Types of Sampling Methods

Sampling is one of the most successful methods for conducting market research. Sampling takes data from a small group, such as a simple random sample, and applies it to a much broader target audience, allowing marketers to draw conclusions. 

 

When we hear the word "sampling," what comes to mind? Surveys, research, and vast populations come to mind. And why do we think about them in the first place? The main definition of "sampling" encapsulates the reason behind this. Let's see what we can learn about sampling.

 

 

What is Sampling?

 

A statistical analysis procedure in which a certain number of observations are selected from a larger population is known as sampling. Depending on the type of study being undertaken, the method for sampling from a larger population may include simple random sampling or systematic sampling.

 

Now, in order to comprehend this, we must first comprehend the two concepts that are associated with it. The population and the sample.

 

A population is a collection of goods with one or more qualities in common. The number of elements in the population determines the population size. A subset of a population is referred to as a sample. The act of selecting a sample is known as sampling. The number of items in the sample determines the sample size.

 

Consider the situation where we must select all of the lawyers from a throng on a random street. The people in this room represent our population, and the number of lawyers is a sampling. Sampling is the process of selecting a representative sample from a larger population.

 

In this blog, we'll discuss the different forms of sampling, or how we isolate our sample from the rest of the population.

 

Also Read | What is Sampling Distribution?

 

Need of Sampling

 

Sampling is used to form conclusions about populations based on samples, and it allows us to identify the features of a population by directly seeing only a subset (or sample) of the population.

 

1. It takes less time to select a sample than it does to select every item in a population.

 

2. Sample selection is a low-cost technique.

 

3. A sample analysis is less time-consuming and more feasible than a population-wide analysis.

 

Also Read | Conditional Probability: Definition, Properties and Examples

 

Different Types of Sampling Techniques

 

There are different sampling methods. All of them are grouped into two main categories-

 

1. Non-Probability Sampling

 

Some members of a target population have a higher chance of being chosen for a study under non-probability sampling methods. Rather than selecting respondents at random from a larger population or subset, researchers use their judgment to pick respondents. 

 

Non-probability sampling methods are typically faster and less expensive than probability-based approaches. Their drawback is that they are prone to sampling error and non-representative sample frames.

 

2. Probability Sampling

 

All members of the population have an equal chance of being chosen for a study in this type of sampling. Participants are picked at random in probability sampling, which reduces the risk of sampling bias and downstream sampling errors. Because random sampling can be time-consuming and costly, some researchers prefer non-probability sampling approaches.

 

Also Read | Applications of Statistical Techniques

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Different types of Sampling Method

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Types of Probability Sampling

 

1. Simple Random Sampling

 

Every element has an equal probability of being chosen as a part sample. It is applied when we do not have any prior knowledge about the target demographic. The Simple Random Sampling method is one of the greatest probability sampling approaches for saving time and resources. 

 

It's a trustworthy method of gathering data in which each and every member of a population is chosen at random and solely by chance. Each person has the same chance of being chosen to participate in a sample.

 

Simple random sampling is simple and inexpensive, as it eliminates all bias from the sample process. However, it provides no control for the researcher and may result in the selection of unrepresentative categories by chance.

 

2. Stratified Sampling

 

This methodology separates the population's elements into tiny subgroups (strata) based on similarity, such that the components within the group are homogeneous and heterogeneous among the other subgroups generated. The components are then drawn at random from each of these layers. 

 

To establish subgroups, we require previous knowledge of the population. To avoid overlapping items in subgroups, these subsets are mutually exclusive and collectively exhaustive. Age, occupation, location, gender, and other factors can be utilized to identify these subsets. 

 

After defining the population subgroups, the researcher uses SRS to choose items from each of these subsets. When a researcher wishes to ensure that particular sections of the population are properly represented in the study, systematic sampling is a critical social research strategy. 

 

For example, we have 10 fans of cricket, 10 football fans, and 10 tennis fans among a group of 30. If we were to select 2 fans of each sport, we would first divide them into subgroups based on the sport they like. This way, we will be able to randomly pick from the subgroups of Strata now.

 

3. Cluster Sampling

 

Our whole population is split into clusters or sections, and the clusters are then chosen at random. For sampling, all of the cluster's elements are employed. 

 

Details such as age, gender, and geography are used to identify clusters. If it's feasible, you may add every single person from each sampled cluster. If the clusters are big, one of the above strategies can be used to sample individuals from inside each cluster. Multistage sampling is the term for this method.

 

Cluster sampling can be done in the following ways-

 

• Single Stage Cluster Sampling

 

In it, we randomly select elements from the cluster. I.e the entire cluster is selected randomly.

 

• Two-Stage Cluster Sampling

 

Here first a cluster is chosen randomly, and then from it, we select random elements. This is what we call Two-Stage Cluster Sampling.

 

Also Read | Clustering Methods and Applications

 

4. Systematic Cluster Sampling

 

Except for the first element, the selection of elements is methodical rather than random. A sample's elements are picked at regular intervals from the population. All of the items are initially arranged in a sequence in which each element has an equal probability of being chosen.

 

Systematic sampling entails selecting a random beginning point in the total population and randomly selecting sample members at regular intervals. For example, if a researcher had a list of every resident of a 300,000-person city, they could select every 100th person on the list to obtain a random sample of people. A total of 3,000 persons will be polled in this case.
 

5. Multi-stage Sampling

 

It is the combination of one or more of the preceding procedures. The population is separated into different clusters, which are subsequently subdivided and organized into numerous subgroups (strata) depending on resemblance. 

 

Each stratum can have one or more clusters chosen at random. This technique is repeated until the cluster can no longer be separated. For example, a country may be split into states, cities, urban, and rural regions, and all regions with comparable features may be combined to form a stratum.

 

Also Read | Types of Statistical Analysis

 

 

Types of Non Probability Sampling:

 

Unlike probability sampling, Non-Probability Sampling doesn’t rely on randomization. 

 

This method is highly dependent on the researcher's ability to choose items for a sample. The outcome of sampling may be skewed, making it difficult for all aspects of the population to be included in the sample equitably. This is sometimes referred to as non-random sampling.

 

There are generally four types of Non-Probability Sampling is divided into these four types-

 

1. Convenience Sampling

 

The samples in this case are chosen depending on their availability. This approach is employed when sample availability is limited and also expensive. As a result, samples are chosen depending on their convenience. 

 

This strategy is reliant on the ease with which people can be surveyed, such as mall consumers or pedestrians on a busy roadway. 

 

Because of the simplicity with which the researcher can conduct it and contact the subjects, it is commonly referred to as convenience sampling. Researchers have almost no power over the sample items they choose, and they do so only on the basis of proximity rather than representativeness. 

 

When gathering feedback, this non-probability sampling method is employed because of time and expense constraints. Convenience sampling is utilized in instances where resources are limited, such as the early phases of research.

 

 As an example: This is used by researchers throughout the early phases of survey research since it is rapid and straightforward to produce data.

 

2. Purposive Sampling

 

Convenience sampling, sometimes known as incidental sampling, is a non-probability sampling method used at the discretion of the researcher. 

 

The researcher selects respondents whenever and wherever they are encountered. When there is a time constraint or specific elements of the population are difficult to locate, this sampling approach is used.

 

3. Quota Sampling

 

This sort of sampling is based on a predetermined standard. It draws a representative sample from the entire population. The proportion of characteristics/traits in the sample should be the same as in the population. 

 

Elements are chosen until correct quantities of particular sorts of data are achieved, or until adequate data in various categories is gathered.

 

This strategy, like the probability-based stratified sampling method, seeks to establish a distribution over the target population by designating who should be recruited for a survey based on specific categories or criteria. 

 

For example, your quota could include a specific amount of males and females, or persons from specific age ranges or ethnic groupings.

 

4. Referral or Snowball Sampling

 

This strategy is employed when the population is entirely unknown and scarce. As a result, we will enlist the assistance of the first element chosen for the population and ask him to identify other elements that will suit the description of the sample required. As a result of this referral approach, the population grows like a snowball.

 

For example, suppose there is a survey regarding covid patients. If we go on asking people about their covid positivity, there is a chance that most of them will not tell us about it. Most of them will not be able to talk about it openly.

 

In that case, we use the Snowball technique. To know about the exact numbers, we contact their relatives or volunteers or doctors, or any person which can help us gather information. This technique is used when we don’t have access to sufficient people with the desired characteristics. 

 

Also Read | Types of Data in Statistics

 

Conclusion

 

Sampling helps a lot in surveys and research, where we have to take a sample from a large population. Different techniques of Sampling are there to give different types of desired results. 

 

In this blog, we learned about different Sampling techniques and how they are used. On the end note, we must keep in mind that Sampling techniques should be used according to the case taken, keeping in mind this case we must use the desired sampling techniques.