Sampling

Introduction

Economists, statisticians, and market researchers, among others, need to learn various attributes of a population that lives in certain demography. However, it may not be possible for them to collect data from each individual due to the size and volume of data and/or population. In such a circumstance, sampling of the population under study is done.

What is Sampling?

Sampling is a statistical technique where members or subsets of populations are selected to get inferences about the whole population regarding a matter of study. Sampling is useful because it may not be possible for the researchers to study the characteristics of each and every member of a demographic system. That is why selected members or a subset of the population is selected for research and the idea for the entire demography is formed after following various statistical measures.

The idea of sampling is valuable in market research and economics because depending on sampling, the researchers make insights that lead to action. That is why sampling must be accurate. In order to get actionable insights, various types of sampling techniques are available.

Major Types of Sampling

The two major types of sampling are probability sampling and non-probability sampling which are further divided depending on their nature.

• Probability Sampling: In probability sampling, researchers set a few selection criteria and then select the members of the sampling population randomly. In such a sampling method, all members have an equal opportunity to be part of the sample.

• Non-probability sampling: In non-probability sampling, the members are selected randomly, but there are no predefined or pre-fixed criteria for selection. In such sampling methods, all types of populations from demography cannot have an equal opportunity for participation.

Types of Probability Sampling

In probability sampling use of the theory of probability takes place. As all members get an equal opportunity, probability sampling has no bias.

• Simple random sampling It is one of the most useful yet very simple processes of probability sampling. It is reliable and information can be obtained from each member of the population in simple random sampling.

  In this type of sampling, each member of the population is selected randomly. So, each individual in the population has an equal chance to be part of the population that is sampled arbitrarily.

• Cluster sampling: In cluster sampling, the sampling data is classified into various clusters or groups. Clusters are formed and identified by demographic variables, such as age, sex, location, etc. It helps the researchers gain insight into specific populations very easily as the data is already classified into actionable groups.

• Systematic sampling: In systematic sampling, the members of the sampling population are chosen after regular intervals. In this type of sampling, the selection starting point and the interval must be applied to get accurate data after each interval. Systematic sampling has a predefined range and consumes the least amount of time.

• Stratified random sampling: In stratified random sampling, the population is divided into some strata that do not overlap each other but cover the entire range of the population. It is built in such a manner that every member of the population will fall into one stratum. These strata can finally be compared to check the findings, such as which stratum has the highest population under its coverage, etc.

Types of Non-Probability Sampling

Non-probability sampling is carried out by collecting samples from the researcher. It is less time-consuming and does not cost too much.

However, the chances of non-probability sampling being skewed are more than probability sampling.

• Convenience sampling: Convenience sampling is done by the researchers in places such as busy streets and malls to collect easy data from a purely random population. It is based more on proximity than authority and representation. It is a valuable sampling method when time and cost constraints exist.

• Purposive sampling: Also known as judgmental sampling, this type of research matches the requirements of respondents with the required samples of the research. If there is a mismatch, the respondent is not included in the research. For instance, when a respondent answers “no” to a specific question in the research questionnaire, he/she may be omitted from the research.

• Snowball sampling: Snowball sampling is used to track extremely sensitive and confidential topics. This technique uses third parties who may be able to provide information about the required sample.

  Using snowball theory, the researchers may be able to interview a few selected categories and derive results from them.

• Quota sampling: As the name suggests, in quota sampling, a quota with all the attributes of the required population is selected and tested for the entire population. This method of sampling is used for rapid sample collection.

Importance of Sampling

Sampling has an overwhelming influence on data collection and analysis.

Sampling is used to derive results that are meant for a wide range of populations

In fact, it is impossible for researchers to collect data from each individual in a large economy. So, smaller groups must be formed efficiently to get the desired and accurate results that suit the entire population.

Sampling is invaluable in many fields of study

As smaller groups of populations are considered to get larger economic results, sampling offers insights into economies in a less costly and efficient manner. Researchers can use sampling in various ways. It is used in almost all fields of science to get accurate insights that are actionable and perspective-based.

Sampling is also quite important in Economics

Various calculations of economic studies are based on sampling. Without sampling, economics will be incomplete. For example, in considering the demand for wheat in a market, sampling the populations who use wheat can be implemented. This will offer a better insight into the demand and needs of the market.

Sampling indirectly helps to realize the demand of markets

For countries like India, estimating the needs of the people in various parts of the country would be impossible to assume. In such circumstances, sampling can be used to better understand the needs and wants of the populations. This indirectly helps to realize the demand of markets on which marketing departments of companies can rely.

Sampling is widely used in healthcare

Sampling is widely used in healthcare and it helps scientists prepare invaluable medications and life-saving drugs. Sampling is therefore an indispensable tool in many schools of thought and its importance cannot be ignored in the daily lives of people.

Conclusion

As a statistical tool of estimation, sampling plays a vital role in keeping societies and communities intact. The role of sampling in economics cannot be ignored too. It is a science that helps procure data from large populations in an easy and affordable manner. That is why, sampling will remain an indispensable tool for economists to run economies smoothly without having to take large groups of populations under consideration. Sampling is, therefore, important for social well-being too.

FAQs

Q1. What are the two different types of sampling methods used to estimate the characteristics of populations?

Ans. The two main types of sampling methods that are used for the estimation of characteristics of large populations are probability and non-probability sampling.

Q2. What is the biggest advantage of sampling?

Ans. The biggest advantage of sampling is that it can be used to find the characteristics of a large population by taking into consideration a smaller one. Therefore, researchers can focus on a small group population to identify characteristics of the original, large population.