What is Sampling Theory? Definition, Terms, and Applications

Introduction 

As a statistical technique, sampling theory falls under the category of statistical analysis. This theory was formulated in the year 1928. It is hard to manage all the data of each individual when research is conducted on a group of people. Here, the sampling theory concept comes into play, and there is its relevance. This article aims to open up a discussion about sampling theory to our readers and help them understand what it is all about in a detailed manner. 

In the latter part of this article, you will find the sampling theory definition, types of sampling theory, sampling theory and applications, etc. 

What Is Sampling? 

Statistics begins with creating a sample set from an entire population set before any estimation can be done. The sample set can be viewed as the tree branch in which the actual analysis of observations and estimation of data sampling is done. Population Set could be viewed as the entire data collection tree. 

The population tree is a very large set; analyzing observations made on it can be very challenging, both from a time and a financial standpoint. The sample set is thus created from the population set in order to save time and resources. 

Types of Sampling 

There are three different sampling types in statistics that are discussed below: 

  • Simple Random Sampling: As a form of probability sampling, simple random sampling is a sample taken from a population by the researcher randomly selecting a subset of survey participants from that population. The chances of being selected are the same for every member of the population. After identifying a random subset of the population, data is collected from as large a percentage as possible of this selection. 
  • Systematic Sampling: A systematic sampling method randomly selects sample members from a larger population but with a fixed, periodic interval based on a random starting point. The sampling interval is calculated by dividing the population by the sample size. 
  • Stratified Sampling: This sampling method is known as stratified random sampling because it involves dividing a population into smaller groups known as strata to perform the sampling. Stratified random sampling, also called stratification, is a form of sampling where individuals are separated into groups according to their shared attributes or characteristics, such as their educational attainment or income. 

How Is Sampling Used? 

Now let us discuss how sampling theory is used through the process of sampling: 

  • A population set needs to be identified. 
  • The size of our sample set is determined based on the number of participants in the study. 
  • Providing a medium on the basis of which samples are selected from a population based on certain selection criteria. 
  • Sampling can be performed using any of many different methods, such as Simple Random Sampling, Systematic Sampling, or Stratified Sampling, to pick out samples from the medium. 
  • It should be checked that the sample set that is formed includes elements similar to the different attributes of the population set, with no large variations between them. 
  • Ensure that the sample set has been formed correctly and that there aren’t errors or incorrect estimations that may have happened during the sampling process. 
  • As a result of performing the above steps, we will get a set of data that we can use as part of the Sample Set. 

What Is the Purpose of Sampling? 

A sample may be particularly helpful when analyzing large data sets that cannot be analyzed in their entirety — such as in big data analytics applications or surveys — because the data sets are too large to be analyzed efficiently. A representative sample of your target audience can be identified and analyzed much more efficiently and cost-effectively than surveying the entire data set or population of your target audience as a whole. 

As important as the sample size required, it is important to remember that it is possible to introduce a data sampling error into the data if the sample is too small. When it comes to analyzing a data set, sometimes the most significant information can be gleaned from a small sample. However, there are instances where a larger sample can be a more likely indicator of accurately representing the data overall, even if the larger sample size may hinder the ease of interpretation and manipulation. 

Why Is Sampling Important for Researchers? 

The experience of working on a research project will tell you that there are a limited amount of resources, including money, time, and people; none of these are unlimited in supply. Therefore, it is common for research projects to aim at collecting data from a sample of people rather than from the entire population (the census is an exception to this rule). The reason for this is that researchers can use sampling to: 

  • Save Time 

It takes a lot of time and effort to contact every single person in a population. As it turns out, it is inevitable that some people will not respond to the first attempt to contact them, meaning that researchers have to spend additional time on follow-up in order to get their responses. It is much easier and faster to get a random sample of a population than to survey the whole population, and it is almost always easier and faster to obtain a non-random sample than a random sample. Consequently, researchers benefit a lot from sampling because it saves them a lot of time. 

  • Save Money 

In order to determine the cost of a study, it is necessary to know how many people a researcher contacts. By utilizing a sample of the population, researchers are able to gather the same answers from a sample of the population that they would be able to gather if they used the full sample. Compared to random sampling, non-random sampling is significantly cheaper since it does not require the same expense to locate people and collect data from them as random sampling does. In order to conduct research on a budget, it is important to save money wherever possible. 

  • Collect Richer Data 

It is not unusual for researchers to set out to gather a little bit of data from a large number of people (e.g., a poll) as part of their research. Other times, however, the purpose is to gather the most information from a relatively small number of people (e.g., an ethnographic interview or a user study). Regardless of the type of sampling used, the researchers are able to ask participants more questions and collect richer data than they would be able to gather if they contacted everyone in the study population. 

The Importance of Knowing Where to Sample 

 Using an efficient sampling method of sampling theory, researchers can gain benefits by utilizing an efficient sampling method. Knowledge of how to sample is important. Some participants are more suitable than others for taking part in a research project. Researchers need to find participants that will be able to contribute to the research project. 

Consider the case of an online research project, for instance. A research team can select several sources of participants when deciding to conduct a study online. The sample size provided by some sources is random, whereas the sample size provided by many others is not random. As a researcher, you have a wide range of options when it comes to selecting a non-random sample. 

An online panel has a number of advantages that have made it especially well-suited for some studies, as it offers access to millions of people across the world. Some studies, however, are more appropriate for crowdsourced sites since they usually have fewer participants overall, but they have more flexibility when it comes to fostering participant involvement during the study. 

In order to give a better sense of the types of online samples, researchers may choose to use, let’s look at a few use cases where different online samples might be used. 

Different Use Cases of Online Sampling 

  • Academic Research 

Academic researchers collect online samples in various ways. The probability sampling method can be used in some projects to select random samples. Non-random samples are used in most other projects. Using online panels, researchers can selectively target members of specific groups or sample a general audience from crowdsourcing websites. Numerous types of online samples can be found in academia due to the variety of research projects. 

  • Market Research 

Customers and potential customers are often the focus of market researchers’ studies. In order to reach tens of millions of people, most market research is conducted online through online panels. In some cases, researchers may be able to get more participant engagement by using crowdsourcing sites, such as Amazon Mechanical Turk, since they allow participants to be selected based on their experience and awarded bonuses. 

  • Public Polling 

Randomly selected samples of the population are the most accurate for public polling. Because of this, a lot of public polling is conducted with samples that are national in scope. However, non-random samples are becoming more common in opinion polls. Following the collection of data, researchers usually adjust for known sources of bias when they survey people using non-random methods. 

  • User Testing 

A website or product must be tested by engaging users. The best way to get participants to engage deeply with a study is to conduct user testing on platforms that allow participants to do so. Users who are willing to participate in a longer study can be rewarded with compensation through crowdsourcing platforms. 

Conclusion 

The concept of sampling is applied in many fields, such as communication, due to its vast implementation. There is a lot of valuable information about sampling theory in all the above information, which constitutes a vital part of that theory. 

If you’re willing to learn more about the sampling theory, you must check out the Data Science course by UNext Jigsaw. 

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