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What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods  

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The  sample  represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population. 

There are two most common sampling methods: 

• Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population. 
• Non-probability sampling:  Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population. 

  Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories. 

What is probability sampling?  

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.   

Types of probability sampling  

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods: 

• Simple random sampling:  In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population. 

For example,  A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study. 

• Systematic sampling:  The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.  

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.  

• Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample. 

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals. 

• Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging. 

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey. 

Use s of probability sampling  

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following: 

• Representativeness  

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions. 

• Statistical inference  

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample. 

• Precision and reliability  

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations. 

• Generalizability  

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations. 

• Minimization of Selection Bias  

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population. 

What is non-probability sampling?  

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research. 

Types of Non-probability Sampling   

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail. 

• Convenience sampling:  In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation. 

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

• Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements. 

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample. 

• Quota sampling:  The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.  

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions. 

• Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes. 

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.  

• Snowball sampling:  This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations. 

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling  

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes: 

• Generating a hypothesis  

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.  

• Qualitative research  

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.  

• Convenience and pragmatism  

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.  

Probability vs Non-probability Sampling Methods  

Frequently asked questions  

• What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.  
• What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
• How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
• What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
• Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.  

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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.

Table of contents

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

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 .

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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.

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

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.

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.

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, 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.

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

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.

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.

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 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

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.

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.

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.

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.

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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

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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

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

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

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

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

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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

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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.

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 .

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!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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

Excellent and helpful for junior researcher!

Grad Coach tutorials are excellent – I recommend them to everyone doing research. I will be working with a sample of imprisoned women and now have a much clearer idea concerning sampling. Thank you to all at Grad Coach for generously sharing your expertise with students.

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Methodology

Systematic Sampling | A Step-by-Step Guide with Examples

Published on October 2, 2020 by Lauren Thomas . Revised on December 18, 2023.

Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k ) determined in advance.

If the population order is random or random-like (e.g., alphabetical), then this method will give you a representative sample that can be used to draw conclusions about your population of interest.

Table of contents

When to use systematic sampling, step 1: define your population, step 2: decide on your sample size, step 3: calculate sampling interval k, step 4: select the sample and collect data, other interesting articles, frequently asked questions about systematic sampling.

Systematic sampling is a method that imitates many of the randomization benefits of simple random sampling , but is slightly easier to conduct.

You can use systematic sampling with a list of the entire population , like you would in simple random sampling. However, unlike with simple random sampling, you can also use this method when you’re unable to access a list of your population in advance.

Order of the population

When using systematic sampling with a population list, it’s essential to consider the order in which your population is listed to ensure that your sample is valid .

If your population is in ascending or descending order, using systematic sampling should still give you a fairly representative sample, as it will include participants from both the bottom and top ends of the population.

For example, if you are sampling from a list of individuals ordered by age, systematic sampling will result in a population drawn from the entire age spectrum. If you instead used simple random sampling, it is possible (although unlikely) that you would end up with only younger or older individuals.

You should not use systematic sampling if your population is ordered cyclically or periodically, as your resulting sample cannot be guaranteed to be representative.

Systematic sampling without a population list

You can use systematic sampling to imitate the randomization of simple random sampling when you don’t have access to a full list of the population in advance.

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Like other methods of sampling, you must decide upon the population that you are studying.

In systematic sampling, you have two choices for data collection :

• You can select your sample ahead of time from a list and then approach the selected subjects to collect data, or
• You can approach every k th member of your target population to ask them to participate in your study.

Listing the population in advance

Ensure that your list contains the entire population and is not in a periodic or cyclic order. Ideally, it should be in a random or random-like (such as alphabetical) order, which will allow you to imitate the randomization benefits of simple random sampling .

Selecting your sample on the spot

If you cannot access a list in advance, but you are able to physically observe the population, you can also use systematic sampling to select subjects at the moment of data collection.

In this case, ensure that the timing and location of your sampling procedure covers the full population to avoid bias in the results.

Before you choose your interval, you must first decide on your sample size. It’s important to choose a representative number in order to avoid sampling bias . There are several different ways to choose a sample size, but one of the most common involves using a sample size calculator .

Once you have chosen your desired margin of error and confidence level , estimated total size of the population, and the standard deviation of the variables you are attempting to measure, this calculator will provide you with the sample size you should aim for.

When you know your target sample size, you can calculate your interval, k , by dividing your total estimated population size by your sample size. This can be a rough estimate rather than an exact calculation.

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If you already have a list of your population, randomly select a starting point on your list, and from there, select every k th member of the population to include in your sample.

If you don’t have a list, you choose every k th member of the population for your sample at the same time as collecting the data for your study.

As in simple random sampling , you should try to make sure every individual you have chosen for your sample actually participates in your study. If those who decide to participate do so for reasons connected with the variables that you are collecting, this could cause research bias to affect your study.

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

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 .

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

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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.

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Sampling Methods: A guide for researchers

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• 1 Arizona School of Dentistry & Oral Health A.T. Still University, Mesa, AZ, USA [email protected].
• PMID: 37553279

Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research question. Characteristics of individuals included in the sample population should be clearly defined to determine eligibility for study participation and improve power. Sample selection methods differ based on study design. The purpose of this short report is to review common sampling considerations and related errors.

Keywords: research design; sample size; sampling.

Copyright © 2023 The American Dental Hygienists’ Association.

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Welcome to our comprehensive guide on sampling methods! If you’re curious about how researchers gather data and draw meaningful conclusions, you’ve come to the right place. Sampling is a crucial aspect of research and data analysis, allowing us to select a subset of individuals or elements from a larger population. 

It is also very important to mention the sampling strategy that you use, in your research methodology section. In this guide, we’ll explore different sampling methods, and strategies, and provide real-life examples to help you understand and apply these techniques effectively!

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In this article, we will be looking at:

What are sampling methods?

Advantages of using sampling methods, difference between population and sample, sampling strategies and types.

Sampling methods are techniques used by researchers to select a smaller group of individuals or elements from a larger population. It’s like taking a small portion of something to represent the whole. Sampling methods help researchers collect data more efficiently and cost-effectively.

They allow us to study a subset of the population and draw meaningful insights without having to survey or observe every single individual or element. So, in a nutshell, sampling methods are tools that researchers use to choose a representative group from a larger population, making it easier to study and draw conclusions about the whole.

Sampling Frame

A sampling frame is a list or database of all the members of a population from which a sample can be drawn. The sampling frame is crucial because it helps ensure that every member of the population has a chance of being selected for the sample.

For example: Imagine you want to conduct a survey about the favorite ice cream flavors among students in your school. To do this, you need a list of all the students enrolled in the school. This list is your sampling frame. From this sampling frame, you can then select a subset of students who will participate in your survey. This subset is your sample.

Using sampling techniques in research and statistics offers several advantages. Here are some of the key benefits:

1. Efficiency: Sampling saves time and resources compared to studying an entire population.

2. Cost-effectiveness: Collecting data from a smaller group reduces expenses.

3. Feasibility: Sampling makes research possible when studying large or inaccessible populations.

4. Accuracy: Well-designed samples provide representative data for drawing valid conclusions.

5. Timeliness: Sampling enables faster data collection and analysis for timely decision-making.

6. Detailed information: Focusing on a smaller sample allows for more comprehensive data collection.

7. Flexibility: Different sampling techniques accommodate various research needs and populations.

A population is the entire group of individuals, objects, or events that you want to study or make conclusions about. It includes all members of a defined group.

On the other hand, a sample is a smaller subset of the population that is selected for study. It is a portion of the population that is used to represent the entire group.

For example:

Imagine you have a big jar full of candies of different colors. The jar with all the candies is the population. If you take a handful of candies from the jar, that handful is your sample. You can use the sample to make estimates or draw conclusions about the characteristics of all the candies in the jar (the population) without having to examine each and every candy.

In research and statistics, various types of sampling methods are used because it is often impractical, time-consuming, or impossible to study the entire population. By studying a representative sample, researchers can make inferences and generalizations about the larger population.

Sample size

A sample size refers to the number of individuals or observations included in a sample, which is a subset of the entire population being studied.

For example: Imagine you have a bag containing 100 marbles of different colors. The bag with all the marbles is your population. Now, you want to estimate the proportion of blue marbles in the bag.  You reach into the bag and randomly pick out 10 marbles. The number of marbles you picked (10) is your sample size. Based on the colors of these 10 marbles, you can make an estimate of the proportion of blue marbles in the entire bag.

Probability sampling

Probability sampling methods are ways to choose a sample from a population in a fair and unbiased manner. These methods make sure that every person or thing in the population has an equal chance of being picked for the sample.

Think of it like a lottery. In a lottery, every ticket has the same chance of being drawn. Similarly, in probability sampling, every individual in the population has the same chance of being selected for the sample.

Types of probability sampling methods

Simple random sampling.

Let’s start with the basics – simple random sampling. In this method, every individual in the population has an equal chance of being selected for the sample. It’s fair, and unbiased, and ensures that everyone gets a chance at being included.

To select a sample of 10 students from a class of 50 students, you can use simple random sampling:

a. Number each student from 1 to 50.

b. Use a random number generator or draw numbers from a hat to select 10 numbers.

c. The students corresponding to the selected numbers will be the sample.

d. Every student in the class had an equal probability of being chosen for the sample, and the selection was made randomly.

Stratified sampling

Stratified sampling involves dividing the population into distinct subgroups or strata based on certain characteristics. Then, we select a proportional number of individuals from each stratum to form the sample. This method ensures that our sample reflects the  diversity within the population and allows for more accurate analysis within each subgroup.

Imagine you have a box of 100 toys. The box contains three types of toys: 50 cars, 30 dolls, and 20 puzzles. You want to select a sample of 20 toys from the box to estimate the proportion of each type of toy in the entire box.

To do stratified sampling, you would:

a. Divide the toys into three strata (subgroups) based on their type: cars, dolls, and puzzles.

b. Calculate the proportion of each stratum in the sample. Since you want a sample of 20 toys, and the box has 100 toys, you’ll select 20% of each stratum:

Cars: 50 × 20% = 10 cars

Dolls: 30 × 20% = 6 dolls

Puzzles: 20 × 20% = 4 puzzles

c. Randomly select the calculated number of toys from each stratum:

Randomly pick 10 cars from the 50 cars

Randomly pick 6 dolls from the 30 dolls

Randomly pick 4 puzzles from the 20 puzzles

d. Combine the selected toys from each stratum to form your stratified sample of 20 toys.

Systematic sampling

In systematic sampling, we start with a random starting point and then select every ‘nth’ individual from the population. This method is relatively easy to implement and provides a representative sample when there’s no specific pattern or order in the population.

Imagine you have a shelf with 100 books arranged in a row. You want to select a sample of 10 books from the shelf to estimate the average number of pages in all the books.

To do systematic sampling, you would:

a. Determine the sampling interval by dividing the population size by the desired sample size. In this case, 100 books ÷ 10 books = 10.

b. Choose a random starting point between 1 and the sampling interval (10). Let’s say you randomly pick the number 4.

c. Select every 10th book starting from the 4th book until you have a sample of 10 books.

Cluster sampling

In cluster sampling, we divide the population into clusters or groups, and instead of selecting individuals, we randomly choose entire clusters to be included in the sample. Cluster sampling can be more cost-effective and time-efficient, especially when dealing with large populations.

Imagine you are a school administrator, and you want to survey students about their favorite subject. The school has 1000 students divided into 50 classrooms of 20 students each. Instead of surveying all 1000 students, you decide to use cluster sampling.

To do cluster sampling, you would:

a. Define the clusters: In this case, each classroom is a cluster, so there are 50 clusters in total.

b. Randomly select a sample of clusters: Let’s say you decide to select 5 classrooms (clusters) out of the 50. You can use a random number generator or a hat to randomly pick 5 numbers between 1 and 50, corresponding to the classroom numbers.

c. Include all members of the selected clusters in your sample: If the randomly selected classrooms are 3, 12, 27, 35, and 48, you would survey all 20 students in each of these classrooms.

d. Your final sample size would be 100 students (5 classrooms × 20 students per classroom).

Non-probability sampling

In non-probability sampling, the researcher chooses who to include in the sample based on their judgment or what is easy for them, rather than using random selection where everyone has an equal chance.

This means that the sample might not fairly represent the entire population, and the results of the study might not apply to everyone. However, non-probability sampling can still be helpful in some situations and can provide useful information for the researcher.

Types of non-probability sampling methods

Purposive sampling.

In purposive sampling, we select participants based on specific criteria or characteristics that are relevant to the study. It’s like handpicking individuals who can provide valuable insights and information related to our research objectives. This method is commonly used in qualitative research or when studying a specific subgroup within a population.

Imagine you are doing a research project on the challenges faced by students with visual impairments in your school. You want to interview some students to get their perspectives and experiences.

To do purposive sampling, you would:

a. Define the specific characteristics or criteria that are important for your study. In this case, you are looking for students who have visual impairments.

b. Purposefully select individuals who meet these criteria. You would reach out to the school’s disability services office or teachers to help identify students with visual impairments who might be willing to participate in your study.

c. Choose the students who you believe will provide the most relevant and valuable information for your research. For example, you might select students from different grade levels or with varying degrees of visual impairment to get a diverse range of perspectives.

d. Continue selecting participants until you have enough information to answer your research questions or until you reach saturation (when new interviews stop providing new insights).

Snowball sampling

In snowball sampling, we start with a small number of individuals who meet our criteria and ask them to refer other potential participants. It’s like creating a snowball effect as more participants join the sample. Snowball sampling is often used in studies involving hard-to-reach populations, such as individuals with rare diseases or marginalized communities.

Imagine you are conducting a study on the experiences of international students at your university. You want to interview some international students to learn about their challenges and successes.

To do snowball sampling, you would:

a. Start with a few initial participants who meet your criteria (in this case, being an international student). These initial participants are often called “seeds.”

b. After interviewing the seeds, ask them to refer other international students they know who might be willing to participate in your study. This is where the “snowball” effect comes in – your sample grows as each participant refers to others.

c. Contact the referred students and invite them to participate in your study. If they agree, interview them and then ask them to refer other international students they know.

d. Continue this process of interviewing and asking for referrals until you have enough participants to answer your research questions or until you stop getting new referrals.

Quota sampling

Quota sampling is a non-probability sampling technique that involves selecting individuals based on pre-defined quotas or characteristics. In quota sampling, the researcher identifies specific characteristics or traits that are relevant to the study. These characteristics could be demographic factors like age, gender, occupation, or any other relevant criteria. The researcher then sets quotas for each characteristic, specifying the desired number of participants to be included in each category.

Imagine you are surveying favorite pizza toppings in your neighborhood. You want to make sure that your sample includes equal numbers of men and women to get a balanced perspective.

To do quota sampling, you would:

a. Define the key characteristics (quotas) that you want to be represented in your sample. In this case, you want equal numbers of men and women.

b. Determine the total sample size you want. Let’s say you want to survey 100 people.

c. Divide the total sample size by the number of quotas to determine how many participants you need for each quota. In this case, you need 50 men and 50 women.

d. Once you have surveyed 50 men, you stop surveying men and focus on finding women to survey until you reach 50 women.

e. Once you have 50 men and 50 women, your quota sample is complete.

Convenience sampling

Convenience sampling, also known as accidental or grab sampling, involves selecting individuals who are readily available and easy to include in the sample. However, convenience sampling may introduce bias and may not accurately represent the population as a whole. So, tread carefully when interpreting the results.

Imagine you are a student doing a project on the reading habits of your classmates. You need to collect data quickly and don’t have a lot of time or resources.

To use convenience sampling, you would:

a. Decide to survey the people who are most accessible and easy for you to reach. In this case, you might choose to survey your friends, classmates sitting near you, or people you see in the library.

b. Approach these people and ask if they are willing to participate in your survey.

c. If they agree, have them complete your survey about their reading habits.

d. Continue surveying people until you feel like you have enough data for your project or until you run out of time.

Voluntary response sampling

Voluntary response sampling, also known as self-selection sampling is a non-probability sampling technique where individuals self-select or volunteer to be part of the sample. In voluntary sampling, participants have the freedom to decide whether or not they want to be included in the study. 

Researchers typically advertise or make the opportunity to participate known, and individuals who are interested or motivated to be part of the study voluntarily come forward. This type of sampling is commonly used in surveys, online questionnaires, or studies where individuals can choose to participate based on their willingness or interest. 

Imagine you are a researcher interested in studying the experiences of people who have adopted a vegan lifestyle. You want to gather data through an online survey.

To use voluntary sampling, you would:

a. Create an online survey about the experiences of being vegan.

b. Promote the survey through various channels, such as vegan social media groups, and vegan forums, or by asking vegan friends to share the survey link.

c. In your survey promotion, clearly state that you are looking for vegans to voluntarily participate in your study.

d. As people come across your survey invitation, they can choose to click on the link and complete the survey if they are interested and meet the criteria (being vegan).

e. Collect responses from those who voluntarily completed the survey.

Sampling methods play a crucial role in research and statistics, allowing us to gain valuable insights from a smaller subset of the population. By efficiently selecting representative samples, researchers can save time, reduce costs, and still obtain accurate and meaningful results. 

So, as you embark on your research journey, remember the power of sampling methods in unlocking valuable insights. And don’t forget to give your work the final polish it deserves with the help of expert editing and proofreading services , like PaperTrue. Happy researching!

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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.

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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.

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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.

Related Articles

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Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research

10 Pages Posted: 31 Jul 2018

Hamed Taherdoost

Hamta Group

Date Written: April 10, 2016

In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases. Thus, there is a need to select a sample. This paper presents the steps to go through to conduct sampling. Furthermore, as there are different types of sampling techniques/methods, researcher needs to understand the differences to select the proper sampling method for the research. In the regards, this paper also presents the different types of sampling techniques and methods.

Keywords: Sampling Method, Sampling Technique, Research Methodology, Probability Sampling, Non-Probability Sampling

Suggested Citation: Suggested Citation

Hamed Taherdoost (Contact Author)

Hamta group ( email ).

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Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis

Albine moser.

a Faculty of Health Care, Research Centre Autonomy and Participation of Chronically Ill People , Zuyd University of Applied Sciences , Heerlen, The Netherlands

b Faculty of Health, Medicine and Life Sciences, Department of Family Medicine , Maastricht University , Maastricht, The Netherlands

Irene Korstjens

c Faculty of Health Care, Research Centre for Midwifery Science , Zuyd University of Applied Sciences , Maastricht, The Netherlands

In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.

Key points on sampling, data collection and analysis

• The data collection plan needs to be broadly defined and open during data collection.
• Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used.
• Data saturation determines sample size and is different for each study.
• The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions.
• Analyses of ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory or a descriptive summary, respectively.

Introduction

This article is the third paper in a series of four articles aiming to provide practical guidance to qualitative research. In an introductory paper, we have described the objective, nature and outline of the Series [ 1 ]. Part 2 of the series focused on context, research questions and design of qualitative research [ 2 ]. In this paper, Part 3, we address frequently asked questions (FAQs) about sampling, data collection and analysis.

What is a sampling plan?

A sampling plan is a formal plan specifying a sampling method, a sample size, and procedure for recruiting participants ( Box 1 ) [ 3 ]. A qualitative sampling plan describes how many observations, interviews, focus-group discussions or cases are needed to ensure that the findings will contribute rich data. In quantitative studies, the sampling plan, including sample size, is determined in detail in beforehand but qualitative research projects start with a broadly defined sampling plan. This plan enables you to include a variety of settings and situations and a variety of participants, including negative cases or extreme cases to obtain rich data. The key features of a qualitative sampling plan are as follows. First, participants are always sampled deliberately. Second, sample size differs for each study and is small. Third, the sample will emerge during the study: based on further questions raised in the process of data collection and analysis, inclusion and exclusion criteria might be altered, or the sampling sites might be changed. Finally, the sample is determined by conceptual requirements and not primarily by representativeness. You, therefore, need to provide a description of and rationale for your choices in the sampling plan. The sampling plan is appropriate when the selected participants and settings are sufficient to provide the information needed for a full understanding of the phenomenon under study.

Sampling strategies in qualitative research. Based on Polit & Beck [ 3 ].

Some practicalities: a critical first step is to select settings and situations where you have access to potential participants. Subsequently, the best strategy to apply is to recruit participants who can provide the richest information. Such participants have to be knowledgeable on the phenomenon and can articulate and reflect, and are motivated to communicate at length and in depth with you. Finally, you should review the sampling plan regularly and adapt when necessary.

What sampling strategies can I use?

Sampling is the process of selecting or searching for situations, context and/or participants who provide rich data of the phenomenon of interest [ 3 ]. In qualitative research, you sample deliberately, not at random. The most commonly used deliberate sampling strategies are purposive sampling, criterion sampling, theoretical sampling, convenience sampling and snowball sampling. Occasionally, the ‘maximum variation,’ ‘typical cases’ and ‘confirming and disconfirming’ sampling strategies are used. Key informants need to be carefully chosen. Key informants hold special and expert knowledge about the phenomenon to be studied and are willing to share information and insights with you as the researcher [ 3 ]. They also help to gain access to participants, especially when groups are studied. In addition, as researcher, you can validate your ideas and perceptions with those of the key informants.

What is the connection between sampling types and qualitative designs?

The ‘big three’ approaches of ethnography, phenomenology, and grounded theory use different types of sampling.

In ethnography, the main strategy is purposive sampling of a variety of key informants, who are most knowledgeable about a culture and are able and willing to act as representatives in revealing and interpreting the culture. For example, an ethnographic study on the cultural influences of communication in maternity care will recruit key informants from among a variety of parents-to-be, midwives and obstetricians in midwifery care practices and hospitals.

Phenomenology uses criterion sampling, in which participants meet predefined criteria. The most prominent criterion is the participant’s experience with the phenomenon under study. The researchers look for participants who have shared an experience, but vary in characteristics and in their individual experiences. For example, a phenomenological study on the lived experiences of pregnant women with psychosocial support from primary care midwives will recruit pregnant women varying in age, parity and educational level in primary midwifery practices.

Grounded theory usually starts with purposive sampling and later uses theoretical sampling to select participants who can best contribute to the developing theory. As theory construction takes place concurrently with data collection and analyses, the theoretical sampling of new participants also occurs along with the emerging theoretical concepts. For example, one grounded theory study tested several theoretical constructs to build a theory on autonomy in diabetes patients [ 4 ]. In developing the theory, the researchers started by purposefully sampling participants with diabetes differing in age, onset of diabetes and social roles, for example, employees, housewives, and retired people. After the first analysis, researchers continued with theoretically sampling, for example, participants who differed in the treatment they received, with different degrees of care dependency, and participants who receive care from a general practitioner (GP), at a hospital or from a specialist nurse, etc.

In addition to the ‘big three’ approaches, content analysis is frequently applied in primary care research, and very often uses purposive, convenience, or snowball sampling. For instance, a study on peoples’ choice of a hospital for elective orthopaedic surgery used snowball sampling [ 5 ]. One elderly person in the private network of one researcher personally approached potential respondents in her social network by means of personal invitations (including letters). In turn, respondents were asked to pass on the invitation to other eligible candidates.

Sampling is also dependent on the characteristics of the setting, e.g., access, time, vulnerability of participants, and different types of stakeholders. The setting, where sampling is carried out, is described in detail to provide thick description of the context, thereby, enabling the reader to make a transferability judgement (see Part 3: transferability). Sampling also affects the data analysis, where you continue decision-making about whom or what situations to sample next. This is based on what you consider as still missing to get the necessary information for rich findings (see Part 1: emergent design). Another point of attention is the sampling of ‘invisible groups’ or vulnerable people. Sampling of these participants would require applying multiple sampling strategies, and more time calculated in the project planning stage for sampling and recruitment [ 6 ].

How do sample size and data saturation interact?

A guiding principle in qualitative research is to sample only until data saturation has been achieved. Data saturation means the collection of qualitative data to the point where a sense of closure is attained because new data yield redundant information [ 3 ].

Data saturation is reached when no new analytical information arises anymore, and the study provides maximum information on the phenomenon. In quantitative research, by contrast, the sample size is determined by a power calculation. The usually small sample size in qualitative research depends on the information richness of the data, the variety of participants (or other units), the broadness of the research question and the phenomenon, the data collection method (e.g., individual or group interviews) and the type of sampling strategy. Mostly, you and your research team will jointly decide when data saturation has been reached, and hence whether the sampling can be ended and the sample size is sufficient. The most important criterion is the availability of enough in-depth data showing the patterns, categories and variety of the phenomenon under study. You review the analysis, findings, and the quality of the participant quotes you have collected, and then decide whether sampling might be ended because of data saturation. In many cases, you will choose to carry out two or three more observations or interviews or an additional focus group discussion to confirm that data saturation has been reached.

When designing a qualitative sampling plan, we (the authors) work with estimates. We estimate that ethnographic research should require 25–50 interviews and observations, including about four-to-six focus group discussions, while phenomenological studies require fewer than 10 interviews, grounded theory studies 20–30 interviews and content analysis 15–20 interviews or three-to-four focus group discussions. However, these numbers are very tentative and should be very carefully considered before using them. Furthermore, qualitative designs do not always mean small sample numbers. Bigger sample sizes might occur, for example, in content analysis, employing rapid qualitative approaches, and in large or longitudinal qualitative studies.

Data collection

What methods of data collection are appropriate.

The most frequently used data collection methods are participant observation, interviews, and focus group discussions. Participant observation is a method of data collection through the participation in and observation of a group or individuals over an extended period of time [ 3 ]. Interviews are another data collection method in which an interviewer asks the respondents questions [ 6 ], face-to-face, by telephone or online. The qualitative research interview seeks to describe the meanings of central themes in the life world of the participants. The main task in interviewing is to understand the meaning of what participants say [ 5 ]. Focus group discussions are a data collection method with a small group of people to discuss a given topic, usually guided by a moderator using a questioning-route [ 8 ]. It is common in qualitative research to combine more than one data collection method in one study. You should always choose your data collection method wisely. Data collection in qualitative research is unstructured and flexible. You often make decisions on data collection while engaging in fieldwork, the guiding questions being with whom, what, when, where and how. The most basic or ‘light’ version of qualitative data collection is that of open questions in surveys. Box 2 provides an overview of the ‘big three’ qualitative approaches and their most commonly used data collection methods.

Qualitative data collection methods.

What role should I adopt when conducting participant observations?

What is important is to immerse yourself in the research setting, to enable you to study it from the inside. There are four types of researcher involvement in observations, and in your qualitative study, you may apply all four. In the first type, as ‘complete participant’, you become part of the setting and play an insider role, just as you do in your own work setting. This role might be appropriate when studying persons who are difficult to access. The second type is ‘active participation’. You have gained access to a particular setting and observed the group under study. You can move around at will and can observe in detail and depth and in different situations. The third role is ‘moderate participation’. You do not actually work in the setting you wish to study but are located there as a researcher. You might adopt this role when you are not affiliated to the care setting you wish to study. The fourth role is that of the ‘complete observer’, in which you merely observe (bystander role) and do not participate in the setting at all. However, you cannot perform any observations without access to the care setting. Such access might be easily obtained when you collect data by observations in your own primary care setting. In some cases, you might observe other care settings, which are relevant to primary care, for instance observing the discharge procedure for vulnerable elderly people from hospital to primary care.

How do I perform observations?

It is important to decide what to focus on in each individual observation. The focus of observations is important because you can never observe everything, and you can only observe each situation once. Your focus might differ between observations. Each observation should provide you with answers regarding ‘Who do you observe?’, ‘What do you observe’, ‘Where does the observation take place?’, ‘When does it take place?’, ‘How does it happen?’, and ‘Why does it happen as it happens?’ Observations are not static but proceed in three stages: descriptive, focused, and selective. Descriptive means that you observe, on the basis of general questions, everything that goes on in the setting. Focused observation means that you observe certain situations for some time, with some areas becoming more prominent. Selective means that you observe highly specific issues only. For example, if you want to observe the discharge procedure for vulnerable elderly people from hospitals to general practice, you might begin with broad observations to get to know the general procedure. This might involve observing several different patient situations. You might find that the involvement of primary care nurses deserves special attention, so you might then focus on the roles of hospital staff and primary care nurses, and their interactions. Finally, you might want to observe only the specific situations where hospital staff and primary care nurses exchange information. You take field notes from all these observations and add your own reflections on the situations you observed. You jot down words, whole sentences or parts of situations, and your reflections on a piece of paper. After the observations, the field notes need to be worked out and transcribed immediately to be able to include detailed descriptions.

Further reading on interviews and focus group discussion.

Qualitative data analysis.

What are the general features of an interview?

Interviews involve interactions between the interviewer(s) and the respondent(s) based on interview questions. Individual, or face-to-face, interviews should be distinguished from focus group discussions. The interview questions are written down in an interview guide [ 7 ] for individual interviews or a questioning route [ 8 ] for focus group discussions, with questions focusing on the phenomenon under study. The sequence of the questions is pre-determined. In individual interviews, the sequence depends on the respondents and how the interviews unfold. During the interview, as the conversation evolves, you go back and forth through the sequence of questions. It should be a dialogue, not a strict question–answer interview. In a focus group discussion, the sequence is intended to facilitate the interaction between the participants, and you might adapt the sequence depending on how their discussion evolves. Working with an interview guide or questioning route enables you to collect information on specific topics from all participants. You are in control in the sense that you give direction to the interview, while the participants are in control of their answers. However, you need to be open-minded to recognize that some relevant topics for participants may not have been covered in your interview guide or questioning route, and need to be added. During the data collection process, you develop the interview guide or questioning route further and revise it based on the analysis.

The interview guide and questioning route might include open and general as well as subordinate or detailed questions, probes and prompts. Probes are exploratory questions, for example, ‘Can you tell me more about this?’ or ‘Then what happened?’ Prompts are words and signs to encourage participants to tell more. Examples of stimulating prompts are eye contact, leaning forward and open body language.

Further reading on qualitative analysis.

What is a face-to-face interview?

A face-to-face interview is an individual interview, that is, a conversation between participant and interviewer. Interviews can focus on past or present situations, and on personal issues. Most qualitative studies start with open interviews to get a broad ‘picture’ of what is going on. You should not provide a great deal of guidance and avoid influencing the answers to fit ‘your’ point of view, as you want to obtain the participant’s own experiences, perceptions, thoughts, and feelings. You should encourage the participants to speak freely. As the interview evolves, your subsequent major and subordinate questions become more focused. A face-to-face or individual interview might last between 30 and 90 min.

Most interviews are semi-structured [ 3 ]. To prepare an interview guide to enhance that a set of topics will be covered by every participant, you might use a framework for constructing a semi-structured interview guide [ 10 ]: (1) identify the prerequisites to use a semi-structured interview and evaluate if a semi-structured interview is the appropriate data collection method; (2) retrieve and utilize previous knowledge to gain a comprehensive and adequate understanding of the phenomenon under study; (3) formulate a preliminary interview guide by operationalizing the previous knowledge; (4) pilot-test the preliminary interview guide to confirm the coverage and relevance of the content and to identify the need for reformulation of questions; (5) complete the interview guide to collect rich data with a clear and logical guide.

The first few minutes of an interview are decisive. The participant wants to feel at ease before sharing his or her experiences. In a semi-structured interview, you would start with open questions related to the topic, which invite the participant to talk freely. The questions aim to encourage participants to tell their personal experiences, including feelings and emotions and often focus on a particular experience or specific events. As you want to get as much detail as possible, you also ask follow-up questions or encourage telling more details by using probes and prompts or keeping a short period of silence [ 6 ]. You first ask what and why questions and then how questions.

You need to be prepared for handling problems you might encounter, such as gaining access, dealing with multiple formal and informal gatekeepers, negotiating space and privacy for recording data, socially desirable answers from participants, reluctance of participants to tell their story, deciding on the appropriate role (emotional involvement), and exiting from fieldwork prematurely.

What is a focus group discussion and when can I use it?

A focus group discussion is a way to gather together people to discuss a specific topic of interest. The people participating in the focus group discussion share certain characteristics, e.g., professional background, or share similar experiences, e.g., having diabetes. You use their interaction to collect the information you need on a particular topic. To what depth of information the discussion goes depends on the extent to which focus group participants can stimulate each other in discussing and sharing their views and experiences. Focus group participants respond to you and to each other. Focus group discussions are often used to explore patients’ experiences of their condition and interactions with health professionals, to evaluate programmes and treatment, to gain an understanding of health professionals’ roles and identities, to examine the perception of professional education, or to obtain perspectives on primary care issues. A focus group discussion usually lasts 90–120 mins.

You might use guidelines for developing a questioning route [ 9 ]: (1) brainstorm about possible topics you want to cover; (2) sequence the questioning: arrange general questions first, and then, more specific questions, and ask positive questions before negative questions; (3) phrase the questions: use open-ended questions, ask participants to think back and reflect on their personal experiences, avoid asking ‘why’ questions, keep questions simple and make your questions sound conversational, be careful about giving examples; (4) estimate the time for each question and consider: the complexity of the question, the category of the question, level of participant’s expertise, the size of the focus group discussion, and the amount of discussion you want related to the question; (5) obtain feedback from others (peers); (6) revise the questions based on the feedback; and (7) test the questions by doing a mock focus group discussion. All questions need to provide an answer to the phenomenon under study.

You need to be prepared to manage difficulties as they arise, for example, dominant participants during the discussion, little or no interaction and discussion between participants, participants who have difficulties sharing their real feelings about sensitive topics with others, and participants who behave differently when they are observed.

How should I compose a focus group and how many participants are needed?

The purpose of the focus group discussion determines the composition. Smaller groups might be more suitable for complex (and sometimes controversial) topics. Also, smaller focus groups give the participants more time to voice their views and provide more detailed information, while participants in larger focus groups might generate greater variety of information. In composing a smaller or larger focus group, you need to ensure that the participants are likely to have different viewpoints that stimulate the discussion. For example, if you want to discuss the management of obesity in a primary care district, you might want to have a group composed of professionals who work with these patients but also have a variety of backgrounds, e.g. GPs, community nurses, practice nurses in general practice, school nurses, midwives or dieticians.

Focus groups generally consist of 6–12 participants. Careful time management is important, since you have to determine how much time you want to devote to answering each question, and how much time is available for each individual participant. For example, if you have planned a focus group discussion lasting 90 min. with eight participants, you might need 15 min. for the introduction and the concluding summary. This means you have 75 min. for asking questions, and if you have four questions, this allows a total of 18 min. of speaking time for each question. If all eight respondents participate in the discussion, this boils down to about two minutes of speaking time per respondent per question.

How can I use new media to collect qualitative data?

New media are increasingly used for collecting qualitative data, for example, through online observations, online interviews and focus group discussions, and in analysis of online sources. Data can be collected synchronously or asynchronously, with text messaging, video conferences, video calls or immersive virtual worlds or games, etcetera. Qualitative research moves from ‘virtual’ to ‘digital’. Virtual means those approaches that import traditional data collection methods into the online environment and digital means those approaches take advantage of the unique characteristics and capabilities of the Internet for research [ 10 ]. New media can also be applied. See Box 3 for further reading on interview and focus group discussion.

Can I wait with my analysis until all data have been collected?

You cannot wait with the analysis, because an iterative approach and emerging design are at the heart of qualitative research. This involves a process whereby you move back and forth between sampling, data collection and data analysis to accumulate rich data and interesting findings. The principle is that what emerges from data analysis will shape subsequent sampling decisions. Immediately after the very first observation, interview or focus group discussion, you have to start the analysis and prepare your field notes.

Why is a good transcript so important?

First, transcripts of audiotaped interviews and focus group discussions and your field notes constitute your major data sources. Trained and well-instructed transcribers preferably make transcripts. Usually, e.g., in ethnography, phenomenology, grounded theory, and content analysis, data are transcribed verbatim, which means that recordings are fully typed out, and the transcripts are accurate and reflect the interview or focus group discussion experience. Most important aspects of transcribing are the focus on the participants’ words, transcribing all parts of the audiotape, and carefully revisiting the tape and rereading the transcript. In conversation analysis non-verbal actions such as coughing, the lengths of pausing and emphasizing, tone of voice need to be described in detail using a formal transcription system (best known are G. Jefferson’s symbols).

To facilitate analysis, it is essential that you ensure and check that transcripts are accurate and reflect the totality of the interview, including pauses, punctuation and non-verbal data. To be able to make sense of qualitative data, you need to immerse yourself in the data and ‘live’ the data. In this process of incubation, you search the transcripts for meaning and essential patterns, and you try to collect legitimate and insightful findings. You familiarize yourself with the data by reading and rereading transcripts carefully and conscientiously, in search for deeper understanding.

Are there differences between the analyses in ethnography, phenomenology, grounded theory, and content analysis?

Ethnography, phenomenology, and grounded theory each have different analytical approaches, and you should be aware that each of these approaches has different schools of thought, which may also have integrated the analytical methods from other schools ( Box 4 ). When you opt for a particular approach, it is best to use a handbook describing its analytical methods, as it is better to use one approach consistently than to ‘mix up’ different schools.

In general, qualitative analysis begins with organizing data. Large amounts of data need to be stored in smaller and manageable units, which can be retrieved and reviewed easily. To obtain a sense of the whole, analysis starts with reading and rereading the data, looking at themes, emotions and the unexpected, taking into account the overall picture. You immerse yourself in the data. The most widely used procedure is to develop an inductive coding scheme based on actual data [ 11 ]. This is a process of open coding, creating categories and abstraction. In most cases, you do not start with a predefined coding scheme. You describe what is going on in the data. You ask yourself, what is this? What does it stand for? What else is like this? What is this distinct from? Based on this close examination of what emerges from the data you make as many labels as needed. Then, you make a coding sheet, in which you collect the labels and, based on your interpretation, cluster them in preliminary categories. The next step is to order similar or dissimilar categories into broader higher order categories. Each category is named using content-characteristic words. Then, you use abstraction by formulating a general description of the phenomenon under study: subcategories with similar events and information are grouped together as categories and categories are grouped as main categories. During the analysis process, you identify ‘missing analytical information’ and you continue data collection. You reread, recode, re-analyse and re-collect data until your findings provide breadth and depth.

Throughout the qualitative study, you reflect on what you see or do not see in the data. It is common to write ‘analytic memos’ [ 3 ], write-ups or mini-analyses about what you think you are learning during the course of your study, from designing to publishing. They can be a few sentences or pages, whatever is needed to reflect upon: open codes, categories, concepts, and patterns that might be emerging in the data. Memos can contain summaries of major findings and comments and reflections on particular aspects.

In ethnography, analysis begins from the moment that the researcher sets foot in the field. The analysis involves continually looking for patterns in the behaviours and thoughts of the participants in everyday life, in order to obtain an understanding of the culture under study. When comparing one pattern with another and analysing many patterns simultaneously, you may use maps, flow charts, organizational charts and matrices to illustrate the comparisons graphically. The outcome of an ethnographic study is a narrative description of a culture.

In phenomenology, analysis aims to describe and interpret the meaning of an experience, often by identifying essential subordinate and major themes. You search for common themes featuring within an interview and across interviews, sometimes involving the study participants or other experts in the analysis process. The outcome of a phenomenological study is a detailed description of themes that capture the essential meaning of a ‘lived’ experience.

Grounded theory generates a theory that explains how a basic social problem that emerged from the data is processed in a social setting. Grounded theory uses the ‘constant comparison’ method, which involves comparing elements that are present in one data source (e.g., an interview) with elements in another source, to identify commonalities. The steps in the analysis are known as open, axial and selective coding. Throughout the analysis, you document your ideas about the data in methodological and theoretical memos. The outcome of a grounded theory study is a theory.

Descriptive generic qualitative research is defined as research designed to produce a low inference description of a phenomenon [ 12 ]. Although Sandelowski maintains that all research involves interpretation, she has also suggested that qualitative description attempts to minimize inferences made in order to remain ‘closer’ to the original data [ 12 ]. Descriptive generic qualitative research often applies content analysis. Descriptive content analysis studies are not based on a specific qualitative tradition and are varied in their methods of analysis. The analysis of the content aims to identify themes, and patterns within and among these themes. An inductive content analysis [ 11 ] involves breaking down the data into smaller units, coding and naming the units according to the content they present, and grouping the coded material based on shared concepts. They can be represented by clustering in treelike diagrams. A deductive content analysis [ 11 ] uses a theory, theoretical framework or conceptual model to analyse the data by operationalizing them in a coding matrix. An inductive content analysis might use several techniques from grounded theory, such as open and axial coding and constant comparison. However, note that your findings are merely a summary of categories, not a grounded theory.

Analysis software can support you to manage your data, for example by helping to store, annotate and retrieve texts, to locate words, phrases and segments of data, to name and label, to sort and organize, to identify data units, to prepare diagrams and to extract quotes. Still, as a researcher you would do the analytical work by looking at what is in the data, and making decisions about assigning codes, and identifying categories, concepts and patterns. The computer assisted qualitative data analysis (CAQDAS) website provides support to make informed choices between analytical software and courses: http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing . See Box 5 for further reading on qualitative analysis.

The next and final article in this series, Part 4, will focus on trustworthiness and publishing qualitative research [ 13 ].

Acknowledgements

The authors thank the following junior researchers who have been participating for the last few years in the so-called ‘Think tank on qualitative research’ project, a collaborative project between Zuyd University of Applied Sciences and Maastricht University, for their pertinent questions: Erica Baarends, Jerome van Dongen, Jolanda Friesen-Storms, Steffy Lenzen, Ankie Hoefnagels, Barbara Piskur, Claudia van Putten-Gamel, Wilma Savelberg, Steffy Stans, and Anita Stevens. The authors are grateful to Isabel van Helmond, Joyce Molenaar and Darcy Ummels for proofreading our manuscripts and providing valuable feedback from the ‘novice perspective’.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

• DOI: 10.2139/SSRN.3205035
• Corpus ID: 146053130

Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research

• Hamed Taherdoost
• Published 10 April 2016

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The Sample and Sampling Techniques

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Collecting data using an appropriate sampling technique is a challenging task for a researcher to do. The researchers will be unable to collect data from all possible situations, which will preclude them from answering the study’s research questions in their current form. In light of the enormous number and variety of sampling techniques/methods available, the researcher must be knowledgeable about the differences to select the most appropriate sampling technique/method for the specific study under consideration. In this context, this study also looks into the basic concepts in probability sampling, kinds of probability sampling techniques with their advantages and disadvantages. Social science researchers will benefit from this study since it will assist them in choosing the most suitable probability sampling technique(s) for completing their research smoothly and successfully.

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The Manual for Sampling Techniques used in Social Sciences is an effort to describe various types of sampling methodologies that are used in researches of social sciences in an easy and understandable way. Characteristics, benefits, crucial issues/ draw backs, and examples of each sampling type are provided separately.

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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.

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

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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 randomisation 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 anonymising 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 unrecognised 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 specialised 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 specialised 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.

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Sampling and non-sampling errors 10 min read, how to determine sample size 16 min read, convenience sampling 15 min read, non-probability sampling 17 min read, probability sampling 8 min read, stratified random sampling 13 min read, simple random sampling 10 min read, request demo.

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Evidential uncertainty sampling strategies for active learning

• Published: 27 June 2024

Cite this article

• Arthur Hoarau 1 ,
• Vincent Lemaire 2 ,
• Yolande Le Gall 1 ,
• Jean-Christophe Dubois 1 &
• Arnaud Martin 1  

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Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simplify the computational process while eliminating the dependence on observations. Crucially, the inherent uncertainty in the labels is considered, i.e. the uncertainty of the oracles. Two strategies are proposed, sampling by Klir uncertainty, which tackles the exploration–exploitation dilemma, and sampling by evidential epistemic uncertainty, which extends the concept of reducible uncertainty within the evidential framework, both using the theory of belief functions. Experimental results in active learning demonstrate that our proposed method can outperform uncertainty sampling.

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Epistemic Uncertainty Sampling

How to measure uncertainty in uncertainty sampling for active learning

On Several New Dempster-Shafer-Inspired Uncertainty Measures Applicable for Active Learning

Data availability.

All data used is this study are available publicly online. The datasets were extracted directly in the repositories available with the links in the folloing section.

Code availability

The code for theoretical experiments is available at this link: https://anonymous.4open.science/r/evidential-uncertainty-sampling-D453 . Link to the code for the experimental part in active learning: https://anonymous.4open.science/r/evidential-active-learning-B266 .

For details on experiments conducted in theoretical sections, visit: https://anonymous.4open.science/r/evidential-uncertainty-sampling-D453 .

From now on, the model used is K -NN ( K -Nearest Neighbors), with a probabilistic output and on the distance-weighted version available with scikit-learn (Pedregosa et al., 2011 ), every other parameters are scikit-learn default parameters. The uncertainty used is the least confidence measure given in Eq. ( 5 ).

In the example, the word “tails” is written in Finnish, the word “heads” is called Kruuna .

The notion of plausibility within the theory of belief functions used in the proposed methods differs from the one presented here and will be discussed in greater detail in Sect.  4 .

The uncertainty no longer depends on observations, but the model does.

From now, the Evidential K -nearest Neighbors model of (Deœux, 1995 ) is considered.

This representation also applies to labeling performed by a machine.

Experiments where conducted according to the following code: https://anonymous.4open.science/r/evidential-active-learning-B266 .

An entropy of 1 means that the classes are perfectly equidistributed and an entropy of 0 would indicate the total over-representation of one of the classes.

Although it can also be to maximize performance given a cost.

Abdar, M., Pourpanah, F., Hussain, S., et al. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76 , 243–297.

Article   Google Scholar  

Abe, N., Zadrozny, B., & Langford, J. (2006). Outlier detection by active learning. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006 , 504–509.

Aggarwal, C., Kong, X., Gu, Q., et al. (2014). Active Learning: A Survey, Data Classification: Algorithms and Applications . CRC Press.

Google Scholar  

Bondu, A., Lemaire, V. & Boullé, M. (2010). Exploration vs. exploitation in active learning: A Bayesian approach. In The 2010 international joint conference on neural networks (IJCNN) (pp. 1–7).

Charpentier, B., Zügner, D., & Günnemann, S., et al. (2020). Posterior network: Uncertainty estimation without OOD samples via density-based pseudo-counts. In H. Larochelle, M. Ranzato, & R. Hadsell (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 1356–1367). Curran Associates Inc.

Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38 (2), 325–339.

Article   MathSciNet   Google Scholar  

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7 , 1–30.

MathSciNet   Google Scholar  

Deng, Y. (2020). Uncertainty measure in evidence theory. Science China Information Sciences, 63 , 210201.

Denœux, T. (1995). A k-nearest neighbor classification rule based on Dempster–Shafer theory. IEEE Transactions on Systems, Man and Cybernetics , 219 .

Denoeux, T., & Bjanger, M. (2000). Induction of decision trees from partially classified data using belief functions. Systems, Man, and Cybernetics, 4 , 2923–2928.

Dua, D. & Graff, C. (2017). UCI ML Repository. https://archive.ics.uci.edu/

Dubois, D., & Prade, H. (1987). Properties of measures of information in evidence and possibility theories. Fuzzy Sets and Systems, 24 (2), 161–182.

Elouedi, Z., Mellouli, K., & Smets, P. (2001). Belief decision trees: Theoretical foundations. International Journal of Approximate Reasoning, 28 (2), 91–124.

Hacohen, G., Dekel, A. & Weinshall, D. (2022). Active learning on a budget: Opposite strategies suit high and low budgets. In Chaudhuri, K., Jegelka, S., Song, L., et al. (Eds.), International conference on machine learning, 2022, Baltimore, Maryland, USA, proceedings of machine learning research (vol. 162, pp. 8175–8195). PMLR.

Hoarau, A., Martin, A., Dubois, J. C., et al. (2022). Imperfect labels with belief functions for active learning. In Belief functions: Theory and applications . Springer.

Hoarau, A., Martin, A., Dubois, J. C., et al. (2023a). Evidential random forests. Expert Systems with Applications, 230 .

Hoarau, A., Thierry, C., Martin, A., et al. (2023b). Datasets with rich labels for machine learning. In 2023 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1–6).

Hora, S. C. (1996). Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliability Engineering & System Safety, 54 (2), 217–223. Treatment of Aleatory and Epistemic Uncertainty.

Huang, L., Ruan, S. & Xing, Y., et al. (2023). A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods.

Hüllermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning, 110 , 457–506.

Hüllermeier, E., Destercke, S. & Shaker, M.H. (2022). Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison. In Cussens, J., & Zhang, K. (Eds.), Proceedings of the thirty-eighth conference on uncertainty in artificial intelligence, proceedings of machine learning research (vol. 180, pp. 548–557). PMLR.

Kendall, A. & Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision? In NIPS .

Klir, G. J., & Wierman, M. J. (1998). Uncertainty-based information: Elements of generalized information theory . Springer.

Kottke, D., Calma, A., Huseljic, D., et al. (2017). Challenges of reliable, realistic and comparable active learning evaluation. In Proceedings of the workshop and tutorial on interactive adaptive learning (pp. 2–14).

Lewis, D. D., & Gale, W.. A. (1994). A sequential algorithm for training text classifiers. In SIGIR .

Martens, T., Perini, L., & Davis, J. (2023). Semi-supervised learning from active noisy soft labels for anomaly detection. Machine learning and knowledge discovery in databases: Research track: European conference, ECML PKDD 2023, Turin (pp. 219–236). Springer-Verlag.

Martin, A. (2019). Conflict management in information fusion with belief functions. In E. Bossé & G. L. Rogova (Eds.), Information quality in information fusion and decision making. Information fusion and data science (pp. 79–97). Springer.

Nguyen, V. L., Shaker, M. H., & Hüllermeier, E. (2022). How to measure uncertainty in uncertainty sampling for active learning. Machine Learning, 111 , 89–122.

Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12 , 2825–2830.

Senge, R., Bösner, S., Dembczynski, K., et al. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Science, 255 , 16–29.

Sensoy, M., Kaplan, L., & Kandemir, M., et al. (2018). Evidential deep learning to quantify classification uncertainty. In S. Bengio, H. Wallach, & H. Larochelle (Eds.), Advances in Neural Information Processing Systems. (Vol. 31). Curran Associates Inc.

Settles, B. (2009). Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.

Shafer, G. (1976). A mathematical theory of evidence . Princeton University Press.

Book   Google Scholar  

Smets, P. & Kennes, R. (1994). The transferable belief model. Artificial Intelligence .

Thierry, C., Hoarau, A., Martin, A,. et al. (2022). Real bird dataset with imprecise and uncertain values. In 7th International conference on belief functions .

Yuan, B., Yue, X., Lv, Y., et al. (2020). Evidential deep neural networks for uncertain data classification. In: Knowledge science, engineering and management (proceedings of KSEM 2020). Lecture notes in computer science . Springer Verlag.

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This work is funded by the Brittany region and the Côtes-d’Armor department. The authors also received funding from IRISA, the University of Rennes, DRUID and Orange SA.

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MINNEAPOLIS / ST. PAUL (07/01/2024)—University of Minnesota College of Science and Engineering Dean Andrew Alleyne has named four new department heads in the college. All bring a wealth of academic, research, and leadership abilities to their departments.

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Dorfman joined the University of Minnesota faculty in January of 2006 and was quickly promoted up the ranks, receiving tenure in 2011, promotion to professor in 2015, and named a Distinguished McKnight Professor in 2020. He previously served as the director of undergraduate studies in chemical engineering from 2018-2022, where he headed a large-scale revision of the chemical engineering curriculum and saw the department through its most recent ABET accreditation. 

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Ghate is an expert in operations research and most recently served as the Fluor Endowed Chair in the Department of Industrial Engineering at Clemson University. Previously, he was a professor of industrial and systems engineering at the University of Washington. He has won several research and teaching awards, including an NSF CAREER Award. 

Ghate’s research in optimization spans areas as varied as health care, transportation and logistics, manufacturing, economics, and business analytics. He also served as a principal research scientist at Amazon working on supply chain optimization technologies. 

Ghate received bachelor’s and master’s degrees, both in chemical engineering, from the Indian Institute of Technology. He also received a master’s degree in management science and engineering from Stanford University and a Ph.D. in industrial and operations engineering from the University of Michigan.

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Professor Chris Hogan has been appointed as the new department head for the Department of Mechanical Engineering. Hogan started his five-year term on July 1, 2024.

Hogan, who currently holds the Carl and Janet Kuhrmeyer Chair, joined the University of Minnesota in 2009, and since then has taught fluid mechanics and heat transfer to nearly 1,000 undergraduates, advised 25+ Ph.D. students and postdoctoral associates, and served as the department’s director of graduate studies from 2015-2020. He most recently served as associate department head. 

He is a leading expert in particle science with applications including supersonic-to-hypersonic particle impacts with surfaces, condensation and coagulation, agricultural sprays, and virus aerosol sampling and control technologies. He has authored and co-authored more than 160 papers on these topics. He currently serves as the editor-in-chief of the Journal of Aerosol Science . Hogan received the University of Minnesota College of Science and Engineering’s George W. Taylor Award for Distinguished Research in 2023.

Hogan holds a bachelor’s degree Cornell University and a Ph.D. from Washington University in Saint Louis.

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Since joining the School of Physics and Astronomy in 1988, Kakalios has built a research program in experimental condensed matter physics, with particular emphasis on complex and disordered systems. His research ranges from the nano to the neuro with experimental investigations of the electronic and optical properties of nanostructured semiconductors and fluctuation phenomena in neurological systems.

During his time at the University of Minnesota, Kakalios has served as both director of undergraduate studies and director of graduate studies. He has received numerous awards and professorships including the University’s Taylor Distinguished Professorship, Andrew Gemant Award from the American Institute of Physics, and the Award for Public Engagement with Science from the American Association for the Advancement of Science (AAAS). He is a fellow of both the American Physical Society and AAAS. 

In addition to numerous research publications, Kakalios is the author of three popular science books— The Physics of Superheroes , The Amazing Story of Quantum Mechanics , and The Physics of Everyday Things .

Kaklios received a bachelor’s degree from City College of New York and master’s and Ph.D. degrees from the University of Chicago.

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Constrained spectral–spatial attention residual network and new cross-scene dataset for hyperspectral classification.

1. Introduction

• Samples selected from the same image have little spatial variability. As a result, there is an overfitting problem in hyperspectral classification caused by the spatial distribution of training and testing samples being too similar.
• Many cross-scene hyperspectral classification methods focus on domain adaptation and generalization to mitigate the significant data distribution shift between datasets. The lack of univariate cross-scene datasets makes exploring generalization methods for individual variables difficult.

2. Related Works

2.1. hyperspectral image classification, 2.2. cross-scene hyperspectral image classification, 3. proposed method, 3.1. overview, 3.2. spectral feature learning module, 3.3. spatial feature learning module, 3.4. feature fusion and loss function, 4.1. data description and evaluation metrics, 4.1.1. data description.

• Indian Pines : The Indian Pines (IP) dataset has a spatial size of 145 × 145 and contains 200 spectral bands that can be used for experiments. The wavelength range of this dataset is 400–2500 nm. A total of 10,249 samples from 16 different classes are included. The number of training and testing samples on the IP dataset is listed in Table 1 . Figure 9 a,b show the false-color composite image and ground truth of the IP dataset, respectively.
• Salinas : The Salinas (SA) dataset has a spatial size of 512 × 217 and contains 204 spectral bands that can be used for experiments. The wavelength range of this dataset is 400–2500 nm. A total of 54,129 samples from 16 different classes are included. Table 2 shows the number of training and testing samples on the SA dataset. Figure 10 a,b show the false-color composite image and ground truth of the SA dataset, respectively.
• GF14-C17&C16 : The GF14-C17&C16 dataset was obtained by the GF-14 satellite from Zhoukou City, Henan Province, China, in April 2022. After atmospheric and radiometric correction, it was manually labeled based on spectral information collected in the field in the city on the day of shooting. The GF14-C17&C16 dataset contains 70 spectral channels. The spatial resolution is 5 m per pixel. The spatial size of GF14-C17&C16 is 2048 × 2048. The wavelength range of this dataset is 450–900 nm. There are 80,653 samples in the GF14-C17, which contains 17 categories. A total of 58,550 samples from 16 different classes are included in GF14-C16. Table 3 shows the number of training and testing samples on the GF14-C17&C16 dataset. In this paper, 1606 samples are randomly selected in GF14-C17 as training samples, and the remaining samples of GF14-C17 with all the samples of GF14-C16 are used as test samples. Figure 11 a,b show the false-color composite image and ground truth of the GF14-C17 dataset, respectively. The false-color composite image and ground truth of the GF14-C16 dataset are respectively shown in Figure 12 a,b.

4.1.2. Evaluation Metrics

• Average accuracy (AA): AA = ∑ i = 1 n M i , i ∑ j = 1 n M i , j n (9) where M ∈ R n × n denotes the confusion matrix. n represents the number of classes.
• Overall accuracy (OA): OA = ∑ i = 1 n M i , i ∑ i = 1 n ∑ j = 1 n M i , j (10)
• Kappa coefficient ( κ ): κ = OA − p e 1 − p e (11) p e = ∑ k = 1 n ∑ i = 1 n M i , k · ∑ j = 1 n M k , j ∑ i = 1 n ∑ j = 1 n M i , j 2 (12) where M i , j represents the i -th row and j -th column of the matrix M . The value of M i , j denotes the i -th category is classified as the j -th class. ∑ stands for summation.

4.2. Experimental Setup and Compared Methods

4.2.1. experimental setup, 4.2.2. compared methods, 4.3. parameter analysis and ablation experiments, 4.3.1. parameter analysis, 4.3.2. ablation experiments.

• Base: This network is the CSSARN after removing the constrained spectral attention and the constrained spatial attention.
• Constrained Spatial Attention Network (SpaANet): The constrained spatial attention module is added to the baseline network.
• Constrained Spectral Attention Network (SpecANet): The constrained spectral attention module is added to the baseline network.
• CSSARN: The whole network.

4.4. Experimental Results

5. discussion, 6. conclusions, author contributions, data availability statement, conflicts of interest.

• Zhang, L.; Zhang, L. Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities. IEEE Geosci. Remote Sens. Mag. 2022 , 10 , 270–294. [ Google Scholar ] [ CrossRef ]
• Crowson, M.; Hagensieker, R.; Waske, B. Mapping land cover change in northern Brazil with limited training data. Int. J. Appl. Earth Obs. Geoinf. 2019 , 78 , 202–214. [ Google Scholar ] [ CrossRef ]
• Wang, N.; Shi, Y.; Li, H.; Zhang, G.; Li, S.; Liu, X. Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection. Remote Sens. 2023 , 15 , 4430. [ Google Scholar ] [ CrossRef ]
• Shi, Y.; Wang, N.; Yang, F.; Zhang, G.; Li, S.; Liu, X. Multispectral Images Deblurring via Interchannel Correlation Relationship. In Proceedings of the 2021 4th International Conference on Information Communication and Signal Processing (ICICSP), Shanghai, China, 24–26 September 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 458–462. [ Google Scholar ]
• Nguyen, C.; Sagan, V.; Maimaitiyiming, M.; Maimaitijiang, M.; Bhadra, S.; Kwasniewski, M.T. Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning. Sensors 2021 , 21 , 742. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fang, J.; Yuan, Y.; Lu, X.; Feng, Y. Robust space–frequency joint representation for remote sensing image scene classification. IEEE Trans. Geosci. Remote Sens. 2019 , 57 , 7492–7502. [ Google Scholar ] [ CrossRef ]
• Ahmad, M.; Shabbir, S.; Roy, S.K.; Hong, D.; Wu, X.; Yao, J.; Khan, A.M.; Mazzara, M.; Distefano, S.; Chanussot, J. Hyperspectral image classification—Traditional to deep models: A survey for future prospects. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021 , 15 , 968–999. [ Google Scholar ] [ CrossRef ]
• Singh, S.; Kasana, S.S. A Pre-processing framework for spectral classification of hyperspectral images. Multimed. Tools Appl. 2021 , 80 , 243–261. [ Google Scholar ] [ CrossRef ]
• Manifold, B.; Men, S.; Hu, R.; Fu, D. A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. Nat. Mach. Intell. 2021 , 3 , 306–315. [ Google Scholar ] [ CrossRef ]
• Roy, S.K.; Krishna, G.; Dubey, S.R.; Chaudhuri, B.B. HybridSN: Exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 2019 , 17 , 277–281. [ Google Scholar ] [ CrossRef ]
• Wei, W.; Song, C.; Zhang, L.; Zhang, Y. Lightweighted hyperspectral image classification network by progressive bi-quantization. IEEE Trans. Geosci. Remote Sens. 2023 , 61 , 1–14. [ Google Scholar ] [ CrossRef ]
• Feng, H.; Wang, Y.; Li, Z.; Zhang, N.; Zhang, Y.; Gao, Y. Information leakage in deep learning-based hyperspectral image classification: A survey. Remote Sens. 2023 , 15 , 3793. [ Google Scholar ] [ CrossRef ]
• Li, J.; Huang, X.; Tu, L. WHU-OHS: A benchmark dataset for large-scale Hersepctral Image classification. Int. J. Appl. Earth Obs. Geoinf. 2022 , 113 , 103022. [ Google Scholar ] [ CrossRef ]
• Audebert, N.; Le Saux, B.; Lefevre, S. Deep Learning for Classification of Hyperspectral Data: A Comparative Review. IEEE Geosci. Remote Sens. Mag. 2019 , 7 , 159–173. [ Google Scholar ] [ CrossRef ]
• Sun, H.; Zheng, X.; Lu, X. A supervised segmentation network for hyperspectral image classification. IEEE Trans. Image Process. 2021 , 30 , 2810–2825. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tobler, W.R. A Computer Movie Simulating Urban Growth in the Detroit Region. Econ. Geogr. 1970 , 46 , 234–240. [ Google Scholar ] [ CrossRef ]
• Liang, J.; Zhou, J.; Qian, Y.; Wen, L.; Bai, X.; Gao, Y. On the sampling strategy for evaluation of spectral-spatial methods in hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2016 , 55 , 862–880. [ Google Scholar ] [ CrossRef ]
• Li, G.; Ma, S.; Li, K.; Zhou, M.; Lin, L. Band selection for heterogeneity classification of hyperspectral transmission images based on multi-criteria ranking. Infrared Phys. Technol. 2022 , 125 , 104317. [ Google Scholar ] [ CrossRef ]
• Moharram, M.A.; Sundaram, D.M. Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: A survey. Environ. Sci. Pollut. Res. 2023 , 30 , 5580–5602. [ Google Scholar ] [ CrossRef ]
• Gewali, U.B.; Monteiro, S.T.; Saber, E. Machine learning based hyperspectral image analysis: A survey. arXiv 2018 , arXiv:1802.08701. [ Google Scholar ]
• Zhong, C.; Zhang, J.; Wu, S.; Zhang, Y. Cross-Scene Deep Transfer Learning With Spectral Feature Adaptation for Hyperspectral Image Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020 , 13 , 2861–2873. [ Google Scholar ] [ CrossRef ]
• Samat, A.; Gamba, P.; Abuduwaili, J.; Liu, S.; Miao, Z. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer. Remote Sens. 2016 , 8 , 234. [ Google Scholar ] [ CrossRef ]
• Shen, J.; Cao, X.; Li, Y.; Xu, D. Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification. IEEE Geosci. Remote Sens. Lett. 2018 , 15 , 622–626. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Li, W.; Zhang, M.; Qu, Y.; Tao, R.; Qi, H. Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification. IEEE Trans. Neural Networks Learn. Syst. 2023 , 34 , 2817–2830. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, Y.; Li, W.; Sun, W.; Tao, R.; Du, Q. Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification. IEEE Trans. Image Process. 2023 , 32 , 1498–1512. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zheng, X.; Sun, H.; Lu, X.; Xie, W. Rotation-invariant attention network for hyperspectral image classification. IEEE Trans. Image Process. 2022 , 31 , 4251–4265. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Melgani, F.; Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004 , 42 , 1778–1790. [ Google Scholar ] [ CrossRef ]
• Gomez-Chova, L.; Calpe, J.; Soria, E.; Camps-Valls, G.; Martin, J.; Moreno, J. CART-based feature selection of hyperspectral images for crop cover classification. In Proceedings of the 2003 International Conference on Image Processing (Cat. No.03CH37429), Barcelona, Spain, 14–17 September 2003; Volume 3, p. III-589. [ Google Scholar ]
• Ham, J.; Chen, Y.; Crawford, M.M.; Ghosh, J. Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2005 , 43 , 492–501. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Hu, Q.; Cai, Y.; Cai, Z. Extreme learning machine-based ensemble transfer learning for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020 , 13 , 3892–3902. [ Google Scholar ] [ CrossRef ]
• Li, S.; Song, W.; Fang, L.; Chen, Y.; Ghamisi, P.; Benediktsson, J.A. Deep learning for hyperspectral image classification: An overview. IEEE Trans. Geosci. Remote Sens. 2019 , 57 , 6690–6709. [ Google Scholar ] [ CrossRef ]
• Paoletti, M.E.; Haut, J.M.; Fernandez-Beltran, R.; Plaza, J.; Plaza, A.J.; Pla, F. Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2018 , 57 , 740–754. [ Google Scholar ] [ CrossRef ]
• Yang, X.; Ye, Y.; Li, X.; Lau, R.Y.; Zhang, X.; Huang, X. Hyperspectral image classification with deep learning models. IEEE Trans. Geosci. Remote Sens. 2018 , 56 , 5408–5423. [ Google Scholar ] [ CrossRef ]
• Hu, W.; Huang, Y.; Wei, L.; Zhang, F.; Li, H. Deep convolutional neural networks for hyperspectral image classification. J. Sensors 2015 , 2015 , 258619. [ Google Scholar ] [ CrossRef ]
• Chen, Y.; Lin, Z.; Zhao, X.; Wang, G.; Gu, Y. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014 , 7 , 2094–2107. [ Google Scholar ] [ CrossRef ]
• Paoletti, M.E.; Haut, J.M.; Plaza, J.; Plaza, A. Deep learning classifiers for hyperspectral imaging: A review. ISPRS J. Photogramm. Remote Sens. 2019 , 158 , 279–317. [ Google Scholar ] [ CrossRef ]
• Yu, S.; Jia, S.; Xu, C. Convolutional neural networks for hyperspectral image classification. Neurocomputing 2017 , 219 , 88–98. [ Google Scholar ] [ CrossRef ]
• Mou, L.; Ghamisi, P.; Zhu, X.X. Deep recurrent neural networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2017 , 55 , 3639–3655. [ Google Scholar ] [ CrossRef ]
• Wu, H.; Prasad, S. Convolutional recurrent neural networks for hyperspectral data classification. Remote Sens. 2017 , 9 , 298. [ Google Scholar ] [ CrossRef ]
• Wambugu, N.; Chen, Y.; Xiao, Z.; Tan, K.; Wei, M.; Liu, X.; Li, J. Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review. Int. J. Appl. Earth Obs. Geoinf. 2021 , 105 , 102603. [ Google Scholar ] [ CrossRef ]
• Li, J.; Zhao, X.; Li, Y.; Du, Q.; Xi, B.; Hu, J. Classification of hyperspectral imagery using a new fully convolutional neural network. IEEE Geosci. Remote Sens. Lett. 2018 , 15 , 292–296. [ Google Scholar ] [ CrossRef ]
• Haut, J.M.; Paoletti, M.E.; Plaza, J.; Plaza, A.; Li, J. Hyperspectral image classification using random occlusion data augmentation. IEEE Geosci. Remote Sens. Lett. 2019 , 16 , 1751–1755. [ Google Scholar ] [ CrossRef ]
• Xu, Y.; Du, B.; Zhang, F.; Zhang, L. Hyperspectral image classification via a random patches network. ISPRS J. Photogramm. Remote Sens. 2018 , 142 , 344–357. [ Google Scholar ] [ CrossRef ]
• Bera, S.; Shrivastava, V.K.; Satapathy, S.C. Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review. Comput. Model. Eng. Sci. 2022 , 133 , 219–250. [ Google Scholar ] [ CrossRef ]
• Ghamisi, P.; Maggiori, E.; Li, S.; Souza, R.; Tarablaka, Y.; Moser, G.; De Giorgi, A.; Fang, L.; Chen, Y.; Chi, M.; et al. New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning. IEEE Geosci. Remote Sens. Mag. 2018 , 6 , 10–43. [ Google Scholar ] [ CrossRef ]
• Liu, H.; Li, W.; Xia, X.G.; Zhang, M.; Gao, C.Z.; Tao, R. Central attention network for hyperspectral imagery classification. IEEE Trans. Neural Netw. Learn. Syst. 2022 , 34 , 8989–9003. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lei, S.; Shi, Z.; Zou, Z. Coupled Adversarial Training for Remote Sensing Image Super-Resolution. IEEE Trans. Geosci. Remote Sens. 2020 , 58 , 3633–3643. [ Google Scholar ] [ CrossRef ]
• Mei, S.; Ji, J.; Geng, Y.; Zhang, Z.; Li, X.; Du, Q. Unsupervised spatial–spectral feature learning by 3D convolutional autoencoder for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 2019 , 57 , 6808–6820. [ Google Scholar ] [ CrossRef ]
• Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv 2020 , arXiv:2010.11929. [ Google Scholar ]
• He, J.; Zhao, L.; Yang, H.; Zhang, M.; Li, W. HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Trans. Geosci. Remote Sens. 2019 , 58 , 165–178. [ Google Scholar ] [ CrossRef ]
• Zhong, Z.; Li, Y.; Ma, L.; Li, J.; Zheng, W.S. Spectral–spatial transformer network for hyperspectral image classification: A factorized architecture search framework. IEEE Trans. Geosci. Remote Sens. 2021 , 60 , 1–15. [ Google Scholar ] [ CrossRef ]
• Roy, S.K.; Deria, A.; Shah, C.; Haut, J.M.; Du, Q.; Plaza, A. Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens. 2023 , 61 , 1–15. [ Google Scholar ] [ CrossRef ]
• Moharram, M.A.; Sundaram, D.M. Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions. Neurocomputing 2023 , 536 , 90–113. [ Google Scholar ] [ CrossRef ]
• Shi, Y.; Fu, B.; Wang, N.; Cheng, Y.; Fang, J.; Liu, X.; Zhang, G. Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images. Drones 2023 , 7 , 240. [ Google Scholar ] [ CrossRef ]
• Zhan, Y.; Wu, K.; Dong, Y. Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022 , 15 , 7171–7186. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Song, T.; Xie, Y.; Roy, S.K. A probabilistic neighbourhood pooling-based attention network for hyperspectral image classification. Remote Sens. Lett. 2022 , 13 , 65–75. [ Google Scholar ]
• Liu, B.; Yu, A.; Gao, K.; Tan, X.; Sun, Y.; Yu, X. DSS-TRM: Deep spatial–spectral transformer for hyperspectral image classification. Eur. J. Remote Sens. 2022 , 55 , 103–114. [ Google Scholar ] [ CrossRef ]
• Lu, Z.; Xu, B.; Sun, L.; Zhan, T.; Tang, S. 3-D channel and spatial attention based multiscale spatial–spectral residual network for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020 , 13 , 4311–4324. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Shang, S.; Tang, X.; Feng, J.; Jiao, L. Spectral partitioning residual network with spatial attention mechanism for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2021 , 60 , 1–14. [ Google Scholar ] [ CrossRef ]
• Mei, S.; Li, X.; Liu, X.; Cai, H.; Du, Q. Hyperspectral image classification using attention-based bidirectional long short-term memory network. IEEE Trans. Geosci. Remote Sens. 2021 , 60 , 1–12. [ Google Scholar ]
• Shi, Y.; Fu, B.; Wang, N.; Chen, Y.; Fang, J. Multispectral Image Quality Improvement Based on Global Iterative Fusion Constrained by Meteorological Factors. Cogn. Comput. 2023 , 16 , 404–424. [ Google Scholar ] [ CrossRef ]
• Veit, A.; Wilber, M.J.; Belongie, S. Residual networks behave like ensembles of relatively shallow networks. In Advances in Neural Information Processing Systems ; Curran Associates, Inc.: New York, NY, USA, 2016; Volume 29. [ Google Scholar ]
• Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708. [ Google Scholar ]
• Yang, B.; Bender, G.; Le, Q.V.; Ngiam, J. Condconv: Conditionally parameterized convolutions for efficient inference. In Advances in Neural Information Processing Systems ; Curran Associates, Inc.: New York, NY, USA, 2019; Volume 32. [ Google Scholar ]
• Li, C.; Zhou, A.; Yao, A. Omni-dimensional dynamic convolution. arXiv 2022 , arXiv:2209.07947. [ Google Scholar ]
• Chen, Y.; Dai, X.; Liu, M.; Chen, D.; Yuan, L.; Liu, Z. Dynamic convolution: Attention over convolution kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020; pp. 11030–11039. [ Google Scholar ]
• Sun, H.; Li, Q.; Yu, J.; Zhou, D.; Chen, W.; Zheng, X.; Lu, X. Deep feature reconstruction learning for open-set classification of remote sensing imagery. IEEE Geosci. Remote. Sens. Lett. 2023 , 20 , 1–5. [ Google Scholar ] [ CrossRef ]
• Mei, S.; Song, C.; Ma, M.; Xu, F. Hyperspectral image classification using group-aware hierarchical transformer. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 1–14. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Zhang, H.; Shen, Q. Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 2017 , 9 , 67. [ Google Scholar ] [ CrossRef ]
• Song, W.; Li, S.; Fang, L.; Lu, T. Hyperspectral image classification with deep feature fusion network. IEEE Trans. Geosci. Remote Sens. 2018 , 56 , 3173–3184. [ Google Scholar ] [ CrossRef ]
• Zhu, M.; Jiao, L.; Liu, F.; Yang, S.; Wang, J. Residual spectral–spatial attention network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2020 , 59 , 449–462. [ Google Scholar ] [ CrossRef ]
• Hong, D.; Han, Z.; Yao, J.; Gao, L.; Zhang, B.; Plaza, A.; Chanussot, J. SpectralFormer: Rethinking hyperspectral image classification with transformers. IEEE Trans. Geosci. Remote Sens. 2021 , 60 , 1–15. [ Google Scholar ] [ CrossRef ]

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Li, S.; Chen, B.; Wang, N.; Shi, Y.; Zhang, G.; Liu, J. Constrained Spectral–Spatial Attention Residual Network and New Cross-Scene Dataset for Hyperspectral Classification. Electronics 2024 , 13 , 2540. https://doi.org/10.3390/electronics13132540

Li S, Chen B, Wang N, Shi Y, Zhang G, Liu J. Constrained Spectral–Spatial Attention Residual Network and New Cross-Scene Dataset for Hyperspectral Classification. Electronics . 2024; 13(13):2540. https://doi.org/10.3390/electronics13132540

Li, Siyuan, Baocheng Chen, Nan Wang, Yuetian Shi, Geng Zhang, and Jia Liu. 2024. "Constrained Spectral–Spatial Attention Residual Network and New Cross-Scene Dataset for Hyperspectral Classification" Electronics 13, no. 13: 2540. https://doi.org/10.3390/electronics13132540

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