Statistical research allows for a wide range of conclusions to be drawn based on the experts’ requirements. A specific purpose is served by using the data collected in this way. Different sampling techniques and methods can be used in statistics to collect data.
Statistical research, however, uses a particular sampling technique depending on its objective. There are two types of statistical research:
In this article, we will discuss what sampling is and the different types of sampling techniques.
Sampling refers to identifying individual members of the population or a subset of the entire population to make statistical inferences from them and estimate the entire population’s characteristics based on the results. In order to collect actionable insights for their clients, market research researchers use various sampling techniques and methods to avoid having to conduct research on the entire population.
The use of the various sampling methods is also time-saving and cost-effective, which makes it an excellent sampling technique in research design. For an optimal derivation of survey results, research survey software can be used to implement the sampling techniques.
For Example: Think about the case where you want to conduct some research on everyone in India, and it would be nearly impossible to ask everyone. A survey across multiple states and in different languages, followed by the collection and analysis of the results, would take a long time and be very expensive, even if everyone said “yes.”
By using a smaller number of individuals to represent the whole population, sampling allows large-scale research to be conducted at a lower cost and in a shorter time frame.
We will discuss in detail the different types of sampling techniques and methods further in this article, but first, we’ll discuss the difference between population and sampling.
As the name implies, the “population” refers to the entire group from which you would like to draw conclusions. In “sampling,” you will collect data from the sample of individuals you have selected based on your research questions.
Various characteristics of a population can be described, including the area of residence, income, age, and a number of other characteristics. There are many kinds of research projects: some can be very broad, while others can be very narrow.
For example, you may want to draw inferences about the entire adult population of your country; you may be investigating the customers of a specific company, patients with a specific health condition, or students in a particular school.
Defining the target audience of your project is essential based on the purpose and practicalities of the project as well as your project’s objective.
It may be difficult to get a representative sample of the population if it is demographically mixed, very large, and geographically scattered throughout the country.
It is imperative to select the correct method of sampling if you want to draw valid conclusions regarding the population. In estimating a population’s parameters, it is used to refer to the entire group of individuals. The purpose of a sample is to represent a subset of a population in a way that represents the characteristics of that population as a whole as it represents the characteristics of the subset. A sampling technique is one of the most prominent methods that are used in statistics. The following are some of them:
Now, we will discuss a wide range of sampling methods, both probability-based and non-probability-based, which can be used for a variety of market research studies.
The different types of probability sampling techniques and methods are as follows:
Simple Random Sampling
The simple random sampling technique can be described as the process of selecting all members of a population at random, with equal chances of being chosen by all members of the population. As a matter of fact, there is no way to control the outcome of a selection made using this sampling method. Various methods can be used to administer a sampling technique like this, including random number generators or other methods based solely on a chance to conduct this process. It is also considered the most widely used sampling method.
Example: A 500-employee organization is most likely to prefer picking chits from a bowl as team-building activities if the HR team decides to arrange them. There is an equal opportunity for each of the 500 employees to be selected in this case.
It is a method of sampling in which the researcher divides the entire population into subgroups called clusters. Ideally, each subgroup should have the same characteristics as the whole sample in order to be considered similar. Rather than selecting individuals at random from an entire group, the researcher chooses clusters from the whole group to form the sample. As a result of such a method of sampling, it is often used for large populations. However, it has a higher probability of error due to the fact that each cluster may contain significant differences with regard to the others.
Example: Residents in different states can be clustered if the government wants to evaluate the number of immigrants living there. It will be more effective to conduct a survey this way because results will be organized by state and provide insightful information about immigration.
As part of systematic sampling, each population entity is assigned a number, and individuals are randomly selected at regular intervals from this population to collect data. In relation to such a sampling technique, there is a predefined range, a set starting point, and the ability to repeat the sampling size at regular intervals throughout the sampling range.
Example: An experiment will be conducted on 500 people out of a population of 5000. The researcher will select every 10th individual from the population, numbering each element from 1-5000.
Stratified Random Sampling
Researchers stratify a population using stratified random sampling by dividing it into non-overlapping subgroups. On the basis of the population proportions, the researcher calculates the number of entities to sample from each subgroup. Each subgroup is then selected separately by either simple or systematic random sampling. In this way, each subgroup is accurately represented.
Example: Strata (groups) will be created based on the annual family income of a researcher who is analyzing the characteristics of people in different income divisions. The researcher is able to conclude the characteristics of people from different income groups by doing this. It is possible for marketers to create a roadmap that yields fruitful results by analyzing the income groups to target and eliminating the ones that are not productive.
The different types of non-probability sampling techniques and methods are as follows:
During the convenience sampling technique, the subjects are asked for data based on the ease with which they can access it. To put it another way, a sample is comprised of entities that the researcher can easily access. In order to gather initial data cost-effectively, this sampling method is used. Although this sampling technique is widely used, the data it produces may not be representative of the entire population.
Example: Startups and NGOs will utilize convenience sampling at mall entrances to distribute leaflets about upcoming events or promote a cause.
When researchers need data for a specific purpose, judgmental or purposive sampling is used to gather data for that specific purpose. The researcher is able to decide from which target audience the sample will be drawn, but the decision will depend on the research question. An analysis of a phenomenon is done using this method of sampling when it is necessary to obtain detailed information about that phenomenon.
Example: Imagine a situation where a researcher wishes to find out what students with disabilities experience at school. For the purpose of collecting this data, he/she will focus on asking only those students who are physically disabled to share their experiences with her.
As a type of non-probability sampling, snowball sampling is a method where the researchers cannot easily access the subjects during the sampling process. In this instance, they may either monitor a few categories for interviews or recruit participants with the help of other participants. This sampling technique is very effective when it comes to studies based on sensitive topics or involving many difficulties, such as surveys.
Example: Surveys to gather HIV Aids information. It is unlikely that many victims will be willing to answer the questions. Researchers can still collect information from victims by getting in touch with people they know or volunteering for the cause.
This sampling method uses a set of standards to create the samples so that they are based on the same attributes as the entire population and will have the same characteristics.
Example: An example of quota sampling would be to select five men, five women, ten girls, and ten boys to determine the average number of hours they watch television per day.
Hoping that you are now well aware of various sampling techniques and methods that are used in statistics, along with their examples. Data sampling is a statistical analysis approach that is used to choose, modify, and analyze a representative selection of data points to find patterns and trends in the broader data set being examined. Its scope in Data Analytics is tremendous. You surely need to check out the UNext Jigsaw PG Certificate Program in Data Science and Machine Learning to push start your career in the field of Data Science!