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.
Cluster Sampling Technique Pdf 16
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.
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.
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.
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.
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.
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.
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.
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.
If individuals are sampled completely at random, and without replacement, then each group of a given size is just as likely to be selected as all the other groups of that size. This is called a simple random sample (SRS). In contrast, a systematic sample would not allow for sibling students to be selected, because of having the same last name. In a simple random sample, sibling students would have just as much of a chance of both being selected as any other pair of students. Therefore, there may be subtle sources of bias in using a systematic sampling plan.
A simple random sample is the easiest way to base a selection on randomness. There are other, more sophisticated, sampling techniques that utilize randomness that are often preferable in real-life circumstances. Any plan that relies on random selection is called a probability sampling plan (or technique). The following three probability sampling plans are among the most commonly used:
Suppose that the city has 10 hospitals. Choose one of the 10 hospitals at random and interview all the nurses in that hospital regarding their job satisfaction. This is an example of cluster sampling, in which the hospitals are the clusters.
Choose a random sample of 50 nurses from each of the 10 hospitals and interview these 50 * 10 = 500 regarding their job satisfaction. This is an example of stratified sampling, in which each hospital is a stratum.
On the other hand, say that instead of job satisfaction, our study focuses on the age or weight of hospital nurses. In this case, it is probably not as crucial to get representation from the different hospitals, and therefore the more easily obtained cluster sample might be preferable.
For example, say you would like to study the exercise habits of college students in the state of California. You might choose 8 colleges (clusters) at random, but you are certainly not going to use all the students in these 8 colleges as your sample. It is simply not realistic to conduct your study that way. Instead you move on to Stage 2 of your sampling plan, in which you choose a random sample of 100 males and a random sample of 100 females from each of the 8 colleges you selected in Stage 1.
Multistage sampling can have more than 2 stages. For example, to obtain a random sample of physicians in the United States, you choose 10 states at random (Stage 1, cluster). From each state you choose at random 8 hospitals (Stage 2, cluster). Finally, from each hospital, you choose 5 physicians from each sub-specialty (Stage 3, stratified).
When choosing a probability sample design, the goal is to minimize the sampling error of the estimates for the most important survey variables, while simultaneously minimizing the time and cost of conducting the survey. Some operational constraints can also have an impact on that choice, such as characteristics of the survey frame.
In simple random sampling (SRS), each sampling unit of a population has an equal chance of being included in the sample. Consequently, each possible sample also has an equal chance of being selected. To select a simple random sample, you need to list all of the units in the survey population.
SRS is the most commonly used method. The advantage of this technique is that it does not require any information on the survey frame other than the complete list of units of the survey population along with contact information. Also, since SRS is a simple method and the theory behind it is well established, standard formulas exist to determine the sample size, the estimates and so on, and these formulas are easy to use. 2ff7e9595c
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