different research sampling techniques

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

Knowledge Base

Methodology

Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style
Vague sentences
Style consistency

See an example

Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. 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.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Sampling Methods | Types, Techniques & Examples. Scribbr. Retrieved March 25, 2024, from https://www.scribbr.com/methodology/sampling-methods/

Is this article helpful?

Shona McCombes

Other students also liked, population vs. sample | definitions, differences & examples, simple random sampling | definition, steps & examples, sampling bias and how to avoid it | types & examples, unlimited academic ai-proofreading.

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Buy Me a Coffee

Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

Define the population: Identify the population of interest and clearly define its boundaries.
Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

The population of interest is too large to study in its entirety.
The cost and time required to study the entire population are prohibitive.
The population is geographically dispersed or difficult to access.
The research question requires specialized or hard-to-find individuals.
The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Stratified Random Sampling – Definition, Method...

Purposive Sampling – Methods, Types and Examples

Non-probability Sampling – Types, Methods and...

Cluster Sampling – Types, Method and Examples

Systematic Sampling – Types, Method and Examples

Snowball Sampling – Method, Types and Examples

Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

Sampling : the process of selecting a representative group from the population under study.
Target population : the total group of individuals from which the sample might be drawn.
Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

The advantages are that your sample should represent the target population and eliminate sampling bias.
The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

This is a quick and easy way of choosing participants (advantage)
It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology
Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

Published on 3 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias .
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis.

Population vs sample, probability sampling methods, non-probability sampling methods, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, and many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

You are doing research on working conditions at Company X. Your population is all 1,000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee.

Sample size

Prevent plagiarism, run a free check.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample , every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

2. Systematic sampling

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

3. Stratified sampling

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender, age range, income bracket, job role).

The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

4. Cluster sampling

The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

In a non-probability sample , individuals are selected based on non-random criteria, and not every individual has a chance of being included.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalisable results.

You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

2. Voluntary response sampling

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

You send out the survey to all students at your university and many students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.

3. Purposive sampling

Purposive sampling , also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

You want to know more about the opinions and experiences of students with a disability at your university, so you purposely select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to ‘snowballs’ as you get in contact with more people.

You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people she knows in the area.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Sampling Methods | Types, Techniques, & Examples. Scribbr. Retrieved 25 March 2024, from https://www.scribbr.co.uk/research-methods/sampling/

Is this article helpful?

Shona McCombes

Other students also liked, what is quantitative research | definition & methods, a quick guide to experimental design | 5 steps & examples, controlled experiments | methods & examples of control.

Sampling Techniques: Definition, Types, and Examples

Sampling is an inherent human trait we follow whenever we want to study something, but the domain is huge enough to force us to base our study on a sub-sample only. This is true for most of the studies in the practical world. The whole population is never accessible; even if it is, it’s not worth going through all of it.

Sampling lets us cleverly study the characteristics of a vast population without actually going through it all. Not only does this make the studies quicker and more practical, but it also helps reduce costs considerably. So, today in this article, we will see in-depth all the different techniques one can employ to do sampling. Moreover, we’ll also see how you can choose the best sampling technique depending on your scenario. So, let’s jump in!

Why Do We Need Sampling Techniques?

Research benefits greatly from sampling. It is one of the most crucial elements that affect how accurate your study or survey results are. If your sample contains any errors, the outcome will be affected accordingly. Depending on the situation and necessity, numerous methodologies aid in sample collection. But before diving into the topic, let’s look at some essential statistical terms you might need to remember.

Population:

A population is a group of related objects or occurrences relevant to a particular topic or experiment.

It is the particular group from whom you will get data. The sample size is always smaller than the population as a whole.

Sample unit:

It is the object or person being observed.

Sampling frame:

It is a list of everything in the population that can be observed, whether it be people or other objects.

What is a Sampling Technique?

It is seldom possible to gather data from every member of a group of individuals when conducting research on them. So, what do you do? Well, you pick a sample instead. The population that will actually take part in the study is the sample.

The sampling technique is the method you employ while choosing a sample from a population. For example, you could select every 3 rd person, everyone in a particular age group, and so on. You must carefully consider your study before choosing an appropriate sampling technique. It has a significant effect on your results. For example, some sampling techniques might be intentionally biased. So, selecting a suitable sampling technique is essential to draw accurate conclusions from your data.

What are the different types of Sampling Techniques?

There are two significant types of sampling techniques which are then divided into sub-types:

1. Probability Sampling

2. non-probability sampling techniques.

Let’s go through both, along with their sub-types.

Probability Sampling Techniques

Using a set of predetermined criteria and a random selection of population members, a researcher uses the sampling technique known as probability sampling. With this selection criteria, each member has an equal chance of being included in the sample. Our best shot at producing a sample that is accurately representative of the population and enables us to draw robust statistical conclusions about the entire group is through probability sampling. Random sampling is another name for it. It has four sub-divisions:

1. Simple Random Sampling Technique:

Every person in the population has an equal probability of getting chosen in a simple random sampling. The entire population should be part of your sampling frame. The Simple Random Sampling method is one of the top probability sampling approaches that aid in time and resource conservation. It is a reliable way to gather information.

The fact that this method is the most straightforward for probability sampling is a significant benefit. It does, however, come with a disclaimer: it might not choose enough people who fit our criteria. We use it when we don’t know anything about the target population beforehand.

A company has decided to give a bonus to 10 of its employees. These employees will be selected randomly through any method from the whole company.

2. Systematic Sampling Technique:

In systematic sampling , the first person is chosen randomly, and the others are selected according to a predetermined sampling interval. Put each person, in the population, in some kind of order and select every nth member to be in the sample from a random starting point.

This approach is easy to use, especially with large populations. A possible disadvantage could be if there is an underlying pattern in how we are choosing objects from the population, it could potentially result in bias.

Suppose you need to choose a sample of 50 people from a population of 100. You will select every 2nd person on the list.

3. Stratified Sampling Technique:

Stratified sampling entails breaking the population up into smaller groups that might have significant differences. Ensuring that each subgroup is fairly represented in the sample enables you to reach more accurate findings. You can employ this sampling technique by dividing the population into smaller groups or strata according to the pertinent property (e.g., gender, age range, residence area, etc.). You determine the appropriate number of individuals to sample from each subgroup based on the population’s overall proportions. Then you choose a sample from each subgroup using random or systematic sampling.

We employ this sort of sampling when seeking representation from all the population’s subgroups. However, stratified sampling necessitates thorough familiarity with demographic characteristics.

A researcher wants to know the number of people in a country who went to college. He\she would divide the country into cities and then further divide the cities into age groups. He\she would then randomly select a sample to get information about the topic.

4. Cluster Sampling Technique:

In cluster sampling , we break down the overall population into smaller groups, each of which shares the features of the population as a whole. We also choose the entire subgroups randomly rather than merely picking people. You might incorporate each person from each sampled group if it is practically feasible. If the clusters are large, you can also sample people from each cluster using one of the methods mentioned above.

The sample has a higher chance of mistakes because there may be significant differences between clusters, but it is pretty helpful for handling oversized and dispersed populations. It is challenging to ensure that the sampled clusters accurately reflect the entire population.

A mobile company is looking to survey people from a country about the usage of phones. It would divide the country into cities, known as clusters, and then further divide the cities into areas (clusters) that are more populated.

Non-probability Sampling Techniques

1. convenience sampling technique:.

Because participants are chosen based on their availability and willingness to participate, this sampling technique may be the simplest. Only those people who are easily accessible and available to participate in the study are included in convenience sampling .

Although it is quick and affordable, this method cannot yield generalizable conclusions because it is impossible to determine whether the sample reflects the population. Considering how simple it was for the researcher to conduct the study and contact the subjects, it is frequently referred to as convenience sampling. Researchers with almost no authority choose the sample components, and they are selected entirely based on accessibility rather than representativeness.

When gathering feedback is time and money-constrained, this non-probability sampling technique is used.

For example, if a person is conducting a study about the use of shampoo, they would go to the people they know instead of the general public.

2. Purposive Sampling Technique:

In the purposive sampling technique , the researcher uses their knowledge to choose a sample that will be most helpful to the research’s objectives. This sort of sampling is also known as selective or judgment sampling. It is frequently employed when the researcher prefers to learn in-depth information on a particular occurrence versus drawing general conclusions from statistics or when the population is relatively tiny and focused.

For example, if a researcher wants to gather information about a particular religion, they should go to the area where it is practiced the most.

3. Snowball Sampling Technique:

When subjects are challenging to trace, researchers use the snowball sampling technique . To discover people who are interested in participating in the study, the researcher contacts other people they know. Using the snowball theory, researchers can follow a few categories to interview and gather data in situations where it is challenging to survey people on a particular topic. This sampling strategy is also used by researchers when the subject is highly delicate and taboo . The population expands like a snowball as a result of this referral strategy. This sampling technique works well when it’s challenging to pinpoint a sampling frame.

Snowball sampling carries a considerable risk of selection bias because the people who are referred will have characteristics in common with the person who refers them.

For example, if a researcher is conducting a study about the psychological effects of STDs, the snowball sampling technique would be useful as STDs are considered taboo in most areas.

4. Quota Sampling Technique:

The quota sampling technique is conducted based on a predetermined criterion. From the entire population, a representative sample is taken.

This approach divides the sample into groups based on traits and then interviews. The sample should reflect the population regarding the proportion of traits and attributes. The researcher stops collecting data once each group has adequate sample units. This sampling technique has numerous benefits, including its ability to compare groups within the population, quick and uncomplicated execution, and lack of need for a sample frame. The division of the groups may not be correct, and there is a possibility of some bias.

For example, if our population is composed of 50% women and 50% men, our sample should be composed of the same proportion of males and females.

Sampling is a very extensive yet one of the most undermined areas in research and general statistical studies. People don’t realize how important it is to choose suitable sampling methods to achieve the correct results. Most use the same one or two generic approaches, regardless of their use case, resulting in improper results.

So, to achieve your research objectives properly, selecting a sampling technique carefully while taking everything into consideration is crucial. In order to help you make your decision on a broader category, I’ve made up a table to help you choose the right approach for you, highlighting the major differences between probability vs. non-probability sampling techniques .

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

Try Qualtrics for free

Sampling methods, types & techniques.

15 min read Your comprehensive guide to the different sampling methods available to researchers – and how to know which is right for your research.

Author: Will Webster

What is sampling?

In survey research, sampling is the process of using a subset of a population to represent the whole population. To help illustrate this further, let’s look at data sampling methods with examples below.

Let’s say you wanted to do some research on everyone in North America. To ask every person would be almost impossible. Even if everyone said “yes”, carrying out a survey across different states, in different languages and timezones, and then collecting and processing all the results , would take a long time and be very costly.

Sampling allows large-scale research to be carried out with a more realistic cost and time-frame because it uses a smaller number of individuals in the population with representative characteristics to stand in for the whole.

However, when you decide to sample, you take on a new task. You have to decide who is part of your sample list and how to choose the people who will best represent the whole population. How you go about that is what the practice of sampling is all about.

Sampling definitions

Population: The total number of people or things you are interested in
Sample: A smaller number within your population that will represent the whole
Sampling: The process and method of selecting your sample

Free eBook: 2024 Market Research Trends

Why is sampling important?

Although the idea of sampling is easiest to understand when you think about a very large population, it makes sense to use sampling methods in research studies of all types and sizes. After all, if you can reduce the effort and cost of doing a study, why wouldn’t you? And because sampling allows you to research larger target populations using the same resources as you would smaller ones, it dramatically opens up the possibilities for research.

Sampling is a little like having gears on a car or bicycle. Instead of always turning a set of wheels of a specific size and being constrained by their physical properties, it allows you to translate your effort to the wheels via the different gears, so you’re effectively choosing bigger or smaller wheels depending on the terrain you’re on and how much work you’re able to do.

Sampling allows you to “gear” your research so you’re less limited by the constraints of cost, time, and complexity that come with different population sizes.

It allows us to do things like carrying out exit polls during elections, map the spread and effects rates of epidemics across geographical areas, and carry out nationwide census research that provides a snapshot of society and culture.

Types of sampling

Sampling strategies in research vary widely across different disciplines and research areas, and from study to study.

There are two major types of sampling methods: probability and non-probability sampling.

Probability sampling , also known as random sampling , is a kind of sample selection where randomization is used instead of deliberate choice. Each member of the population has a known, non-zero chance of being selected.
Non-probability sampling techniques are where the researcher deliberately picks items or individuals for the sample based on non-random factors such as convenience, geographic availability, or costs.

As we delve into these categories, it’s essential to understand the nuances and applications of each method to ensure that the chosen sampling strategy aligns with the research goals.

Probability sampling methods

There’s a wide range of probability sampling methods to explore and consider. Here are some of the best-known options.

1. Simple random sampling

With simple random sampling , every element in the population has an equal chance of being selected as part of the sample. It’s something like picking a name out of a hat. Simple random sampling can be done by anonymizing the population – e.g. by assigning each item or person in the population a number and then picking numbers at random.

Pros: Simple random sampling is easy to do and cheap. Designed to ensure that every member of the population has an equal chance of being selected, it reduces the risk of bias compared to non-random sampling.

Cons: It offers no control for the researcher and may lead to unrepresentative groupings being picked by chance.

2. Systematic sampling

With systematic sampling the random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked.

Best practice is to sort your list in a random way to ensure that selections won’t be accidentally clustered together. This is commonly achieved using a random number generator. If that’s not available you might order your list alphabetically by first name and then pick every fifth name to eliminate bias, for example.

Next, you need to decide your sampling interval – for example, if your sample will be 10% of your full list, your sampling interval is one in 10 – and pick a random start between one and 10 – for example three. This means you would start with person number three on your list and pick every tenth person.

Pros: Systematic sampling is efficient and straightforward, especially when dealing with populations that have a clear order. It ensures a uniform selection across the population.

Cons: There’s a potential risk of introducing bias if there’s an unrecognized pattern in the population that aligns with the sampling interval.

3. Stratified sampling

Stratified sampling involves random selection within predefined groups. It’s a useful method for researchers wanting to determine what aspects of a sample are highly correlated with what’s being measured. They can then decide how to subdivide (stratify) it in a way that makes sense for the research.

For example, you want to measure the height of students at a college where 80% of students are female and 20% are male. We know that gender is highly correlated with height, and if we took a simple random sample of 200 students (out of the 2,000 who attend the college), we could by chance get 200 females and not one male. This would bias our results and we would underestimate the height of students overall. Instead, we could stratify by gender and make sure that 20% of our sample (40 students) are male and 80% (160 students) are female.

Pros: Stratified sampling enhances the representation of all identified subgroups within a population, leading to more accurate results in heterogeneous populations.

Cons: This method requires accurate knowledge about the population’s stratification, and its design and execution can be more intricate than other methods.

4. Cluster sampling

With cluster sampling, groups rather than individual units of the target population are selected at random for the sample. These might be pre-existing groups, such as people in certain zip codes or students belonging to an academic year.

Cluster sampling can be done by selecting the entire cluster, or in the case of two-stage cluster sampling, by randomly selecting the cluster itself, then selecting at random again within the cluster.

Pros: Cluster sampling is economically beneficial and logistically easier when dealing with vast and geographically dispersed populations.

Cons: Due to potential similarities within clusters, this method can introduce a greater sampling error compared to other methods.

Non-probability sampling methods

The non-probability sampling methodology doesn’t offer the same bias-removal benefits as probability sampling, but there are times when these types of sampling are chosen for expediency or simplicity. Here are some forms of non-probability sampling and how they work.

1. Convenience sampling

People or elements in a sample are selected on the basis of their accessibility and availability. If you are doing a research survey and you work at a university, for example, a convenience sample might consist of students or co-workers who happen to be on campus with open schedules who are willing to take your questionnaire .

This kind of sample can have value, especially if it’s done as an early or preliminary step, but significant bias will be introduced.

Pros: Convenience sampling is the most straightforward method, requiring minimal planning, making it quick to implement.

Cons: Due to its non-random nature, the method is highly susceptible to biases, and the results are often lacking in their application to the real world.

2. Quota sampling

Like the probability-based stratified sampling method, this approach aims to achieve a spread across the target population by specifying who should be recruited for a survey according to certain groups or criteria.

For example, your quota might include a certain number of males and a certain number of females. Alternatively, you might want your samples to be at a specific income level or in certain age brackets or ethnic groups.

Pros: Quota sampling ensures certain subgroups are adequately represented, making it great for when random sampling isn’t feasible but representation is necessary.

Cons: The selection within each quota is non-random and researchers’ discretion can influence the representation, which both strongly increase the risk of bias.

3. Purposive sampling

Participants for the sample are chosen consciously by researchers based on their knowledge and understanding of the research question at hand or their goals.

Also known as judgment sampling, this technique is unlikely to result in a representative sample , but it is a quick and fairly easy way to get a range of results or responses.

Pros: Purposive sampling targets specific criteria or characteristics, making it ideal for studies that require specialized participants or specific conditions.

Cons: It’s highly subjective and based on researchers’ judgment, which can introduce biases and limit the study’s real-world application.

4. Snowball or referral sampling

With this approach, people recruited to be part of a sample are asked to invite those they know to take part, who are then asked to invite their friends and family and so on. The participation radiates through a community of connected individuals like a snowball rolling downhill.

Pros: Especially useful for hard-to-reach or secretive populations, snowball sampling is effective for certain niche studies.

Cons: The method can introduce bias due to the reliance on participant referrals, and the choice of initial seeds can significantly influence the final sample.

What type of sampling should I use?

Choosing the right sampling method is a pivotal aspect of any research process, but it can be a stumbling block for many.

Here’s a structured approach to guide your decision.

1) Define your research goals

If you aim to get a general sense of a larger group, simple random or stratified sampling could be your best bet. For focused insights or studying unique communities, snowball or purposive sampling might be more suitable.

2) Assess the nature of your population

The nature of the group you’re studying can guide your method. For a diverse group with different categories, stratified sampling can ensure all segments are covered. If they’re widely spread geographically , cluster sampling becomes useful. If they’re arranged in a certain sequence or order, systematic sampling might be effective.

3) Consider your constraints

Your available time, budget and ease of accessing participants matter. Convenience or quota sampling can be practical for quicker studies, but they come with some trade-offs. If reaching everyone in your desired group is challenging, snowball or purposive sampling can be more feasible.

4) Determine the reach of your findings

Decide if you want your findings to represent a much broader group. For a wider representation, methods that include everyone fairly (like probability sampling ) are a good option. For specialized insights into specific groups, non-probability sampling methods can be more suitable.

5) Get feedback

Before fully committing, discuss your chosen method with others in your field and consider a test run.

Avoid or reduce sampling errors and bias

Using a sample is a kind of short-cut. If you could ask every single person in a population to take part in your study and have each of them reply, you’d have a highly accurate (and very labor-intensive) project on your hands.

But since that’s not realistic, sampling offers a “good-enough” solution that sacrifices some accuracy for the sake of practicality and ease. How much accuracy you lose out on depends on how well you control for sampling error, non-sampling error, and bias in your survey design . Our blog post helps you to steer clear of some of these issues.

How to choose the correct sample size

Finding the best sample size for your target population is something you’ll need to do again and again, as it’s different for every study.

To make life easier, we’ve provided a sample size calculator . To use it, you need to know your:

Population size
Confidence level
Margin of error (confidence interval)

If any of those terms are unfamiliar, have a look at our blog post on determining sample size for details of what they mean and how to find them.

Unlock the insights of yesterday to shape tomorrow

In the ever-evolving business landscape, relying on the most recent market research is paramount. Reflecting on 2022, brands and businesses can harness crucial insights to outmaneuver challenges and seize opportunities.

Equip yourself with this knowledge by exploring Qualtrics’ ‘2022 Market Research Global Trends’ report.

Delve into this comprehensive study to unearth:

How businesses made sense of tricky situations in 2022
Tips that really helped improve research results
Steps to take your findings and put them into action

Related resources

How to determine sample size 12 min read, selection bias 11 min read, systematic random sampling 15 min read, convenience sampling 18 min read, probability sampling 8 min read, non-probability sampling 17 min read, stratified random sampling 12 min read, request demo.

Ready to learn more about Qualtrics?

En español – ExME
Em português – EME

What are sampling methods and how do you choose the best one?

Posted on 18th November 2020 by Mohamed Khalifa

This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.

Introduction to sampling methods

Examples of different sampling methods, choosing the best sampling method.

It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding .

However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique.

What is a sampling frame?

A sampling frame is a record of the target population containing all participants of interest. In other words, it is a list from which we can extract a sample.

What makes a good sample?

A good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study.

We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. For the purposes of this blog we will be focusing on random sampling methods .

Example: We want to conduct an experimental trial in a small population such as: employees in a company, or students in a college. We include everyone in a list and use a random number generator to select the participants

Advantages: Generalisable results possible, random sampling, the sampling frame is the whole population, every participant has an equal probability of being selected

Disadvantages: Less precise than stratified method, less representative than the systematic method

Simple sampling method example in stick men.

Example: Every nth patient entering the out-patient clinic is selected and included in our sample

Advantages: More feasible than simple or stratified methods, sampling frame is not always required

Disadvantages: Generalisability may decrease if baseline characteristics repeat across every nth participant

Systematic sampling method example in stick men

Example: We have a big population (a city) and we want to ensure representativeness of all groups with a pre-determined characteristic such as: age groups, ethnic origin, and gender

Advantages: Inclusive of strata (subgroups), reliable and generalisable results

Disadvantages: Does not work well with multiple variables

Stratified sampling method example stick men

Example: 10 schools have the same number of students across the county. We can randomly select 3 out of 10 schools as our clusters

Advantages: Readily doable with most budgets, does not require a sampling frame

Disadvantages: Results may not be reliable nor generalisable

Cluster sampling method example with stick men

How can you identify sampling errors?

Non-random selection increases the probability of sampling (selection) bias if the sample does not represent the population we want to study. We could avoid this by random sampling and ensuring representativeness of our sample with regards to sample size.

An inadequate sample size decreases the confidence in our results as we may think there is no significant difference when actually there is. This type two error results from having a small sample size, or from participants dropping out of the sample.

In medical research of disease, if we select people with certain diseases while strictly excluding participants with other co-morbidities, we run the risk of diagnostic purity bias where important sub-groups of the population are not represented.

Furthermore, measurement bias may occur during re-collection of risk factors by participants (recall bias) or assessment of outcome where people who live longer are associated with treatment success, when in fact people who died were not included in the sample or data analysis (survivors bias).

By following the steps below we could choose the best sampling method for our study in an orderly fashion.

Research objectiveness

Firstly, a refined research question and goal would help us define our population of interest. If our calculated sample size is small then it would be easier to get a random sample. If, however, the sample size is large, then we should check if our budget and resources can handle a random sampling method.

Sampling frame availability

Secondly, we need to check for availability of a sampling frame (Simple), if not, could we make a list of our own (Stratified). If neither option is possible, we could still use other random sampling methods, for instance, systematic or cluster sampling.

Study design

Moreover, we could consider the prevalence of the topic (exposure or outcome) in the population, and what would be the suitable study design. In addition, checking if our target population is widely varied in its baseline characteristics. For example, a population with large ethnic subgroups could best be studied using a stratified sampling method.

Random sampling

Finally, the best sampling method is always the one that could best answer our research question while also allowing for others to make use of our results (generalisability of results). When we cannot afford a random sampling method, we can always choose from the non-random sampling methods.

To sum up, we now understand that choosing between random or non-random sampling methods is multifactorial. We might often be tempted to choose a convenience sample from the start, but that would not only decrease precision of our results, and would make us miss out on producing research that is more robust and reliable.

References (pdf)

Mohamed Khalifa

No Comments on What are sampling methods and how do you choose the best one?

Thank you for this overview. A concise approach for research.

really helps! am an ecology student preparing to write my lab report for sampling.

I learned a lot to the given presentation.. It’s very comprehensive… Thanks for sharing…

Very informative and useful for my study. Thank you

Oversimplified info on sampling methods. Probabilistic of the sampling and sampling of samples by chance does rest solely on the random methods. Factors such as the random visits or presentation of the potential participants at clinics or sites could be sufficiently random in nature and should be used for the sake of efficiency and feasibility. Nevertheless, this approach has to be taken only after careful thoughts. Representativeness of the study samples have to be checked at the end or during reporting by comparing it to the published larger studies or register of some kind in/from the local population.

Thank you so much Mr.mohamed very useful and informative article

Subscribe to our newsletter

You will receive our monthly newsletter and free access to Trip Premium.

How to read a funnel plot

This blog introduces you to funnel plots, guiding you through how to read them and what may cause them to look asymmetrical.

Internal and external validity: what are they and how do they differ?

Is this study valid? Can I trust this study’s methods and design? Can I apply the results of this study to other contexts? Learn more about internal and external validity in research to help you answer these questions when you next look at a paper.

Cluster Randomized Trials: Concepts

This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.

Sampling Methods & Strategies 101

Everything you need to know (including examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to research, sooner or later you’re bound to wander into the intimidating world of sampling methods and strategies. If you find yourself on this page, chances are you’re feeling a little overwhelmed or confused. Fear not – in this post we’ll unpack sampling in straightforward language , along with loads of examples .

Overview: Sampling Methods & Strategies

What is sampling in a research context?
The two overarching approaches

Simple random sampling

Stratified random sampling, cluster sampling, systematic sampling, purposive sampling, convenience sampling, snowball sampling.

How to choose the right sampling method

What (exactly) is sampling?

At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group . For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent (good luck with that!) – but you could potentially collect data from a smaller subset of this group.

In technical terms, the larger group is referred to as the population , and the subset (the group you’ll actually engage with in your research) is called the sample . Put another way, you can look at the population as a full cake and the sample as a single slice of that cake. In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. In other words, you’d want to cut a perfect cross-sectional slice of cake, such that the slice reflects every layer of the cake in perfect proportion.

Achieving a truly representative sample is, unfortunately, a little trickier than slicing a cake, as there are many practical challenges and obstacles to achieving this in a real-world setting. Thankfully though, you don’t always need to have a perfectly representative sample – it all depends on the specific research aims of each study – so don’t stress yourself out about that just yet!

With the concept of sampling broadly defined, let’s look at the different approaches to sampling to get a better understanding of what it all looks like in practice.

The two overarching sampling approaches

At the highest level, there are two approaches to sampling: probability sampling and non-probability sampling . Within each of these, there are a variety of sampling methods , which we’ll explore a little later.

Probability sampling involves selecting participants (or any unit of interest) on a statistically random basis , which is why it’s also called “random sampling”. In other words, the selection of each individual participant is based on a pre-determined process (not the discretion of the researcher). As a result, this approach achieves a random sample.

Probability-based sampling methods are most commonly used in quantitative research , especially when it’s important to achieve a representative sample that allows the researcher to generalise their findings.

Non-probability sampling , on the other hand, refers to sampling methods in which the selection of participants is not statistically random . In other words, the selection of individual participants is based on the discretion and judgment of the researcher, rather than on a pre-determined process.

Non-probability sampling methods are commonly used in qualitative research , where the richness and depth of the data are more important than the generalisability of the findings.

If that all sounds a little too conceptual and fluffy, don’t worry. Let’s take a look at some actual sampling methods to make it more tangible.

Need a helping hand?

Probability-based sampling methods

First, we’ll look at four common probability-based (random) sampling methods:

Importantly, this is not a comprehensive list of all the probability sampling methods – these are just four of the most common ones. So, if you’re interested in adopting a probability-based sampling approach, be sure to explore all the options.

Simple random sampling involves selecting participants in a completely random fashion , where each participant has an equal chance of being selected. Basically, this sampling method is the equivalent of pulling names out of a hat , except that you can do it digitally. For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 numbers (each number, reflecting a participant) and then use that dataset as your sample.

Thanks to its simplicity, simple random sampling is easy to implement , and as a consequence, is typically quite cheap and efficient . Given that the selection process is completely random, the results can be generalised fairly reliably. However, this also means it can hide the impact of large subgroups within the data, which can result in minority subgroups having little representation in the results – if any at all. To address this, one needs to take a slightly different approach, which we’ll look at next.

Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly , but from within certain pre-defined subgroups (i.e., strata) that share a common trait . For example, you might divide the population into strata based on gender, ethnicity, age range or level of education, and then select randomly from each group.

The benefit of this sampling method is that it gives you more control over the impact of large subgroups (strata) within the population. For example, if a population comprises 80% males and 20% females, you may want to “balance” this skew out by selecting a random sample from an equal number of males and females. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So, depending on your research aims, the stratified approach could work well.

Next on the list is cluster sampling. As the name suggests, this sampling method involves sampling from naturally occurring, mutually exclusive clusters within a population – for example, area codes within a city or cities within a country. Once the clusters are defined, a set of clusters are randomly selected and then a set of participants are randomly selected from each cluster.

Now, you’re probably wondering, “how is cluster sampling different from stratified random sampling?”. Well, let’s look at the previous example where each cluster reflects an area code in a given city.

With cluster sampling, you would collect data from clusters of participants in a handful of area codes (let’s say 5 neighbourhoods). Conversely, with stratified random sampling, you would need to collect data from all over the city (i.e., many more neighbourhoods). You’d still achieve the same sample size either way (let’s say 200 people, for example), but with stratified sampling, you’d need to do a lot more running around, as participants would be scattered across a vast geographic area. As a result, cluster sampling is often the more practical and economical option.

If that all sounds a little mind-bending, you can use the following general rule of thumb. If a population is relatively homogeneous , cluster sampling will often be adequate. Conversely, if a population is quite heterogeneous (i.e., diverse), stratified sampling will generally be more appropriate.

The last probability sampling method we’ll look at is systematic sampling. This method simply involves selecting participants at a set interval , starting from a random point .

For example, if you have a list of students that reflects the population of a university, you could systematically sample that population by selecting participants at an interval of 8 . In other words, you would randomly select a starting point – let’s say student number 40 – followed by student 48, 56, 64, etc.

What’s important with systematic sampling is that the population list you select from needs to be randomly ordered . If there are underlying patterns in the list (for example, if the list is ordered by gender, IQ, age, etc.), this will result in a non-random sample, which would defeat the purpose of adopting this sampling method. Of course, you could safeguard against this by “shuffling” your population list using a random number generator or similar tool.

Systematic sampling simply involves selecting participants at a set interval (e.g., every 10th person), starting from a random point.

Non-probability-based sampling methods

Right, now that we’ve looked at a few probability-based sampling methods, let’s look at three non-probability methods :

Again, this is not an exhaustive list of all possible sampling methods, so be sure to explore further if you’re interested in adopting a non-probability sampling approach.

First up, we’ve got purposive sampling – also known as judgment , selective or subjective sampling. Again, the name provides some clues, as this method involves the researcher selecting participants using his or her own judgement , based on the purpose of the study (i.e., the research aims).

For example, suppose your research aims were to understand the perceptions of hyper-loyal customers of a particular retail store. In that case, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes .

Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience . Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad .

Purposive sampling involves the researcher selecting participants using their own judgement, based on the purpose of the study.

Next up, we have convenience sampling. As the name suggests, with this method, participants are selected based on their availability or accessibility . In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.

Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. However, as you’d expect, this sampling method is unlikely to produce a representative sample and will of course be vulnerable to researcher bias , so it’s important to approach it with caution.

Last but not least, we have the snowball sampling method. This method relies on referrals from initial participants to recruit additional participants. In other words, the initial subjects form the first (small) snowball and each additional subject recruited through referral is added to the snowball, making it larger as it rolls along .

Snowball sampling is often used in research contexts where it’s difficult to identify and access a particular population. For example, people with a rare medical condition or members of an exclusive group. It can also be useful in cases where the research topic is sensitive or taboo and people are unlikely to open up unless they’re referred by someone they trust.

Simply put, snowball sampling is ideal for research that involves reaching hard-to-access populations . But, keep in mind that, once again, it’s a sampling method that’s highly prone to researcher bias and is unlikely to produce a representative sample. So, make sure that it aligns with your research aims and questions before adopting this method.

How to choose a sampling method

Now that we’ve looked at a few popular sampling methods (both probability and non-probability based), the obvious question is, “ how do I choose the right sampling method for my study?”. When selecting a sampling method for your research project, you’ll need to consider two important factors: your research aims and your resources .

As with all research design and methodology choices, your sampling approach needs to be guided by and aligned with your research aims, objectives and research questions – in other words, your golden thread. Specifically, you need to consider whether your research aims are primarily concerned with producing generalisable findings (in which case, you’ll likely opt for a probability-based sampling method) or with achieving rich , deep insights (in which case, a non-probability-based approach could be more practical). Typically, quantitative studies lean toward the former, while qualitative studies aim for the latter, so be sure to consider your broader methodology as well.

The second factor you need to consider is your resources and, more generally, the practical constraints at play. If, for example, you have easy, free access to a large sample at your workplace or university and a healthy budget to help you attract participants, that will open up multiple options in terms of sampling methods. Conversely, if you’re cash-strapped, short on time and don’t have unfettered access to your population of interest, you may be restricted to convenience or referral-based methods.

In short, be ready for trade-offs – you won’t always be able to utilise the “perfect” sampling method for your study, and that’s okay. Much like all the other methodological choices you’ll make as part of your study, you’ll often need to compromise and accept practical trade-offs when it comes to sampling. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.

Let’s recap…

In this post, we’ve covered the basics of sampling within the context of a typical research project.

Sampling refers to the process of defining a subgroup (sample) from the larger group of interest (population).
The two overarching approaches to sampling are probability sampling (random) and non-probability sampling .
Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling.
Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
When choosing a sampling method, you need to consider your research aims , objectives and questions, as well as your resources and other practical constraints .

If you’d like to see an example of a sampling strategy in action, be sure to check out our research methodology chapter sample .

Last but not least, if you need hands-on help with your sampling (or any other aspect of your research), take a look at our 1-on-1 coaching service , where we guide you through each step of the research process, at your own pace.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project.

You Might Also Like:

Research constructs: construct validity and reliability

Excellent and helpful. Best site to get a full understanding of Research methodology. I’m nolonger as “clueless “..😉

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Print Friendly

Join thousands of product people at Insight Out Conf on April 11. Register free.

Insights hub solutions

Analyze data

Uncover deep customer insights with fast, powerful features, store insights, curate and manage insights in one searchable platform, scale research, unlock the potential of customer insights at enterprise scale.

Make magic with your customer data in Dovetail

Four ways Dovetail helps Product Managers master continuous product discovery

Product updates

Dovetail retro: our biggest releases from the past year

Events and videos

An overview of sampling methods

Last updated

27 February 2023

Reviewed by

Cathy Heath

When researching perceptions or attributes of a product, service, or people, you have two options:

Survey every person in your chosen group (the target market, or population), collate your responses, and reach your conclusions.

Select a smaller group from within your target market and use their answers to represent everyone. This option is sampling .

Sampling saves you time and money. When you use the sampling method, the whole population being studied is called the sampling frame .

The sample you choose should represent your target market, or the sampling frame, well enough to do one of the following:

Generalize your findings across the sampling frame and use them as though you had surveyed everyone

Use the findings to decide on your next step, which might involve more in-depth sampling

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

How was sampling developed?

Valery Glivenko and Francesco Cantelli, two mathematicians studying probability theory in the early 1900s, devised the sampling method. Their research showed that a properly chosen sample of people would reflect the larger group’s status, opinions, decisions, and decision-making steps.

They proved you don't need to survey the entire target market, thereby saving the rest of us a lot of time and money.

Why is sampling important?

We’ve already touched on the fact that sampling saves you time and money. When you get reliable results quickly, you can act on them sooner. And the money you save can pay for something else.

It’s often easier to survey a sample than a whole population. Sample inferences can be more reliable than those you get from a very large group because you can choose your samples carefully and scientifically.

Sampling is also useful because it is often impossible to survey the entire population. You probably have no choice but to collect only a sample in the first place.

Because you’re working with fewer people, you can collect richer data, which makes your research more accurate. You can:

Ask more questions

Go into more detail

Seek opinions instead of just collecting facts

Observe user behaviors

Double-check your findings if you need to

In short, sampling works! Let's take a look at the most common sampling methods.

Types of sampling methods

There are two main sampling methods: probability sampling and non-probability sampling. These can be further refined, which we'll cover shortly. You can then decide which approach best suits your research project.

Probability sampling method

Probability sampling is used in quantitative research , so it provides data on the survey topic in terms of numbers. Probability relates to mathematics, hence the name ‘quantitative research’. Subjects are asked questions like:

How many boxes of candy do you buy at one time?

How often do you shop for candy?

How much would you pay for a box of candy?

This method is also called random sampling because everyone in the target market has an equal chance of being chosen for the survey. It is designed to reduce sampling error for the most important variables. You should, therefore, get results that fairly reflect the larger population.

Non-probability sampling method

In this method, not everyone has an equal chance of being part of the sample. It's usually easier (and cheaper) to select people for the sample group. You choose people who are more likely to be involved in or know more about the topic you’re researching.

Non-probability sampling is used for qualitative research. Qualitative data is generated by questions like:

Where do you usually shop for candy (supermarket, gas station, etc.?)

Which candy brand do you usually buy?

Why do you like that brand?

Probability sampling methods

Here are five ways of doing probability sampling:

Simple random sampling (basic probability sampling)

Systematic sampling

Stratified sampling.

Cluster sampling

Multi-stage sampling

Simple random sampling.

There are three basic steps to simple random sampling:

Choose your sampling frame.

Decide on your sample size. Make sure it is large enough to give you reliable data.

Randomly choose your sample participants.

You could put all their names in a hat, shake the hat to mix the names, and pull out however many names you want in your sample (without looking!)

You could be more scientific by giving each participant a number and then using a random number generator program to choose the numbers.

Instead of choosing names or numbers, you decide beforehand on a selection method. For example, collect all the names in your sampling frame and start at, for example, the fifth person on the list, then choose every fourth name or every tenth name. Alternatively, you could choose everyone whose last name begins with randomly-selected initials, such as A, G, or W.

Choose your system of selecting names, and away you go.

This is a more sophisticated way to choose your sample. You break the sampling frame down into important subgroups or strata . Then, decide how many you want in your sample, and choose an equal number (or a proportionate number) from each subgroup.

For example, you want to survey how many people in a geographic area buy candy, so you compile a list of everyone in that area. You then break that list down into, for example, males and females, then into pre-teens, teenagers, young adults, senior citizens, etc. who are male or female.

So, if there are 1,000 young male adults and 2,000 young female adults in the whole sampling frame, you may want to choose 100 males and 200 females to keep the proportions balanced. You then choose the individual survey participants through the systematic sampling method.

Clustered sampling

This method is used when you want to subdivide a sample into smaller groups or clusters that are geographically or organizationally related.

Let’s say you’re doing quantitative research into candy sales. You could choose your sample participants from urban, suburban, or rural populations. This would give you three geographic clusters from which to select your participants.

This is a more refined way of doing cluster sampling. Let’s say you have your urban cluster, which is your primary sampling unit. You can subdivide this into a secondary sampling unit, say, participants who typically buy their candy in supermarkets. You could then further subdivide this group into your ultimate sampling unit. Finally, you select the actual survey participants from this unit.

Uses of probability sampling

Probability sampling has three main advantages:

It helps minimizes the likelihood of sampling bias. How you choose your sample determines the quality of your results. Probability sampling gives you an unbiased, randomly selected sample of your target market.

It allows you to create representative samples and subgroups within a sample out of a large or diverse target market.

It lets you use sophisticated statistical methods to select as close to perfect samples as possible.

Non-probability sampling methods

To recap, with non-probability sampling, you choose people for your sample in a non-random way, so not everyone in your sampling frame has an equal chance of being chosen. Your research findings, therefore, may not be as representative overall as probability sampling, but you may not want them to be.

Sampling bias is not a concern if all potential survey participants share similar traits. For example, you may want to specifically focus on young male adults who spend more than others on candy. In addition, it is usually a cheaper and quicker method because you don't have to work out a complex selection system that represents the entire population in that community.

Researchers do need to be mindful of carefully considering the strengths and limitations of each method before selecting a sampling technique.

Non-probability sampling is best for exploratory research , such as at the beginning of a research project.

There are five main types of non-probability sampling methods:

Convenience sampling

Purposive sampling, voluntary response sampling, snowball sampling, quota sampling.

The strategy of convenience sampling is to choose your sample quickly and efficiently, using the least effort, usually to save money.

Let's say you want to survey the opinions of 100 millennials about a particular topic. You could send out a questionnaire over the social media platforms millennials use. Ask respondents to confirm their birth year at the top of their response sheet and, when you have your 100 responses, begin your analysis. Or you could visit restaurants and bars where millennials spend their evenings and sign people up.

A drawback of convenience sampling is that it may not yield results that apply to a broader population.

This method relies on your judgment to choose the most likely sample to deliver the most useful results. You must know enough about the survey goals and the sampling frame to choose the most appropriate sample respondents.

Your knowledge and experience save you time because you know your ideal sample candidates, so you should get high-quality results.

This method is similar to convenience sampling, but it is based on potential sample members volunteering rather than you looking for people.

You make it known you want to do a survey on a particular topic for a particular reason and wait until enough people volunteer. Then you give them the questionnaire or arrange interviews to ask your questions directly.

Snowball sampling involves asking selected participants to refer others who may qualify for the survey. This method is best used when there is no sampling frame available. It is also useful when the researcher doesn’t know much about the target population.

Let's say you want to research a niche topic that involves people who may be difficult to locate. For our candy example, this could be young males who buy a lot of candy, go rock climbing during the day, and watch adventure movies at night. You ask each participant to name others they know who do the same things, so you can contact them. As you make contact with more people, your sample 'snowballs' until you have all the names you need.

This sampling method involves collecting the specific number of units (quotas) from your predetermined subpopulations. Quota sampling is a way of ensuring that your sample accurately represents the sampling frame.

Uses of non-probability sampling

You can use non-probability sampling when you:

Want to do a quick test to see if a more detailed and sophisticated survey may be worthwhile

Want to explore an idea to see if it 'has legs'

Launch a pilot study

Do some initial qualitative research

Have little time or money available (half a loaf is better than no bread at all)

Want to see if the initial results will help you justify a longer, more detailed, and more expensive research project

The main types of sampling bias, and how to avoid them

Sampling bias can fog or limit your research results. This will have an impact when you generalize your results across the whole target market. The two main causes of sampling bias are faulty research design and poor data collection or recording. They can affect probability and non-probability sampling.

Faulty research

If a surveyor chooses participants inappropriately, the results will not reflect the population as a whole.

A famous example is the 1948 presidential race. A telephone survey was conducted to see which candidate had more support. The problem with the research design was that, in 1948, most people with telephones were wealthy, and their opinions were very different from voters as a whole. The research implied Dewey would win, but it was Truman who became president.

Poor data collection or recording

This problem speaks for itself. The survey may be well structured, the sample groups appropriate, the questions clear and easy to understand, and the cluster sizes appropriate. But if surveyors check the wrong boxes when they get an answer or if the entire subgroup results are lost, the survey results will be biased.

How do you minimize bias in sampling?

To get results you can rely on, you must:

Know enough about your target market

Choose one or more sample surveys to cover the whole target market properly

Choose enough people in each sample so your results mirror your target market

Have content validity . This means the content of your questions must be direct and efficiently worded. If it isn’t, the viability of your survey could be questioned. That would also be a waste of time and money, so make the wording of your questions your top focus.

If using probability sampling, make sure your sampling frame includes everyone it should and that your random sampling selection process includes the right proportion of the subgroups

If using non-probability sampling, focus on fairness, equality, and completeness in identifying your samples and subgroups. Then balance those criteria against simple convenience or other relevant factors.

What are the five types of sampling bias?

Self-selection bias. If you mass-mail questionnaires to everyone in the sample, you’re more likely to get results from people with extrovert or activist personalities and not from introverts or pragmatists. So if your convenience sampling focuses on getting your quota responses quickly, it may be skewed.

Non-response bias. Unhappy customers, stressed-out employees, or other sub-groups may not want to cooperate or they may pull out early.

Undercoverage bias. If your survey is done, say, via email or social media platforms, it will miss people without internet access, such as those living in rural areas, the elderly, or lower-income groups.

Survivorship bias. Unsuccessful people are less likely to take part. Another example may be a researcher excluding results that don’t support the overall goal. If the CEO wants to tell the shareholders about a successful product or project at the AGM, some less positive survey results may go “missing” (to take an extreme example.) The result is that your data will reflect an overly optimistic representation of the truth.

Pre-screening bias. If the researcher, whose experience and knowledge are being used to pre-select respondents in a judgmental sampling, focuses more on convenience than judgment, the results may be compromised.

How do you minimize sampling bias?

Focus on the bullet points in the next section and:

Make survey questionnaires as direct, easy, short, and available as possible, so participants are more likely to complete them accurately and send them back

Follow up with the people who have been selected but have not returned their responses

Ignore any pressure that may produce bias

How do you decide on the type of sampling to use?

Use the ideas you've gleaned from this article to give yourself a platform, then choose the best method to meet your goals while staying within your time and cost limits.

If it isn't obvious which method you should choose, use this strategy:

Clarify your research goals

Clarify how accurate your research results must be to reach your goals

Evaluate your goals against time and budget

List the two or three most obvious sampling methods that will work for you

Confirm the availability of your resources (researchers, computer time, etc.)

Compare each of the possible methods with your goals, accuracy, precision, resource, time, and cost constraints

Make your decision

The takeaway

Effective market research is the basis of successful marketing, advertising, and future productivity. By selecting the most appropriate sampling methods, you will collect the most useful market data and make the most effective decisions.

Get started today

Go from raw data to valuable insights with a flexible research platform

Editor’s picks

Last updated: 21 December 2023

Last updated: 16 December 2023

Last updated: 17 February 2024

Last updated: 19 November 2023

Last updated: 5 March 2024

Last updated: 15 February 2024

Last updated: 11 March 2024

Last updated: 12 December 2023

Last updated: 6 March 2024

Last updated: 10 April 2023

Last updated: 20 December 2023

Latest articles

Sampling Techniques for Quantitative Research

Moniruzzaman Sarker ORCID: orcid.org/0000-0003-3595-5838 4 &
Mohammed Abdulmalek AL-Muaalemi 5
First Online: 27 October 2022

2838 Accesses

6 Citations

In quantitative research, collecting data from an entire population of a study is impractical in many instances. It squanders resources like time and money which can be minimized by choosing suitable sampling techniques between probability and non-probability methods. The chapter outlines a brief idea about the different categories of sampling techniques with examples. Sensibly selecting among the sampling techniques allows the researcher to generalize the findings to a specific study context. Although probability sampling is more appealing to draw a representative sample, non-probability sampling techniques also enable the researcher to generalize the findings upon implementing the sampling strategy wisely. Moreover, adopting probability sampling techniques is not feasible in many situations. The chapter suggests selecting sampling techniques should be guided by research objectives, study scope, and availability of sampling frame rather than looking at the nature of sampling techniques.

Sampling techniques
Quantitative study
Probability sampling
Non-probability sampling

This is a preview of subscription content, log in via an institution .

Buying options

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). Oxford University Press.

Google Scholar

Clottey, T. A., & Grawe, S. J. (2014). Non-response bias assessment in logistics survey research: Use fewer tests? International Journal of Physical Distribution & Logistics Management, 44 (5), 412–426. https://doi.org/10.1108/IJPDLM-10-2012-0314

Article Google Scholar

Collier, J. E., & Bienstock, C. C. (2007). An analysis of how nonresponse error is assessed in academic marketing research. Marketing Theory, 7 (2), 163–183. https://doi.org/10.1177/1470593107076865

Cooper, D. R., Schindler, P. S., & Sun, J. (2006). Business research methods (9th ed.). McGraw-Hill Irwin.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications, Incorporated.

Feild, L., Pruchno, R. A., Bewley, J., Lemay, E. P., & Levinsky, N. G. (2006). Using probability versus nonprobability sampling to identify hard-to-access participants for health-related research: Costs and contrasts. Journal of Aging and Health, 18 (4), 565–583. https://doi.org/10.1177/0898264306291420.

Hulland, J., Baumgartner, H., & Smith, K. M. (2017). Marketing survey research best practices: Evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46 (1), 92–108. https://doi.org/10.1007/s11747-017-0532-y

Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education, 42 (4), 43–53.

Malhotra, N. K., & Das, S. (2010). Marketing research: An applied orientation (6th ed.). Pearson Education.

Book Google Scholar

Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13 (6), 522–526. https://doi.org/10.1093/fampra/13.6.522

Memon, M. A., Ting, H., Ramayah, T., Chuah, F., & Cheah, J.-H. (2017). A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario. Journal of Applied Structural Equation Modeling, 1 (1), i–xiii.

Rowley, J. (2014). Designing and using research questionnaires. Management Research Review, 37 (3), 308–330. https://doi.org/10.1108/MRR-02-2013-0027

Sarstedt, M., Bengart, P., Shaltoni, A. M., & Lehmann, S. (2018). The use of sampling methods in advertising research: A gap between theory and practice. International Journal of Advertising, 37 (4), 650–663. https://doi.org/10.1080/02650487.2017.1348329

Sax, L. J., Gilmartin, S. K., & Bryant, A. N. (2003). Assessing response rates and nonresponse bias in web and paper surveys. Research in Higher Education, 44 (4), 409–432. https://doi.org/10.1023/a:1024232915870

Seddon, P. B., & Scheepers, R. (2012). Towards the improved treatment of generalization of knowledge claims in IS research: Drawing general conclusions from samples. European Journal of Information Systems, 21 (1), 6–21.

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach (7th ed.). John Wiley & Sons.

Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods (9th ed.). Cengage Learning.

Download references

Author information

Authors and affiliations.

Assistant Professor of Marketing, Southamton Malysia Business School, University of Southamton Malysia, Johor Bahru, Malaysia

Moniruzzaman Sarker

Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur, Malaysia

Mohammed Abdulmalek AL-Muaalemi

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moniruzzaman Sarker .

Editor information

Editors and affiliations.

Centre for Family and Child Studies, Research Institute of Humanities and Social Sciences, University of Sharjah, Sharjah, United Arab Emirates

M. Rezaul Islam

Department of Development Studies, University of Dhaka, Dhaka, Bangladesh

Niaz Ahmed Khan

Department of Social Work, School of Humanities, University of Johannesburg, Johannesburg, South Africa

Rajendra Baikady

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Cite this chapter.

Moniruzzaman Sarker, AL-Muaalemi, M.A. (2022). Sampling Techniques for Quantitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_15

Download citation

DOI : https://doi.org/10.1007/978-981-19-5441-2_15

Published : 27 October 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-5219-7

Online ISBN : 978-981-19-5441-2

eBook Packages : Social Sciences

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Skip to main content
Skip to primary sidebar
Skip to footer
QuestionPro

Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Sampling Methods: Guide To All Types with Examples

Sampling is an essential part of any research project. The right sampling method can make or break the validity of your research, and it’s essential to choose the right method for your specific question. In this article, we’ll take a closer look at some of the most popular sampling methods and provide real-world examples of how they can be used to gather accurate and reliable data.

LEARN ABOUT: Research Process Steps

From simple random sampling to complex stratified sampling , we’ll explore each method’s pros, cons, and best practices. So, whether you’re a seasoned researcher or just starting your journey, this article is a must-read for anyone looking to master sampling methods. Let’s get started!

Content Index

What is sampling?

Types of sampling: sampling methods, types of probability sampling with examples:, uses of probability sampling, types of non-probability sampling with examples, uses of non-probability sampling, how do you decide on the type of sampling to use, difference between probability sampling and non-probability sampling methods.

Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights.

It is also a time-convenient and cost-effective method and hence forms the basis of any research design . Sampling techniques can be used in research survey software for optimum derivation.

For example, suppose a drug manufacturer would like to research the adverse side effects of a drug on the country’s population. In that case, it is almost impossible to conduct a research study that involves everyone. In this case, the researcher decides on a sample of people from each demographic and then researches them, giving him/her indicative feedback on the drug’s behavior.

Learn more about Audience by QuestionPro

Sampling in market action research is of two types – probability sampling and non-probability sampling. Let’s take a closer look at these two methods of sampling.

Probability sampling: Probability sampling is a sampling technique where a researcher selects a few criteria and chooses members of a population randomly. All the members have an equal opportunity to participate in the sample with this selection parameter.
Non-probability sampling: In non-probability sampling, the researcher randomly chooses members for research. This sampling method is not a fixed or predefined selection process. This makes it difficult for all population elements to have equal opportunities to be included in a sample.

This blog discusses the various probability and non-probability sampling methods you can implement in any market research study.

LEARN ABOUT: Survey Sampling

Probability sampling is a technique in which researchers choose samples from a larger population based on the theory of probability. This sampling method considers every member of the population and forms samples based on a fixed process.

For example, in a population of 1000 members, every member will have a 1/1000 chance of being selected to be a part of a sample. Probability sampling eliminates sampling bias in the population and allows all members to be included in the sample.

There are four types of probability sampling techniques:

Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the 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, they would likely 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 representing a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inferences from the feedback. For example, suppose the United States government wishes to evaluate the number of immigrants living in the Mainland US. In that case, they can divide it into clusters based on 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 and provide insightful immigration data.
Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, 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. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).
Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then draw a sample from each group separately. For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results.

LEARN ABOUT: Purposive Sampling

There are multiple uses of probability sampling:

Reduce Sample Bias: Using the probability sampling method, the research bias in the sample derived from a population is negligible to non-existent. The sample selection mainly depicts the researcher’s understanding and inference. Probability sampling leads to higher-quality data collection as the sample appropriately represents the population.
Diverse Population: When the population is vast and diverse, it is essential to have adequate representation so that the data is not skewed toward one demographic . For example, suppose Square would like to understand the people that could make their point-of-sale devices. In that case, a survey conducted from a sample of people across the US from different industries and socio-economic backgrounds helps.
Create an Accurate Sample: Probability sampling helps the researchers plan and create an accurate sample. This helps to obtain well-defined data.

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

Four types of non-probability sampling explain the purpose of this sampling method in a better manner:

Convenience sampling: This method depends 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 because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations with 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 mall entrance and giving out pamphlets randomly.
Judgmental or purposive sampling: Judgmental or purposive samples are formed at the researcher’s discretion. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample.
Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, surveying shelterless people or illegal immigrants will be extremely challenging. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method when the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids. Not many victims will readily respond to the questions. Still, 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 , members in this sampling technique selection happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples.

Non-probability sampling is used for the following:

Create a hypothesis: Researchers use the non-probability sampling method to create an assumption when limited to no prior information is available. This method helps with the immediate return of data and builds a base for further research.
Exploratory research: Researchers use this sampling technique widely when conducting qualitative research, pilot studies, or exploratory research .
Budget and time constraints: The non-probability method when there are budget and time constraints, and some preliminary data must be collected. Since the survey design is not rigid, it is easier to pick respondents randomly and have them take the survey or questionnaire .

For any research, it is essential to choose a sampling method accurately to meet the goals of your study. The effectiveness of your sampling relies on various factors. Here are some steps expert researchers follow to decide the best sampling method.

Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy.
Identify the effective sampling techniques that might potentially achieve the research goals.
Test each of these methods and examine whether they help achieve your goal.
Select the method that works best for the research.

Unlock the power of accurate sampling!

We have looked at the different types of sampling methods above and their subtypes. To encapsulate the whole discussion, though, the significant differences between probability sampling methods and non-probability sampling methods are as below:

Now that we have learned how different sampling methods work and are widely used by researchers in market research so that they don’t need to research the entire population to collect actionable insights, let’s go over a tool that can help you manage these insights.

LEARN ABOUT: 12 Best Tools for Researchers

QuestionPro understands the need for an accurate, timely, and cost-effective method to select the proper sample; that’s why we bring QuestionPro Software, a set of tools that allow you to efficiently select your target audience , manage your insights in an organized, customizable repository and community management for post-survey feedback.

Don’t miss the chance to elevate the value of research.

FREE TRIAL LEARN MORE

MORE LIKE THIS

Unlocking Creativity With 10 Top Idea Management Software

Mar 23, 2024

20 Best Website Optimization Tools to Improve Your Website

Mar 22, 2024

15 Best Digital Customer Experience Software of 2024

15 Best Product Experience Software of 2024

Other categories.

Academic Research
Artificial Intelligence
Assessments
Brand Awareness
Case Studies
Communities
Consumer Insights
Customer effort score
Customer Engagement
Customer Experience
Customer Loyalty
Customer Research
Customer Satisfaction
Employee Benefits
Employee Engagement
Employee Retention
Friday Five
General Data Protection Regulation
Insights Hub
Life@QuestionPro
Market Research
Mobile diaries
Mobile Surveys
New Features
Online Communities
Question Types
Questionnaire
QuestionPro Products
Release Notes
Research Tools and Apps
Revenue at Risk
Survey Templates
Training Tips
Uncategorized
Video Learning Series
What’s Coming Up
Workforce Intelligence

What We Offer

With a comprehensive suite of qualitative and quantitative capabilities and 55 years of experience in the industry, Sago powers insights through adaptive solutions.

Recruitment
Communities
Methodify® Automated research
QualBoard® Digital Discussions
QualMeeting® Digital Interviews
Global Qualitative
Global Quantitative
In-Person Facilities
Research Consulting
Europe Solutions
Clinical Research
Human Factors
Neuromarketing Tools
Trial & Jury Consulting

Who We Serve

Form deeper customer connections and make the process of answering your business questions easier. Sago delivers unparalleled access to the audiences you need through adaptive solutions and a consultative approach.

Consumer Packaged Goods
Financial Services
Media Technology
Marketing Research

With a 55-year legacy of impact, Sago has proven we have what it takes to be a long-standing industry leader and partner. We continually advance our range of expertise to provide our clients with the highest level of confidence.

Global Offices
Partnerships & Certifications
News & Media
Researcher Events

Steve Schlesinger, Quirks Lifetime Achievement Award

Sago Executive Chairman Steve Schlesinger to Receive Quirk’s Lifetime Achievement Award

16 Ways to Prepare for the Next Holiday Campaign

Sago Unveils Electric Vehicle Panel to Drive Industry Success

Drop into your new favorite insights rabbit hole and explore content created by the leading minds in market research.

Case Studies
Knowledge Kit

Moderator Minutes: Maximizing Efficient with QualBoard – Streamline Your Projects with Support & AI

business team strategizing with research

2024 Trends in Research: Fact or Fiction?

Get in touch

Account Logins

Different Types of Sampling Techniques in Qualitative Research

Resources , Blog

Key Takeaways:

Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling.
Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results.
It’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique for your qualitative research.

Qualitative research seeks to understand social phenomena from the perspective of those experiencing them. It involves collecting non-numerical data such as interviews, observations, and written documents to gain insights into human experiences, attitudes, and behaviors. While qualitative research can provide rich and nuanced insights, the accuracy and generalizability of findings depend on the quality of the sampling process. Sampling is a critical component of qualitative research as it involves selecting a group of participants who can provide valuable insights into the research questions.

This article explores different types of sampling techniques used in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques used in qualitative research. and then compare and contrast these techniques to provide guidance on choosing the most appropriate method for a particular study. Additionally, you’ll find best practices for sampling and learn about ethical considerations researchers need to consider in selecting a sample. Overall, this article aims to help researchers conduct effective and high-quality sampling in qualitative research.

Factors to Consider When Choosing a Sampling Technique

Practical approaches to sampling: recommended practices, final thoughts, get expert guidance on your sample needs.

Want expert input on the best sampling technique for your qualitative research project? Book a consultation for trusted advice.

Request a consultation

4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques used in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We’ll break down the definition of each technique, when to use it, and its advantages and disadvantages.

1. Purposive Sampling

Purposive sampling, or judgmental sampling, is a non-probability sampling technique commonly used in qualitative research. In purposive sampling, researchers intentionally select participants with specific characteristics or unique experiences related to the research question. The goal is to identify and recruit participants who can provide rich and diverse data to enhance the research findings.

Purposive sampling is used when researchers seek to identify individuals or groups with particular knowledge, skills, or experiences relevant to the research question. For instance, in a study examining the experiences of cancer patients undergoing chemotherapy, purposive sampling may be used to recruit participants who have undergone chemotherapy in the past year. Researchers can better understand the phenomenon under investigation by selecting individuals with relevant backgrounds.

Purposive Sampling: Strengths and Weaknesses

Purposive sampling is a powerful tool for researchers seeking to select participants who can provide valuable insight into their research question. This method is advantageous when studying groups with technical characteristics or experiences where a random selection of participants may yield different results.

One of the main advantages of purposive sampling is the ability to improve the quality and accuracy of data collected by selecting participants most relevant to the research question. This approach also enables researchers to collect data from diverse participants with unique perspectives and experiences related to the research question.

However, researchers should also be aware of potential bias when using purposive sampling. The researcher’s judgment may influence the selection of participants, resulting in a biased sample that does not accurately represent the broader population. Another disadvantage is that purposive sampling may not be representative of the more general population, which limits the generalizability of the findings. To guarantee the accuracy and dependability of data obtained through purposive sampling, researchers must provide a clear and transparent justification of their selection criteria and sampling approach. This entails outlining the specific characteristics or experiences required for participants to be included in the study and explaining the rationale behind these criteria. This level of transparency not only helps readers to evaluate the validity of the findings, but also enhances the replicability of the research.

2. Convenience Sampling

When time and resources are limited, researchers may opt for convenience sampling as a quick and cost-effective way to recruit participants. In this non-probability sampling technique, participants are selected based on their accessibility and willingness to participate rather than their suitability for the research question. Qualitative research often uses this approach to generate various perspectives and experiences.

During the COVID-19 pandemic, convenience sampling was a valuable method for researchers to collect data quickly and efficiently from participants who were easily accessible and willing to participate. For example, in a study examining the experiences of university students during the pandemic, convenience sampling allowed researchers to recruit students who were available and willing to share their experiences quickly. While the pandemic may be over, convenience sampling during this time highlights its value in urgent situations where time and resources are limited.

Convenience Sampling: Strengths and Weaknesses

Convenience sampling offers several advantages to researchers, including its ease of implementation and cost-effectiveness. This technique allows researchers to quickly and efficiently recruit participants without spending time and resources identifying and contacting potential participants. Furthermore, convenience sampling can result in a diverse pool of participants, as individuals from various backgrounds and experiences may be more likely to participate.

While convenience sampling has the advantage of being efficient, researchers need to acknowledge its limitations. One of the primary drawbacks of convenience sampling is that it is susceptible to selection bias. Participants who are more easily accessible may not be representative of the broader population, which can limit the generalizability of the findings. Furthermore, convenience sampling may lead to issues with the reliability of the results, as it may not be possible to replicate the study using the same sample or a similar one.

To mitigate these limitations, researchers should carefully define the population of interest and ensure the sample is drawn from that population. For instance, if a study is investigating the experiences of individuals with a particular medical condition, researchers can recruit participants from specialized clinics or support groups for that condition. Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.

3. Snowball Sampling

Snowball sampling, also called referral sampling, is a unique approach researchers use to recruit participants in qualitative research. The technique involves identifying a few initial participants who meet the eligibility criteria and asking them to refer others they know who also fit the requirements. The sample size grows as referrals are added, creating a chain-like structure.

Snowball sampling enables researchers to reach out to individuals who may be hard to locate through traditional sampling methods, such as members of marginalized or hidden communities. For instance, in a study examining the experiences of undocumented immigrants, snowball sampling may be used to identify and recruit participants through referrals from other undocumented immigrants.

Snowball Sampling: Strengths and Weaknesses

Snowball sampling can produce in-depth and detailed data from participants with common characteristics or experiences. Since referrals are made within a network of individuals who share similarities, researchers can gain deep insights into a specific group’s attitudes, behaviors, and perspectives.

4. Theoretical Sampling

Theoretical sampling is a sophisticated and strategic technique that can help researchers develop more in-depth and nuanced theories from their data. Instead of selecting participants based on convenience or accessibility, researchers using theoretical sampling choose participants based on their potential to contribute to the emerging themes and concepts in the data. This approach allows researchers to refine their research question and theory based on the data they collect rather than forcing their data to fit a preconceived idea.

Theoretical sampling is used when researchers conduct grounded theory research and have developed an initial theory or conceptual framework. In a study examining cancer survivors’ experiences, for example, theoretical sampling may be used to identify and recruit participants who can provide new insights into the coping strategies of survivors.

Theoretical Sampling: Strengths and Weaknesses

One of the significant advantages of theoretical sampling is that it allows researchers to refine their research question and theory based on emerging data. This means the research can be highly targeted and focused, leading to a deeper understanding of the phenomenon being studied. Additionally, theoretical sampling can generate rich and in-depth data, as participants are selected based on their potential to provide new insights into the research question.

Participants are selected based on their perceived ability to offer new perspectives on the research question. This means specific perspectives or experiences may be overrepresented in the sample, leading to an incomplete understanding of the phenomenon being studied. Additionally, theoretical sampling can be time-consuming and resource-intensive, as researchers must continuously analyze the data and recruit new participants.

To mitigate the potential for bias, researchers can take several steps. One way to reduce bias is to use a diverse team of researchers to analyze the data and make participant selection decisions. Having multiple perspectives and backgrounds can help prevent researchers from unconsciously selecting participants who fit their preconceived notions or biases.

Another solution would be to use reflexive sampling. Reflexive sampling involves selecting participants aware of the research process and provides insights into how their biases and experiences may influence their perspectives. By including participants who are reflexive about their subjectivity, researchers can generate more nuanced and self-aware findings.

Choosing the proper sampling technique is one of the most critical decisions a researcher makes when conducting a study. The preferred method can significantly impact the accuracy and reliability of the research results.

For instance, purposive sampling provides a more targeted and specific sample, which helps to answer research questions related to that particular population or phenomenon. However, this approach may also introduce bias by limiting the diversity of the sample.

Conversely, convenience sampling may offer a more diverse sample regarding demographics and backgrounds but may also introduce bias by selecting more willing or available participants.

Snowball sampling may help study hard-to-reach populations, but it can also limit the sample’s diversity as participants are selected based on their connections to existing participants.

Theoretical sampling may offer an opportunity to refine the research question and theory based on emerging data, but it can also be time-consuming and resource-intensive.

Additionally, the choice of sampling technique can impact the generalizability of the research findings. Therefore, it’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique. By doing so, researchers can select the most appropriate method for their research question and ensure the validity and reliability of their findings.

Tips for Selecting Participants

When selecting participants for a qualitative research study, it is crucial to consider the research question and the purpose of the study. In addition, researchers should identify the specific characteristics or criteria they seek in their sample and select participants accordingly.

One helpful tip for selecting participants is to use a pre-screening process to ensure potential participants meet the criteria for inclusion in the study. Another technique is using multiple recruitment methods to ensure the sample is diverse and representative of the studied population.

Ensuring Diversity in Samples

Diversity in the sample is important to ensure the study’s findings apply to a wide range of individuals and situations. One way to ensure diversity is to use stratified sampling, which involves dividing the population into subgroups and selecting participants from each subset. This helps establish that the sample is representative of the larger population.

Maintaining Ethical Considerations

When selecting participants for a qualitative research study, it is essential to ensure ethical considerations are taken into account. Researchers must ensure participants are fully informed about the study and provide their voluntary consent to participate. They must also ensure participants understand their rights and that their confidentiality and privacy will be protected.

A qualitative research study’s success hinges on its sampling technique’s effectiveness. The choice of sampling technique must be guided by the research question, the population being studied, and the purpose of the study. Whether purposive, convenience, snowball, or theoretical sampling, the primary goal is to ensure the validity and reliability of the study’s findings.

By thoughtfully weighing the pros and cons of each sampling technique, researchers can make informed decisions that lead to more reliable and accurate results. In conclusion, carefully selecting a sampling technique is integral to the success of a qualitative research study, and a thorough understanding of the available options can make all the difference in achieving high-quality research outcomes.

If you’re interested in improving your research and sampling methods, Sago offers a variety of solutions. Our qualitative research platforms, such as QualBoard and QualMeeting, can assist you in conducting research studies with precision and efficiency. Our robust global panel and recruitment options help you reach the right people. We also offer qualitative and quantitative research services to meet your research needs. Contact us today to learn more about how we can help improve your research outcomes.

Find the Right Sample for Your Qualitative Research

Trust our team to recruit the participants you need using the appropriate techniques. Book a consultation with our team to get started .

north carolina state flag on flag pole against blue sky

The Deciders February 2024: African American voters in North Carolina

The Swing Voter Project in Michigan – February 2024

hands holding phone and touching page with data on it

OnDemand: 5 Steps to High-Quality Data: Mitigating the Challenges of Data Quality in Quantitative Research

How Leading Brands Use Insights to Win Market Share

The Deciders January 2024: Trump-Voting Women Who Oppose Dobbs

The State of New Year’s Resolutions: A Closer Look at Mindsets and Habits

It’s Time to Change the Way We Write Screeners

A Closer Look at New Year’s Resolutions

The Swing Voter Project in Nevada – January 2024

OnDemand: The Secret Sauce to Finding Perfect Participants: Strategies for Recruiting Hard-to-Reach Audiences

Take a deep dive into your favorite market research topics

How can we help support you and your research needs?

BEFORE YOU GO

Have you considered how to harness AI in your research process? Check out our on-demand webinar for everything you need to know

No internet connection.

All search filters on the page have been cleared., your search has been saved..

All content
Dictionaries
Encyclopedias
Expert Insights
Foundations
How-to Guides
Journal Articles
Little Blue Books
Little Green Books
Project Planner
Tools Directory
Sign in to my profile My Profile

Sign in Signed in
My profile My Profile

Offline Playback link

Have you created a personal profile? sign in or create a profile so that you can create alerts, save clips, playlists and searches.

Overview of Different Survey Sampling Techniques

Watching now: Chapter 1: Probability Sampling Technique Start time: 00:00:00 End time: 00:03:27
Chapter 2: Non-Probability Sampling Technique Start time: 00:03:27 End time: 00:06:31
Chapter 3: Sampling With and Without Replacement Start time: 00:06:31 End time: 00:09:47

Video Type: Tutorial

(Academic). (2021). Overview of different survey sampling techniques [Video]. Sage Research Methods. https:// doi. org/10.4135/9781529695182

"Overview of Different Survey Sampling Techniques." In Sage Video . : Betazeta Pty Ltd ATF Lawoko Holdings Trust, 2021. Video, 00:09:47. https:// doi. org/10.4135/9781529695182.

, 2021. Overview of Different Survey Sampling Techniques , Sage Video. [Streaming Video] London: Sage Publications Ltd. Available at: <https:// doi. org/10.4135/9781529695182 & gt; [Accessed 25 Mar 2024].

Overview of Different Survey Sampling Techniques . Online video clip. SAGE Video. London: SAGE Publications, Ltd., 30 Jan 2024. doi: https:// doi. org/10.4135/9781529695182. 25 Mar 2024.

Overview of Different Survey Sampling Techniques [Streaming video]. 2021. doi:10.4135/9781529695182. Accessed 03/25/2024

Please log in from an authenticated institution or log into your member profile to access the email feature.

Add this content to your learning management system or webpage by copying the code below into the HTML editor on the page. Look for the words HTML or </>. Learn More about Embedding Video icon link (opens in new window)

Sample View:

This is an image of a Sage Research Methods video on a Learning Management System

Download PDF opens in new window
icon/tools/download-video icon/tools/video-downloaded Download video Downloading... Video downloaded

Dr. Charles Lawoko gives an overview of survey sampling methods, including both probability and non-probability methods.

Chapter 1: Probability Sampling Technique

Start time: 00:00:00
End time: 00:03:27

Chapter 2: Non-Probability Sampling Technique

Start time: 00:03:27
End time: 00:06:31

Chapter 3: Sampling With and Without Replacement

Start time: 00:06:31
End time: 00:09:47
Product: Sage Research Methods Video: Quantitative and Mixed Methods
Type of Content: Tutorial
Title: Overview of Different Survey Sampling Techniques
Publisher: Betazeta Pty Ltd ATF Lawoko Holdings Trust
Series: Introduction to Quantitative Research Methods
Publication year: 2021
Online pub date: January 30, 2024
Discipline: Sociology , Communication and Media Studies , Business and Management , Nursing , Marketing , Science , History , Technology , Education , Computer Science , Economics , Criminology and Criminal Justice , Mathematics , Medicine , Dentistry , Public Health , Psychology , Counseling and Psychotherapy , Health , Anthropology , Social Policy and Public Policy , Political Science and International Relations , Social Work , Engineering , Geography
Methods: Quantitative data collection , Quantitative data analysis , Survey research , Sampling , Probability sampling , Nonprobability sampling
Duration: 00:09:47
DOI: https:// doi. org/10.4135/9781529695182
Keywords: probability sample , probability sampling , replacement control , Sampling , Sampling techniques Show all Show less

Sign in to access this content

Get a 30 day free trial, more like this, sage recommends.

We found other relevant content for you on other Sage platforms.

Have you created a personal profile? Login or create a profile so that you can save clips, playlists and searches

Navigating away from this page will delete your results

Please save your results to "My Self-Assessments" in your profile before navigating away from this page.

You must have a valid academic email address to sign up.

Get off-campus access

View or download all content my institution has access to.

view my profile
view my lists

Statistics Made Easy

Types of Sampling Methods (With Examples)

Researchers are often interested in answering questions about populations like:

What is the average height of a certain species of plant?
What is the average weight of a certain species of bird?
What percentage of citizens in a certain city support a certain law?

One way to answer these questions is to go around and collect data on every single individual in the population of interest.

However, this is typically too costly and time-consuming which is why researchers instead take a sample of the population and use the data from the sample to draw conclusions about the population as a whole.

Example of taking a sample from a population

There are many different methods researchers can potentially use to obtain individuals to be in a sample. These are known as sampling methods .

In this post we share the most commonly used sampling methods in statistics, including the benefits and drawbacks of the various methods.

Probability Sampling Methods

The first class of sampling methods is known as probability sampling methods because every member in a population has an equal probability of being selected to be in the sample.

Simple random sample

Definition: Every member of a population has an equal chance of being selected to be in the sample. Randomly select members through the use of a random number generator or some means of random selection.

Example: We put the names of every student in a class into a hat and randomly draw out names to get a sample of students.

Benefit: Simple random samples are usually representative of the population we’re interested in since every member has an equal chance of being included in the sample.

Stratified random sample

Definition: Split a population into groups. Randomly select some members from each group to be in the sample.

Example: Split up all students in a school according to their grade – freshman, sophomores, juniors, and seniors. Ask 50 students from each grade to complete a survey about the school lunches.

Benefit: Stratified random samples ensure that members from each group in the population are included in the survey.

Cluster random sample

Definition: Split a population into clusters. Randomly select some of the clusters and include all members from those clusters in the sample.

Example: A company that gives whale watching tours wants to survey its customers. Out of ten tours they give one day, they randomly select four tours and ask every customer about their experience.

Benefit: Cluster random samples get every member from some of the groups, which is useful when each group is reflective of the population as a whole.

Systematic random sample

Definition: Put every member of a population into some order. Choosing a random starting point and select every n th member to be in the sample.

Example: A teacher puts students in alphabetical order according to their last name, randomly chooses a starting point, and picks every 5th student to be in the sample.

Benefit: Systematic random samples are usually representative of the population we’re interested in since every member has an equal chance of being included in the sample.

Non-probability Sampling Methods

Another class of sampling methods is known as non-probability sampling methods because not every member in a population has an equal probability of being selected to be in the sample.

This type of sampling method is sometimes used because it’s much cheaper and more convenient compared to probability sampling methods. It’s often used during exploratory analysis when researchers simply want to gain an initial understanding of a population.

However, the samples that result from these sampling methods cannot be used to draw inferences about the populations they came from because they typically aren’t representative samples.

Convenience sample

Definition: Choose members of a population that are readily available to be included in the sample.

Example: A researcher stands in front of a library during the day and polls people that happen to walk by.

Drawback: Location and time of day will affect the results. More than likely, the sample will suffer from undercoverage bias since certain people (e.g. those who work during the day) will not be represented as much in the sample.

Voluntary response sample

Definition: A researcher puts out a request for volunteers to be included in a study and members of a population voluntarily decide to be included in the sample or not.

Example: A radio host asks listeners to go online and take a survey on his website.

Drawback: People who voluntarily respond will likely have stronger opinions (positive or negative) than the rest of the population, which makes them an unrepresentative sample. Using this sampling method, the sample is likely to suffer from nonresponse bias – certain groups of people are simply less likely to provide responses.

Snowball sample

Definition: Researchers recruit initial subjects to be in a study and then ask those initial subjects to recruit additional subjects to be in the study. Using this approach, the sample size “snowballs” bigger and bigger as each additional subject recruits more subjects.

Example: Researchers are conducting a study of individuals with rare diseases, but it’s difficult to find individuals who actually have the disease. However, if they can find just a few initial individuals to be in the study then they can ask them to recruit further individuals they may know through a private support group or through some other means.

Drawback: Sampling bias is likely to occur. Because initial subjects recruit additional subjects, it’s likely that many of the subjects will share similar traits or characteristics that might be unrepresentative of the larger population under study. Thus, findings from the sample can’t be extrapolated to the population.

Read more about snowballing sampling here .

Purposive sample

Definition: Researchers recruit individuals based on who they think will be most useful based on the purpose of their study.

Example: Researchers want to know about the opinions that individuals in a city have about a potential new rock climbing gym being placed in the city square so they purposely seek out individuals that hang out at other rock climbing gyms around the city.

Drawback: The individuals in the sample are unlikely to be representative of the overall population. Thus, findings from the sample can’t be extrapolated to the population.

Published by Zach

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

thank you.. helped me a lot..

Glad to help!

Thankyou so much for this info. It helped me a lot

What are Sampling Techniques? Different Types and Methods

Home Blog Data Science What are Sampling Techniques? Different Types and Methods

Data is the backbone of the majority of what researchers and data scientists do, and they require data to undertake experiments, analyze scenarios, and test ideas. The data samples come from the study population, and samples are the selected portion of the data that represent the whole population. However, dealing with enormous amounts of data is one of the main challenges in data analytics . It is unnecessary and even impractical to investigate the entire population when researching a specific group. Data sampling is the process of analyzing data from a small group of individuals in a larger group. Data sampling allows you to research using various sampling techniques in data analytics without looking at the complete dataset. But what is the sampling technique? Essentially, it's the methods used to obtain a subset of data from a larger set for analysis. However, It is crucial for a person with a career to make sense of data, navigate it, and use it to impact a world filled with data. KnowledgeHut is an online platform focused on providing outcome-based immersive learning experiences to learners.

Now, build the required skills to learn data science and analytics with the Data Science Certificate online from KnowledgeHut and pursue your lucrative tech career seamlessly.

What is Sampling?

Statistics defines sampling as the process of gathering information about a population from a subset, like a selected individual or a small group and analyzing that information to study the whole population. The sample space constitutes the foundation of data which in turn is responsible for determining the accuracy of the study or research. Sampling, however, is not as simple as it seems. To land an accurate result, the sample size needs to be accurate, followed by implementing the right sampling methods based on the sample size. based on sample size.

Sampling Steps

An analyst needs to follow certain steps in order to reach conclusions from a broader perspective. The Sampling steps include the following -

Step 1: Identity and clearly define the target group/population.
Step 2: Create a specific sampling frame.
Step 3: Select the right sampling methods to be used.
Step 4: Specify the sample size.
Step 5: Collect the required sampled data.

Major Types of Sampling Methods

There are two types of sampling methods used in market action research -

1. Probability Sampling

In the probability sampling approach, a researcher selects a few criteria and randomly selects individuals from a population. Using this selection parameter, each member has an equal chance of participating in the sample.

2. Non-Probability Sampling

In this type of sampling, randomly chosen participants are used by researchers. This type of sampling is not a set or predetermined selection procedure. As a result, it is difficult for all parts of a population to have equal chances of being included in a sample.

The expert-designed Bootcamp for Data Science can help you pursue your dream career in data science with comprehensive real-world skills and knowledge.

Different Types of Sampling Techniques

To choose and reach every unit in the population, probability sampling is typically favored when conducting large-scale investigations, particularly when a sample frame is available. We can measure the standard deviation of estimations, create confidence intervals, and formally test hypotheses using probability sampling.

The different sampling methods in probability sampling include:

A. Simple random sampling

Simple random sampling gives each member of the population an equal chance of being chosen for the sample. It's similar to drawing a name out of a bowl. Simple random sampling can be performed by anonymizing the population, for example, assigning a number to each object or person in the population and selecting numbers randomly.

Simple random sampling eliminates any bias from the sampling process and is inexpensive, simple, and quick to use. It also provides the researcher with no means of control, increasing the likelihood that unrepresentative groupings will be chosen randomly.

Applications

Lottery techniques,
Split and Train in machine learning.

Advantages

Little bias due to the random nature of the sample collection
Given the usage of random generators, sample selection is straightforward.
Due to representativeness, the findings can be broadly interpreted.

Disadvantage

All responders' potential availability might be expensive and time-consuming.
Huge sample size

B. Cluster sampling

Cluster sampling involves selecting portions of the target population randomly from groupings rather than from single units. These might be already established groupings like residents of particular postal codes or students who attend a particular academic year.

However, in the case of a two-stage cluster sampling, the cluster can be randomly chosen in the first stage, and then the cluster can be randomly chosen again in the second stage.

Applications

One Stage Cluster
Two Stage Cluster
Reduces time and money.
Practical and easy to use.
Larger sample sizes can be used.
Increased errors in sampling
The sample frame's variety might not be well reflected.

C. Systematic sampling

In systematic sampling, sometimes called systematic clustering, only the first item is subject to random selection. Afterward, every nth thing or person is chosen according to a rule. Although there is some element of randomness, the researcher may control the frequency at which things are chosen, ensuring that the picks won't unintentionally group.

Quality Control: To statistically check the quality of their goods, industrial companies frequently utilize systematic sampling. Here, a sample is gathered by periodically grabbing something from the present production stream.
Auditing: The most obvious method to sample an account list for an audit of savings accounts is to look for conformity with accounting processes.
Cost- and time-effective
increases the sample's distribution across the population.

Disadvantage

It is important to know the full population.
Probable sample bias in case the dataset contains periodic patterns.

D. Stratified random sampling.

Stratified sampling uses random selection within established groupings. Knowing information about the target population helps researchers stratify it for research purposes. Although stratified sampling offers advantages, it also raises the issue of subdividing a population, increasing the chance of bias.

The three types of stratified random sampling are:

Proportionate: When compared to the overall population, the sample size for each stratum in this method is proportionate to the number of the stratum's population.
Disproportionate: A proportionate stratified random sampling differs from a disproportionate stratified random sampling only by its sampling fraction. Disproportionate sampling results in different sampling fractions for different strata.
Optimal: Variable standard deviation determines the size of these strata in optimal stratified random sampling.

Advantages

A greater percentage of all groups represented.
The estimations can be as precise if there is uniformity within strata and variation across strata.
Complex methodology
Possibly more costly and time-consuming
Requires understanding of strata membership.

Non-probability sampling techniques are selected when the precision of the results is not crucial. Non-probability sampling doesn't need a frame, is affordable, and is simple. The bias in the results can be lessened if a non-probability sample is appropriately implemented. Making assumptions about the entire population is hazardous to make, according to the fundamental drawback of non-probability sampling.

The different types of sampling techniques in non-probability sampling include:

A. Convenience sampling

The simplest sampling technique is convenience sampling, where participants are picked up based on their availability and desire to participate in the survey. The sample could not be representative of the population as a whole. Hence the results are subject to severe bias.

This type of sampling technique is usually conducted in offices and social networking sites. Example of sampling techniques includes online surveys, product surveys etc.

Obtaining a sample is comparatively easy.
Cost-effective
Participants are easily accessible.
Results cannot be generalized.
Possibility of an imbalance in the population's representation
Rise of substantial prejudice or biases in the sample frame

B. Judgmental or purposive sampling

In judgment (or purposeful) sampling, a researcher uses judgment to select individuals from the population to take part in the study. Researchers frequently think they can use good judgment to gather a representative sample while saving time and money.

There is a likelihood that the results will be extremely accurate with a small margin of error because the researcher's expertise is essential for establishing a group in this sampling approach.

This sampling method is used for a small group of chosen groups.

Relatively inexpensive and less time consuming
Enables researchers to directly contact their target market.
Near-real-time outcomes
Risk of the researcher making mistakes in judgment
Bias levels are high, and dependability is low.
Difficulty in generalizing study results

3. Snowball sampling

This sampling technique entails primary data sources proposing other prospective primary data sources that may be employed in the study. To create more subjects, the snowball sampling approach relies on referrals from the original participants. As a result, using this sampling technique, sample group members are chosen by chain referral.

When examining difficult-to-reach groups, the social sciences frequently adopt this sampling methodology. As more subjects who are known to the existing subjects are nominated, the sample grows in size like a snowball. For instance, participants can be asked to suggest more users for interviews while researching risk behaviors among intravenous drug users.

Three sub-parts of snowball sampling include -

Linear snowball sampling - Only one subject is recruited, and the subject only makes one referral.
Non-discriminatory exponential snowball sampling - One subject is recruited, and that one subject offers several references.
Exponentially discriminative snowball sampling - One subject is recruited, who generates several references. However, only one topic is chosen from the recommendations.
Researchers can access uncommon subjects in a certain community.
Inexpensive and simple to execute.
The additional subjects can be recruited without the assistance of recruiting staff.
It's possible that the sample isn't representative.
Bias in sampling might exist.
It might be challenging to infer conclusions about the wider population with certainty since the sample is liable to biases.

D. Quota sampling

Quota sampling is the most used sampling technique used by most market researchers. The survey population is split up into subgroups that are mutually exclusive by the researchers. These categories are chosen based on well-known characteristics, qualities, or interests. The researcher chooses representative samples from each class.

Quota sampling is carried out in the following steps -

Separate the population into distinct subgroups.
Determine the percentage of auxiliary groupings in the population.
Choose unique subjects for every group of subgroups.
Make sure the sample represents the population.
Controlled quota sampling - This limit the samples that researchers can choose by introducing certain restrictions.
Uncontrolled quota sampling - In uncontrolled quota sampling, a researcher is allowed to select the individuals of the sample group.
Independent of sample frames
Provides researchers with the opportunity to study a particular subgroup.
Possibility of an oversized sample
Impossible to calculate the sampling error.
Researchers' incompetence and/or lack of experience may lead to biases and substandard work.

Factors While Choosing Probability and Non-Probability Samples

To achieve the objectives of the study accurately, it is critical to pick a sampling technique carefully for every research project. However, it is important to note that different sampling methods require different elements to form the sample frame. The efficiency of the sample depends on several variables, which include types of sampling methods require different elements to form the sample frame. The efficiency of the sample depends on a number of variables, which include:

To answer a research question, the sample size should be large enough but not so large that it becomes inefficient to sample.
The margin of error.
Depending on the study or use case, determine the best sampling method.
Deviate from any pre-established sample guidelines to rule out biases.
Omit hard-to-reach target groups.
Low response rates.

Difference Between Probability Sampling and Non-probability Sampling Methods

The various sampling techniques in research and their subtypes have already been considered. To summarise the entire subject, however, the key distinctions between probability sampling techniques and non-probability sampling techniques are as follows:

Importance of Sampling

In data analytics , sampling is the process of selecting a representative subset of a larger population. Sampling is a relatively easier technique to study a population closely from drawn samples. "What is sampling techniques in research?" is a common question among budding researchers. Sampling helps an analyst determine a given population's characteristics more cost-effectively and practically.

Sampling aims to collect data that can be used to draw conclusions about the wider population. There are many reasons why sampling is important in data analytics -

Sampling allows the data analysts to work with a smaller dataset, which can be more manageable and easier to work with.
Sampling can help to ensure that the data is representative of the population as a whole, which is important because it means that the conclusions drawn from the data are more likely to be accurate.
Sampling can help to reduce bias in data analysis. This is because Sampling allows analysts to select a representative subset of a population, which can help to reduce the skew that may be present in the data.
Sampling is an important tool that helps analysts to collect accurate and reliable data.

Important Terminologies Need to Know

1. population.

The population in statistics is the entire group of items from which a sample can be drawn. The population can be defined in terms of geographical location, age, gender, occupation, etc. A population can be very large, making it impractical or impossible to study all group members.

A sample is a set of data collected and used to determine the population parameters. Once the sample has been selected, data can be collected and analyzed in order to make inferences or predictions about the population as a whole.

3. Sampling

Sampling is the process of selecting a representative group from a larger population. The main purpose of sampling is to provide information about a population that can be used to make inferences or predictions about that population.

Research and surveys require a sample from a large population. Sampling is a great way to achieve that. As many might wonder, "What is sampling techniques?", it's essential to know that sampling techniques vary depending on the type of results that one wants. It is important to remember that different sampling techniques should be applied according to the case taken; keeping this in mind, we must choose the appropriate sampling techniques. You would have a highly accurate (and time-consuming) undertaking on your hands if you could ask everyone in a population to participate in your study and have everyone respond. You would have a similarly accurate effort if you could ask every single person in a population to participate and have each one respond.

But since that isn't practicable, sampling provides a "good enough" option that trades some accuracy for convenience and usability. How well you account for bias, non-sampling error, and sampling error in your survey design will determine how much precision you lose. KnowledgeHut is a trusted online platform dedicated to skill-based learning modules.

Learn Machine Learning with Python , data science, and programming courses with KnowledgeHut’s expert-backed structure and start adding new feathers to your portfolio for that dream career in no time.

Frequently Asked Questions (FAQs)

In statistical analysis, sampling techniques meaning is the process of selecting a specified number of observations from a larger population. Depending on the sort of study being done, a variety of methods, including systematic sampling and simple random sampling, may be employed to draw samples from a broader population.

Certain limitations of sampling techniques include the following -

Probability of biased results
Selecting a good sample frame can be difficult at times
Different sampling techniques demand different time frames

Data sampling in data science is a statistical analysis approach used to pick, move, and analyze a representative selection of data points in order to spot trends and patterns in the larger data set being looked at.

The salary range of a data scientist can be anywhere between ₹4 Lakhs to ₹24 Lakhs annually. The average salary of a data scientist in India is ₹11 Lakhs per annum.

With the rapid growth of automation and technology in business modules, data scientists are very much in demand, which is projected to grow even more in the coming years. So it can be said that data science is a very promising career with tremendous growth opportunities shortly.

Ashish Gulati

Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

Avail your free 1:1 mentorship session.

Something went wrong

Upcoming Data Science Batches & Dates

IMAGES

Sampling Method
PPT
Sampling Methods/Techniques of Sampling
A Data Scientist's Guide to 8 Types of Sampling Techniques
PPT
Types Of Sampling Sampling Methods With Examples

VIDEO

SAMPLING PROCEDURE AND SAMPLE (QUALITATIVE RESEARCH)
Sampling Techniques (Part 1): Random Sampling Techniques
Sampling Techniques
Sampling Methods
Sampling Techniques and Sample.Classification,Charecteristics with Example.|| Statistics
Types of Sampling Techniques #researchmethodology #biotechnology #samplingtechniques

COMMENTS

Sampling Methods
Sampling methods are crucial for conducting reliable research. In this article, you will learn about the types, techniques and examples of sampling methods, and how to choose the best one for your study. Scribbr also offers free tools and guides for other aspects of academic writing, such as citation, bibliography, and fallacy.
Sampling Methods
Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research. Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of ...
Sampling Methods In Reseach: Types, Techniques, & Examples
Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.
Sampling Methods
1. Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.
Sampling Techniques: Definition, Types, and Examples
The sampling technique is the method you employ while choosing a sample from a population. For example, you could select every 3rd person, everyone in a particular age group, and so on. You must carefully consider your study before choosing an appropriate sampling technique. It has a significant effect on your results.
Sampling Methods, Types & Techniques
Sampling strategies in research vary widely across different disciplines and research areas, and from study to study. There are two major types of sampling methods: probability and non-probability sampling. Probability sampling, also known as random sampling, is a kind of sample selection where randomization is used instead of deliberate choice ...
What are sampling methods and how do you choose the best one?
We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include ...
What are Sampling Methods? Techniques, Types, and Examples
Understand sampling methods in research, from simple random sampling to stratified, systematic, and cluster sampling. Learn how these sampling techniques boost data accuracy and representation, ensuring robust, reliable results. Check this article to learn about the different sampling method techniques, types and examples.
Sampling Methods: Different Types in Research
A sample is the subset of the population that you actually measure, test, or evaluate and base your results. Sampling methods are how you obtain your sample. Before beginning your study, carefully define the population because your results apply to the target population. You can define your population as narrowly as necessary to meet the needs ...
Sampling Methods & Strategies 101 (With Examples)
Stratified random sampling. Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly, but from within certain pre-defined subgroups (i.e., strata) that share a common trait.For example, you might divide the population into strata based on gender, ethnicity, age range or ...
Types of sampling methods
1. Cluster sampling- she puts 50 into random groups of 5 so we get 10 groups then randomly selects 5 of them and interviews everyone in those groups --> 25 people are asked. 2. Stratified sampling- she puts 50 into categories: high achieving smart kids, decently achieving kids, mediumly achieving kids, lower poorer achieving kids and clueless ...
Sampling Methods Explained: 10 Types of Sampling Methods
9. Snowball sampling: In this variation on convenience sampling, respondents may be asked if they know other people who might qualify to be part of the sample frame. Researchers then contact people whose names were provided and try to recruit them to participate in survey research.
Sampling Methods for Research: Types, Uses, and Examples
Evaluate your goals against time and budget. List the two or three most obvious sampling methods that will work for you. Confirm the availability of your resources (researchers, computer time, etc.) Compare each of the possible methods with your goals, accuracy, precision, resource, time, and cost constraints.
Sampling Techniques for Quantitative Research
Abstract. In quantitative research, collecting data from an entire population of a study is impractical in many instances. It squanders resources like time and money which can be minimized by choosing suitable sampling techniques between probability and non-probability methods. The chapter outlines a brief idea about the different categories of ...
Sampling Methods
Abstract. Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration.
Sampling Methods: Guide To All Types with Examples
Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and cost-effective method and hence forms the basis of any research design. Sampling techniques can be used in research survey software for optimum ...
Different Types of Sampling Techniques in Qualitative Research
Key Takeaways: Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling. Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results. It's crucial to consider the potential impact on the bias, sample diversity, and generalizability when ...
Overview of Different Survey Sampling Techniques
Product: Sage Research Methods Video: Quantitative and Mixed Methods; Type of Content: Tutorial Title: Overview of Different Survey Sampling Techniques Publisher: Betazeta Pty Ltd ATF Lawoko Holdings Trust Series: Introduction to Quantitative Research Methods; Publication year: 2021
Types of Sampling Methods (With Examples)
Stratified random sample. Definition: Split a population into groups. Randomly select some members from each group to be in the sample. Example: Split up all students in a school according to their grade - freshman, sophomores, juniors, and seniors. Ask 50 students from each grade to complete a survey about the school lunches.
8 Types of Sampling Techniques
Systematic Sampling. You can implement it using python as shown below — population = 100 step = 5 sample = [element for element in range(1, population, step)] print (sample) Multistage sampling. Under Multistage sampling, we stack multiple sampling methods one after the other. For example, at the first stage, cluster sampling can be used to choose clusters from the population and then we can ...
Sampling methods in Clinical Research; an Educational Review
Sampling types. There are two major categories of sampling methods ( figure 1 ): 1; probability sampling methods where all subjects in the target population have equal chances to be selected in the sample [ 1, 2] and 2; non-probability sampling methods where the sample population is selected in a non-systematic process that does not guarantee ...
Methodology Series Module 5: Sampling Strategies
The method by which the researcher selects the sample is the ' Sampling Method'. There are essentially two types of sampling methods: 1) probability sampling - based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling - based on researcher's choice, population that accessible & available.
Types of Sampling Methods in Research: Briefly Explained
The main goal of any marketing or statistical research is to provide quality results that are a reliable basis for decision-making. That is why the different types of sampling methods and techniques have a crucial role in research methodology and statistics. Your sample is one of the key factors that determine if your findings are accurate.
What are Sampling Techniques? Different Types and Methods
The Sampling steps include the following -. Step 1: Identity and clearly define the target group/population. Step 2: Create a specific sampling frame. Step 3: Select the right sampling methods to be used. Step 4: Specify the sample size. Step 5: Collect the required sampled data.

Have a language expert improve your writing

Sampling Methods | Types, Techniques & Examples

Table of contents

Sampling frame

Sample size

Receive feedback on language, structure, and formatting

1. Simple random sampling

2. Systematic sampling

3. Stratified sampling

4. Cluster sampling

1. Convenience sampling

2. Voluntary response sampling

3. Purposive sampling

4. Snowball sampling

5. Quota sampling

Cite this Scribbr article

Is this article helpful?

Shona McCombes

Sampling Methods – Types, Techniques and Examples

Sampling Methods

Types of Sampling Methods

Probability Sampling

Non-probability Sampling

Applications of Sampling Methods

Examples of Sampling Methods

How to Conduct Sampling Methods

When to use Sampling Methods

Purpose of Sampling Methods

Characteristics of Sampling Methods

Advantages of Sampling Methods

Limitations of Sampling Methods

About the author

Muhammad Hassan

You may also like

Stratified Random Sampling – Definition, Method...

Purposive Sampling – Methods, Types and Examples

Non-probability Sampling – Types, Methods and...

Cluster Sampling – Types, Method and Examples

Systematic Sampling – Types, Method and Examples

Snowball Sampling – Method, Types and Examples

Sampling Methods In Reseach: Types, Techniques, & Examples

The Purpose of Sampling

Random Sampling

Stratified Sampling

Opportunity Sampling

Systematic Sampling

Sample size

Reliability and Validity

Practical Considerations

Have a language expert improve your writing

Sampling Methods | Types, Techniques, & Examples

Table of contents

Sampling frame

Sample size

Prevent plagiarism, run a free check.

1. Simple random sampling

2. Systematic sampling

3. Stratified sampling

4. Cluster sampling

1. Convenience sampling

2. Voluntary response sampling

3. Purposive sampling

4. Snowball sampling

Cite this Scribbr article

Is this article helpful?

Shona McCombes

Sampling Techniques: Definition, Types, and Examples

Why Do We Need Sampling Techniques?

What is a Sampling Technique?

What are the different types of Sampling Techniques?

1. Probability Sampling

Probability Sampling Techniques

1. Simple Random Sampling Technique:

2. Systematic Sampling Technique:

3. Stratified Sampling Technique:

4. Cluster Sampling Technique:

Non-probability Sampling Techniques

2. Purposive Sampling Technique:

3. Snowball Sampling Technique:

4. Quota Sampling Technique: