One of the biggest hurdles they face in data analytics is processing vast amounts of data. When conducting research on a specific demographic group, it would be impractical and impossible to study the entire population. So how to overcome this problem? Is there a way you can select a subset of the data that represents the whole dataset? As it turns out, there is. In data analysis, there are various different types of sampling techniques that you can use for research without having to examine the entire data set. Before we start with the sampling techniques in data analysis, let us first understand What is Sampling ?
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What is Sampling?
Sampling is a technique of statistical analysis in which a predetermined number of observations are drawn from a larger population. The process used to sample from the wider population varies by study. However, it may involve simple random sampling or systematic sampling.
Let’s try to comprehend two terms that are related to understanding this: Population and Sample.
A population is a collection of things with one or more shared traits. The quantity of population elements determines the population size.
An element of the population is a sample. Taking a sample is the act of sampling. The number of items in the sample will determine the sample size.
Consider the situation where we must choose all lawyers from a group of people gathered on a street. The crowd is our population, and the number of lawyers is the sample. The process of selecting our sample from this population is called sampling.
The Need for Sampling
Sampling is used to make inferences about populations based on samples.
It allows us to identify features of a population by directly seeing only a subset (or sample) of the population.
Selecting a sample takes less time than choosing each item in the file.
Sampling is a low-cost strategy.
The analysis of samples takes less time and is more useful than population analysis.
Different Types of Sampling Techniques
After choosing your sample size, you must select the appropriate sampling method to select a representative sample from the population. Ultimately, each type of sampling falls into two broad categories:
A.Probability Sampling Methods
Every member of the population has a chance of being chosen in a probability sample. Mostly quantitative research uses it. If you want to get findings that are typical of the entire population, probability sampling techniques are the best option.
There are four primary types of probability sampling.
1. Simple Random Selection
Each person in the population has an equal probability of being chosen in a simple random sampling. To carry out this kind of sampling, you can utilize instruments like random number generators or other methods that solely rely on chance.
For illustration, use random selection.
100 employees of Company X should be chosen randomly from a sample size of 100. Each employee in the company database is given a number between 1 and 1000, and 100 are chosen randomly using a random number generator.
2. Consistent Sampling
Unlike a simple random sample, systematic sampling is typically more straightforward. Each person in the population is assigned a number, but people are chosen at predetermined intervals instead of randomly assigning numbers.
For instance, systematic sampling.
An alphabetical list of every employee of the company is provided. You randomly choose the number 6 as your starting point from the first 10 digits. Every tenth individual on the list is chosen, starting at number 6 (6, 16, 26, 36, etc.), creating a sample of 100 persons.
If you use this technique, it is essential to ensure that there is no hidden pattern in the list that could bias the sample. For example : suppose the HR database groups employees by the team, and the team members are listed in order of seniority. In such a case, there is a risk that your interval may skip people in lower roles, biasing the sample towards higher-ranking employees.
3. Stratified Selection
Stratified sampling entails splitting the population into subpopulations that might have substantial differences. Making sure that each subgroup fairly represents the sample enables you to reach appropriate conclusions.
To apply this sampling technique, you separate the population into smaller groups (referred to as strata) based on an important attribute (e.g., gender, age range, income group, job role).
Based on the proportions of the entire population, you determine how many people ought to be chosen from each category. Then, you select a sample from each grouping using random or systematic sampling.
Example: Stratified sampling
There are 200 and 800 employees at the business. You divide the population into two strata depending on gender to ensure the sample accurately reflects the gender balance in the community. Then, using random sampling, you select 20 men and 80 women from each group, yielding a representative sample of 100 people.
4. Group Sampling
The population is divided into smaller groups for cluster sampling, but each subset should represent the whole sample. Instead of sampling individuals from each subset, you randomly select exclusive subgroups.
If practicable, you may include every individual from each sampled cluster. If the groups are large, you can also sample individuals from each cluster using the above techniques. This is called multistage sampling.
This method is suitable for working with large and dispersed populations, but there is a greater risk of sampling error because there may be substantial differences between clusters. It is difficult to guarantee that the selected files represent the entire population.
Example: Cluster sampling
The business has locations in ten different American cities (all with roughly the same number of employees in similar roles). Since you can’t visit every office to gather data, you randomly select 3 offices as your clusters.
Unlike probability sampling, non-probability sampling does not rely on randomization.
This method relies heavily on the researcher’s ability to select items for the sample. The result of sampling can be biased, making it difficult to reasonably include all aspects of the population in the sample. This is sometimes called non-random sampling.
Non-Probability Sampling is Divided into these Four Types.
1. Convenient Sampling
In this case, samples are selected depending on their availability. This approach is used when sample availability is limited and also expensive. As a result, samples are selected based on their convenience.
As an example: This is used by researchers in the early stages of exploration because it is quick and straightforward to generate data.
2. Purposive Sampling
This depends on the goal or objective of the investigation. Only those components of the population most suitable for our research will be selected.
For example: if we have to select a group of people to form a football team, we ask them the question: “do you play football?”
If their answer is “No”, they will automatically be excluded from our sample.
3. Quota Sampling
This kind of sampling is based on a predetermined standard. It selects a representative sample from the entire population. The sample’s proportion of traits/traits should be the same as in the population. Elements are selected until the correct amount of specific data is reached or until adequate data is collected in different categories.
For example: if our population contains 65 percent women and 35 percent men, our sample should be an equal proportion of men and women.
4. Referral or Snowball Sampling
This strategy is used when the population is entirely unknown and rare. As a result, we ask the first element selected for the population to help identify other aspects that will fit the desired description of the sample. The population is growing rapidly as a result of this referral strategy.
Consider a poll regarding COVID patients as an illustration. There’s a probability that most people won’t answer our questions concerning their COVID positive if we ask them repeatedly. The majority of them won’t be able to discuss it freely.
In that situation, we employ the Snowball method. We get in touch with their relatives, volunteers, physicians, or anybody else who can provide information to find out the precise figures.
This technique is used when we cannot access a sufficient number of people with the desired characteristics.
Conclusion
Sampling is quite helpful in surveys and studies where we must select a sample from a big population. Different sampling methods deliver different kinds of desired outcomes.
This blog taught us about several sampling methods and their applications. In conclusion, we must remember that sampling techniques should be used according to the chosen case. With this case in mind, we must use the required sampling techniques.
