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  • The Online Researcher’s Guide To Sampling

Pros and Cons of Different Sampling Methods

Pros and Cons of Different Sampling Methods2@2x

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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

research sampling advantages and disadvantages

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

research sampling advantages and disadvantages

Part 2: How to Reduce Sampling Bias in Research

research sampling advantages and disadvantages

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

Simple sampling method example in stick men.

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

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

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

Systematic sampling method example in stick men

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

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

Disadvantages: Does not work well with multiple variables

Stratified sampling method example stick men

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

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

Disadvantages: Results may not be reliable nor generalisable

Cluster sampling method example with stick men

How can you identify sampling errors?

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

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

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

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

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

Research objectiveness

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

Sampling frame availability

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

Study design

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

Random sampling

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

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

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

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

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

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Very informative and useful for my study. Thank you

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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.

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

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Methodology

  • Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

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

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

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

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

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

Table of contents

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

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

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

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

Population vs sample

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

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

Sampling frame

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

Sample size

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

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

There are four main types of probability sample.

Probability sampling

1. Simple random sampling

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

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

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

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

3. Stratified sampling

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

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

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

4. Cluster sampling

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

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

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

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

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

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

Non probability sampling

1. Convenience sampling

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

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

2. Voluntary response sampling

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

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

3. Purposive sampling

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

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

4. Snowball sampling

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

5. Quota sampling

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

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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Principles of Social Research Methodology pp 221–234 Cite as

Sampling Techniques for Quantitative Research

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

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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.

  • Sampling techniques
  • Quantitative study
  • Probability sampling
  • Non-probability sampling

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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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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.

Sample Target Population

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.).

Probability and Non-Probability Samples

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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Systematic Sampling: Advantages and Disadvantages

research sampling advantages and disadvantages

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT).

research sampling advantages and disadvantages

There are distinct advantages and disadvantages of using systematic sampling as a statistical sampling method when conducting research of a survey population.

Systematic Sampling: An Overview

Systematic sampling is simpler and more straightforward than random sampling . It can also be more conducive to covering a wide study area. On the other hand, systematic sampling introduces certain arbitrary parameters in the data. This can cause over- or under-representation of particular patterns.

Systematic sampling is popular with researchers because of its simplicity. Researchers generally assume the results are representative of most normal populations , unless a random characteristic disproportionately exists with every "nth" data sample (which is unlikely).

To begin, a researcher selects a starting integer on which to base the system. This number needs to be smaller than the population as a whole (e.g., they don't pick every 500th yard to sample for a 100-yard football field). After a number has been selected, the researcher picks the interval, or spaces between samples in the population.

Key Takeaways

  • Because of its simplicity, systematic sampling is popular with researchers.
  • Other advantages of this methodology include eliminating the phenomenon of clustered selection and a low probability of contaminating data.
  • Disadvantages include over- or under-representation of particular patterns and a greater risk of data manipulation.

Systematic Sampling Example

In a systematic sample, chosen data is evenly distributed. For example, in a population of 10,000 people, a statistician might select every 100th person for sampling. The sampling intervals can also be systematic, such as choosing one new sample every 12 hours.

Advantages of Systematic Sampling

The pros of systematic sampling include:

Easy to Execute and Understand

Systematic samples are relatively easy to construct, execute, compare, and understand. This is particularly important for studies or surveys that operate with tight budget constraints.

Control and Sense of Process

A systematic method also provides researchers and statisticians with a degree of control and sense of process. This might be particularly beneficial for studies with strict parameters or a narrowly formed hypothesis, assuming the sampling is reasonably constructed to fit certain parameters .

Clustered Selection Eliminated

Clustered selection, a phenomenon in which randomly chosen samples are uncommonly close together in a population, is eliminated in systematic sampling. Random samples can only deal with this by increasing the number of samples or running more than one survey. These can be expensive alternatives.

Low Risk Factor

Perhaps the greatest strength of a systematic approach is its low risk factor. The primary potential disadvantages of the system carry a distinctly low probability of contaminating the data.

Disadvantages of Systematic Sampling

There are also drawbacks to this research method:

Assumes Size of Population Can Be Determined

The systematic method assumes the size of the population is available or can be reasonably approximated . For instance, suppose researchers want to study the size of rats in a given area. If they don't have any idea how many rats there are, they cannot systematically select a starting point or interval size.

Need for Natural Degree of Randomness

A population needs to exhibit a natural degree of randomness along the chosen metric. If the population has a type of standardized pattern, the risk of accidentally choosing very common cases is more apparent.

For a simple hypothetical situation, consider a list of favorite dog breeds where (intentionally or by accident) every evenly numbered dog on the list was small and every odd dog was large. If the systematic sampler began with the fourth dog and chose an interval of six, the survey would skip the large dogs.

Greater Risk of Data Manipulation

There is a greater risk of data manipulation with systematic sampling because researchers might be able to construct their systems to increase the likelihood of achieving a targeted outcome rather than letting the random data produce a representative answer. Any resulting statistics could not be trusted.

research sampling advantages and disadvantages

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

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Sampling Methods

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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Table of Contents

  • 1 What is Sampling?
  • 2.1 1. Low cost of sampling
  • 2.2 2. Less time consuming in sampling
  • 2.3 3. Scope of sampling is high
  • 2.4 4. Accuracy of data is high
  • 2.5 5. Organization of convenience
  • 2.6 6. Intensive and exhaustive data
  • 2.7 7. Suitable in limited resources
  • 2.8 8. Better rapport
  • 3.1 1. Chances of bias
  • 3.2 2. Difficulties in selecting a truly representative sample
  • 3.3 3. In adequate knowledge in the subject
  • 3.4 4. Changeability of units
  • 3.5 5. Impossibility of sampling
  • 4 Infographic on meaning, advantages and disadvantages of Sampling

What is Sampling?

Sampling may be defined as the procedure in which a sample is selected from an individual or a group of people of certain kind for research purpose. In sampling, the population is divided into a number of parts called sampling units.

Sampling

Advantages of sampling

Sampling ensures convenience, collection of intensive and exhaustive data, suitability in limited resources and better rapport. In addition to this, sampling has the following advantages also.

1. Low cost of sampling

If data were to be collected for the entire population, the cost will be quite high. A sample is a small proportion of a population. So, the cost will be lower if data is collected for a sample of population which is a big advantage.

2. Less time consuming in sampling

Use of sampling takes less time also. It consumes less time than census technique. Tabulation, analysis etc., take much less time in the case of a sample than in the case of a population.

3. Scope of sampling is high

The investigator is concerned with the generalization of data. To study a whole population in order to arrive at generalizations would be impractical.

Some populations are so large that their characteristics could not be measured. Before the measurement has been completed, the population would have changed. But the process of sampling makes it possible to arrive at generalizations by studying the variables within a relatively small proportion of the population.

4. Accuracy of data is high

Having drawn a sample and computed the desired descriptive statistics, it is possible to determine the stability of the obtained sample value. A sample represents the population from which its is drawn. It permits a high degree of accuracy due to a limited area of operations. Moreover, careful execution of field work is possible. Ultimately, the results of sampling studies turn out to be sufficiently accurate.

5. Organization of convenience

Organizational problems involved in sampling are very few. Since sample is of a small size, vast facilities are not required. Sampling is therefore economical in respect of resources. Study of samples involves less space and equipment.

6. Intensive and exhaustive data

In sample studies, measurements or observations are made of a limited number. So, intensive and exhaustive data are collected.

7. Suitable in limited resources

The resources available within an organization may be limited. Studying the entire universe is not viable. The population can be satisfactorily covered through sampling. Where limited resources exist, use of sampling is an appropriate strategy while conducting marketing research.

8. Better rapport

An effective research study requires a good rapport between the researcher and the respondents. When the population of the study is large, the problem of rapport arises. But manageable samples permit the researcher to establish adequate rapport with the respondents.

Disadvantages of sampling

The reliability of the sample depends upon the appropriateness of the sampling method used. The purpose of sampling theory is to make sampling more efficient. But the real difficulties lie in selection, estimation and administration of samples.

Disadvantages of sampling may be discussed under the heads:

  • Chances of bias
  • Difficulties in selecting truly a representative sample
  • Need for subject specific knowledge
  • changeability of sampling units
  • impossibility of sampling.

1. Chances of bias

The serious limitation of the sampling method is that it involves biased selection and thereby leads us to draw erroneous conclusions. Bias arises when the method of selection of sample employed is faulty. Relative small samples properly selected may be much more reliable than large samples poorly selected.

2. Difficulties in selecting a truly representative sample

Difficulties in selecting a truly representative sample produces reliable and accurate results only when they are representative of the whole group. Selection of a truly representative sample is difficult when the phenomena under study are of a complex nature. Selecting good samples is difficult.

3. In adequate knowledge in the subject

Use of sampling method requires adequate subject specific knowledge in sampling technique . Sampling involves statistical analysis and calculation of probable error. When the researcher lacks specialized knowledge in sampling, he may commit serious mistakes. Consequently, the results of the study will be misleading.

4. Changeability of units

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.

Some of the cases of sample may not cooperate with the researcher and some others may be inaccessible. Because of these problems, all the cases may not be taken up. The selected cases may have to be replaced by other cases. Changeability of units stands in the way of results of the study.

5. Impossibility of sampling

Deriving a representative sample is difficult, when the universe is too small or too heterogeneous. In this case, census study is the only alternative. Moreover, in studies requiring a very high standard of accuracy, the sampling method may be unsuitable. There will be chances of errors even if samples are drawn most carefully.

Infographic on meaning, advantages and disadvantages of Sampling

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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.

Sampling: Meaning, Characteristics, Types, Advantages and Disadvantages

Meaning of sampling.

Sampling refers to the method of selecting a small pattern of data from large population for the purpose of carrying out an investigation. The selected pattern is termed as sample which is a small and manageable version of large set of data. Sampling is most widely used in statistical testing where size of population is too large such that it is impossible to include each individual observation in test. 

Under this technique, to ease the process of doing a research on whole population, it is divided into small sampling unit. These sampling units represent the characteristics of whole population and should not reflect bias towards a particular attribute. Samples drawn from population are used by researcher for making statistical inferences and estimating the information about whole population. Methodology to be used for the technique of sampling depends upon type of analysis being conducted by researcher. Probability and non-probability sampling are two common sampling methodologies. Sampling is mostly used by businesses for studying the needs and preferences of people in market.

Characteristics of Sampling

Various characteristics of sampling are discussed in points given below: –

  • Goal-oriented: Design of sampling should be goal oriented. It must align clearly with the objectives of research being conducted and should be in accordance with conditions of survey. 
  • Proper universe representation: Sample chosen should adequately represent the characteristics of whole population from which it is taken. It should fairly represent details about all units without any biasness. There are different methods of choosing a sample and it need to be chosen with utmost care as improper sampling would lead to error in survey.  
  • Proportional: Size of sample should be proportional with the size of population. It should be large enough for representing the whole universe and must provide statistical reliability. Sample must ensure proper accuracy for carrying out the particular research study.
  • Economical: Process of sampling should be economical requiring minimum cost and efforts for attaining the objectives of survey.
  • Random selection: Sample units should be selected on a random basis under which every unit has an equal chance of being chosen. It will ensure that sample is a fair representative of whole population.
  • Practical: Design of sample should be simple and practical. It must be capable of easily understood and applicable in fieldwork.

Types of Sampling

Various types of sampling are as discussed below: –

  • Random sampling: Random sampling is a technique under which every member of population has equal chance of being selected in sample units. It is most reliable method which ensures fairness and eliminates any biasness. Under random sampling, whole population need to be properly numbered or names should be allotted to it and then a raffle method is used for making the sample.
  • Convenience sampling: It is a technique under which individual from target population is chosen on the basis of their easy availability and willingness to take part in survey. Convenience sampling is an easy and inexpensive method under which participants are chosen by researcher on the basis of their easy accessibility. However, this method may not represent whole population accurately and involve biasness.  
  • Systematic sampling: Systematic sampling is method in which participants are selected from population using a systematic/orderly manner. All members are properly numbered and then chosen at regular intervals instead of randomly generating numbers. This sampling technique is less time-consuming as it has predefined range.
  • Stratified sampling: Stratified sampling is a type of sampling under which whole population is divided into distinct small sub-groups based on various individual traits such as gender, age, job role and income. Groups are formed in such a way that it does not overlap. Peoples in each sub-group are included on the basis of overall proportion of population.   
  • Judgmental or Purposive sampling: Under this type of sampling, judgements of researcher is used for choosing sample units. It is also termed as selective sampling in which samples are formed at the discretion of researcher. 

Advantages of Sampling

Various advantages of sampling are as discussed below: –

  • Lower sampling cost: Sampling reduces the overall cost involved in doing research. The cost for collecting data about entire population is quite high. Sampling reduces the population into small manageable units. Acquiring data about sample of population involves lower cost which is one of the major advantage. 
  • Less time consuming: Sampling reduces the overall time by reducing the size of population. Data is not collected about every member in population but only related to sample is gathered. It is less time-consuming in comparison to census technique. 
  • Higher accuracy of data: A sample represents the whole population from which it is drawn. It is used for calculation of desired descriptive statistics and a stability of derived sample value can be easily determined. Samples permit a high level of accuracy because of limited area of operations. It enables in proper execution of field work and results of studies conducted on the basis of theses sample units turn out to be accurate.
  • Higher scope of sampling: Sampling enables investigators to easily arrive at generalizations about set of data. It would be totally impractical to study whole population as it is too large for measuring characteristics of all individual members. Process of sampling by analyzing variables within small proportion of population ease in arriving at generalizations.
  • Intensive and exhaustive data: In studies based on sample units, observations are made of a limited number. Therefore, exhaustive and intensive data are collected.
  • Suitable in case of limited resources: Sampling is very effective technique of collecting information in presence of limited resources with organization. Studying the whole population requires large amount of resources both in term of money and time. Sampling makes it possible to cover whole population satisfactorily even by employing limited resources. 
  • Better rapport: Good rapport in between the researcher and respondents is must for carrying out an effective research study. In presence of large population, various issues of rapport arise. 

Disadvantages of Sampling

Accuracy of sample is dependent upon appropriateness of sample method used. Theory of sampling focuses on improving the efficiency of sampling. Major difficulties are pose at the time of estimation, selection and administration of samples. Various disadvantages of sampling process are discussed in points given below: – 

  • Chance of Bias: Major limitation that arises with sampling is chance of biasness in choosing sample units. Selection of samples is a judgmental task as it is based on mindset of individual choosing them. These biased selection does not truly represent the whole population and may lead to faulty conclusions by researcher. 
  • Difficulty in choosing a truly representative sample: Choosing an adequate and reliable sample that is a truly representative of population remains a difficult task. In case the phenomena under study is of complex nature involving heterogeneous data, it becomes difficult to select proper samples.
  • Lack of adequate subject knowledge: Application of sampling process requires proper knowledge regarding sampling technique by individual selecting sample units. This process requires computation of probable error and statistical analysis. There are chances of serious mistakes being committed by researcher in case if he lacks specialized knowledge about sampling. Consequently, overall results of research study conducted will be misleading.
  • Impossibility of sampling: Process of sampling is not applicable in cases where universe is too small consisting of heterogeneous set of data. It is difficult to derive a representative sample in such cases. Census study is the only alternative for doing study for such phenomena. Also, sampling is inadequate for studies that needs a high degree of accuracy. There are always chance of errors in sampling even if sample units are chosen with utmost care.

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16 Key Advantages and Disadvantages of Cluster Sampling

Cluster sampling is a statistical method used to divide population groups or specific demographics into externally homogeneous, internally heterogeneous groups. Each cluster then provides a miniature representation of the entire population. After researchers identify the clusters, specific ones get chosen through random sampling while others remain unrepresented. Then each investigator must choose the most appropriate method of element sampling from each group.

Cluster sampling typically occurs through two methods: one- or two-stage sampling. The first option requires all of the elements in selected clusters to get sampled. When researchers use the latter option, then simple random sampling happens within each cluster to create subsamples for the project.

It is essential to avoid confusing cluster sampling with the stratified approach. The latter option divides the population into mutually exclusive groups that are the reverse of this method.

When we look at the advantages and disadvantages of cluster sampling, it is important to remember that the groups are similar to each other. They simply have different internal composition.

List of the Advantages of Cluster Sampling

1. Cluster sampling requires fewer resources. A cluster sampling effort will only choose specific groups from within an entire population or demographic. That means this method requires fewer resources to complete the research work. That’s why it is one of the cheapest investigatory options that’s available right now, even when compared to simple randomization or stratified sampling. Even when the costs of obtaining data are similar, cluster sampling typically requires fewer administrative and travel expenses.

2. It is a feasible way to collect statistical information. The division of a demographic or an entire population into homogenous groups increases the feasibility of the process for researchers. Because every cluster is a direct representation of the people being studied, it is easy to include more subjects in the project as needed to obtain the correct level of information.

The design of cluster samples makes it a simple process to manage massive data input. It takes large population groups into account with its design to ensure that the extrapolated information gets collected into usable formats.

3. The cluster sampling approach reduces variabilities. Every research effort creates estimates as the discovered statistics get extrapolated to the rest of the population. When investigators use cluster samples to generate this information, then the estimation has more accuracy to it when compared to the other methods of collection.

Researchers must make their best effort to ensure that each cluster is a direct representation of the population or demographic to achieve this benefit. Then the data obtained from this method offers reduced variability with its results since the findings are closer to a direct reflection of the entire group.

4. Researchers can conduct cluster sampling almost anywhere. When resources are tight and research is required, cluster sampling is a popular method to use because of its structures. You can take a representative sample from anywhere in the world to generate the results that you want. Although geographic variability will increase the error rate in the sample by a small margin, it also opens the door to localized efforts that can still be useful to the overall demographic.

5. You receive the benefits of stratified and random sampling with this method. Cluster sampling is a popular research method because it includes all of the benefits of stratified and random approaches without as many disadvantages. This benefit works to reduce the potential for bias in the collected data because it simplifies the information assembly work required of the investigators. Because there are fewer risks of adverse influences creating random variations, the results of the work can generate exclusive conclusions when applied to the overall population.

6. It gives researchers a large data sample from which to work. When you work with a larger population group, then you’re creating more usable data that can eventually lead to unique findings. After researchers design and place the cluster sampling method on their preferred demographic, then similar information gets collected from each group. Investigators can then compare data points between the clusters to look for specific conclusions within a particular population group.

This advantage generates tracking data that looks at how individual clusters evolve in the future when compared to the rest of the population group. Then researchers can use that variability to understand more of the differences that can lead to a higher error rate.

7. Cluster sampling allows for data collection when a complete list of elements isn’t possible. Cluster sampling should only be considered when there are economic justifications to use this approach. If reduced costs can be used to overcome precision losses, then it can be a useful tool. This advantage occurs most often when the construction of a complete list of the population elements is impossible, expensive, or too difficult to organize.

Instead of trying to list all of the customers that shop at a Walmart, a stage 1 cluster group would select a subset of operating stores. Then a stage 2 cluster would speak with a random sample of customers who visit the selected stores.

List of the Disadvantages of Cluster Sampling

1. Biased samples are easy to create in cluster sampling. If the clusters in each sample get formed with a biased opinion from the researchers, then the data obtained can be easily manipulated to convey the desired message. It creates an inference within the information about the entire population or demographic, creating a bias in that segment simultaneously.

The participants of a cluster sample can offer their own bias in the results without the researchers realizing what is happening. It is a method that makes it difficult to root out people who have an agenda that want to follow.

2. There can be high sampling error rates. The samples drawn from the clustering method are prone to a higher sampling error rate. Even when there is randomization in a two-stage process using this method, the results obtained aren’t always reflective of the general population. That’s why great care must be taken when using the statistics from a research effort such as this because there will be elements within the same population that feel completely the opposite.

3. Unconscious bias is almost impossible to detect with this approach. Unconscious bias is a social stereotype about a specific group of people. Everyone forms this prejudice, which is also called “implicit bias,” that people hold about individuals who are outside of their conscious awareness. It is an issue that develops because of humanity’s tendency to organize our social worlds through categorizing. Because cluster sampling is already susceptible to bias, finding these implicit pressures can be almost impossible when reviewing a study.

This disadvantage boosts the potential error rate of a cluster sample study even higher. When researchers are under time pressure or must multitask when collecting information, this issue can become even more prevalent in the information.

4. Most clusters get formed based on the information provided by participants. Cluster sampling usually occurs when participants provide information to researchers about themselves and their families. That means each group can influence the quality of the information that researchers gather when they intentionally or unintentionally misrepresent their standing. Something as simple as an artificially-inflated income can be enough to cause the error rate of the info to skyrocket.

Common areas of misrepresentation involve political preferences, family ethnicity, and employment status. If researchers only use this data to design and implement structures, then the statistical outcomes can become skewed, inaccurate, and potentially useless.

5. Cluster sampling creates several overlapping data points. Researchers use cluster sampling to reduce the information overlaps that occur in other study methods. When you have repetitive data in a study, then the findings may not have the integrity levels needed for publication. Since clusters already have similarities because everyone gets pulled from the same population group, the levels of variability within the work can be minimal if everyone comes from the same region.

Imagine researchers are looking at families who eat fast food three times per week. What reasons do these people have when making this dining decision? If all of the individuals for the cluster sampling came from the same neighborhood, then the answers received would be very similar. That result could mean the error rate got high enough that the conclusions would get invalidated.

6. Researchers can only apply their findings to one population group. Cluster sampling can provide a wonderful dataset that applies to a large population group. It is also essential to remember that the findings of researchers can only apply to that specific demographic. That’s why generalized findings that apply to everyone cannot be obtained when using this method. One neighborhood is not reflective of an entire city, just as a single state or province isn’t reflective of an entire country.

Researchers must have robust definitions in place when creating their clusters to ensure the accuracy of the information that gets collected. Then more structures must be in place to ensure the extrapolation applies to the correct larger specific group.

7. Cluster sampling requires size equality. The representative samples in the clustering approach must have the same representative size to be a useful research tool. Any discrepancies in this area will create over- and under-representation in the conclusions that investigators reach with this work. If this disadvantage isn’t caught during the structuring process of the study, then data disparities are almost certain to happen. Then a significant sampling error would occur that could be challenging to identify, leading everyone toward false conclusions that seem to be true.

8. This method requires a minimum number of examples to provide accurate results. Cluster sampling provides valid results when it has multiple research points to use. If the structure of the research includes people from the same population group with similar perspectives that are a minority in the larger demographic, then the findings will not have the desired accuracy. There must be a minimum number of examples from each perspective in this approach to create usable statistics.

When this disadvantage is present, then the risk of obtaining one-side information becomes much higher.

9. Cluster sampling requires unit identification to be effective. The cluster sampling process works best when people get classified into “units” instead of as individuals. That’s why political samples that use this approach often segregate people into their preferred party when creating results. If investigators were to avoid this separation, then the findings could get flawed because an over-representation of one specific group might take place without anyone realizing what was happening.

The advantages and disadvantages of cluster sampling show us that researchers can use this method to determine specific data points from a large population or demographic. It doesn’t have the sample expense or time commitments as other methods of information collection while avoiding many of the issues that take place when working with specific groups.

The best results occur when researchers use defined controls in combination with their experiences and skills to gather as much information as possible. Without these tools in the toolbox, the error rate of the collected data can be high enough where the findings are no longer usable.

That’s why experienced researchers who are familiar with cluster samples are typically the people hired to design these projects. That outcome in itself can lead to implicit bias, which is why any findings generated by this process should be considered carefully.

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Marketing Research - Sampling

Last updated 22 Mar 2021

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What is sampling? In market research, sampling means getting opinions from a number of people, chosen from a specific group, in order to find out about the whole group. Let's look at sampling in more detail and discuss the most popular types of sampling used in market research.

It would be expensive and time-consuming to collect data from the whole population of a market. Therefore, market researchers make extensive of sampling from which, through careful design and analysis, marketers can draw information about their chosen market.

Sample Design

Sample design covers:

  • Method of selection
  • Sample structure
  • Plans for analysing and interpreting the results.

Sample designs can vary from simple to complex. They depend on the type of information required and the way the sample is selected.

Sample design affects the size of the sample and the way in which analysis is carried out; in simple terms the more precision the market researcher requires, the more complex the design and larger the sample size will be.

The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative . It may be necessary to draw a larger sample than would be expected from some parts of the population: for example, to select more from a minority grouping to ensure that sufficient data is obtained for analysis on such groups.

Many sample designs are built around the concept of random selection . This permits justifiable inference from the sample to the population, at quantified levels of precision. Random selection also helps guard against sample bias in a way that selecting by judgement or convenience cannot.

Defining the Population

The first step in good sample design is to ensure that the specification of the target population is as clear and complete as possible. This is to ensure that all elements within the population are represented.

The target population is sampled using a sampling frame .

Often, the units in the population can be identified by existing information such as pay-rolls, company lists, government registers etc.

A sampling frame could also be geographical. For example, postcodes have become a well-used means of selecting a sample.

Sample Size

For any sample design, deciding upon the appropriate sample size will depend on several key factors:

  • No estimate taken from a sample is expected to be exact: assumptions about the overall population based on the results of a sample will have an attached margin of error
  • To lower the margin of error usually requires a larger sample size: the amount of variability in the population, ie the range of values or opinions, will also affect accuracy and therefore size of the sample
  • The confidence level is the likelihood that the results obtained from the sample lie within a required precision: the higher the confidence level, the more certain you wish to be that the results are not atypical. Statisticians often use a 95% confidence level to provide strong conclusions
  • Population size does not normally affect sample size: in fact the larger the population size, the lower the proportion of that population needs to be sampled to be representative. It's only when the proposed sample size is more than 5% of the population that the population size becomes part of the formulae to calculate the sample size

Types of Sampling

There are many different types of sampling methods, here's a summary of the most common:

Cluster sampling

Units in the population can often be found in certain geographic groups or "clusters" for example, primary school children in Derbyshire.

A random sample of clusters is taken, then all units within the cluster are examined.

  • Quick and easy
  • Doesn't need complete population information
  • Good for face-to-face surveys

Disadvantages

  • Expensive if the clusters are large
  • Greater risk of sampling error

Convenience sampling

Uses those who are willing to volunteer and easiest to involve in the study.

  • Subjects are readily available
  • Large amounts of information can be gathered quickly
  • The sample is not representative of the entire population, so results can't speak for them - inferences are limited. future data
  • Prone to volunteer bias

Judgement sampling

A deliberate choice of a sample - the opposite of random

  • Good for providing illustrative examples or case studies
  • Very prone to bias
  • Samples often small
  • Cannot extrapolate from sample

Quota sampling

The aim is to obtain a sample that is "representative" of the overall population.

The population is divided ("stratified") by the most important variables such as income, age and location. The required quota sample is then drawn from each stratum.

  • Quick and easy way of obtaining a sample
  • Not random, so some risk of bias
  • Need to understand the population to be able to identify the basis of stratification

Simply random sampling

This makes sure that every member of the population has an equal chance of selection.

  • Simple to design and interpret
  • Can calculate both estimate of the population and sampling error
  • Need a complete and accurate population listing
  • May not be practical if the sample requires lots of small visits over the country

Systematic sampling

After randomly selecting a starting point from the population between 1 and * n , every nth unit is selected.

* n equals the population size divided by the sample size.

  • Easier to extract the sample than via simple random
  • Ensures sample is spread across the population
  • Can be costly and time-consuming if the sample is not conveniently located
  • Secondary research
  • Quantitative research
  • Qualitative research
  • Marketing research

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Majorities in most countries surveyed say social media is good for democracy.

A man records a video for a legislative candidate's social media account on Jan. 10, 2024, in Tangerang, Banten province, Indonesia. (Bay Ismoyo/AFP via Getty Images)

Social media has increased public access to information and created platforms for political activism. Yet some also say it is harmful to democracy .

This Pew Research Center analysis focuses on the perceived impact of social media on democracy in 27 countries in North America, Europe, the Middle East, the Asia-Pacific region, sub-Saharan Africa and Latin America.

For non-U.S. data, this report draws on surveys of 20,944 respondents across 18 advanced economies conducted from Feb. 14 to June 3, 2022, and surveys of 10,235 respondents across eight emerging and developing economies (Argentina, Brazil, India, Indonesia, Kenya, Mexico, Nigeria and South Africa) conducted from Feb. 25 to May 22, 2023. The data was collected both face-to-face and over the phone. In Australia, we used a mixed-mode probability-based online panel.

In the United States, we surveyed 3,581 U.S. adults from March 21 to March 27, 2022. Everyone who took part in the U.S. survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

To compare educational groups across countries, we standardize education levels based on the UN’s International Standard Classification of Education (ISCED).

  • In India, Indonesia, Kenya, Nigeria, South Africa and Brazil, the lower education category is below secondary education, and the higher category is secondary or more.
  • In Belgium, Canada, France, Germany, Greece, Hungary, Italy, the Netherlands, Poland, Spain, Sweden, the UK, Australia, Japan, Malaysia, Singapore, South Korea, Israel, Argentina and Mexico, the lower education category is secondary education or less, and the higher category is postsecondary or more.
  • In the U.S., the lower education category is some college or less, and the higher category is a college degree or more.

Here is the question used for the analysis , along with responses, and the survey methodology .

A diverging bar chart showing that, in most countries surveyed, large shares say social media has been good for their democracy.

As social media use becomes more widespread globally , people in 27 countries surveyed by Pew Research Center between 2022 and 2023 generally see it as more of a good thing than a bad thing for democracy. In 20 of these countries, in fact, majorities say social media has benefited democracy in their nation.

People in emerging economies are particularly likely to say social media has advanced their democracy. Assessments are especially positive in Nigeria and Mexico, where nearly eight-in-ten (77% each) say social media has had a positive effect on democracy.

People are far less certain in other countries, including the Netherlands and France, where more say social media has had a negative effect on democracy than say it’s had positive effect. French President Emmanuel Macron has called for social media regulation to curb the spread of misinformation. In 2023, he also suggested that access to social media should be cut during times of social unrest , including during riots over police violence in France.

Meanwhile, Americans are the least likely to evaluate social media positively. Just 34% of U.S. adults say social media has been a good thing for democracy in the United States, while nearly twice as many (64%) say it has been a bad thing.

Related: Social Media Seen as Mostly Good for Democracy Across Many Nations, but U.S. is a Major Outlier

The role of social media in spreading misinformation has been widely discussed ahead of key U.S. elections. And though majorities in both parties say social media has been a bad thing for democracy in the U.S., Republicans and Republican-leaning independents are more likely to say this than Democrats and Democratic leaners (74% vs. 57%).

How do views on social media and democracy vary by age, education and other factors?

A dot plot showing that young adults are more likely than older people to say social media has been a good thing for democracy.

In 14 countries surveyed, younger adults are more likely than older people to say social media has been a good thing for democracy.

This difference is most prevalent in Poland, where 86% of adults under 40 say social media has benefited democracy in their country, compared with 56% of those ages 40 and older. Double-digit differences exist in 10 additional countries surveyed.

In 13 countries, adults with more education are more likely than those with less schooling to say that social media has been a good thing for democracy. In South Africa, for example, there is a 22-percentage-point difference on this question between those with more education and those with less.

(Education systems differ by country, so in this analysis, levels of attainment for “more education” and “less education” also vary. Read the “ How we did this ” section for more information.)

In some countries, adults with higher incomes are more likely than those with lower incomes to say social media is a good thing for democracy. In Belgium and the U.S., however, the reverse is true.

Social media use

A dot plot showing that social media users are significantly more likely than non-users to say social media benefits democracy.

Those who use social media are significantly more likely than non-users to say that social media has benefited democracy in their country. In every country surveyed, there is a difference of at least 10 points between social media users and non-users on this question. Non-users, however, are also less likely to offer an opinion on this question in most places.

For example, in Israel, social media users are 77 percentage points more likely than non-users to say social media has been a good thing for democracy (82% vs. 5%). But about a quarter of non-social media users in Israel decline to provide a response, compared with just 5% of those who do use social media.

And in Poland, South Africa, Australia, Japan and elsewhere, social media users are far more likely than non-users to express a positive view of its effect on democracy. In every country surveyed, social media users are at least 10 points more likely to take this stance than non-users.

Note: Here is the question used for the analysis , along with responses, and the survey methodology .

research sampling advantages and disadvantages

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Purposive sampling: complex or simple? Research case examples

Steve campbell.

Professor of Clinical Redesign, Nursing, Associate Head Research, School of Nursing, University of Tasmania, College of Health and Medicine, Australia

Melanie Greenwood

Associate Professor, Director Post Graduate Courses, School of Nursing, University of Tasmania, College of Health and Medicine, Australia

Sarah Prior

Lecturer, Tasmanian School of Medicine, University of Tasmania, College of Health and Medicine, Australia

Toniele Shearer

Lecturer, PhD Candidate, School of Nursing, University of Tasmania, College of Health and Medicine, Australia

Kerrie Walkem

Sarah young.

Professor of Health Care Improvement, School of Nursing, University of Tasmania, College of Health and Medicine, School of Health Science, Australia

Danielle Bywaters

PhD Candidate, School of Nursing, University of Tasmania, College of Health and Medicine, Australia

Purposive sampling has a long developmental history and there are as many views that it is simple and straightforward as there are about its complexity. The reason for purposive sampling is the better matching of the sample to the aims and objectives of the research, thus improving the rigour of the study and trustworthiness of the data and results. Four aspects to this concept have previously been described: credibility, transferability, dependability and confirmability.

The aim of this paper is to outline the nature and intent of purposive sampling, presenting three different case studies as examples of its application in different contexts.

Presenting individual case studies has highlighted how purposive sampling can be integrated into varying contexts dependent on study design. The sampling strategies clearly situate each study in terms of trustworthiness for data collection and analysis. The selected approach to purposive sampling used in each case aligns to the research methodology, aims and objectives, thus addressing each of the aspects of rigour.

Conclusions

Making explicit the approach used for participant sampling provides improved methodological rigour as judged by the four aspects of trustworthiness. The cases presented provide a guide for novice researchers of how rigour may be addressed in qualitative research.

Introduction

Novice nurse researchers tend to see purposive sampling as either simple or too difficult ( Tuckett, 2004 ) and may therefore default to using a convenience sample for the wrong reasons. Attempting to ensure that nursing research has the right sample is crucial to good processes. This paper came out of the ongoing work of a research group, made up largely of nurses, at the University of Tasmania. The group ranged in experience from PhD students and early career researchers to experienced full professors and the research ranged similarly from PhD studies to funded research. A number of the group were using purposive sampling techniques under different circumstances and with different challenges. The lessons learnt by the individuals and by the group as a whole are interweaved into this paper and the case studies using purposive sampling are used to exemplify the different uses of purposive sampling, and the way in which each context has been handled.

Purposive sampling

In terms of sampling, the strategy for participant selection should be integrated into the overall logic of any study ( Punch, 2004 ) and the rationale for sample selection needs to be aligned from an ontological, epistemological and axiological perspective with the overarching aims of the study. In a qualitative study, a relatively small and purposively selected sample may be employed ( Miles and Huberman, 1994 ), with the aim of increasing the depth (as opposed to breadth) of understanding ( Palinkas et al., 2015 ). Purposive sampling is ‘used to select respondents that are most likely to yield appropriate and useful information’ ( Kelly, 2010 : 317) and is a way of identifying and selecting cases that will use limited research resources effectively ( Palinkas et al., 2015 ).

Purposive sampling strategies move away from any random form of sampling and are strategies to make sure that specific kinds of cases of those that could possibly be included are part of the final sample in the research study. The reasons for adopting a purposive strategy are based on the assumption that, given the aims and objectives of the study, specific kinds of people may hold different and important views about the ideas and issues at question and therefore need to be included in the sample ( Mason, 2002 ; Robinson, 2014 ; Trost, 1986 ).

With respect to research involving multiple cases, the most popular forms of purposive sampling are stratified, cell, quota and theoretical sampling. The different nature of these approaches is described in brief below.

Stratified sampling selects specific kinds or groups of participants that need to be part of the final sample. The sample is then stratified by the characteristic of the participant or group, with a specific number allocated to each stratification. (The number allocated to each category is also clearly important, particularly when allocation to separate groups is different.) Categories might be age, size of family, IQ, etc. However, and importantly, there needs to be a clear reason linked to the aims and objectives of the study to show why each group is different. Moreover, in terms of interviews, they must have something to add to the study.

Cell sampling is similar to stratified sampling but differs in that the categories for stratification are discrete, and in cell sampling they can overlap like a Venn diagram ( Miles and Huberman, 1994 ). For example, in a study of children with chronic disease, one cell might be obese children and the other might be children with diabetes and the overlap will be obese children with diabetes.

In quota sampling, there is greater flexibility – rather than fixed numbers of cases being required with particular criteria, quota sampling specifies categories and the minimum number needed for each one ( Mason, 2002 ). As the study proceeds, numbers in each area are monitored for fulfilment of the quota. For example, in a study, again of children with chronic illness, there might be quota for kinds of chronic illness and for kinds of family. The research team would specify a minimum for each of the quota. (A minimum of five children each with diabetes, leukaemia, arthritis, etc., and for the kind of family, 10 from a nuclear family, 15 from a reconstituted family, etc.) The use of minimum quota makes sure that key participants are part of the final sample. It is argued that this approach is also more flexible in shaping the final sample and easier, in recruitment terms, compared with stratified and cell sampling ( Robinson, 2014 ).

Theoretical sampling is different by being part of the collection and analysis of the data, following provisional sampling and analysis of some data ( Coyne, 1997 ; Robinson, 2014 ; Strauss, 1987 ). Theoretical sampling originally came from Grounded Theory but is applied to other methods as well ( Mason, 2002 ). The process involves either identifying cases from new groups, which might amount to being a comparison or a contrast with other groups, or reshaping the sample into a new set of criteria as a result of the analysis, and in so doing replacing the original sampling strategy chosen a-priori ( Draucker et al., 2007 ; Robinson, 2014 ).

This paper now introduces three different research studies in which the processes and challenges of purposive sampling are taken up in each instance.

Research study 1: Co-led redesign of stroke services in North West Tasmania

This example relates to the redesign of stroke services and is reported at the point when all patient interviews have been collected. Co-led redesign initiatives in healthcare service provision rely on experience-based feedback from patients and their families as well as sourcing information from healthcare staff and data collected specifically for the purpose of a service redesign (Prior and Campbell, 2018). The stroke service co-led redesign project utilised a purposive sampling method developed by Reed et al. (1996) based on stakeholder sampling ( Ovretveit, 1998 ), termed the Matrix sampling method. Matrix sampling empowers the stakeholders, allowing them to select categories of participants who they determine to be representative of the service users, essentially creating a trustworthy sample. For example, the stroke patient interviews consisted of 50% of patients over age 65 and 50% of those aged 65 or under. The stakeholder group identified that these two groups of patients require differing types of acute and rehabilitative stroke care in some instances and placed a high level of importance on being able to achieve the levels of care required for different age groups. The stakeholders included senior medical and nursing management, medical consultants, nursing unit managers, the director of allied health and the research team. The research team is then able to perform the interviews with selected patients on behalf of the stakeholders and report the findings to the group via thematic analysis.

Matrix sampling strengthens qualitative research by providing a structured and purposive method for nominating participants. It creates maximum variability based on stakeholder knowledge of the population and the intended research outcomes. Previously utilised in healthcare redesign research in the United Kingdom ( Campbell et al., 2004 ) as part of a patient journey approach, Matrix sampling is a cost-effective and time-efficient method allowing the stakeholders a level of control over the selected sample. This method of sampling was selected to capture a relevant participant group, representing stroke patients in North West Tasmania. A number of clinical and demographic variables were considered when determining the appropriate stroke patient participants, influenced by the local population and a quantitative data analysis determining the numbers and types of stroke patients admitted. Exclusion criteria were set prior to the sampling process; these included mini strokes (transient ischaemic attacks), patients who were living in a nursing home at the time of their stroke and deceased patients. As with other purposive sampling methods, Matrix sampling utilises the specific characteristic of stroke to provide a potential pool of participants. Other characteristics of importance noted during the participant selection phase for this project included the number of risk factors associated with each stroke patient, mode of arrival to the hospital, whether the patient was transferred into or out of a specific hospital and the type of stroke for which the patient was admitted (haemorrhagic or ischaemic). These specific criteria, determined by the stakeholders, allowed the research team to find candidates for the interviews to represent the patient group who could provide the most appropriate input into stroke service redesign for this particular population area.

Although this sampling method fulfils the needs of the stakeholders by allowing them to make the decisions over the sample population, there are also some weaknesses or disadvantages to the Matrix sampling method. If it is not possible to recruit participants to a selected criterion, gaps appear in the data. In the project it was noted that one particular criterion, patients who were transferred between hospitals, was more difficult to ‘fill’ due to smaller numbers of admitted patients fitting this description, purely due to the population being sampled. The dependability of the data, then, can be difficult to control; however, to overcome this issue, discussions with the stakeholder group suggested other recruitment methods, such as clinicians identifying patients and requesting consent. If these patients were unable to be identified, the group was satisfied that all was done to ensure the stakeholder view was utilised to the best abilities of the research team and the results delivered still reflected a representative population.

The Matrix sampling method is an easily transferable approach for qualitative research, which allows the input of the stakeholder(s) to determine the output of the research through the provision of local information and knowledge. Matrix sampling is a form of stratified sampling, but it is also quota driven. It is a form of stakeholder sampling where the views of the stakeholders are paramount, as they have to be reassured of the adequacy of the sampling so they regard the evidence as adequate and credible.

Research study 2: Child and family health nurses and safety and wellbeing of young children

This example is from a PhD study (Young, 2020 [unpublished thesis]) focusing on the response of child and family health (CFH) nurses to concerns around the safety and wellbeing of young children aged from birth to 5 years within the family, using Interpretive Description (ID) as the methodological approach. The setting in which the study is situated is that of a CFH nursing service provided by an Australian state-wide health department.

ID methodology, developed by Thorne et al. (1997) , is a way of generating increased understanding of clinical phenomena that are complex and experiential. ID studies generate an ID of the themes and patterns captured within subjective perceptions around a phenomena of clinical interest ( Thorne et al., 2004 ) and produce practice-relevant knowledge that can be immediately applied in the clinical context ( Thorne, 2016 ; Hunt, 2009 ). When using ID methodology, researchers identify who should be included in the study, so the eventual findings allow better understanding of the phenomenon of interest ( Hunt, 2009 ; Thorne, 2016 ). Purposive sampling is an accepted and often used initial sampling strategy in ID methodology as it allows settings and people to be recruited based on their expected contribution to the study ( Schensul, 2011 ) and by virtue of some angle of the phenomenon that they might help us better understand ( Hunt, 2009 ; Thorne, 2016 ). Participants are those who are most likely to have in-depth knowledge and experience of the phenomenon being studied. With this in mind, the inclusion criteria developed for this study were that participants must be nurses currently employed as CFH nurses with a minimum of 2 years recent (within the last 5 years) experience working in this specialist area of nursing. This was to help to ensure the opinions obtained were those of experienced CFH nurses with exposure to relevant practice experiences in a range of situations. Excluded from the study were those nurses who did not have at least 2 years recent post-graduate experience as a CFH nurse.

In developing the sample subset, an awareness was maintained of how this might either privilege or silence particular angles or perspectives and thus impact the eventual findings of the study and its credibility ( Thorne, 2016 ). To enhance credibility, care was taken to clearly, transparently and explicitly describe the logic used in selecting the sample subset ( Robinson, 2014 ; Thorne, 2016 ). Furthermore, a critical awareness of the nature of the selected sample and how this might impact on any findings generated was maintained throughout the study to help ensure claims beyond the sample subset were not made ( Robinson, 2014 ; Thorne, 2016 ).

Transferability was enhanced by the way in which study participants were clearly identified in terms of inclusion and exclusion criteria and demographic information. This helps others to determine whether the findings are applicable to other situations and population groups ( Shenton, 2004 ; Amankwaa, 2016 ). A sample that is fully contextualised helps prevent unwarranted generalisation ( Robinson, 2014 ). Dependability was enhanced by the description of participants using clear inclusion and exclusion criteria ( Shenton, 2014 ). In addition, a well-accepted sampling strategy appropriate to an ID study was used ( Thorne, 2016 ). Confirmability was enhanced by the provision of a rationale for the choice of inclusion and exclusion criteria, so that the integrity of the process could be determined by others ( Shenton, 2014 ).

Research study 3: How can mental wellbeing for new mothers be achieved?

This example is from a PhD study (Young, 2020 [unpublished thesis]) about women's experiences after childbirth, where recruitment is about to commence. This research aims to determine what influences mothers’ mental wellbeing in the year after the birth of a first baby and asks, ‘how can mental wellbeing for new mothers be achieved?’ Narrative inquiry involving three or four in-depth interviews with ∼10 women will be used to answer this question. The interviews will be conducted longitudinally over a period of 9–12 months and will aim to capture a rich, deep picture of the first year after childbirth. It is hoped that the major influences impacting mental wellbeing will be identified.

To determine which women to include in this study, purposive sampling will be employed. Specific inclusion and exclusion criteria will be indicated, making the inclusion of participants in this study non-probabilistic, and indeed purposive, in nature. Women will be recruited for involvement from the antenatal clinic at the local public hospital by way of response to a posted flyer. Although there is an element of convenience sampling involved in this process, the very specific nature of the criteria for involvement make this design purposive. Inclusion criteria will include considerations such as first-time mothers only, singleton pregnancy, maternal age over 18 years and gestational due date within a specified timeframe to facilitate the longitudinal interview schedule. Exclusion criteria will include anyone who has had a previous mental health issue or a pregnancy-related health complication (e.g., gestational diabetes, placenta praevia, known foetal issues, etc.).

The trustworthiness and rigour of the data will be enhanced by the purposive sampling design. In terms of credibility, this method of sampling supports the likelihood that ‘member checking’ may occur, which will increase the credibility of the findings ( Guba, 1981 ). Because women will self-select for participation in the study, this degree of interest and investment increases the likelihood of their willingness to remain involved for the duration of the research.

Both the transferability and dependability of the data will be enhanced by the specific nature of the inclusion and exclusion criteria laid out for this research. Transferability will be affected because these detailed criteria will allow readers to develop a clear picture of participants involved. Guba notes the importance of ‘full description of all the contextual factors impinging on the inquiry’ (1981: 70) and the participants themselves can be considered a ‘contextual factor’ in the research. In a similar vein, the detailed nature of the criteria will form part of the audit trail that contributes to dependability in a study ( Baillie, 2015 ; Guba, 1981 ). A risk to trustworthiness in interview-based research is the role of the interviewer themselves and the influence of their own beliefs and perspectives ( Haga et al., 2012 ; Shenton, 2004 ).

When determining the sample size for a study of this nature, several factors are considered. Morse notes that the scope of the study, the nature of the topic, the quality of the data, the study design and the use of shadowed data all require consideration (2000). Relatedly, Morse (2000 : 4) emphasises that ‘the quality of data and the number of interviews per participant determine the amount of useable data obtained. There is an inverse relationship between the amount of useable data obtained from each participant and the number of participants’. This is an important consideration with a longitudinal study where, for example, four interviews with 10 participants would amass data very quickly. With these considerations in mind, a sample size of 10 participants will be the aim.

Implications for research in nursing and health

The sample, particularly for qualitative research, is often not analysed by the nursing reader of practice papers ( Gelling et al., 2014 ). The sample itself, the context and the process are all important issues to consider when reading a paper and considering its impact, particularly when making potential policy changes. Therefore, novice nursing researchers need to ensure the sampling process fits the needs of the study and be clear about the actual process that ensued. For instance: does the sample in the nursing research strategy match the patients who are being considered? The context of sampling in nursing research, as in all research, is a key issue.

Each of these research studies has considered purposive sampling in very different contexts. However, all of them, although purposive, have a convenience element to them given the voluntary nature of all consent processes, where the researcher is at the mercy of the pool of potential participants. However, the voluntary nature of the participation means the researchers can characterise them as fitting not only the inclusion criteria of the study, but also being interested in the topic and motivated to take part out of this interest and their potential to contribute to development of knowledge in this arena.

The Co-Led Stroke Redesign sampling process was about interviewing a representative sample that was persuasive enough to inform change of practice in the stakeholders. The CFH nurse study is the simplest of the designs cited in this paper and has power in this simplicity. However, the analysis of the data is already showing important differences in the nature of the sample. The identification of the right mothers to gain their views of motherhood shows the lengths researchers can go to when considering complex forms of purposive sampling, only to discard them for a simpler process. However, this process of considering options is important in developing high-quality research designs rather than settling for standard approaches.

A continued narrative for all of the research studies that have been exemplified in this paper was whether being purposive in some more complex manner was actually necessary. The only clarity was that all studies were purposive with the intent of recruiting participants who could inform the researchers' aims and objectives. The argument was that the reader of the research would be able to make the judgements about the relevance of the research, if the nature of the sample was transparent. This is another example of the context of research being all important in qualitative research. In combination, the case studies highlight important elements researchers should consider when using purposive sampling techniques to address the four elements of trustworthiness for the research design.

Key points for policy, practice and/or research

  • Novice nurse researchers need to ensure purposive sampling is used where appropriate and not default to a convenience sample.
  • The context of the data collection is an important consideration in purposive sampling for trustworthiness of data in nursing research.
  • Nurse researchers adopt theoretical positions that are reflected in purposive sampling techniques and assist policy makers to understand the relevance of the research.
  • The voluntary nature of nursing research supports the purposive sampling approach, it does not mitigate against it.

Acknowledgements

The authors thank the Patient Involvement Group, School of Health Sciences, University of Tasmania.

Steve Campbell joined the University of Tasmania in January 2013 as the Head of Nursing and Midwifery and then Head of the School of Health Sciences until 2016. With the reestablishment of the School of Nursing in 2019, Steve is now the Research Director/Associate Head of Research for the school and Professor of Clinical Redesign, Nursing.

Melanie Greenwood is an Associate Professor within the School of Nursing at the University of Tasmania and leads the school’s extensive postgraduate framework. She has over 20 years’ critical care nursing expertise in researching into recognition and response to deteriorating patients with a quality and safety in healthcare focus.

Sarah Prior is an academic with the School of Medicine, coordinating the postgraduate, workplace integrated healthcare quality and safety courses. Sarah’s research interests include patient involvement, co-design, rural health service delivery and health service improvement.

Toniele Shearer has worked as a critical care nurse in Australia for around 17 years in the Intensive Care/Coronary Care setting. Toniele teaches in both postgraduate and undergraduate programs offered in the School of Nursing at the University of Tasmania. She is also a PhD candidate.

Kerrie Walkem is a lecturer in the School of Nursing. She coordinates and teaches the postgraduate child and family health nursing stream, as well as other related nursing units across the postgraduate and undergraduate areas. She is also a PhD candidate.

Sarah Young is a PhD candidate with the University of Tasmania’s School of Nursing. Her PhD thesis aims to contribute to the development of a picture of women's experiences after having their first baby.

Danielle Bywaters is a nursing lecturer in the School of Nursing and a photographer who is currently a PhD Candidate. Her PhD study is interdisciplinary and uses a visual method to explore communication in nursing.

Kim Walker is a nurse and a former Professor of Healthcare Improvement, a position he held between the University of Tasmania (nursing discipline) and St Vincent’s Private hospital in Sydney.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article..

Ethical approval: This paper is a methodological paper, therefore ethics approval was not needed.

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

ORCID iDs: Steve Campbell https://orcid.org/0000-0003-4830-8488 Melanie Greenwood https://orcid.org/0000-0001-5840-0750 Sarah Prior

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VIDEO

  1. ADVANTAGES AND DISADVANTAGES OF SAMPLING METHOD LESSON 12

  2. Methodology of the Study

  3. 8.3 Advantages and Disadvantages of Sampling

  4. Sampling techniques and sampling distribution ch 11 lec 7

  5. ADVANTAGES AND DISADVANTAGES OF SAMPLING

  6. Simple Random Sampling

COMMENTS

  1. Pros & Cons of Different Sampling Methods

    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.

  2. Sampling methods in Clinical Research; an Educational Review

    Figure 1 Sampling methods. Probability sampling method Simple random sampling This method is used when the whole population is accessible and the investigators have a list of all subjects in this target population. The list of all subjects in this population is called the "sampling frame".

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

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

  4. More than Just Convenient: The Scientific Merits of Homogeneous

    Despite their disadvantages in generalizability relative to probability samples, much of this research relies on convenience samples-a fact that does not bode well for the field of developmental science ( Bornstein, Jager, & Putnick, 2013 ). Developmental scientists should rely more on probability samples, for reasons we describe below.

  5. Types of Sampling in Research : Journal of the Practice of

    D. Slesinger and M. Stephenson in the Encyclopaedia of the Social Sciences define research as "the manipulation of things, concepts or symbols for the purpose of generalising to extend, correct or verify knowledge, whether that knowledge aids in construction of theory or in the practice of an art."

  6. Sampling in Developmental Science: Situations, Shortcomings, Solutions

    Given the advantages and disadvantages of the four sampling strategies, it is important to note how sociodemographic characteristics can affect study outcomes and the interpretation of study results. ... Sampling Implications for Research Not Focused on Sociodemographic Factors as a Source of Heterogeneity. Even for developmental research not ...

  7. Sampling Methods

    Sampling methods are crucial for conducting reliable research. In this article, you will learn about the types, techniques and examples of sampling methods, and how to choose the best one for your study. Scribbr also offers free tools and guides for other aspects of academic writing, such as citation, bibliography, and fallacy.

  8. Sampling Methods

    Abstract. Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration.

  9. Sampling Techniques for Quantitative Research

    Abstract. In quantitative research, collecting data from an entire population of a study is impractical in many instances. It squanders resources like time and money which can be minimized by choosing suitable sampling techniques between probability and non-probability methods. The chapter outlines a brief idea about the different categories of ...

  10. Sampling Methods In Reseach: Types, Techniques, & Examples

    Sample size 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.

  11. 17 Advantages and Disadvantages of Random Sampling

    1. It offers a chance to perform data analysis that has less risk of carrying an error. Random sampling allows researchers to perform an analysis of the data that is collected with a lower margin of error. This is allowed because the sampling occurs within specific boundaries that dictate the sampling process.

  12. Simple Random Sampling Definition, Advantages and Disadvantage

    Major advantages include its simplicity and lack of bias. Among the disadvantages are difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur...

  13. Systematic Sampling: Advantages and Disadvantages

    Key Takeaways. Because of its simplicity, systematic sampling is popular with researchers. Other advantages of this methodology include eliminating the phenomenon of clustered selection and a low ...

  14. 13 Advantages and Disadvantages of Systematic Sampling

    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.

  15. Sampling Methods

    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:

  16. Advantages and Disadvantages of Sampling

    2.1 1. Low cost of sampling 2.2 2. Less time consuming in sampling 2.3 3. Scope of sampling is high 2.4 4. Accuracy of data is high 2.5 5. Organization of convenience 2.6 6. Intensive and exhaustive data 2.7 7. Suitable in limited resources 2.8 8. Better rapport 3 Disadvantages of sampling 3.1 1. Chances of bias 3.2 2.

  17. Purposeful sampling for qualitative data collection and analysis in

    Principles of Purposeful Sampling. Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources (Patton, 2002).This involves identifying and selecting individuals or groups of individuals that are especially knowledgeable about or experienced with a phenomenon of interest ...

  18. What Are The Advantages And Disadvantages Of Sampling In Research

    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.

  19. Sampling: Meaning, Characteristics, Types, Advantages and Disadvantages

    Practical: Design of sample should be simple and practical. It must be capable of easily understood and applicable in fieldwork. Types of Sampling Various types of sampling are as discussed below: - Random sampling: Random sampling is a technique under which every member of population has equal chance of being selected in sample units.

  20. 16 Key Advantages and Disadvantages of Cluster Sampling

    They simply have different internal composition. List of the Advantages of Cluster Sampling 1. Cluster sampling requires fewer resources. A cluster sampling effort will only choose specific groups from within an entire population or demographic. That means this method requires fewer resources to complete the research work.

  21. Snowball Sampling

    Advantages And Disadvantages. The advantages of snowball sampling are: It is cost-effective because the samples are gathered and referred by primary data sources. ... Snowball sampling in research depends on the initial sample for other referrals. In comparison, a researcher uses their knowledge and understanding for purposive sampling, but ...

  22. Marketing Research

    In market research, sampling means getting opinions from a number of people, chosen from a specific group, in order to find out about the whole group. ... Advantages. Subjects are readily available; Large amounts of information can be gathered quickly; ... Can calculate both estimate of the population and sampling error; Disadvantages

  23. Majorities in most countries surveyed say social ...

    As social media use becomes more widespread globally, people in 27 countries surveyed by Pew Research Center between 2022 and 2023 generally see it as more of a good thing than a bad thing for democracy.In 20 of these countries, in fact, majorities say social media has benefited democracy in their nation. People in emerging economies are particularly likely to say social media has advanced ...

  24. On-Site Bioaerosol Sampling and Airborne Microorganism Detection ...

    There are many analytical techniques used for such bioaerosol monitoring, and their advantages and disadvantages have been described systematically by Santarpia and coworkers . Thus, bioaerosol monitoring essentially requires effective sampling methods followed by appropriate detection techniques for the collected samples.

  25. Sample Size and its Importance in Research

    The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary ...

  26. Federal Register :: Workforce Innovation and Opportunity Act

    Many commenters addressed the pilot program in a myriad of ways, including discussing the advantages and disadvantages of the piloted approaches for measuring effectiveness in serving employers, making alternative recommendations, requesting flexibilities, and seeking an extension of certain aspects of the pilot.

  27. Citizen Athletics on Instagram: "Optimizing Training: Unraveling the

    18 likes, 1 comments - citizenathletics1 on February 15, 2024: "Optimizing Training: Unraveling the Mystery of Workout Splits . Get ready for a spicy episode whe..."

  28. Purposive sampling: complex or simple? Research case examples

    Purposive sampling has a long developmental history and there are as many views that it is simple and straightforward as there are about its complexity. The reason for purposive sampling is the better matching of the sample to the aims and objectives of the research, thus improving the rigour of the study and trustworthiness of the data and ...

  29. Research on optimization method of railway construction ...

    Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar.