# Advantages And Disadvantages Of Probability Sampling Methods Pdf

File Name: advantages and disadvantages of probability sampling methods .zip
Size: 29014Kb
Published: 18.01.2021

## Methods of sampling from a population

Conversations about sampling methods and sampling bias often take place at 60, feet. 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. 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. 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. 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. After those people complete the study, the researchers ask each person to recommend a few others who also meet the study criteria.

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.

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. 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.

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.

Hence, when using judgment sampling, researchers exert some effort to ensure their sample represents the population being studied. 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. 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.

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.

Then, using a computer to randomly dial numbers, the researchers could sample a group of people, ensuring a simple random sample. 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. 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. 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.

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. 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. 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. 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.

Similar to cluster sampling, researchers who study people within organizations or large groups often find multistage sampling useful. 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. 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.

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. 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.

If you were a researcher studying human behavior 30 years ago, your options for identifying participants for your studies were limited. If you worked at a university, you might be As a researcher, you are aware that planning studies, designing materials and collecting data each take a lot of work.

So when you get your hands on a new dataset, Related Articles.

## More than Just Convenient: The Scientific Merits of Homogeneous Convenience Samples

A simple random sample is used by researchers to statistically measure a subset of individuals selected from a larger group or population to approximate a response from the entire group. This research method has both benefits and drawbacks. Unlike other forms of surveying techniques, simple random sampling is an unbiased approach to garner the responses from a large group. Although there are distinct advantages to using a simple random sample in research, it has inherent drawbacks. These disadvantages include the time needed to gather the full list of a specific population, the capital necessary to retrieve and contact that list, and the bias that could occur when the sample set is not large enough to adequately represent the full population.

Advantages and Disadvantages of Probability. Sampling Methods in Social Research. Saroj Kumar Singh. Deptt. Of Rural Economics, S. N. S. R. K. S. College.

## Simple Random Sample: Advantages and Disadvantages

When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study. Ensures a high degree of representativeness of all the strata or layers in the population. Possibly, members of units are different from one another, decreasing the techniques effectiveness.

Probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. Collectively, these units form the sample that the researcher studies [see our article, Sampling: The basics , to learn more about terms such as unit , sample and population ]. A core characteristic of probability sampling techniques is that units are selected from the population at random using probabilistic methods. This enables researchers to make statistical inferences i.

Conversations about sampling methods and sampling bias often take place at 60, feet. 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. However, most online research does not qualify as pure convenience sampling.

It would normally be impractical to study a whole population, for example when doing a questionnaire survey. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association. Calculation of sample size is addressed in section 1B statistics of the Part A syllabus. If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population.

Convenience sampling also known as grab sampling , accidental sampling , or opportunity sampling is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach.

0 Response