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• The Online Researcher’s Guide To Sampling

Pros and Cons of Different Sampling Methods

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Non-random sampling methods, voluntary sampling, snowball sampling, quota sampling, judgment sampling, random sampling techniques, simple random sampling, systematic sampling, cluster sampling, multistage sampling, stratified sampling.

By Aaron Moss, PhD, Cheskie Rosenzweig, MS, & Leib Litman, PhD

Online Researcher’s Sampling Guide, Part 4: Pros and Cons of Different Sampling Methods

Conversations about sampling methods and sampling bias often take place at 60,000 feet. That is, researchers like to talk about the theoretical implications of sampling bias and to point out the potential ways that bias can undermine a study’s conclusions. Although these conversations are important, it is good to occasionally talk about what sampling looks like on the ground. At a practical level, what methods do researchers use to sample people and what are the pros and cons of each?

Non-random sampling techniques lead researchers to gather what are commonly known as convenience samples. Convenience samples are often based on who it’s easy for the researchers to contact. However, most online research does not qualify as pure convenience sampling. Often, researchers use non-random convenience sampling methods but strive to control for potential sources of bias. Here are some different ways that researchers can sample:

Voluntary sampling occurs when researchers seek volunteers to participate in studies. Volunteers can be solicited in person, over the internet, via public postings, and a variety of other methods. A researcher using voluntary sampling typically makes little effort to control sample composition.

Pros and Cons:

• Feasibility: Finding volunteers is often a relatively fast and affordable way to collect data.
• Subject to bias: Voluntary sampling is highly susceptible to bias, because researchers make little effort to control sample composition. The people who volunteer for the study may be very different than those who do not volunteer.

An Example of Voluntary Sampling:

A common form of voluntary sampling is the customer satisfaction survey. After a business provides a service or good, they often ask customers to report on their satisfaction. Because the business is asking all customers to volunteer their thoughts, the sample is voluntary and susceptible to bias.

Who Uses Voluntary Sampling?

Within academia, researchers often seek volunteer samples by either asking students to participate in research or by looking for people in the community. Within industry, companies seek volunteer samples for a variety of research purposes. Because volunteer samples are inexpensive, researchers across industries use them for a variety of different types of research.

Snowball sampling begins when researchers contact a few people who meet a study’s criteria. After those people complete the study, the researchers ask each person to recommend a few others who also meet the study criteria. By building on each participant’s social network, the hope is that data collection will snowball until the researchers reach enough people for their study.

• Ability to reach small or stigmatized groups: By drawing on people’s social networks, snowball sampling can be an effective way to study hard-to-reach groups. Once researchers gain the trust of a few members of the group, those people can help the researchers recruit other people.
• Non-random: A snowball sample will likely provide results that are hard to generalize beyond the sample studied.
• Slow: Because snowball sampling relies on each participant to recommend others, the data collection process is typically slow when compared to other methods.

An Example of Snowball Sampling:

Snowball sampling is an effective way to find people who belong to groups that are difficult to locate. For example, psychologists may use snowball sampling to study members of marginalized groups, such as homeless people, closeted gay people, or people who belong to a support group, such as Alcoholics Anonymous. After gaining the trust of a few people, the researchers could ask the participants to recommend some other members of the group. By proceeding from one recommendation to the next, the researchers may be able to gain a large enough sample for their project.

Who Uses Snowball Sampling?

Snowball sampling is most common among researchers who seek to conduct qualitative research with hard-to-reach groups . Academic researchers might use snowball sampling to study the members of a stigmatized group, while industry researchers might use snowball sampling to study customers who belong to elite groups, such as a private club.

When researchers engage in quota sampling, they identify subsets of the population that are important to represent and then sample participants within each subset.

• Representation: Quota sampling ensures representation of important groups within the population being studied.
• Mitigates confounds: Setting quotas within a study is a purposeful action that can help researchers eliminate potential confounds.
• Potential for bias: Because participants within each quota are not randomly drawn, it’s impossible to know how well they represent the groups in the population.

An Example of Quota Sampling:

If you wanted to study Americans’ beliefs about economic mobility, it would be important to sample people from different steps on the economic ladder. That is, you would want to make sure your sample included people who make a lot of money, people who make a moderate amount of money, and some people who make a little bit of money. To obtain this sample, you might set up quotas that are stratified by people’s income . That is what one researcher recently did using CloudResearch’s Prime Panels.

Who Uses Quota Sampling?

Quota sampling is extremely common in both academic and industry research. Sometimes, researchers set simple quotas to ensure there is an equal balance of men and women within a study. At other times, researchers want to represent several groups and, therefore, set up more extensive quotas that allow them to represent several important demographic groups within a sample.

Judgment sampling occurs when a researcher uses his or her own judgment to select participants from the population of interest. The researcher’s goal is to balance sampling people who are easy to find with obtaining a sample that represents the group of interest. Hence, when using judgment sampling, researchers exert some effort to ensure their sample represents the population being studied.

• Efficiency: Judgment sampling is often used when the population of interest is rare or hard to find. By exercising judgment in who to sample, the researcher is able to save time and money when compared to broader sampling strategies.
• Unsystematic: Judgment sampling is vulnerable to errors in judgment by the researcher, leading to bias.

An Example of Judgment Sampling:

Imagine a research team that wants to know what it’s like to be a university president. Compared to the entire population, very few people are or have been employed as the president of a university. Rather than rely on other sampling techniques that have a low probability of contacting university presidents, the researchers may choose a list of university presidents to contact for their study. By using their judgment in who to contact, the researchers hope to save resources while still obtaining a sample that represents university presidents.

Who Uses Judgment Sampling?

Researchers within industry and academia sometimes rely on judgment sampling. Whenever researchers choose to restrict their data collection to the members of some special group, they may be engaged in judgment sampling.

Random sampling techniques lead researchers to gather representative samples, which allow researchers to understand a larger population by studying just the people included in a sample. Although there are a number of variations to random sampling, researchers in academia and industry are more likely to rely on non-random samples than random samples.

Simple random sampling is the most basic form of probability sampling. In a simple random sample, every member of the population being studied has an equal chance of being selected into the study, and researchers use some random process to select participants.

• Strong external validity: Allows researchers to generalize results from the sample to the entire population being studied.
• Relative speed and efficiency compared to the census: A simple random sample allows researchers to learn about an entire population much faster and more efficiently than collecting data from every member of the population.
• Expensive: Contacting a large, randomly selected group of people requires lots of resources.
• Time consuming: Although this method is faster than conducting a census, gathering data from a large, random sample is often slow when compared to other methods.
• Not always possible: Researchers may wish to study a group for which there is no organized list (sampling frame) to randomly sample from.

An Example of Simple Random Sampling:

Researchers who want to know what Americans think about a particular topic might use simple random sampling. The researchers could begin with a list of telephone numbers from a database of all cell phones and landlines in the U.S. Then, using a computer to randomly dial numbers, the researchers could sample a group of people, ensuring a simple random sample.

Who Uses Simple Random Sampling?

Simple random sampling is sometimes used by researchers across industry, academia and government. The Census Bureau uses random sampling to gather detailed information about the U.S. population. Organizations like Pew and Gallup routinely use simple random sampling to gauge public opinion, and academic researchers sometimes use simple random sampling for research projects. However, because simple random sampling is expensive and many projects can arrive at a reasonable answer to their question without using random sampling, simple random sampling is often not the sampling plan of choice for most researchers.

Systematic sampling is a version of random sampling in which every member of the population being studied is given a number. Then, researchers randomly select a number from the list as the first participant. After the first participant, the researchers choose an interval, say 10, and sample every tenth person on the list.

• External validity: Allows generalization from the sample to the population being studied.
• Relative speed: Faster than contacting all members of the population or simple random sampling.
• Limited feasibility: This sampling method is not possible without a list of all members of the population.

An Example of Systematic Sampling

Colleges and universities sometimes conduct campus-wide surveys to gauge people’s attitudes toward things like campus climate. To conduct such a survey, a university could use systematic sampling. By starting with a list of all registered students, the university could randomly select a starting point and an interval to sample with. Contacting every student who falls along the interval would ensure a random sample of students.

Who Uses Systematic Sampling?

Systematic sampling is a variant of simple random sampling, which means it is often employed by the same researchers who gather random samples. Researchers engaged in public polling and some government, industry or academic positions may use systematic sampling. But, much more often, researchers in these areas rely on non-random samples.

Cluster sampling occurs when researchers randomly sample people within groups or clusters the people already belong to.

• External validity: The random nature of selecting clusters allows researchers to generalize from the sample to the entire population being studied.
• Speed: Faster and more efficient than sampling all groups or all people in the population.
• Not always possible: There are several groups researchers may want to study for which there is no organized list from which to randomly select participants.

An Example of Cluster Sampling:

Imagine that researchers want to know how many high school students in the state of Ohio drank alcohol last year. The researchers could study this issue by taking a list of all high schools in Ohio and randomly selecting a portion of schools (the clusters). Then, the researchers could sample the students within the selected schools, rather than sampling all students in the state. By randomly selecting from the clusters (i.e., schools), the researchers can be more efficient than sampling all students while still maintaining the ability to generalize from their sample to the population.

Who Uses Cluster Sampling?

Researchers who study people within groups, such as students within a school or employees within an organization, often rely on cluster sampling. By randomly selecting clusters within an organization, researchers can maintain the ability to generalize their findings while sampling far fewer people than the organization as a whole.

Multistage sampling is a version of cluster sampling. Multistage sampling begins when researchers randomly select a set of clusters or groups from a larger population. Then, the researchers randomly select people within those clusters, rather than sampling everyone in the cluster.

• External validity: Multistage sampling maintains the researchers’ ability to generalize from the sample to the entire population being studied.
• Relative speed: By sampling fewer people, multistage sampling is faster and more efficient than cluster sampling.

An Example of Multistage Sampling:

Researchers who want to study work-life balance and employee satisfaction within a large organization might begin by randomly selecting departments or locations within the organization as their clusters. If each cluster is large enough, the researchers could then randomly sample people within each cluster, rather than collecting data from all the people within each cluster.

Who Uses Multistage Sampling?

Similar to cluster sampling, researchers who study people within organizations or large groups often find multistage sampling useful. Multistage sampling maintains the researcher’s ability to generalize their findings to the entire population being studied while dramatically reducing the amount of resources needed to study a topic.

Stratified sampling is a version of multistage sampling, in which a researcher selects specific demographic categories, or strata, that are important to represent within the final sample. Once these categories are selected, the researcher randomly samples people within each category.

• External validity: Maintains the researcher’s ability to generalize from the sample to the entire population being studied.
• Representation: By selecting important groups to sample within before beginning data collection, the researchers can ensure adequate representation of small and minority groups.

An Example of Stratified Sampling:

Researchers at the Pew Research Center regularly ask Americans questions about religious life. To ensure that members of each major religious group are adequately represented in their surveys, these researchers might use stratified sampling. In doing so, researchers would choose the major religious groups that it is important to represent in the study and then randomly sample people who belong to each group. By using this technique, the researchers can ensure that even small religious groups are adequately represented in the sample while maintaining the ability to generalize their results to the larger population.

Who Uses Stratified Sampling?

Stratified sampling is common among researchers who study large populations and need to ensure that minority groups within the population are well-represented. For this reason, stratified sampling tends to be more common in government and industry research than within academic research.

CloudResearch connects researchers with a wide variety of participants. Using our Prime Panels platform , you can sample participants from hard-to-reach demographic groups, gather large samples of thousands of people, or set up quotas to ensure your sample matches the demographics of the U.S. When you use our MTurk Toolkit , you can target people based on several demographic or psychographic characteristics. In addition to these tools, we can provide expert advice to ensure you select a sampling approach fit for your research purposes. Contact us today to learn how we can connect you to the right sample for your research project.

Continue Reading: The Online Researcher’s Guide to Sampling

Part 1: What Is the Purpose of Sampling in Research?

Part 2: How to Reduce Sampling Bias in Research

Part 3: How to Build a Sampling Process for Marketing Research

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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.

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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 In Reseach: Types, Techniques, & Examples

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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.

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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.

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Sampling Techniques for Quantitative Research

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• Moniruzzaman Sarker   ORCID: orcid.org/0000-0003-3595-5838 4 &
• Mohammed Abdulmalek AL-Muaalemi 5  

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

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

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Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia

Julian kirchherr.

1 Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands

2 School of Geography and the Environment, University of Oxford, Oxford, United Kingdom

Katrina Charles

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Snowball sampling is a commonly employed sampling method in qualitative research; however, the diversity of samples generated via this method has repeatedly been questioned. Scholars have posited several anecdotally based recommendations for enhancing the diversity of snowball samples. In this study, we performed the first quantitative, medium- N analysis of snowball sampling to identify pathways to sample diversity, analysing 211 reach-outs conducted via snowball sampling, resulting in 81 interviews; these interviews were administered between April and August 2015 for a research project on anti-dam movements in Southeast Asia. Based upon this analysis, we were able to refine and enhance the previous recommendations (e.g., showcasing novel evidence on the value of multiple seeds or face-to-face interviews). This paper may thus be of particular interest to scholars employing or intending to employ snowball sampling.

Introduction

Snowball sampling is a commonly employed sampling method in qualitative research, used in medical science and in various social sciences, including sociology, political science, anthropology and human geography [ 1 – 3 ]. As is typical of terms adopted by a variety of fields, however, the phrase ‘snowball sampling’ is used inconsistently across disciplines [ 4 ]. The most frequently employed definition, suggested by Patton [ 5 ], Atkinson and Flint [ 6 ], Cohen and Arieli [ 7 ] and Bhattacherjee [ 8 ], is as a sampling method in which one interviewee gives the researcher the name of at least one more potential interviewee. That interviewee, in turn, provides the name of at least one more potential interviewee, and so on, with the sample growing like a rolling snowball if more than one referral per interviewee is provided.

This definition can initially seem self-explanatory, which may explain why snowball sampling is rarely discussed in most peer-reviewed papers that employ it. Various scholars use snowball sampling in their empirical work, but most provide only limited information on the method (see, e.g., [ 9 – 13 ]). Similarly, qualitative research textbooks often lack substantive discussion of snowball sampling (e.g., [ 8 , 14 – 19 ]). Bailey [ 14 ], for instance, devotes only a half-page of his 595-page book on social research methods to snowball sampling, acknowledging that ‘snowball sampling procedures have been rather loosely codified’ ([ 14 ], p. 96), an observation echoed by Penrod et al. [ 3 ].

This paper focuses on snowball sampling procedures, which we define as those actions undertaken to initiate, progress and terminate the snowball sample [ 1 , 20 ]. Despite the lack of substantive writing on snowball sampling as a method, several authors [ 2 , 3 , 21 ] have provided recommendations for enhancing a sample’s diversity in snowball sampling procedures (we discuss this further in Section 4). However, as this advice is not based on a quantitative analysis of evidence, but only on anecdotal evidence, there is a risk that these recommendations are based on coincidence. The aim of this paper is to provide advice on enhancing the sample diversity of a snowball sample. This advice is grounded in a medium- N analysis of relevant evidence, thus reducing the probability of positing advice that is based on coincidence [ 22 ]. A medium- N analysis is generally based on 10–100 cases, whereas anecdotal evidence is usually based only on a handful of cases [ 23 , 24 ]. At the core of our work, we provide descriptive analyses of various commonly prescribed strategies for enhancing the sample diversity of a snowball sample. These analyses are based on reach-outs to 211 individuals via snowball sampling for a research project on anti-dam movements in Southeast Asia, resulting in 81 interviews conducted between April and August 2015. As far as we are aware, ours is the first medium- N analysis to focus on enhancing the sample diversity of a snowball sample.

The remainder of this paper is organised as follows: in Section 2, we discuss snowball sampling as a method; in Section 3, we present the research project on anti-dam movements in Southeast Asia that served as the basis for our medium- N analysis on snowball sampling procedures; in Section 4, we present and discuss insights on snowball sampling procedures based upon this analysis as well as our resulting recommendations; finally, in Section 5, we summarise our argument.

Throughout this paper, we employ social science methodology terminology. We define key terms for this paper such as ‘snowball sampling’ or ‘sampling’, since these terms are not consistently codified in the scholarly literature. Due to limited space, however, we refrain from defining terms we have deemed common in this field of study, referring only to the relevant literature.

On snowball sampling

Traditional sampling methods are comprised of two elements [ 25 , 26 ]. First, a full set of data sources is defined, creating a list of the members of the population to be studied, known as a sampling frame. Second, a specific sample of data is collected from this sampling frame. Snowball sampling defies both elements, since it does not rely upon a sampling frame [ 27 ] (which may indicate that a different term for snowball sampling would be more accurate). Snowball sampling is often employed when no sampling frame can be constructed.

Researchers frequently cannot construct a sampling frame if a difficult-to-reach population is to be studied. Difficult-to-reach-populations are also referred to as ‘hard-to-reach-populations’ [ 28 ], ‘hidden populations’ [ 29 ] or ‘concealed populations’ [ 21 ] in the scholarly literature. Although not all scholars may agree that these terms are interchangeable, we deem them interchangeable for the purposes of this paper. For further discussion of this terminology, see [ 30 , 31 ].

A difficult-to-reach population does not wish to be found or contacted (e.g., illegal drug users, illegal migrants, prostitutes or homeless people [ 6 , 31 ]). Snowball sampling was originally used by researchers to study the structure of social networks [ 32 ]. The earliest empirical account of snowball sampling is from 1955 [ 33 ], with snowball sampling first described as a method in 1958 [ 34 ]. While it is still used to study the structure of social networks [ 35 ], over the last few decades, the method’s key purpose has largely transformed ‘into […] an expedient for locating members of a [difficult-to-reach] population’ ([ 36 ], p. 141).

Researchers grounded in quantitative thinking, such as Lijphart [ 37 ] and King et al. [ 38 ], tend to view the drawing of a random sample from a sampling frame as the gold standard of data collection. Even these researchers may nevertheless consider non-probability sampling methods, such as snowball sampling, a ‘necessary and irreplaceable sampling [method]’ ([ 39 ], p. 367) when confronted with difficult-to-reach populations, particularly if the dismissal of snowball sampling would mean that no research could be conducted at all. Ultimately, ‘an important topic is worth studying even if very little [access to] information is available’ ([ 38 ], p. 6). Still, some of those grounded in quantitative thinking call snowball sampling a method ‘at the margin of research practice’ ([ 6 ], p. 1), since the lack of a sampling frame means that, unlike individuals in a random sample, individuals in a population of interest do not have the same probability of being included in the final sample. Findings from a snowball sample would therefore not be generalisable [ 40 ] (on generalisability, see [ 41 ]).

Several qualitative scholars rebut such criticism. Creswell, for instance, notes that ‘the intent [of qualitative research] is not to generalise to a population, but to develop an in-depth [and contextualised] exploration of a central phenomenon’ ([ 42 ], p. 203). Others [ 1 , 39 ] specifically oppose quantitative scholars’ negative framing of snowball sampling, arguing that this method would ‘generate a unique type of social knowledge’ ([ 1 ], p. 327). Due to the diversity of perspectives gathered, this knowledge would be particularly valuable for an in-depth and contextualised exploration of a central phenomenon. We therefore define the diversity of a sample as a measure of the range of viewpoints that have been gathered on a central phenomenon.

Researchers critical of snowball sampling respond to this defence by arguing that the method is unable to ensure sample diversity, which is a necessary condition for valid research findings. Indeed, some scholars have stated that snowball samples underrepresent and may even exclude those least keen to cooperate, since referrals may not materialise in an interview if a potential interviewee is only somewhat keen or not at all keen to be interviewed [ 3 , 43 ]. Similarly, potential interviewees with smaller networks may be underrepresented, as they are less likely to be referred for an interview [ 31 , 44 ]. Those with smaller networks may also be in a specific network whose different perspectives may be of interest but are excluded in the final sample. Meanwhile, snowball sampling is said to over represent those interviewees (and their respective networks) that the interviewer spoke with first; the relevant literature refers to this as ‘anchoring’ [ 20 , 39 ].

We do not aim to argue the ‘validity’ of the method, but rather to inform snowball sampling methodologies in order to promote sample diversity. From a qualitative perspective, ‘validity’ can be defined as ‘the correctness or credibility of a description, conclusion, explanation, interpretation or other sort of account’ ([ 45 ], p. 87), while quantitative researchers frequently use the terms ‘generalisability’ and ‘(external) validity’ interchangeably [ 46 , 47 ]. The term ‘validity’ is contested among qualitative researchers, and some qualitative researchers entirely reject the concept for qualitative work [ 48 , 49 ]. We do not aim to resolve this debate via this paper; instead, we focus on the (seemingly less-contested) term ‘sample diversity’. While we acknowledge that this term is not codified in qualitative textbooks such as the SAGE Encyclopedia of Qualitative Research Methods , sample diversity is considered desirable by the various qualitative scholars we reviewed. Boulton and Fitzpatrick demand, for instance, that qualitative researchers ‘ensure that the full diversity of individuals […] is included [in their sample]’ ([ 50 ], p. 84), a mandate echoed by other scholars [ 16 , 51 – 53 ].

In order to operationalise the concept of sample diversity, we used five key methodological recommendations to inform our research. In this paper, we use quantitative analyses from our experiences with snowball sampling to further reflect on these recommendations, which are briefly described below.

Prior personal contacts of the researcher are required

Patton ([ 5 ], p. 176) notes that snowball sampling ‘begins by asking well-situated people: “Who knows a lot about ____? Who should I talk to?”‘. In the absence of a sampling frame for the population of interest, however, the researcher must retain at least some prior personal or professional contacts in the population of interest which can serve as the seeds of the snowball sample [ 2 , 54 ]. Waters contends that building a diverse snowball sample ‘depend[s] almost exclusively on the researcher’s [prior personal or professional] contacts’ ([ 39 ], p. 372).

Sample seed diversity is important

Morgan [ 21 ] has claimed that the ‘best defence’ against a lack of sample diversity is to begin the sample with seeds that are as diverse as possible. Others echo this advice [ 3 , 39 , 55 ], arguing that it is ‘compulsory for the researcher to ensure that the initial set of respondents is sufficiently varied’ ([ 55 ], p. 55). The term ‘chain referral sampling’ has been used for snowball samples that are strategically built via multiple varying seeds [ 3 ].

Technology means face-to-face interviews are no longer required

Some researchers have argued that face-to-face interviews are obsolete. For instance, over 25 years ago, it was claimed there were ‘no remarkable differences’ ([ 56 ], p. 211) between information collected via telephone and information collected via face-to-face interviews. The increasing use of telecommunications in recent years is likely to have further reduced barriers to remote interviewing, and various scholars [ 57 , 58 ] continue to claim that ‘evidence is lacking that [telephone interviews] produce lower quality data’ ([ 59 ], p. 391). In particular, they have highlighted the benefits of using Skype for semi-structured interviews [ 57 ].

However, for snowball sampling, face-to-face interviews help to generate the trust that scholars claim is required in order to gain referrals [ 1 , 31 , 39 , 60 ]. Noy argues that ‘the quality of the referring process is naturally related to the quality of the interaction: […] if the researcher did not win the informant’s trust […], the chances the latter will supply the former referrals decrease’ ([ 1 ], p. 334).

Persistence is necessary to secure interviews

Although the value of persistence may be considered self-evident by some scholars, it is seen by multiple academics [ 61 – 63 ] as a central virtue of qualitative researchers. Many young career scholars who embrace snowball sampling are likely to hear such advice as, ‘If you cannot interview your envisaged interviewees initially, don’t give up!’. A ‘helpful hint’ for qualitative researchers seeking informants is, ‘Persevere–repeat contact’ [ 64 ].

More waves of sampling are required to access more reluctant interviewees

As a remedy for snowball sampling’s previously discussed bias towards excluding those least keen to be interviewed, multiple scholars suggest pursuing a snowball sample for multiple waves (with a new sampling wave reached once an interviewee introduces the interviewer to one or more potential interviewees) [ 65 – 68 ]. Those suggesting this remedy assume that pursuing more waves increases the likelihood of being referred to an interviewee from a particularly difficult-to-reach population who is at least somewhat keen to be interviewed.

Approval for this study was granted by the Central University Research Ethics Committee (CUREC) of the University of Oxford. Our population of interest for our research project were stakeholders in Southeast Asia’s dam industry. Since ‘the most dramatic conflicts over how to pursue sustainable development’ ([ 69 ], p. 83) have occurred over the construction of large dams, we see this industry as a conflict environment with widely varying viewpoints. A conflict environment is one in which people perceive their goals and interests to be contradicted by the goals or interests of the opposing side [ 70 ]. The major conflicting parties in the dam industry tend to be local and international non-governmental organisations (NGOs) and academics (usually keen not to construct a particular dam) versus international donors, the private sector and governments (usually keen to construct a particular dam) [ 71 , 72 ]. Each sub-population operating in a conflict environment can be considered difficult to reach since fear and mistrust are often pervasive [ 7 ]. Snowball sampling is a suitable research method in conflict environments because the introductions through trusted social networks that are at the core of this method can help interviewees to overcome fear and mistrust, which, in turn, ensures access [ 7 ]. This access is needed to gather the widely varying viewpoints in the hydropower industry, in particular viewpoints with regards to what constitutes just resettlement [ 73 , 74 ]. Based on this rationale, we chose snowball sampling as the main method for our research.

In order to ensure sample diversity for our research project on anti-dam movements in Southeast Asia, we aimed to gather perspectives mostly from six main sub-populations: (1) local NGOs, (2) international NGOs, (3) international donors, (4) academia, (5) the private sector and (6) the government. We hypothesized that ‘dam developers’, a main sub-category of the interviewee category ‘private sector’, would be the most significant challenge to ensuring the diversity of our sample. Early in our process, many of the scholars with whom we discussed our research project argued that it would be impossible to interview a dam developer from a Chinese institution; meanwhile, researchers from a comparable research project that ended approximately when our project started reported being unable to interview any dam developers from European institutions. We also initially failed to collect data from dam developers: for instance, a survey we initiated that was distributed by Aqua~Media (host of a major global dam developer conference) to more than 1,500 dam developers yielded just five responses, only one of which was complete. We considered this weak response rate to be due, at least in part, to the dam industry’s negative view of academicians since the publication of Ansar et al. [ 75 ], which Nombre ([ 76 ], p. 1), the president of the International Commission on Large Dams (ICOLD), called ‘[highly] misleading’.

None of our researchers had significant direct links to the dam industry upon the start of the project; however, we did retain a variety of indirect links. Our researchers had past links to a management consultancy that serves various dam industry players, (more limited) links to an international donor working in the hydropower sector and links to activists in Myanmar advocating against dam projects.

After a favourable ethics review of our study by the CUREC of the University of Oxford, we commenced semi-structured interviews in April 2015, mostly via cold calls (we include cold e-mails in the term ‘cold calls’ throughout this paper). Initially, we conducted research via telephone only. We then undertook field research in Singapore, Myanmar and Thailand from June to August 2015 and terminated our data collection in late August 2015.

In total, 81 semi-structured interviews were carried out during this period. From a qualitative perspective, this is a relatively large sample size (for instance, the average qualitative PhD dissertation is based on 31 interviews [ 77 ]); from a quantitative perspective, however, the sample size is quite small [ 78 ]. Of our 81 interviews, 48 (59%) were conducted via telephone, 26 (32%) face-to-face and 7 (9%) online, either via e-mail or an online survey. Most of our interviews (57%) were carried out in July in Myanmar. Of our 81 interviewees, only 24 (30%) were women. Researchers who employ snowball sampling frequently employ personal/professional contact seeds and cold call seeds to build their sample (e.g., [ 2 , 79 , 80 ] with a seed defined as the starting point of a sample [ 65 ]). Of the 81 interviews analysed, 53 (65%) were rooted in a personal or professional contact ( Fig 1 ) (i.e. the seed of the interview pathway was a contact we had already retained prior to the research project). The remaining 28 (35%) interviews were rooted in cold calls.

Given the sensitive nature of the interview topic, all interviewees were assured anonymity. Thus, all of the interviews are coded, with the first letter indicating the mode of interview ( T for telephone, F for face-to-face, O for online survey or e-mail), the second letter indicating the category of interviewee ( A for academia, G for government, I for international donor, NI for international NGO, NL for national NGO, P for private sector) and the sequence of numbers indicating the interview number within a particular mode. Researcher A is indicated by RA , Researcher B by RB ; CON represents a conference event. Bold type indicates that an interview was completed, while X that an interview was not completed.

As outlined in the previous section, snowball sampling is sometimes criticised for producing samples that lack sample diversity. To address this criticism, we reviewed the (scarce) literature on enhancing sample diversity via snowball sampling procedures prior to commencing our study. Upon reflection during our research, we chose to pursue our analysis retrospectively in order to challenge some of the recommendations provided in literature. Our analysis is structured alongside the five core pieces of advice found in this literature ( Table 1 ). Our results are based on a quantitative analysis of the 81 interviews we conducted. Although we endeavoured to include all interview attempts, some initial cold calls may have been overlooked in this retrospective approach. Therefore, some of our analysis, particularly in Section 4.4, may be too optimistic. Overall, we were able reconstruct 211 reach-out attempts.

Sample diversity is measured by representation from five identified sub-groups.

Results and discussion

On prior personal and professional contacts.

Our analysis provides evidence that sample diversity can be reached even if no prior personal or professional contacts to the population of interest have been retained. The seeds of the interviews are depicted in Fig 2 , with the left side of the figure depicting the 53 interviews based on a personal or professional contact and the right side depicting the 28 interviews that were based on cold calls. This figure shows two main points of interest: first, both types of seeds include interviews in each interview category; second, the interview sub-category ‘dam developer’, which we hypothesised would be the most difficult to include in the sample, is also covered by both types of seeds. We can therefore conclude that a diverse sample could have been built even if we had relied solely on cold calls.

It is acknowledged, however, that building a snowball sample from cold calls is particularly labour-intensive [ 39 ]: in our research, only 25% of our cold calls led to an interview, compared to 62% of the referrals. Significant differences in the value of referrals persist from one interviewee group to another ( Fig 3 ). We measure the value of referrals via a concept we call ‘network premium’. To gauge the network premium, we subtracted the cold call response rate (i.e., the number of interviews initiated via cold calls divided by the total number of cold calls) from the referral response rate (i.e. the number of interviews initiated via referrals divided by the total number of referrals). Referrals were the most valuable when contacting international donors and private sector players, with network premiums of 74% and 52%, respectively, indicating that these groups are particularly difficult-to-reach populations.

(1) Unable to retrace for 13 identified reach-outs if initiated via referral or cold call; four reach-outs coded as ‘Other’. (2) Unable to retrace for one interview carried out via referral coded as ‘Other’. (3) Including personal contacts and contacts via conferences. (4) Referral response rate–Cold call response rate.

The overall results from these analyses are encouraging for scholars interested in researching a population to which no personal or professional contacts are retained prior to the research project. While personal or professional contacts maintained to the research population of interest can accelerate the research endeavour, our results also showcase that (at least for our topic of interest) a diverse sample can be built from cold calls if a researcher is willing to invest some time in reach-outs.

On seed variation

Our research confirms the scholars’ advice that seed diversity is important. Fig 4 (a variation of Fig 2 ) depicts the completed interviews from a seed perspective, with RA, RB and cold calls as the three main seeds of the sample. The sample built via RA, who has a background in the private sector, is largely biased towards this sector, with 47% of all interviews seeded via RA private sector interviews. RB conducted 57% of interviews, whose background is closest to local NGOs, were with local NGOs. Meanwhile, the sample built via cold calls indicates no significant biases towards any interviewee category. Interviews based on the network of RB included one (TNL17) with a leading activist from a remote area of Myanmar who provided unique insights into the early days of an anti-dam campaign. This insight helped us to develop a narrative of the campaign that was not skewed to the later days of the campaign and the activists prominent in these later days. The sample diversity ensured via RB was thus central to the quality of our research.

It is noteworthy that the three different seeds in Fig 4 include interviews in all interviewee categories, including the sub-category ‘dam developer’ (the sole exception is the interviewee category ‘international NGO, which contains zero interviews for RB). This indicates that, at least for our topic of interest, a fairly diverse sample can be generated even if the researcher is unable to vary her or his seed, although the overall data suggest that seed variation can significantly enhance sample diversity. Fig 3 may therefore be viewed as a case for collaboration among researchers; if researchers with different backgrounds and different personal and professional contacts to the population of interest begin to collaborate, such collaborations are bound to contribute to sample diversity.

On face-to-face interviews

Our descriptive analysis provides evidence to further support the argument that face-to-face interviews are redundant, with our data indicating that face-to-face interviews can lead to more sought referrals than telephone interviews (perhaps since trust may be more readily established via face-to-face conversations than over the telephone). Fig 5 aims to quantify the value of face-to-face interviews. Overall, 30 (37%) of our interviews were initiated via prior face-to-face conversations, while prior telephone conversations and online contact each led to only eight interviews (10%). An examination shows that of the nine interviews conducted with dam developers, the interviewee sub-category deemed most difficult to access, seven (78%) were initiated via prior face-to-face interviews, while not a single telephone interview led to a referral to a dam developer. These interviews proved to be essential for our research. For instance, one Chinese dam developer challenged a claim from numerous NGOs that his company would not engage with NGOs, which, in turn, allowed us to present a more balanced portrayal of the interplay between Chinese dam developers and NGOs.

(1) Comprises interviews with those already retaining a personal or professional contact prior to the research project.

While our research did not investigate whether face-to-face interviews lead to lower-quality data than telephone interviews, our data provide tentative evidence that face-to-face interviews are not obsolete; they can still be helpful for those employing or intending to employ snowball sampling, since these interviews can lead to more sought referrals and thus enhanced sample diversity. We acknowledge, however, that this finding may not be true for all populations. For instance, studies on individuals with sexually transmitted diseases have found that these interviewees (particularly men) tend to report more truthfully in an audio-computer-assisted self-interview (ACASI) than in a face-to-face interview, since interviewees tend to be more comfortable reporting on sexually transmitted diseases to a computer than to a live person [ 81 , 82 ].

On persistence

Our data suggest that persistence can indeed enhance sample diversity, but we can also conclude that excessive persistence does not necessarily yield dividends. Instead of distributing a great many interview reminders during our study, we reached out to the majority of our proposed interview subjects only once. Nevertheless, the scarce data we collected regarding persistence indicates its value. We map this data in Fig 6 , with the left side depicting our success rate in relation to the number of reach-outs (either one, two or three) and the right side depicting a deep dive on success rates achieved with two reach-outs (distinguishing between reach-out attempts to unknown potential interviewees and those to whom we were referred by other interviewees). We sent one interview reminder to 28 of our proposed interviewees. This led to 10 additional interviews, a success rate of 36%, equalling 12% of the total interviews analysed for this paper. Reminders appear to be only somewhat more helpful when contacting referrals in comparison to their usefulness with cold calls–a single reminder led to an interview in 39% of our cases for the former group and 38% for the latter. One of the most valuable interviews for our research gained via a reminder was with the CEO of a Burmese dam developer. This interviewee compared Chinese and European dam developers in Myanmar, which helped us to further refine our narrative on social-safeguard policy adherence by Chinese dam developers in Myanmar.

(1) Number of reach-outs unknown for 32 reach-outs. Eight potential interviewees responded, but refused interview.

Excessive persistence, however, does not appear to be worthwhile. We sent three reminders to seven of our envisaged interviewees, but as Fig 6 shows, this did not lead to a single additional interview. While our data does not suggest that excessive persistence is helpful to researchers, it may also not be recommended for ethical reasons. A potential interviewee who does not respond to an interview request after two reach-outs may be indicating via this non-response that she or he is not interested in participating in the research. If a single request remains unanswered, the researcher may hypothesise that, for instance, the e-mail was overlooked, a hypothesis particularly likely when conducting interviews with time-pressed leaders of organisations. Indeed, all 10 interviews only carried out upon the second reach-out were interviews with interviewees in management positions.

Our data on persistence provide some evidence that those employing or intending to employ snowball sampling can enhance sample diversity if every reach-out is carefully tracked and followed by a reminder. We typically sent a reminder after one week if no response was obtained upon the first reach-out. This persistence may help to include those least keen to be interviewed for a research endeavour.

Our data show some evidence that, for our topic of study, pursuing interviews for even a few waves provided the perspectives of particularly difficult-to-reach populations and thus achieved sample diversity. More than 60% of our interviews were conducted in the zeroth or first wave ( Fig 7 ). These include seven of the nine interviews conducted with dam developers, the sub-category we deemed most challenging to interview. The remaining two interviews with dam developers were conducted in the second wave. However, not a single interview with a dam developer was carried out in the third wave and beyond, although a fifth of our total interviews were carried out in the third or later waves. Pursuing interviews for multiple waves nevertheless yielded novel insights. For instance, interview FNL12, which was conducted in the sixth wave, yielded insights on small dam construction in Myanmar–a topic of (some) interest to our research endeavour, but not covered in detail by previous interviews. Furthermore, we note that our finding regarding the limited value of multiple waves may also be specific to our population, with this finding perhaps indicating a low degree of network segmentation in the population in question [ 83 ]. Meanwhile, a high degree of network segmentation may impede the pursuance of multiple waves, since interviewees may lack the suitable contacts for a referral [ 84 ].

While additional waves can lead to novel insights, our overall data on waves provide some evidence that the number of waves pursued is not a definitive indicator for sample diversity. Even very few waves can yield access to particularly difficult-to-access populations.

Our quantitative analysis of pathways to delivering sample diversity in snowball samples yielded the following revisions to the literature’s recommendations:

• Prior personal contacts are not essential for achieving sample diversity but tend to be helpful, as generating new contacts during research can be labour-intensive.
• Sample seed diversity is important to achieving sample diversity.
• Face-to-face interviews build trust and can help to generate further referrals.
• Persistence (within reason) is helpful in securing interviews.
• Sample diversity is not necessarily enhanced if a seed is advanced over numerous waves.

We do not claim that these insights are comprehensive, but we believe that these interpretations of our data may serve as a starting point for future scholars using snowball sampling procedures. All of the analyses presented in this section are based only on descriptive statistics. This means, for instance, that we cannot control for confounds such as effort [ 85 ]. An experimental research design would yield the most robust insights on sampling procedures to enhance the sampling diversity of a snowball sample (with, for instance, one research project staffed with scholars with relevant personal or professional contacts and another staffed with scholars without relevant contacts).

Overall, this work aims to advance the literature on snowball sampling as a qualitative sampling approach. While snowball sampling procedures may qualify ‘as the least “sexy” facet of qualitative research’ ([ 1 ], p. 328), these procedures are ‘not self-evident or obvious’ ([ 20 ], p. 141), since the snowball sample does not ‘somehow magically’ ([ 20 ], p. 143) start, proceed and terminate when a scholar attempts to develop a diverse sample. Rather, continuous, deliberate effort by the researcher(s) is required. Our paper has attempted to provide some insights on this effort.

Unfortunately, we developed the idea to write this paper only during the course of our research project, and thus some of our data may be skewed. For instance, we may not have been able to trace all original reach-out attempts and our data on persistence may therefore be biased. Some of those scholars grounded in quantitative thinking may also claim that the insights outlined in Section 4 lack external validity since our sample size is relatively small from a quantitative methodological perspective. In addition, our population was very specific and thus may not be comparable to other difficult-to-reach populations, and we also did not adopt an experimental research design as described above. Hence, we encourage scholars to replicate our findings via their respective research projects that employ snowball sampling. With many scholars claiming to feel more pressed than ever to deliver research results with maximum efficiency, we hope that these initial descriptive analyses of snowball sampling procedures provide some valuable insights to those employing or intending to employ this method and aiming to improve their management of it.

Supporting information

Acknowledgments.

We wish to thank our reviewers at PLOS ONE who provided constructive thoughts on this piece of work. We also thank Ralf van Santen for his outstanding contributions to this work as a research assistant.

Funding Statement

The authors received no specific funding for this work.

Data Availability

What is Sampling? Need, Advantages, Limitations

• Post last modified: 9 January 2022
• Reading time: 23 mins read
• Post category: Research Methodology

• What is Sampling?

The terminology “sampling” indicates the selection of a part of a group or an aggregate with a view to obtaining information about the whole. This aggregate or the totality of all members is known as Population although they need not be human beings. The selected part, which is used to ascertain the characteristics of the population, is called Sample.

Table of Content

• 1 What is Sampling?
• 2 Need of Sampling
• 3.1 It must be representative
• 3.2 Homogeneity
• 3.3 Adequate samples
• 3.4 Optimization
• 4.1 Cost effective
• 4.2 Time-saving
• 4.3 Testing of Accuracy
• 4.4 Detailed Research is Possible
• 4.5 Reliability
• 4.6 Exclusive methods in many circumstances
• 4.7 Administrative convenience
• 4.8 More scientific
• 5.1 Biased Conclusion
• 5.2 Experienced Researcher is required
• 5.3 Not suited for Heterogeneous Population
• 5.4 Small Population
• 5.5 Sample Not Representative
• 5.6 Lack of Experts
• 5.7 Conditions of Complete Coverage
• 6.1 Universe or Population
• 6.3 Sampling Unit
• 6.4 Sampling
• 6.5 Parameter
• 6.6 Statistic
• 6.7 Standard Error
• 6.8 Sampling Frame
• 6.9 Sampling Design
• 6.10 Sampling Error
• 6.11 Sample Distribution
• 6.12 Population Distribution

While choosing a sample, the population is assumed to be composed of individual units or members, some of which are included in the sample. The total number of members of the population is called Population Size and the number included in the sample is called Sample Size.

Researchers usually cannot make direct observations of every individual in the population they are studying. Instead, they collect data from a subset of individuals – a sample – and use those observations to make inferences about the entire population.

Ideally, the sample corresponds to the larger population on the characteristic(s) of interest. In that case, the researcher’s conclusions from the sample are probably applicable to the entire population.

This type of correspondence between the sample and the larger population is most important when a researcher wants to know what proportion of the population has a certain characteristic –like a particular opinion or a demographic feature. Public opinion polls that try to describe the percentage of the population that plans to vote for a particular candidate, for example, require a sample that is highly representative of the population.

Need of Sampling

To draw conclusions about populations from samples, we must use inferential statistics which enables us to determine a population’s characteristics by directly observing only a portion (or sample) of the population. We obtain a sample rather than a complete enumeration (a census) of the population for many reasons.

Obviously, it is cheaper to observe a part rather than the whole, but we should prepare ourselves to cope with the dangers of using samples. In this tutorial, we will investigate various kinds of sampling procedures. Some are better than others but all may yield samples that are inaccurate and unreliable. We will learn how to minimize these dangers, but some potential error is the price we must pay for the convenience and savings the samples provide.

Essentials of Sampling

In order to reach a clear conclusion, the sampling should possess the following essentials:

It must be representative

Homogeneity, adequate samples, optimization.

The sample selected should possess similar characteristics to the original universe from which it has been drawn.

Selected samples from the universe should have similar nature and should not have any difference when compared with the universe.

In order to have a more reliable and representative result, a good number of items are to be included in the sample.

All efforts should be made to get maximum results both in terms of cost as well as efficiency. If the size of the sample is larger, there is better efficiency and at the same time the cost is more. A proper size of sample is maintained in order to have optimized results in terms of cost and efficiency.

Advantages of Sampling

The sampling only chooses a part of the units from the population for the same study. The sampling has a number of advantages as compared to complete enumeration due to a variety of reasons.

Sampling has the following advantages:

Cost effective

Time-saving.

• Testing of A ccuracy

Detailed Research is Possible

Reliability, exclusive methods in many circumstances, administrative convenience, more scientific.

This method is cheaper than the Census Research because only a fraction of the population is studied in this method.

There is a saving in time not only in conducting the sampling enquiry but also in the decision making process.

Testing of Accuracy

Testing of accuracy of samples drawn can be made by comparing two or more samples.

Since the data collected under this method is limited but homogeneous, so more time could be spend on decision making.

If samples are taken in proper size and on proper grounds the results of sampling will be almost the same which might have been obtained by Census method.

Where the population is infinite, then the sampling method is the only method of effective research. Also, if the population is perishable or testing units are destructive, then we have to complete our research only through sampling. Example: Estimation of expiry dates of medicines.

The organization and administration of sample survey are easy for the reasons which have been discussed earlier.

Since the methods used to collect data are based on scientific theory and results obtained can be tested, sampling is a more scientific method of collecting data.

Limitations of Sampling

It is not that sampling is free from demerits or shortcomings. There are certain limitations of this method which are discussed below:

Biased Conclusion

Experienced researcher is required, not suited for heterogeneous population, small population, sample not representative, lack of experts, conditions of complete coverage.

If the sample has not been properly taken then the data collected and the decision on such data will lead to wrong conclusion. Samples are like medicines. They can be harmful when they are taken carelessly or without knowledge off their effects.

An efficient sampling requires the services of qualified, skilled and experienced personnel. In the absence of these the results of their search will be biased.

If the populations are mixed or varied, then this method is not suited for research.

Sampling method is not possible when population size is too small. 5. Illusory conclusion: If a sample enquiry is not carefully planned and executed, the conclusions may be inaccurate and misleading.

To make the sample representative is a difficult task. If a representative sample is taken from the universe, the result is applicable to the whole population. If the sample is not representative of the universe the result may be false and misleading.

As there are lack of experts to plan and conduct a sample survey, its execution and analysis, and its results would be unsatisfactory and not trustworthy.

If the information is required for each and every item of the universe, then a complete enumeration survey is better.

Some Fundamental Definitions

Some fundamental concepts related to sampling are discussed as follows:

Universe or Population

Sampling unit, standard error, sampling frame, sampling design, sampling error, sample distribution, population distribution.

The total number of items in any field of study is called the universe. The population refers to the total units or items about which information is required. The attributes that are the object of the study are called the characteristics and the units possessing them are known as elementary units. The aggregate of such units is the population.

All units in any field of study constitute the universe. All elementary units are the population. Often the two terms are used interchangeably, however, research needs a distinction.

The population or universe can be of two types:

• A finite population consists of fixed number of elements and the elements can be enumerated totally, e.g., the number of students in a state. The symbol N is used to depict the number of elements or items of a finite population.
• An infinite population is the one where all the elements cannot be observed, at least theoretically, e.g., the number of stars in the sky. In a sense, a very large finite population is an infinite population.

It is a subset of the population. It comprises only some elements of the population. If out of the 350 mechanical engineers employed in an organization, 30 are surveyed regarding their intention to leave the organization in the next six months, these 30 members would constitute the sample.

A sampling unit is a single member of the sample. If a sample of 50 students is taken from a population of 200 MBA students in a business school, then each of the 50 students is a sampling unit. Another example could be that if a sample of 50 patients is taken from a hospital to understand their perception about the services of the hospital, each of the 50 patients is a sampling unit.

It is a process of selecting an adequate number of elements from the population so that the study of the sample will not only help in understanding the characteristics of the population but will also enable us to generalize the results. We will see later that there are two types of sampling designs—probability sampling design and non-probability sampling design.

As per definition,a parameter is an arbitrary constant whose value characterizes a member of a system (as a family of curves); also it is a quantity (as a mean or variance) that describes a statistical population. A parameter is a value, usually unknown (and which therefore has to be estimated), used to represent a certain population characteristic.

For example, the population mean is a parameter that is often used to indicate the average value of a quantity. Within a population, a parameter is a fixed value which does not vary. Each sample drawn from the population has its own value of any statistic that is used to estimate this parameter.

For example, the mean of the data in a sample is used to give information about the overall mean in the population from which that sample was drawn. Parameters are often assigned Greek letters Sigma (s) whereas statistics are assigned Roman letters (s).

A statistical parameter is a parameter that indexes a family of probability distributions. It can be regarded as a numerical characteristic of a population or a model.

Astatistic( singular) is a single measure of some attributes of a sample, for example, its arithmetic mean value. It is calculated by applying a function (statistical algorithm) to the values of the items of the sample, which are known together as a set of data.

More formally, statistical theory defines a statistic as a function of a sample where the function itself is independent of the sample’s distribution; that is, the function can be stated before the realization of the data. The term statistic is used both for the function and for the value of the function on a given sample.

A statistic is distinct from a statistical parameter, which is not computable because often the population is much too large to examine and measure all its items. However, a statistic, when used to estimate a population parameter, is called an estimator. For example, the sample mean is a statistic that estimates the population mean, which is a parameter.

When a statistic (a function) is being used for a specific purpose, it may be referred to by a name indicating its purpose: in descriptive statistics, a descriptive statistic is used to describe the data; in estimation theory, an estimator is used to estimate a parameter of the distribution (population); in statistical hypothesis testing, a test statistic is used to test a hypothesis.

However, a single statistic can be used for multiple purposes – for example, the sample mean can be used to describe a data set, to estimate the population mean, or to test a hypothesis.

As per definition, the ‘StandardError’ is the standard deviation of the sampling distribution of a statistic. The standard error is a statistical term that measures the accuracy with which a sample represents a population.

In statistics, the sample mean deviates from the actual mean of a population; this deviation is the standard error. Thus the term ‘standard error’ is used to refer to the standard deviation of various sample statistics, such as the mean or median.

The ‘standard error of the mean refers to the standard deviation of the distribution of sample means taken from a population. The smaller the standard error, the more representative the sample will be of the overall population. The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value.

The elementary units that form the basis of the sampling process are known as sampling units. A list of all such sampling units is referred to as the sampling frame. The sampling frame is a list of items from which the sample is drawn.

For research, a frame of the population is to be constructed which will enable the researcher to draw the sample, e.g., names from the census records or telephone directory, etc., for conducting a study on a sample that is drawn from the frame. A telephone directory is a frame, from which names are drawn to get the sample.

Sampling design helps in obtaining a sample from the frame. It is the procedure or technique for obtaining those sampling units from which inferences can be made. The sampling design has to be prepared well in advance before undertaking any research.

Statistic(s) and Parameter(s): A statistic is the characteristic of the sample whereas the parameter is the characteristic of the population. Sampling analysis involves estimating the parameter from the statistic.

This refers to any inaccuracy which is spotted in the information collected because only a small portion of the population is included in the study. The sampling errors are also known as error variances. These arise out of sampling and are usually random variations in the sample estimates around the true population values.

For example, say, from a population of 30,000, a random of 300 people is chosen for a given study. The observed data are arranged in a frequency distribution,e.g., fertility rate. This type of distribution is called sample distribution.

If the fertility rates of all the 30,000 people of the population are obtained and arranged in a frequency distribution, it is known as population distribution. Since the forms and parameters are not ordinarily known, an estimate of these two characteristics of population is made from the sample distribution. So, if the sample distribution is normal, one can assume that the population distribution is also normal.

Business Ethics

( Click on Topic to Read )

• What is Ethics?
• What is Business Ethics?
• Values, Norms, Beliefs and Standards in Business Ethics
• Indian Ethos in Management
• Ethical Issues in Marketing
• Ethical Issues in HRM
• Ethical Issues in IT
• Ethical Issues in Production and Operations Management
• Ethical Issues in Finance and Accounting
• What is Corporate Governance?
• What is Ownership Concentration?
• What is Ownership Composition?
• Types of Companies in India
• Internal Corporate Governance
• External Corporate Governance
• Corporate Governance in India
• What is Enterprise Risk Management (ERM)?
• What is Assessment of Risk?
• What is Risk Register?
• Risk Management Committee

Corporate social responsibility (CSR)

• Theories of CSR
• Arguments Against CSR
• Business Case for CSR
• Importance of CSR in India
• Drivers of Corporate Social Responsibility
• Developing a CSR Strategy
• Implement CSR Commitments
• CSR Marketplace
• CSR at Workplace
• Environmental CSR
• CSR with Communities and in Supply Chain
• Community Interventions
• CSR Monitoring
• CSR Reporting
• Voluntary Codes in CSR
• What is Corporate Ethics?

Lean Six Sigma

• What is Six Sigma?
• What is Lean Six Sigma?
• Value and Waste in Lean Six Sigma
• Six Sigma Team
• MAIC Six Sigma
• Six Sigma in Supply Chains
• What is Binomial, Poisson, Normal Distribution?
• What is Sigma Level?
• What is DMAIC in Six Sigma?
• What is DMADV in Six Sigma?
• Six Sigma Project Charter
• Project Decomposition in Six Sigma
• Critical to Quality (CTQ) Six Sigma
• Process Mapping Six Sigma
• Flowchart and SIPOC
• Gage Repeatability and Reproducibility
• Statistical Diagram
• Lean Techniques for Optimisation Flow
• Failure Modes and Effects Analysis (FMEA)
• What is Process Audits?
• Six Sigma Implementation at Ford
• IBM Uses Six Sigma to Drive Behaviour Change
• Research Methodology
• What is Research?
• What is Hypothesis?
• Sampling Method
• Research Methods
• Data Collection in Research
• Methods of Collecting Data
• Application of Business Research

Levels of Measurement

Hypothesis testing, research report.

• What is Management?
• Planning in Management
• Decision Making in Management
• What is Controlling?
• What is Coordination?
• What is Staffing?
• Organization Structure
• What is Departmentation?
• Span of Control
• What is Authority?
• Centralization vs Decentralization
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13 Advantages and Disadvantages of Systematic Sampling

Systematic sampling is a type of probability sampling that takes members for a larger population from a random starting point. It uses fixed, periodic intervals to create a sampling group that generates data for researchers to evaluate. Each interval gets calculated by dividing the population size by the desired scope of the sample.

That means a population group of nine individuals with a 33% systematic rate would pull the #3, #6, and #9 individuals to collect data. The first person would be randomized, which creates a selection series that reduces bias because the starting point becomes unpredictable. It provides every member of the community an equal opportunity to get selected when using this technique.

Linear and circular systems are both available for researchers to use. The only difference is that the latter option restarts from the randomized starting point once the entire population receives consideration.

Several systematic sampling advantages and disadvantages occur when researchers use this process to collect information.

List of the Advantages of Systematic Sampling

1. It is simple and convenient to use. Researchers can create, analyze, and conduct samples easily when using this method because of its structure. The algorithm to make selections is predetermined, which means the only randomized component of the work involves the selection of the first individual. Then the selection process moves across the linear or circular pattern initiated until the desired population group is ready for review.

That’s why systematic sampling is useful in situations when budget restrictions are in place. It’s well-suited for situations where money is a contributing factor to the research because it is an uncomplicated process to follow.

2. There isn’t a need to number each member of a sample. Researchers can represent an entire population quickly and easily when using systematic sampling. There isn’t a need to number each member of the sample because the goal is to create representative data of the entire group without specific individualized identifiers. This advantage makes it possible to create data for analyzing quickly because the only step necessary to get started is to identify the targeted demographic.

Investigators will still need to assign a starting number to the first participant in the systematic sampling work. Then the research chooses an integer that’s less than the total number of people in the selected demographic to create results. The final integer is the constant difference between any two consecutive numbers.

3. The created samples are based on precision. The samples that get created from systematic sampling have a higher level of precision than other randomized methods. Researchers know specifically who will become part of the research group once the first selection occurs. That means there is a much lower risk of favoritism occurring in the data because the individuals in charge of the research have no control over who gets to have their data included in the work. Everything is predetermined for them once the population group gets chosen.

This advantage also applies to unconscious bias that can occur when researchers have specific social preferences that get followed when selecting participants.

4. It reduces the potential for bias in the information. Other methods of probability sampling can have a high risk of creating highly-biased clusters even when researchers take steps to avoid this issue. The processes of systematic sampling create an advantage here because the selection method is at a fixed distance between each participant. That’s why cluster, convenience, and stratified sampling methods quickly fall out of favor when compared to this process.

5. This method creates an even distribution of members to form samples. The factor of risk that’s involved with this sampling method is quite minimal. Even when the population under review is exceptionally diverse, this process is beneficial because of the structured distribution of members to form the sample. That means the data collected during a research project has a better chance of being an authentic representation of the entire demographic.

That means the samples are relatively simple to compare, construct, and execute to understand the data that comes in from the work. It systematically eliminates the issue of clustered subject selection that other forms of randomization can subconsciously add to the research process.

6. It reduces the risk of favoritism. Researchers have no control over who gets selected for systematic sampling, which means it creates the benefits of randomized selection while providing a buffer against favoritism in the data collection efforts. It provides a low risk of data manipulation during the work collection process while keeping the sampling work highly productive on broad subjects while there’s a negligible risk of error.

This advantage comes about because the researchers maintain a sense of control with the process. When studies have strict parameters or a narrow hypothesis to pursue, then it works well when the sampling can get reasonably constructed to fit those parameters.

List of the Disadvantages of Systematic Sampling

1. This process requires a close approximation of a population. The systematic sampling method must assume that the size of the population in specific demographics is available and measurable. If that isn’t possible, then this method requires a reasonable approximation of the demographic in question. The selection process cannot occur correctly if that figure isn’t available, because the size of the pool pulled for participation comes from the division of that overall figure.

This issue becomes problematic when systematic sampling assumes that the population is larger or smaller than it actually is because that will impact the integrity of the samples in question.

2. Some populations can detect the pattern of sampling. If a smaller population group is under review, then the systematic sampling method can get detected by some participants. When this disadvantage occurs, then it can bias the population as non-participants will be different than those who get to be part of the process. It can encourage some individuals to provide false answers as a way to influence the results for personal purposes, working against the perceived hypothesis under study.

This issue can be severe enough that it compromises the work of the entire study.

3. It creates a fractional chance of selection. The systematic sampling method creates fractional chances for selection, which is not the same as an equal chance. Even the circular method encounters this disadvantage, especially with a small demographic. If people fall between the numbering system in their count, then there is no way for their perspectives to be included in the collected data. Although generalizations are possible with this method that apply to the whole demographic, the representation is not typically 100% accurate to each member.

That means the researchers who use systematic sampling are always going to miss something that could have led them to a new finding. Some participants may not want to take part in this effort if they detect a pattern that also excludes them.

4. A high risk of data manipulation exists. Researchers can construct their systems of systematic sampling to increase the likelihood that a targeted outcome can occur. Instead of letting random data produce the repetitive answer organically, the information comes out with an inherent bias that no one else would recognize upon analysis. That means it is still possible to produce answers that are constructed instead of representative, negating the outcomes that occur with the work. Any statistics produced from a process influenced by this disadvantage could not be trusted.

5. Systematic sampling is less random than a simple random sampling effort. If randomness is the top priority for research, then systematic sampling is not the best option to choose. Although it takes less time and isn’t as tedious as other methods of data collection, there is a predictable nature to its efforts that can influence the final results. The goal is still to reduce the sampling error, but the impact of the work may never get detected. It may not even be an authentic sampling option if mailing questionnaires or surveys because of lost mail or uncooperative subjects.

6. This method can potentially interact with hidden periodic traits. The process of selection in systematic sampling can unintentionally interact with hidden periodic traits found in some demographics and communities. If this issue were to occur at random through the integer selection process, then the sampling technique would coincide with the periodicity of the trait. That means the final data set would not be a random representative of the entire group because it would over-emphasize the nature of the periodic trait.

7. The population group in question must have some randomness to it. The processes of systematic sampling can only work when a population group has some degree of randomness to it. If the demographic has a standardized pattern to it, then there is a significantly high risk of accidentally choosing common cases when conducting research. That means the survey might skip key components of the population group without the researchers even realizing what is happening.

When this disadvantage occurs, it can skew the results in adverse ways that can lead researchers down the wrong direction toward a hypothesis. It can even lead to demographic changes that wouldn’t occur otherwise if the sampling process was more authentic to the results.

Systematic sampling is a probability-based method that provides some specific strengths and weaknesses to consider. It requires the first sample to be chosen randomly to ensure the probability aspect of this approach. If researchers do not take that approach, then those who fall between the regular samples have a chance of not being chosen for this process.

It can be a cost-effective way to conduct research, but this method can also produce an easier way to hide purposeful bias. That’s why independent verification of the randomness involved with this process is a useful component of its authenticity. It is a method of data collection that allows for geographically disperse cases to still receive inclusion in the work.

The advantages and disadvantages of systematic sampling also note that it is only possible to complete if an entire population list is available. If that is not possible, then this method is no longer useful.

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What is Data Sampling – Types, Importance, Best Practices

Data sampling is a fundamental statistical method used in various fields to extract meaningful insights from large datasets. By analyzing a subset of data, researchers can draw conclusions about the entire population with accuracy and efficiency.

This article will explore the concept of data sampling, its importance, techniques, process, advantages, disadvantages, and best practices for effective implementation.

Table of Content

What is Data Sampling?

What is data sampling important, types of data sampling techniques, data sampling process, advantages of data sampling, disadvantages of data sampling, sample size determination, best practices for effective data sampling.

Data Sampling is a statistical method that is used to analyze and observe a subset of data from a larger piece of dataset and configure meaningful information, all the required info from the subset that helps in gaining information, or drawing conclusion for the larger dataset, or it’s parent dataset.

• Sampling in data science helps in finding more better and accurate results and works best when the data size is big.
• Sampling helps in identifying the entire pattern on which the subset of the dataset is based upon and on the basis of that smaller dataset, entire sample size is presumed to hold the same properties.
• It is a quicker and more effective method to draw conclusions.

Data sampling is important for a couple of key reasons:

• Cost and Time Efficiency : Sampling allows researchers to collect and analyze a subset of data rather than the entire population. This reduces the time and resources required for data collection and analysis, making it more cost-effective, especially when dealing with large datasets.
• Feasibility : In many cases, it’s impractical or impossible to analyze the entire population due to constraints such as time, budget, or accessibility. Sampling makes it feasible to study a representative portion of the population while still yielding reliable results.
• Risk Reduction : Sampling helps mitigate the risk of errors or biases that may occur when analyzing the entire population. By selecting a random or systematic sample, researchers can minimize the impact of outliers or anomalies that could skew the results.
• Accuracy:  In some cases, examining the entire population might not even be possible. For instance, testing every single item in a large batch of manufactured goods would be impractical. Data sampling allows researchers to get a good understanding of the whole population by examining a well-chosen subset.

There are mainly two types of Data Sampling techniques which are further divided into 4 sub-categories each. They are as follows:

Probability Data Sampling Technique

Probability Data Sampling technique involves selecting data points from a dataset in such a way that every data point has an equal chance of being chosen. Probability sampling techniques ensure that the sample is representative of the population from which it is drawn, making it possible to generalize the findings from the sample to the entire population with a known level of confidence.

• Simple Random Sampling : In Simple random sampling, every dataset has an equal chance or probability of being selected. For eg. Selection of head or tail. Both of the outcomes of the event have equal probabilities of getting selected.
• Systematic Sampling : In Systematic sampling, a regular interval is chosen each after which the dataset continues for sampling. It is more easier and regular than the previous method of sampling and reduces inefficiency while improving the speed. For eg. In a series of 10 numbers, we have a sampling after every 2nd number. Here we use the process of Systematic sampling.
• Stratified Sampling : In Stratified sampling, we follow the strategy of divide & conquer. We opt for the strategy of dividing into groups on the basis of similar properties and then perform sampling. This ensures better accuracy. For eg. In a workplace data, the total number of employees is divided among men and women.
• Cluster Sampling : Cluster sampling is more or less like stratified sampling. However in cluster sampling we choose random data and form it in groups, whereas in stratified we use strata, or an orderly division takes place in the latter. For eg. Picking up users of different networks from a total combination of users.

Non-Probability Data Sampling

Non-probability data sampling means that the selection happens on a non-random basis, and it depends on the individual as to which data does it want to pick. There is no random selection and every selection is made by a thought and an idea behind it.

• Convenience Sampling: As the name suggests, the data checker selects the data based on his/her convenience. It may choose the data sets that would require lesser calculations, and save time while bringing results at par with probability data sampling technique. For eg. Dataset involving recruitment of people in IT Industry, where the convenience would be to choose the data which is the latest one, and the one which encompasses youngsters more.
• Voluntary Response Sampling: As the name suggests, this sampling method depends on the voluntary response of the audience for the data. For eg. If a survey is being conducted on types of Blood groups found in majority at a particular place, and the people who are willing to take part in this survey, and then if the data sampling is conducted, it will be referred to as the voluntary response sampling.
• Purposive Sampling: The Sampling method that involves a special purpose falls under purposive sampling. For eg. If we need to tackle the need of education, we may conduct a survey in the rural areas and then create a dataset based on people’s responses. Such type of sampling is called Purposive Sampling.
• Snowball Sampling: Snowball sampling technique takes place via contacts. For eg. If we wish to conduct a survey on the people living in slum areas, and one person contacts us to the other and so on, it is called a process of snowball sampling.

The process of data sampling involves the following steps:

• Find a Target Dataset : Identify the dataset that you want to analyze or draw conclusions about. This dataset represents the larger population from which a sample will be drawn.
• Select a Sample Size : Determine the size of the sample you will collect from the target dataset. The sample size is the subset of the larger dataset on which the sampling process will be performed.
• Decide the Sampling Technique : Choose a suitable sampling technique from options such as Simple Random Sampling, Systematic Sampling, Cluster Sampling, Snowball Sampling, or Stratified Sampling. The choice of technique depends on factors such as the nature of the dataset and the research objectives.
• Perform Sampling : Apply the selected sampling technique to collect data from the target dataset. Ensure that the sampling process is carried out systematically and according to the chosen method.
• Draw Inferences for the Entire Dataset : Analyze the properties and characteristics of the sampled data subset. Use statistical methods and analysis techniques to draw inferences and insights that are representative of the entire dataset.
• Extend Properties to the Entire Dataset : Extend the findings and conclusions derived from the sample to the entire target dataset. This involves extrapolating the insights gained from the sample to make broader statements or predictions about the larger population.
• Data Sampling helps draw conclusions, or inferences of larger datasets using a smaller sample space, which concerns the entire dataset.
• It helps save time and is a quicker and faster approach.
• It is a better way in terms of cost effectiveness as it reduces the cost for data analysis, observation and collection. It is more of like gaining the data, applying sampling method & drawing the conclusion.
• It is more accurate in terms of result and conclusion.
• Sampling Error: It is the act of differentiation among the entire sample size and the smaller dataset. There arise some differences in characteristics, or properties among both the datasets that reduce the accuracy and the sample set is unable to represent a larger piece of information. Sampling Error mostly occurs by a chance and is regarded as an error-less term.
• It becomes difficult in a few data sampling methods, such as forming clusters of similar properties.
• Sampling Bias: It is the process of choosing a sample set which does not represent the entire population on a whole. It occurs mostly due to incorrect method of sampling usage and consists of errors as the given dataset is not properly able to draw conclusions for the larger set of data.

Sample size is the universal dataset concerning to which several other smaller datasets are created that help in inferring the properties of the entire dataset. Following are a series of steps that are involved during sample size determination.

• Firstly calculate the population size, as in the total sample space size on which the sampling has to be performed.
• Find the values of confidence levels that represent the accuracy of the data.
• Find the value of error margins if any with respect to the sample space dataset.
• Calculate the deviation from the mean or average value from that of standard deviation value calculated.

Before performing data sampling methods, one should keep in mind the below three mentioned considerations for effective data sampling.

• Statistical Regularity: A larger sample space, or parent dataset means more accurate results. This is because then the probability of every data to be chosen is equal, ie., regular. When picked at random, a larger data ensures a regularity among all the data.
• Dataset must be accurate and verified from the respective sources.
• In Stratified Data Sampling technique, one needs to be clear about the kind of strata or group it will be making.
• Inertia of Large Numbers: As mentioned in the first principle, this too states that the parent data set must be large enough to gain better and clear results.

Data sampling is a powerful tool for extracting insights from large datasets, enabling researchers to make informed decisions and draw accurate conclusions about populations. By understanding the principles, techniques, and best practices of data sampling, researchers can maximize the effectiveness and reliability of their analyses, ultimately leading to better outcomes in research and decision-making processes.

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What are the Advantages and Disadvantages of Sampling in Research Methodology

Back to: Introduction to Educational Research Methodology

Sampling method is selecting a group of people from a population to gather information on a particular topic or product. It is gathering maximum information of a population without surveying every member from it. Sampling method will help in acquiring reliable information while making it convenient. 

Sampling method is used mainly when it is impossible to surveying the whole population. The sample must be the representation of the population the researcher has gathered information from. Sampling will help the researcher get significant research result. Sampling method produces results relatively faster and is less expensive. 

Advantages of Sampling 

The advantages of sampling in research methodology are:

Saves time:

It is a difficult task to contact each person in a population. Sampling method will help in surveying only few members of the population. This will make the work of researchers lot faster and save a lot of time. 

Save money: 

Sampling method is budget friendly. The cost of gathering people and collecting data will cost lower. Hence, it is cost-efficient. 

Reliable and accurate data:

Sampling method will help in collecting richer data than contacting everyone in a population. The researcher will be able to ask more questions by contacting a lesser population. The chances of getting accurate data is higher by sampling method. 

Manageable: 

Sampling method makes collecting data in large population easier. It will help in surveying a manageable number of the population. 

Disadvantages of Sampling 

The disadvantages of Sampling are: 

Biased Answers:

Sampling method involves biased selection of participants which can lead to errors. This is one of the biggest limitation of sampling method. 

Sufficient knowledge about sampling method:

The researcher must have adequate knowledge about using sampling technique. Many mistakes may be committed by the researcher without sufficient knowledge leading to plenty of errors in the study. 

Inconceivability of examining : 

Getting an accurate data can be troublesome when the population is extremely less or diverse. In such cases, census study is the only other option. 

Changeability of Units:

When the population is not uniform, the sampling procedure will be unscientific. Even though number of cases is small it isn’t simple to stick to the chosen cases. 

When the units of the population are not in homogeneous, the sampling technique will be unscientific. In sampling, though the number of cases is small, it is not always easy to stick to the, selected cases. The units of sample may be widely dispersed. Due to this not all the cases may not be taken up. The chosen cases may be replaced by different cases.

REVIEW article

On the advantages and disadvantages of choice: future research directions in choice overload and its moderators.

• 1 Department of Political Science and International Relations (DEMS), University of Palermo, Palermo, Italy
• 2 Atkinson Graduate School of Management, Willamette University, Salem, OR, United States
• 3 Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy

Researchers investigating the psychological effects of choice have provided extensive empirical evidence that having choice comes with many advantages, including better performance, more motivation, and greater life satisfaction and disadvantages, such as avoidance of decisions and regret. When the decision task difficulty exceeds the natural cognitive resources of human mind, the possibility to choose becomes more a source of unhappiness and dissatisfaction than an opportunity for a greater well-being, a phenomenon referred to as choice overload. More recently, internal and external moderators that impact when choice overload occurs have been identified. This paper reviews seminal research on the advantages and disadvantages of choice and provides a systematic qualitative review of the research examining moderators of choice overload, laying out multiple critical paths forward for needed research in this area. We organize this literature review using two categories of moderators: the choice environment or context of the decision as well as the decision-maker characteristics.

Introduction

The current marketing orientation adopted by many organizations is to offer a wide range of options that differ in only minor ways. For example, in a common western grocery store contains 285 types of cookies, 120 different pasta sauces, 175 salad-dressing, and 275 types of cereal ( Botti and Iyengar, 2006 ). However, research in psychology and consumer behavior has demonstrated that when the number of alternatives to choose from becomes excessive (or superior to the decision-makers’ cognitive resources), choice is mostly a disadvantage to both the seller and the buyer. This phenomenon has been called choice overload and it refers to a variety of negative consequences stemming from having too many choices, including increased choice deferral, switching likelihood, or decision regret, as well as decreased choice satisfaction and confidence (e.g., Chernev et al., 2015 ). Choice overload has been replicated in numerous fields and laboratory settings, with different items (e.g., jellybeans, pens, coffee, chocolates, etc.), actions (reading, completing projects, and writing essays), and populations (e.g., Chernev, 2003 ; Iyengar et al., 2004 ; Schwartz, 2004 ; Shah and Wolford, 2007 ; Mogilner et al., 2008 ; Fasolo et al., 2009 ; Misuraca and Teuscher, 2013 ; Misuraca and Faraci, 2021 ; Misuraca et al., 2022 ; see also Misuraca, 2013 ). Over time, we have gained insight into numerous moderators of the choice overload phenomena, including aspects of the context or choice environment as well as the individual characteristics of the decision-maker (for a detailed review see Misuraca et al., 2020 ).

The goal of this review is to summarize important research findings that drive our current understanding of the advantages and disadvantages of choice, focusing on the growing body of research investigating moderators of choice overload. Following a discussion of the advantages and disadvantages of choice, we review the existing empirical literature examining moderators of choice overload. We organize this literature review using two categories of moderators: the choice environment or context of the decision as well as the decision-maker characteristics. Finally, based on this systematic review of research, we propose a variety of future research directions for choice overload investigators, ranging from exploring underlying mechanisms of choice overload moderators to broadening the area of investigation to include a robust variety of decision-making scenarios.

Theoretical background

The advantages of choice.

Decades of research in psychology have demonstrated the many advantages of choice. Indeed, increased choice options are associated with increase intrinsic motivation ( Deci, 1975 ; Deci et al., 1981 ; Deci and Ryan, 1985 ), improved task performance ( Rotter, 1966 ), enhanced life satisfaction ( Langer and Rodin, 1976 ), and improved well-being ( Taylor and Brown, 1988 ). Increased choice options also have the potential to satisfy heterogeneous preferences and produce greater utility ( Lancaster, 1990 ). Likewise, economic research has demonstrated that larger assortments provide a higher chance to find an option that perfectly matches the individual preferences ( Baumol and Ide, 1956 ). In other words, with larger assortments it is easier to find what a decision-maker wants.

The impact of increased choice options extends into learning, internal motivation, and performance. Zuckerman et al. (1978) asked college students to solve puzzles. Half of the participants could choose the puzzle they would solve from six options. For the other half of participants, instead, the puzzle was imposed by the researchers. It was found that the group free to choose the puzzle was more motivated, more engaged and exhibited better performance than the group that could not choose the puzzle to solve. In similar research, Schraw et al. (1998) asked college students to read a book. Participants were assigned to either a choice condition or a non-choice condition. In the first one, they were free to choose the book to read, whereas in the second condition the books to read were externally imposed, according to a yoked procedure. Results demonstrated the group that was free to make decisions was more motivated to read, more engaged, and more satisfied compared to the group that was not allowed to choose the book to read ( Schraw et al., 1998 ).

These effects remain consistent with children and when choice options are constrained to incidental aspects of the learning context. In the study by Cordova and Lepper (1996) , elementary school children played a computer game designed to teach arithmetic and problem-solving skills. One group could make decisions about incidental aspects of the learning context, including which spaceship was used and its name, whereas another group could not make any choice (all the choices about the game’s features were externally imposed by the experimenters). The results demonstrated that the first group was more motivated to play the game, more engaged in the task, learned more of the arithmetical concepts involved in the game, and preferred to solve more difficult tasks compared to the second group.

Extending benefits of choice into health consequences, Langer and Rodin (1976) examined the impact that choice made in nursing home patients. In this context, it was observed that giving patients the possibility to make decisions about apparently irrelevant aspects of their life (e.g., at what time to watch a movie; how to dispose the furniture in their bedrooms, etc.), increased psychological and physiological well-being. The lack of choice resulted, instead, in a state of learned helplessness, as well as deterioration of physiological and psychological functions.

The above studies lead to the conclusion that choice has important advantages over no choice and, to some extent, limited choice options. It seems that providing more choice options is an improvement – it will be more motivating, more satisfying, and yield greater well-being. In line with this conclusion, the current orientation in marketing is to offer a huge variety of products that differ only in small details (e.g., Botti and Iyengar, 2006 ). However, research in psychology and consumer behavior demonstrated that when the number of alternatives to choose from exceeds the decision-makers’ cognitive resources, choice can become a disadvantage.

The disadvantages of choice

A famous field study conducted by Iyengar and Lepper (2000) in a Californian supermarket demonstrated that too much choice decreases customers’ motivation to buy as well as their post-choice satisfaction. Tasting booths were set up in two different areas of the supermarket, one of which displayed 6 different jars of jam while the other displayed 24 options, with customers free to taste any of the different flavors of jam. As expected, the larger assortment attracted more passers-by compared to the smaller assortment; Indeed, 60% of passers-by stopped at the table displaying 24 different options, whereas only 40% of the passers-by stopped at the table displaying the small variety of 6 jams. This finding was expected given that more choice options are appealing. However, out of the 60% of passers-by who stopped at the table with more choices, only 3% of them decided to buy jam. Conversely, 30% of the consumers who stopped at the table with only 6 jars of jam decided to purchase at least one jar. Additionally, these customers expressed a higher level of satisfaction with their choices, compared to those who purchased a jar of jam from the larger assortment. In other words, it seems that too much choice is at the beginning more appealing (attracts more customers), but it decreases the motivation to choose and the post-choice satisfaction.

This classic and seminal example of choice overload was quickly followed by many replications that expanded the findings from simple purchasing decisions into other realms of life. For example, Iyengar and Lepper (2000) , asked college students to write an essay. Participants were randomly assigned to one of the following two experimental conditions: limited-choice condition, in which they could choose from a list of six topics for the essay, and extensive-choice condition, in which they could choose from a list of 30 different topics for the essay. Results showed that a higher percentage of college students (74%) turned in the essay in the first condition compared to the second condition (60%). Moreover, the essays written by the students in the limited-choice conditions were evaluated as being higher quality compared to the essays written by the students in the extensive choice condition. In a separate study, college students were asked to choose one chocolate from two randomly assigned choice conditions with either 6 or 30 different chocolates. Those participants in the limited choice condition reporting being more satisfied with their choice and more willing to purchase chocolates at the end of the experiment, compared to participants who chose from the larger assortment ( Iyengar and Lepper, 2000 ).

In the field of financial decision-making, Iyengar et al. (2004) analyzed 800,000 employees’ decisions about their participation in 401(k) plans that offered from a minimum of 2 to a maximum of 59 different fund options. The researchers observed that as the fund options increased, the participation rate decreased. Specifically, plans offering less than 10 options had the highest participation rate, whereas plans offering 59 options had the lowest participation rate.

The negative consequences of having too much choice driven by cognitive limitations. Simon (1957) noted that decision-makers have a bounded rationality. In other words, the human mind cannot process an unlimited amount of information. Individuals’ working memory has a span of about 7 (plus or minus two) items ( Miller, 1956 ), which means that of all the options to choose from, individuals can mentally process only about 7 alternatives at a time. Because of these cognitive limitations, when the number of choices becomes too high, the comparison of all the available items becomes cognitively unmanageable and, consequently, decision-makers feel overwhelmed, confused, less motivated to choose and less satisfied (e.g., Iyengar and Lepper, 2000 ). However, a more recent meta-analytic work [ Chernev et al., 2015 : see also Misuraca et al. (2020) ] has shown that choice overload occurs only under certain conditions. Many moderators that mitigate the phenomenon have been identified by researchers in psychology and consumer behavior (e.g., Mogilner et al., 2008 ; Misuraca et al., 2016a ). In the next sections, we describe our review methodology and provide a detailed discussion of the main external and internal moderators of choice overload.

Literature search and inclusion criteria

Our investigation consisted of a literature review of peer-reviewed empirical research examining moderators of choice overload. We took several steps to locate and identify eligible studies. First, we sought to establish a list of moderators examined in the choice overload literature. For this, we referenced reviews conducted by Chernev et al. (2015) , McShane and Böckenholt (2017) , as well as Misuraca et al. (2020) and reviewed the references sections of the identified articles to locate additional studies. Using the list of moderators generated from this examination, we conducted a literature search using PsycInfo (Psychological Abstracts), EBSCO and Google Scholar. This search included such specific terms such as choice set complexity, visual preference heuristic, and choice preference uncertainty, as well as broad searches for ‘choice overload’ and ‘moderator’.

We used several inclusion criteria to select relevant articles. First, the article had to note that it was examining the choice overload phenomena. Studies examining other theories and/or related variables were excluded. Second, to ensure that we were including high-quality research methods that have been evaluated by scholars, only peer-reviewed journal articles were included. Third, the article had to include primary empirical data (qualitative or quantitative). Thus, studies that were conceptual in nature were excluded. This process yielded 49 articles for the subsequent review.

Moderators of choice overload

Choice environment and context.

Regarding external moderators of choice overload, several aspects about the choice environment become increasingly relevant. Specifically, these include the perceptual attributes of the information, complexity of the set of options, decision task difficulty, as well as the presence of brand names.

Perceptual characteristics

As Miller (1956) noted, humans have “channel capacity” for information processing and these differ for divergent stimuli: for taste, we have a capacity to accommodate four; for tones, the capacity increased to six; and for visual stimuli, we have the capacity for 10–15 items. Accordingly, perceptual attributes of choice options are an important moderator of choice overload, with visual presentation being one of the most important perceptual attributes ( Townsend and Kahn, 2014 ). The visual preference heuristic refers to the tendency to prefer a visual rather than verbal representation of choice options, regardless of assortment size ( Townsend and Kahn, 2014 ). However, despite this preference, visual presentations of large assortments lead to suboptimal decisions compared to verbal presentations, as visual presentations activate a less systematic decision-making approach ( Townsend and Kahn, 2014 ). Visual presentation of large choice sets is also associated with increased perceptions of complexity and likelihood of decisions deferral. Visual representations are particularly effective with small assortments, as they increase consumers’ perception of variety, improve the likelihood of making a choice, and reduce the time spent examining options ( Townsend and Kahn, 2014 ).

Choice set complexity

Choice set complexity refers to a wide range of aspects of a decision task that affect the value of the available choice options without influencing the structural characteristics of the decision problem ( Payne et al., 1993 ). Thus, choice set complexity does not influence aspects such as the number of options, number of attributes of each option, or format in which the information is presented. Rather, choice set complexity concerns factors such as the attractiveness of options, the presence of a dominant option, and the complementarity or alignability of the options.

Choice set complexity increases when the options include higher-quality, more attractive options ( Chernev and Hamilton, 2009 ). Indeed, when the variability in the relative attractiveness of the choice alternatives increases, the certainty about the choice and the satisfaction with the task increase ( Malhotra, 1982 ). Accordingly, when the number of attractive options increases, more choice options led to a decline in consumer satisfaction and likelihood of a decision being made, but satisfaction increases and decision deferral decreased when the number of unattractive options increases ( Dhar, 1997 ). This occurs when increased choice options make the weakness and strengths of attractive and unattractive options more salient ( Chan, 2015 ).

Similarly, the presence of a dominant option simplifies large choice sets and increased the preference for the chosen option; however, the opposite effect happens in small choice sets ( Chernev, 2003 ). Choice sets containing an ideal option have been associated with increased brain activity in the areas involved in reward and value processing as well as in the integration of costs and benefits (striatum and the anterior cingulate cortex; Reutskaja et al., 2018 ) which could explain why larger choice sets are not always associated with choice overload. As Misuraca et al. (2020 , p. 639) noted, “ the benefits of having an ideal item in the set might compensate for the costs of overwhelming set size in the bounded rational mind of humans . ”

Finally, choice set complexity is impacted by the alignability and complementarity of the attributes that differentiate the options ( Chernev et al., 2015 ). When unique attributes of options exist within a choice set, complexity and choice overload increase as the unique attributes make comparison more difficult and trade-offs more salient. Indeed, feature alignability and complementarity (meaning that the options have additive utility and need to be co-present to fully satisfy the decision-maker’s need) 1 have been associated with decision deferral ( Chernev, 2005 ; Gourville and Soman, 2005 ) and changes in satisfaction ( Griffin and Broniarczyk, 2010 ).

Decision task difficulty

Decision task difficulty refers to the structural characteristics of a decision problem; unlike choice set complexity, decision task difficulty does not influence the value of the choice options ( Payne et al., 1993 ). Decision task difficulty is influenced by the number of attributes used to describe available options, decision accountability, time constraints, and presentation format.

The number of attributes used to describe the available options within an assortment influences decision task difficulty and choice overload ( Hoch et al., 1999 ; Chernev, 2003 ; Greifeneder et al., 2010 ), such that choice overload increases with the number of dimensions upon which the options differ. With each additional dimension, decision-makers have another piece of information that must be attended to and evaluated. Along with increasing the cognitive complexity of the choice, additional dimensions likely increase the odds that each option is inferior to other options on one dimension or another (e.g., Chernev et al., 2015 ).

When individuals have decision accountability or are required to justify their choice of an assortment to others, they tend to prefer larger assortments; However, when individuals must justify their particular choice from an assortment to others, they tend to prefer smaller choice sets ( Ratner and Kahn, 2002 ; Chernev, 2006 ; Scheibehenne et al., 2009 ). Indeed, decision accountability is associated with decision deferral when choice sets are larger compared to smaller ( Gourville and Soman, 2005 ). Thus, decision accountability influences decision task difficulty differently depending on whether an individual is selecting an assortment or choosing an option from an assortment.

Time pressure or constraint is an important contextual factor for decision task difficulty, choice overload, and decision regret ( Payne et al., 1993 ). Time pressure affects the strategies that are used to make decisions as well as the quality of the decisions made. When confronted with time pressure, decision-makers tend to speed up information processing, which could be accomplished by limiting the amount of information that they process and use ( Payne et al., 1993 ; Pieters and Warlop, 1999 ; Reutskaja et al., 2011 ). Decision deferral becomes a more likely outcome, as is choosing at random and regretting the decision later ( Inbar et al., 2011 ).

The physical arrangement and presentation of options and information affect information perception, processing, and decision-making. This moderates the effect of choice overload because these aspects facilitate or inhibit decision-makers’ ability to process a greater information load (e.g., Chernev et al., 2015 ; Anderson and Misuraca, 2017 ). The location of options and structure of presented information allow the retrieval of information about the options, thus allowing choosers to distinguish and evaluate various options (e.g., Chandon et al., 2009 ). Specifically, organizing information into “chunks” facilitates information processing ( Miller, 1956 ) as well as the perception of greater variety in large choice sets ( Kahn and Wansink, 2004 ). Interestingly, these “chunks” do not have to be informative; Mogilner et al. (2008) found that choice overload was mitigated to the same extent when large choice sets were grouped into generic categories (i.e., A, B, etc.) as when the categories were meaningful descriptions of characteristics.

Beyond organization, the presentation order can facilitate or inhibit decision-makers cognitive processing ability. Levav et al. (2010) found that choice overload decreased and choice satisfaction increased when smaller choice sets were followed by larger choice sets, compared to the opposite order of presentation. When sets are highly varied, Huffman and Kahn (1998) found that decision-makers were more satisfied and willing to make a choice when information was presented about attributes (i.e., price and characteristics) rather than available alternatives (i.e., images of options). Finally, presenting information simultaneously, rather than sequentially, increases decision satisfaction ( Mogilner et al., 2013 ), likely due to decision-makers choosing among an available set rather than comparing each option to an imaged ideal option.

Brand names

The presence of brand names is an important moderator of choice overload. As recently demonstrated by researchers in psychology and consumer behavior, choice overload occurs only when options are not associated with brands, choice overload occurs when the same choice options are presented without any brand names ( Misuraca et al., 2019 , 2021a ). When choosing between 6 or 24 different mobile phones, choice overload did not occur in the condition in which phones were associated with a well-known brand (i.e., Apple, Samsung, Nokia, etc.), although it did occur when the same cell phones were displayed without information about their brand. These findings have been replicated with a population of adolescents ( Misuraca et al., 2021a ).

Decision-maker characteristics

Beyond the choice environment and context, individual differences in decision-maker characteristics are significant moderators of choice overload. Several critical characteristics include the decision goal as well as an individual’s preference uncertainty, affective state, decision style, and demographic variables such as age, gender, and cultural background (e.g., Misuraca et al., 2021a ).

Decision goal

A decision goal refers to the extent to which a decision-maker aims to minimize the cognitive resources spent making a decision ( Chernev, 2003 ). Decision goals have been associated with choice overload, with choice overload increasing along with choice set options, likely due to decision-makers unwillingness to make tradeoffs between various options. As a moderator of choice overload, there are several factors which impact the effect of decision goals, including decision intent (choosing or browsing) and decision focus (choosing an assortment or an option) ( Misuraca et al., 2020 ).

Decision intent varies between choosing, with the goal of making a decision among the available options, and browsing, with the goal of learning more about the options. Cognitive overload is more likely to occur than when decision makers’ goal is choosing compared to browsing. For choosing goals, decision-makers need to make trade-offs among the pros and cons of the options, something that demands more cognitive resources. Accordingly, decision-makers whose goal is browsing, rather than choosing, are less likely to experience cognitive overload when facing large assortments ( Chernev and Hamilton, 2009 ). Furthermore, when decision-makers have a goal of choosing, brain research reveals inverted-U-shaped function, with neither too much nor too little choice providing optimal cognitive net benefits ( Reutskaja et al., 2018 ).

Decision focus can target selecting an assortment or selecting an option from an assortment. When selecting an assortment, cognitive overload is less likely to occur, likely due to the lack of individual option evaluation and trade-offs ( Chernev et al., 2015 ). Thus, when choosing an assortment, decision-makers tend to prefer larger assortments that provide more variety. Conversely, decision-makers focused on choosing an option from an assortment report increased decision difficulty and tend to prefer smaller assortments ( Chernev, 2006 ). Decision overload is further moderated by the order of decision focus. Scheibehenne et al. (2010) found that when decision-makers first decide on an assortment, they are more likely to choose an option from that assortment, rather than an option from an assortment they did not first select.

Preference uncertainty

The degree to which decision-makers have preferences varies regarding comprehension and prioritization of the costs and benefits of the choice options. This is referred to as preference uncertainty ( Chernev, 2003 ). Preference uncertainty is influenced by decision-maker expertise and an articulated ideal option, which indicates well-defined preferences. When decision-makers have limited expertise, larger choice sets are associated with weaker preferences as well as increased choice deferral and choice overload compared to smaller choice sets. Conversely, high expertise decision-makers experience weaker preferences and increased choice deferral in the context of smaller choice sets compared to larger ( Mogilner et al., 2008 ; Morrin et al., 2012 ). Likewise, an articulated ideal option, which implies that the decision-maker has already engaged in trade-offs, is associated with reduced decision complexity. The effect is more pronounced in larger choice sets compared to smaller choice sets ( Chernev, 2003 ).

Positive affect

Positive affect tends to moderate the impact of choice overload on decision satisfaction. Indeed, Spassova and Isen (2013) found that decision-makers reporting positive affect did not report experiencing dissatisfaction when choosing from larger choice sets while those with neutral affect reported being more satisfied when choosing from smaller choice sets. This affect may be associated with the affect heuristic, or a cognitive shortcut that enables efficient decisions based on the immediate emotional response to a stimulus ( Slovic et al., 2007 ).

Decision-making tendencies

Satisfaction with extensive choice options may depend on whether one is a maximizer or a satisficer. Maximizing refers to the tendency to search for the best option. Maximizers approach decision tasks with the goal to find the absolute best ( Carmeci et al., 2009 ; Misuraca et al., 2015 , 2016b , 2021b ; Misuraca and Fasolo, 2018 ). To do that, they tend to process all the information available and try to compare all the possible options. Conversely, satisficers are decision-makers whose goal is to select an option that is good enough, rather than the best choice. To find such an option, satisficers evaluate a smaller range of options, and choose as soon as they find one alternative that surpasses their threshold of acceptability ( Schwartz, 2004 ). Given the different approach of maximizers and satisficers when choosing, it is easy to see why choice overload represents more of a problem for maximizers than for satisficers. If the number of choices exceeds the individuals’ cognitive resources, maximizers more than satisficers would feel overwhelmed, frustrated, and dissatisfied, because an evaluation of all the available options to select the best one is cognitively impossible.

Maximizers attracted considerable attention from researchers because of the paradoxical finding that even though they make objectively better decisions than satisficers, they report greater regret and dissatisfaction. Specifically, Iyengar et al. (2006) , analyzed the job search outcomes of college students during their final college year and found that maximizer students selected jobs with 20% higher salaries compared to satisficers, but they felt less satisfied and happy, as well as more stressed, frustrated, anxious, and regretful than students who were satisficers. The reasons for these negative feelings of maximizers lies in their tendency to believe that a better option is among those that they could not evaluate, given their time and cognitive limitations.

Choosing for others versus oneself

When decision-makers must make a choice for someone else, choice overload does not occur ( Polman, 2012 ). When making choices for others (about wines, ice-cream flavors, school courses, etc.), decision makers reported greater satisfaction when choosing from larger assortments rather than smaller assortments. However, when choosing for themselves, they reported higher satisfaction after choosing from smaller rather than larger assortments.

Demographics

Demographic variables such as gender, age, and cultural background moderate reactions concerning choice overload. Regarding gender, men and women may often employ different information-processing strategies, with women being more likely to attend to and use details than men (e.g., Meyers-Levy and Maheswaran, 1991 ). Gender differences also arise in desire for variety and satisfaction depending on choice type. While women were more satisfied with their choice of gift boxes regardless of assortment size, women become more selective than men when speed-dating with larger groups of speed daters compared to smaller groups ( Fisman et al., 2006 ).

Age moderates the choice overload experience such that, when choosing from an extensive array of options, adolescents and adults suffer similar negative consequences (i.e., greater difficulty and dissatisfaction), while children and seniors suffer fewer negative consequences (i.e., less difficulty and dissatisfaction than adolescents and adults) ( Misuraca et al., 2016a ). This could be associated with decision-making tendencies. Indeed, adults and adolescents tend to adopt maximizing approaches ( Furby and Beyth-Marom, 1992 ). This maximizing tendency aligns with their greater perceived difficulty and post-choice dissatisfaction when facing a high number of options ( Iyengar et al., 2006 ). Seniors tend to adopt a satisficing approach when making decisions ( Tanius et al., 2009 ), as well as become overconfident in their judgments ( Stankov and Crawford, 1996 ) and focused on positive information ( Mather and Carstensen, 2005 ). Taken together, these could explain why the negative consequences of too many choice options were milder among seniors. Finally, children tend to approach decisions in an intuitive manner and quickly develop strong preferences ( Schlottmann and Wilkening, 2011 ). This mitigates the negative consequences of choice overload for this age group.

Finally, decision-makers from different cultures have different preferences for variety (e.g., Iyengar, 2010 ). Eastern Europeans report greater satisfaction with larger choice sets than Western Europeans ( Reutskaja et al., 2022 ). Likewise, cultural differences in perception may impact how choice options affect decision-makers from Western and non-Western cultures (e.g., Miyamoto et al., 2006 ).

Future research directions

As researchers continue to investigate the choice overload phenomenon, future investigations can provide a deeper understanding of the underlying mechanisms that influence when and how individuals experience the negative impacts of choice overload as well as illuminate how this phenomenon can affect people in diverse contexts (such as hiring decisions, sports, social media platforms, streaming services, etc.).

For instance, the visual preference heuristic indicates, and subsequent research supports, the human tendency to prefer visual rather than verbal representations of choice options ( Townsend and Kahn, 2014 ). However, in Huffman and Kahn’s (1998) research, decision-makers preferred written information, such as characteristics of the sofa, rather than visual representations of alternatives. Future researchers can investigate the circumstances that underlie when individuals prefer detailed written or verbal information as opposed to visual images.

Furthermore, future researchers can examine the extent to which the mechanisms underlying the impact of chunking align with those underlying the effect of brand names. Research has supported that chunking information reduces choice overload, regardless of the sophistication of the categories ( Kahn and Wansink, 2004 ; Mogilner et al., 2008 ). The presence of a brand name has a seemingly similar effect ( Misuraca et al., 2019 , 2021a ). The extent to which the cognitive processes underlying these two areas of research the similar, as well as the ways in which they might differ, can provide valuable insights for researchers and practitioners.

More research is needed that considers the role of the specific culture and cultural values of the decision-maker on choice overload. Indeed, the traditional studies on the choice overload phenomenon mentioned above predominantly focused on western cultures, which are known for being individualistic cultures. Future research should explore whether choice overload replicates in collectivistic cultures, which value the importance of making personal decisions differently than individualist cultures. Additional cultural values, such as long-term or short-term time orientation, may also impact decision-makers and the extent to which they experience choice overload ( Hofstede and Minkov, 2010 ).

While future research that expands our understanding of the currently known and identified moderators of choice overload can critically inform our understanding of when and how this phenomenon occurs, there are many new and exciting directions into which researchers can expand.

For example, traditional research on choice overload focused on choice scenarios where decision-makers had to choose only one option out of either a small or a large assortment of options. This is clearly an important scenario, yet it represents only one of many scenarios that choice overload may impact. Future research could investigate when and how this phenomenon occurs in a wide variety of scenarios that are common in the real-world but currently neglected in classical studies on choice overload. These could include situations in which the individual can choose more than one option (e.g., more than one type of ice cream or cereal) (see Fasolo et al., 2024 ).

Historically, a significant amount of research on choice overload has focused on purchasing decisions. Some evidence also indicates that the phenomenon occurs in a variety of situations (e.g., online dating, career choices, retirement planning, travel and tourism, and education), potentially hindering decision-making processes and outcomes. Future research should further investigate how choice overload impacts individuals in a variety of untested situations. For instance, how might choice overload impact the hiring manager with a robust pool of qualified applicants? How would the occurrence of choice overload in a hiring situation impact the quality of the decision, making an optimal hire? Likewise, does choice overload play a role in procrastination? When confronted with an overwhelming number of task options, does choice overload play a role in decision deferral? It could be that similar cognitive processes underlie deferring a choice on a purchase and deferring a choice on a to-do list. Research is needed to understand how choice overload (and its moderators) may differ across these scenarios.

Finally, as society continues to adapt and develop, future research will be needed to evaluate the impact these technological and sociological changes have on individual decision-makers. The technology that we interact with has become substantially more sophisticated and omnipresent, particularly in the form of artificial intelligence (AI). As AI is adopted into our work, shopping, and online experiences, future researchers should investigate if AI and interactive decision-aids (e.g., Anderson and Misuraca, 2017 ) can be effectively leveraged to reduce the negative consequences of having too many alternatives without impairing the sense of freedom of decision-makers.

As with technological advancements, future research could examine how new sociological roles contribute to or minimize choice overload. For example, a social media influencer could reduce the complexity of the decision when there is a large number of choice options. If social media influencers have an impact, is that impact consistent across age groups and culturally diverse individuals? Deepening our understanding of how historical and sociological events have impacted decision-makers, along with how cultural differences in our perceptions of the world as noted above, could provide a rich and needed area of future research.

Discussion and conclusion

Research in psychology demonstrated the advantages of being able to make choices from a variety of alternatives, particularly when compared to no choice at all. Having the possibility to choose, indeed, enhances individuals’ feeling of self-determination, motivation, performance, well-being, and satisfaction with life (e.g., Zuckerman et al., 1978 ; Cordova and Lepper, 1996 ). As the world continues to globalize through sophisticated supply chains and seemingly infinite online shopping options, our societies have become characterized by a proliferation of choice options. Today, not only stores, but universities, hospitals, financial advisors, sport centers, and many other businesses offer a huge number of options from which to choose. The variety offered is often so large that decision-makers can become overwhelmed when trying to compare and evaluate all the potential options and experience choice overload ( Iyengar and Lepper, 2000 ). Rather than lose the benefits associated with choice options, researchers and practitioners should understand and leverage the existence of the many moderators that affect the occurrence of choice overload. The findings presented in this review indicate that choice overload is influenced by several factors, including perceptual attributes, choice set complexity, decision task difficulty, and brand association. Understanding these moderators can aid in designing choice environments that optimize decision-making processes and alleviate choice overload. For instance, organizing options effectively and leveraging brand association can enhance decision satisfaction and reduce choice overload. Additionally, considering individual differences such as decision goals, preference uncertainty, affective state, decision-making tendencies, and demographics can tailor decision-making environments to better suit the needs and preferences of individuals, ultimately improving decision outcomes. Future research is needed to fully understand the role of many variables that might be responsible for the negative consequences of choice overload and to better understand under which conditions the phenomenon occurs.

Author contributions

RM: Writing – review & editing, Conceptualization, Data curation, Investigation, Methodology, Writing – original draft. AN: Writing – review & editing. SM: Writing – review & editing. GD: Methodology, Writing – review & editing. CS: Writing – review & editing, Supervision.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ For example, gloves and socks have complementary features, in that they provide warmth to different parts of the body.

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Keywords: choice-overload, decision-making, choice set complexity, decision task difficulty, decision goal, decision-making tendency

Citation: Misuraca R, Nixon AE, Miceli S, Di Stefano G and Scaffidi Abbate C (2024) On the advantages and disadvantages of choice: future research directions in choice overload and its moderators. Front. Psychol . 15:1290359. doi: 10.3389/fpsyg.2024.1290359

Received: 07 September 2023; Accepted: 24 April 2024; Published: 09 May 2024.

Reviewed by:

Copyright © 2024 Misuraca, Nixon, Miceli, Di Stefano and Scaffidi Abbate. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Raffaella Misuraca, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Please note you do not have access to teaching notes, role of supply chain partnership, cross-functional integration, responsiveness and resilience on competitive advantages: empirical evidence from palestine.

The TQM Journal

ISSN : 1754-2731

Article publication date: 17 May 2024

The external business environment of the organization is always changing at a rapid pace. For a firm to adapt to changing client requirements, it must implement the right business procedures and strategies. To improve competitive advantage, this study investigates the roles that supply chain partnerships, cross-functional integration, responsiveness and resilience play in achieving competitive advantages in Palestine.

Design/methodology/approach

Industrial institutions in Palestine constitute the study population. Data are collected by distributing surveys via Google Forms linked to manufacturers in industries such as the Leather and shoe Industry, metal industries, chemical industries, construction industries, textile industries, stone and marble industries, pharmaceutical industry, veterinary industry, food industry, plastic industry, paper industry, major advantages and disadvantages. The SEM-PLS approach is used to analyze the data.

The findings demonstrate that supply chain responsiveness, resilience and cooperation are all improved by cross-functional integration in inventory data integration and immediate operation. Supply chain partnerships improve the supply chain’s responsiveness, resilience and competitive advantage by involving partners in work teams and exchanging best practices. The enhancement of supply chain resilience and competitive advantage is influenced by the company’s capacity to act promptly in response to variations in demands.

Research limitations/implications

This paper faces some limitations and it can be drawn as follows: To enhance supply chain risk management, the study continues to concentrate on manufacturing organizations that have internal integration. It also emphasizes the necessity of supply chain integration, which establishes direct connections with outside partners.

Practical implications

The findings of this study suggest some policy implications, as follows: To provide the manufacturing sector with a competitive edge, operations supervisors must be able to track and assess processes to ensure they are meeting demand. Firms that possess the ability to adjust to novel procedures or advancements in technology gain a competitive edge by guaranteeing consistent and high-quality delivery of products.

Originality/value

By implementing IT integration, this study theoretically and practically advances the understanding of the resource-based view of competitive advantages. This study focuses on providing insights into the nature of the relationship between supply chain partnership, cross-functional integration, responsiveness and flexibility and competitive advantages in the manufacturing sector in the Palestinian market.

• Competitive advantages
• Cross-functional integration
• Supply chain partnership
• Responsiveness

Acknowledgements

The author expresses his appreciation to the Emerald Publishing Group Ltd. The author extends a special thanks to the TQM Journal, which received this paper and helped to publish it. Moreover, the author would like to extend his appreciation to the journal's reviewers and its editorial board for their valuable comments and suggestions, which helped in improving the quality of this paper and brought it to the required level for publication.

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Badwan, N. (2024), "Role of supply chain partnership, cross-functional integration, responsiveness and resilience on competitive advantages: empirical evidence from Palestine", The TQM Journal , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TQM-12-2023-0402

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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