When you’re planning a **research** study, sampling techniques are one of the key considerations. It is essential to identify the correct sample size for your study and conduct appropriate sampling techniques so that the data collected is relevant and valid.

Researcher need to choose from various methods of sampling before they can begin their **research** project. These include simple random sampling, stratified sampling, cluster sampling and quota sampling. Each technique has its own benefits in different contexts and you will need to decide which is most suitable for your needs.

Read on to learn more about these sampling techniques in **research.**

**What is Sampling?**

Sampling is the process of taking a part of a larger group and studying it as though it were the whole group. This allows researchers to make inferences about the larger group based on their findings.

There are two main types of sampling techniques:

- Probability Sampling

- Non-Probability sampling

Lets study these two types of sampling techniques in detail.

Probability sampling gives you an idea of how likely you are to come up with a sample that is representative of the larger group.

Non-probability sampling techniques are useful when you’re not able to use probability sampling.

**Probability Sampling Methods**

Probability sampling is an approach that lets you estimate how accurately your sample will represent the larger group. This is done by calculating the probability that each individual in the larger group has the same chance of being selected in the sample.

There are several types of probability sampling, each of which can be used in different contexts. Fortunately, they’re all fairly straightforward to understand.

The most common types of probability sampling are:

- Simple random sampling

- Stratified sampling

- Cluster sampling

- Systematic Sampling

**Simple Random Sampling**

In simple random sampling, every member of the larger group has an equal chance of being included in the sample. This means that each participant has an equal chance of being selected, regardless of other factors.

Simple random sampling is sometimes referred to as “chance” sampling because it relies on chance to determine which individuals are included in the sample.

If a researcher uses simple random sampling, they will first create a list of all the individuals in the larger group.

Next, they put a number next to each name on the list. These numbers are then used to randomly select sample members.

Simple random sampling is a good choice when you want to ensure that your sample is representative of all members of the larger group. This is particularly important when the sample is small.

Simple random sampling is the selection of subjects based on the selection of every nth individual from a list of all possible study participants. This method is particularly useful when you have a large population and a readily available list of all individuals who fall within the study parameters.

For e**xample**, if you are studying online users in the United States, you can simply select every third user from a readily available list of Internet users in the United States, such as a list of email subscribers.

**Stratified Sampling**

In stratified sampling, the researcher first divides the larger group into subgroups. This is done to ensure that each subgroup is proportionally represented in the sample.

Stratified sampling is commonly used with groups that have uneven representation in the sample.

For **example**, if you’re trying to collect data from a group of employees, you may find that one department has a disproportionate number of members. This means that they will be overrepresented in the sample. To correct this, you could use stratified sampling. In this case, you would first divide the employees into their departments. Then, you would randomly select a number of members from each department. This would help to balance out the representation in the sample. Stratified sampling can also be used to reduce risk in certain situations.

For **example**, let’s say that you want to collect data from a remote area in the western part of the country. You may not be able to access this area easily, which could make it difficult to find a representative sample. In this case, you could use stratified sampling to select members from different areas within the country. This would make it easier to find a representative sample.

For **example**, if your study is on the effectiveness of a new drug, you could divide your population into two groups:

**those over 40 years old and those under 40. **

Next, you could sample subjects from both subgroups by random selection.

**Cluster Sampling**

In cluster sampling, the researcher first identifies “clusters” of individuals within the larger group. These clusters of individuals are then included in the sample.

Cluster sampling is most often used when studying rare subgroups within a larger group.

For **example**, if you’re studying rare or endangered species, it can be difficult to find a representative sample. In this case, it may be easier to identify smaller groups within the larger population and study them instead.

It’s important to note that cluster sampling is often costlier than other sampling techniques. This is because it requires researchers to visit each “cluster” in the larger group to collect data.

For **example**, if your study is on the effectiveness of a new drug, you could select a group of, say, 100 doctors and randomly select 10 of them to serve as subjects in your study.

#### Systematic Sampling

Systematic sampling, is similar to simple random sampling, but it is usually slightly easier, to conduct. Every member, of the population, is listed, with a number, but instead of randomly generating numbers, individuals are chosen, at regular intervals. Systematic sampling is the selection of every nth individual from a list of all possible study participants.

For **example**, if you are studying employees in a company and have their contact information, you can simply select every third employee from the list.

Another **example **is, the company’s employees are all listed in alphabetical order. You choose number 7 as your starting point at random from the first 10 digits. Every tenth individual on the list is chosen to start at number 7 (7, 17, 27, 37 and so on), creating a sample of 100 persons.

**Learn more:** **Research Methods: Definitions, Types**

### **Non-Probability Sampling Methods**

The non-probability sampling methods are those in which the sample is selected based on some specific criteria or methods. As such, these samples do not meet the basic requirements of random or representative sampling.

While non-probability sampling methods can be useful in particular contexts, researchers should use them judiciously, given that the resulting samples are not representative and therefore cannot be generalized to the broader population.

#### Convenience Sampling

Convenience sampling is the selection of study participants based on ease or convenience of access. In other words, convenience sampling involves sampling subjects who are easy to access, such as people who live nearby or who work in the same office.

For **instance**, you may ask your classmates to fill out a **survey** regarding student support services in your university after each of your classes in order to gather their comments. This is a convenient approach to obtain information.

#### Judgement (or Purposive) Sampling

Judgement (or purposive) sampling is the deliberate selection of study participants based on the researcher’s pre-determined criteria.

This sort of sampling, also known as judgement sampling, entails the researcher choosing a sample that is most beneficial to the research’s objectives by using their experience.

It is frequently employed in **qualitative research** when the researcher prefers to learn in-depth information about a particular phenomenon than drawing general conclusions from statistics.

To learn more about the perspectives and experiences of handicapped students at your university, for **instance**, you deliberately choose a number of students with various levels of support requirements in order to collect information on a range of topics, including their interactions with student services.

Another **example** is, if your study is on the effectiveness of a new drug, you may deliberately choose patients who are likely to respond positively to the drug.

**Learn more:** **Qualitative Research: Types and Methods**

**Quota Sampling**

This differs from other types of sampling in that the researcher doesn’t choose the sample members.

Instead, the individuals themselves choose whether or not to participate in the study.

Quota sampling is used in situations where the researcher doesn’t have control over who participates in the study. This can occur in certain industries, such as marketing **research** or journalism, where sample members are self-selected. In this case, the sample members choose whether or not to participate in the study.

Quota sampling is often used when sample members are likely to be biased or may not represent the larger group.

#### Snowball sampling

Snowball sampling is a variation of judgement (or purposive) sampling.

During snowball sampling, the researcher asks each study participant to identify other people who meet the study criteria and then samples from that group.

Snowball sampling can be used to find participants if the population is difficult to reach by using existing participants as recruiters. As you interact with more people, the number of people you have access to “snowballs” in size.

For **example**, if your study is on the effectiveness of a new drug, you might ask your first patient to identify other patients who are also taking the drug.

Another **example** , you might, be looking into local homeless people’s experiences. Probability sampling is not feasible because there is no comprehensive list of all homeless people in the city. One person you meet agrees to take part in the study, and she connects you with other local homeless persons she knows.

### Cons of Non-probability sampling methods

Researchers should be judicious in their use of non-probability sampling methods given that the resulting samples are not representative and cannot be generalized to the broader population.

In other words, non-probability samples cannot serve as a basis for generalizing findings to a broader population because the selection of the sample was not based on chance.

Non-probability sampling methods are often referred to as sampling bias, with the bias stemming from the fact that the sample is not representative of the population from which it was drawn.

Researchers using non-probability sampling should be careful to acknowledge the limitations of their findings by making appropriate statements about generalizability and discussing the selection bias that resulted in the sample being different from the broader population.

**Learn more:** **What is Quantitative research? Types, Pros, and Cons**

**Bottom Line**

In the field of **research**, there are two types of sampling techniques:

First is probability sampling, the researcher can calculate the likelihood that a sample will accurately represent a larger group.

Second is non-probability sampling, the researcher cannot accurately predict whether a sample will accurately represent a larger group.

Having a clear understanding of the different types of sampling techniques will help you to select the best technique for your study. This will ensure that your findings are reliable and valid and will allow you to make strong inferences about the larger group based on your sample findings.

It’s important to remember that the type of sampling technique you use will have an impact on the cost and time of your study.

## Other articles

Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.

**Statistics**

** Methodology**

- Research Methods
- Quantitative Research
- Qualitative Research
- Case Study Research
- Survey Research
- Conclusive Research
- Descriptive Research
- Cross-Sectional Research
- Theoretical Framework
- Conceptual Framework
- Triangulation
- Grounded Theory
- Quasi-Experimental Design
- Mixed Method
- Correlational Research
- Randomized Controlled Trial
- Stratified Sampling
- Ethnography
- Ghost Authorship
- Secondary Data Collection
- Primary Data Collection
- Ex-Post-Facto

** Research **

- Table of Contents
- Dissertation Topic
- Synopsis
- Thesis Statement
- Research Proposal
- Research Questions
- Research Problem
- Research Gap
- Types of Research Gaps
- Variables
- Operationalization of Variables
- Literature Review
- Research Hypothesis
- Questionnaire
- Abstract
- Validity
- Reliability
- Measurement of Scale
- Sampling Techniques
- Acknowledgements

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