# Different Types of Sampling Techniques in Research

Before we learn the different types of sampling techniques, we should learn what sampling is in research field. Read – What is Sampling in Research? Sample Design in Research: Developing a Sample Design for Research Project.

Sampling techniques in research are divided into 2 categories:

# Probability Sampling Methods:

Probability sampling technique selects random members of a population by setting a few selection criteria. These selection parameters allow every member to have the equal opportunities to be a part of various samples.

For example, in a population of 1000 members, each of these members will have 1/1000 chances of being selected to be a part of a sample. It gets rid of bias in the population and gives a fair chance to all members to be included in the sample.

There are five types of probability sampling technique:

• Simple Random Sampling: It is a trustworthy method of obtaining information where every single member of a population is chosen randomly, merely by chance and each individual has the exact same probability of being chosen to be a part of a sample.

For example, in an organization of 500 employees, if the HR team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected.

• Cluster Sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample on the basis of criteria such as age, location, sex etc.

For example, if the government of the United States wishes to evaluate the number of immigrants in North America, they can divide it into clusters on the basis of states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii etc. This way of conducting a survey will be more effective as the results will be organized into states.

• Systematic Sampling: Using systematic sampling method, members of a sample are chosen at regular intervals of a population. It requires selection of a starting point for the sample and sample size which is then repeated at regular intervals and so, this sampling technique is the least time-consuming.

For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. Each element of the population will be numbered from 1-5000 and every 10th individual will be chosen to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).

• Stratified Random Sampling: A social research organization conducts stratified random sampling to bifurcate the population into non- overlapping, distinct groups (strata) and by randomly selecting members of these strata, online surveys can be conducted.

For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions, will create strata (groups) according to annual family income such as – Less than \$20,000, \$21,000 – \$30,000, \$31,000 to \$40,000, \$41,000 to \$50,000 etc.
People belonging to different income groups can be observed to draw conclusions of which income strata have which characteristics. Marketers can analyze which income groups to target and which ones to eliminate in order to create a roadmap that would definitely bear fruitful results.

• Multi-stage sampling: Multi-stage sampling is a further development of the principle of cluster sampling. Suppose we want to investigate the working efficiency of nationalized banks in America and we want to take a sample of few banks for this purpose.
The first stage is to select large primary sampling unit such as states in a country. Then we may select certain districts and interview all banks in the chosen districts. This would represent a two-stage sampling design with the ultimate sampling units being clusters of districts.

# Non-probability Sampling Methods

This is a sampling technique that involves a collection of feedback on the basis of a researcher or statisticians sample selection capabilities and not on a fixed selection process. In most situations, output of a survey conducted with a non-probable sample leads to skewed results, which may not totally represent the desired target population.
But, there are situations such as the preliminary stages of research or where there are cost constraints for conducting research, where non-probability sampling will be much more effective than the other type.

There are 4 types of non-probability sampling which will explain the purpose of this sampling method in a better manner:

• Convenience Sampling: This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling, as it’s carried out on the basis of how easy is it for a researcher to get in touch with the subjects.

Researchers have nearly no authority over selecting elements of the sample and it’s purely done on the basis of proximity and not representations. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations where there are resource limitations such as the initial stages of research, convenience sampling is used.

For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the entrance of the mall and giving out pamphlets randomly.

• Deliberate sampling: This sample is formed by considering the purpose of study along with the understanding of target audience. Also known as deliberate sampling, the participants are selected solely on the basis of research requirements and elements who do not suffice the purpose are kept out of the sample.

For instance, when researchers want to understand the thought process of people who are interested in studying for their master’s degree. The selection criteria will be: “Are you interested in studying for Masters in …?” and those who respond with a “No” will be excluded from the sample.

• Snowball sampling: Snowball sampling is used studies which need to be carried out to understand subjects which are difficult to trace. For example, it will be extremely challenging to survey homeless people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few of that particular category to interview and results will be derived on that basis.

This sampling method is implemented in situations where the topic is highly sensitive and not openly discussed such as conducting surveys to gather information about HIV Aids. Not many victims will readily respond to the questions but researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.

• Quota Sampling: In Quota sampling, selection of members in this sampling technique happens on basis of a preset standard. In this case, as a sample is formed on basis of specific attributes, the created sample will have the same attributes that are found in the total population. It is an extremely quick method of collecting samples.