the samples after some intervals. Those intervals can be anything depending on the type of data. Let’s see how this type works. Let’s say you have a population of 500 people. You set the 5th person as your starting point and 10th as the sampling interval. This number 10 means that every 10th member after the 5th person is part of the research.

Advantages

  • This sampling type is easy to understand as it moves forward as per a system.
  • It has the lowest probability of contaminating the data samples.
  • It allows you to choose a starting point of your own accord.

Disadvantages

  • The element of bias can appear in this sampling type.
  • It requires a natural degree of randomness in the data.
  • The researcher’s bias in choosing the starting point is the major issue with this type.

3.      Stratified sampling

This sampling type is based on the principle of stratification. Stratification means dividing something into layers and subgroups. The same is the case with this type of probability sampling. The researcher divides the population into smaller groups based on some factors. Those factors can be age, gender, race, or monthly income. Each stratum in this type is given a weight proportional to its size. After that, the researcher randomly selects the samples.

Advantages

  • This type of sampling limits the researcher’s bias in the selection of samples.
  • It ensures equal representation of all the strata or subgroups.

Disadvantages

  • It is time-consuming as you will have to divide the whole population into subgroups or layers first.
  • It is difficult to determine which strata are more appropriate for the research.

4.      Cluster sampling

The 4th and last type of probability sampling is cluster sampling. The investigators use this method when the population is too large. The population size in this type of sampling spans different cities. Therefore, all other methods become ineffective. The researcher first divides the data into different naturally occurring subgroups to do this sampling. After that, he randomly chooses a cluster and performs research.

Advantages

  • It reduces variability in the research.
  • It is a very effective sampling type for larger populations.

Disadvantages

  • The biasness is the biggest issue associated with this sampling method.
  • The clusters may have overlapping data points, which is a problem.

Conclusion

Probability sampling uses the principle of randomisation to select the sample sizes. It is an excellent technique to reduce the researcher’s bias in the research. You must read about its different types before doing this sampling.

Author Bio:

Robert Fawl is a professional Content writer & Content Marketer. Based in London, Robert is an author and blogger with experience in encounter composing on various topics including but not limited to Essay Writing, Dissertation Writing, Coursework Writing Services, Thesis Writing Services and Assignment Writing etc.

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