Cluster sampling is often preferred to other random sampling strategies because it
Quick Navigation:Select Option Show
By Aaron Moss, PhD, Cheskie Rosenzweig, MS, & Leib Litman, PhD Online Researcher’s Sampling Guide, Part 4:Pros and Cons of Different Sampling MethodsConversations 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 MethodsNon-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 SamplingVoluntary 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:
An Example of Voluntary Sampling:
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 SamplingSnowball 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. Pros and Cons:
An Example of Snowball Sampling:
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. Quota SamplingWhen researchers engage in quota sampling, they identify subsets of the population that are important to represent and then sample participants within each subset. Pros and Cons:
An Example of Quota Sampling:
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 SamplingJudgment 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. Pros and Cons:
An Example of Judgment Sampling:
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 TechniquesRandom 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 SamplingSimple 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. Pros and Cons:
An Example of Simple Random Sampling:
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 SamplingSystematic 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. Pros and Cons:
An Example of Systematic Sampling
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 SamplingCluster sampling occurs when researchers randomly sample people within groups or clusters the people already belong to. Pros and Cons:
An Example of Cluster Sampling:
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 SamplingMultistage 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. Pros and Cons:
An Example of Multistage Sampling:
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 SamplingStratified 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. Pros and Cons:
An Example of Stratified Sampling:
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. Why does cluster sampling is often preferred to other random sampling strategies?This method is usually conducted when groups that are similar yet internally diverse form a statistical population. Instead of selecting the entire population, cluster sampling allows the researchers to collect data by bifurcating the data into small, more productive groups.
Why is cluster sampling the most preferred sampling?Advantages of Cluster Sampling
Since cluster sampling selects only certain groups from the entire population, the method requires fewer resources for the sampling process. Therefore, it is generally cheaper than simple random or stratified sampling as it requires fewer administrative and travel expenses.
Why is cluster sampling better than convenience?Cluster sampling requires fewer resources (meaning it's often less expensive) and therefore makes the obtaining of data more feasible. It is also easier to collect the data from small groups of respondents, unless you use an AI-powered tool to organize your data.
What is the major difference between cluster sampling strategies and random sampling strategies?Stratified sampling is one, in which the population is divided into homogeneous segments, and then the sample is randomly taken from the segments. Cluster sampling refers to a sampling method wherein the members of the population are selected at random, from naturally occurring groups called 'cluster'.
|