“Sampling” what comes to our mind when we hear this word? We think about surveys and research and large populations. And why exactly do we think about them? The reason behind this sums up the main definition of “sampling”. Let us try to discover more about sampling.
Sampling is a statistical analysis technique in which a preset number of observations are drawn from a larger population. The process used to sample from a broader population varies according to the sort of study being conducted, however, it may include simple random sampling or systematic sampling.
Now, to understand this, let us try to understand the two terms related to it. Sample and population.
A population is a group of items that share one or more characteristics. The population size is determined by the number of elements in the population.
A sample is a subset of a population. Sampling is the process of picking a sample. The sample size is determined by the number of items in the sample.
For example, suppose we have to choose all the lawyers from a crowd standing at some random street. The crowd here is our population and the number of lawyers is the sample. The process involved in picking out our sample from that population is what we call sampling.
In this blog, we are going to talk about the types of sampling, i.e the different ways in which we separate out our sample from the population.
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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.
Choosing a sample takes less time than choosing every item in a population.
Sample selection is a low-cost strategy.
A sample analysis is less time-consuming and more practical than a population analysis.
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There are different sampling methods. All of them are grouped into two main categories-
Probability Sampling
Non-Probability Sampling
Sampling techniques
This Sampling methodology uses randomization to ensure that every member of the population has an equal probability of being included in the selected sample. It is also known as random sampling.
Following are the types of Probability 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.
For example, out of 100 people, 20 are selected randomly. Here each person has an equal probability of getting selected. The probability of selection is 1/100.
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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.
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.
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.
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.
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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.
We divide our population of size N into k subgroups for a sample of size n. Our first element is chosen at random from the first subgroup of k elements.
To pick different example elements, we do the following:
We know that the number of elements in each group is k, which equals N/n.
So, if n1 is our initial element, then
The second element is n1+k, which equals n2.
The third element is n2+k, which equals n3, and so on.
Using N=20 as an example, n=5
We randomly select the first element from the first subgroup.
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.
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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.
Non-Probability Sampling is divided into these four types-
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.
As an example: This is used by researchers throughout the early phases of survey research since it is rapid and straightforward to produce data.
This is dependent on the objective or goal of the investigation. Only those components from the population that are most appropriate for our research will be chosen.
For example, if from a group of people we have to select people to form a football team, we would ask a question to them, that would be “do you play football?”
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If their answer is “No”, then automatically, they will be excluded from our sample.
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.
For example, if our population is composed of 65 percent females and 35 percent men, our sample should be composed of the same proportion of males and females.
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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.
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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.
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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.
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