In this procedure more than two steps were taken in selecting clusters from cluster brainly
The entire population of the study is divided into externally homogeneous but internally heterogeneous groups called clusters Show
What is Cluster Sampling?In statistics, cluster sampling is a sampling method in which the entire population of the study is divided into externally, homogeneous but internally, heterogeneous groups called clusters. Essentially, each cluster is a mini-representation of the entire population. Source: WikicommonsAfter identifying the clusters, certain clusters are chosen using simple random sampling while the others remain unrepresented in a study. After selecting the clusters, a researcher must choose the appropriate method to sample the elements from each selected group. Primary Sampling MethodsThere are primarily two methods of sampling the elements in the cluster sampling method: one-stage and two-stage. In one-stage sampling, all elements in each selected cluster are sampled. In two-stage sampling, simple random sampling is applied within each cluster to select a subsample of elements in each cluster. The cluster method must not be confused with stratified sampling. In stratified sampling, the population is divided into mutually exclusive groups that are externally heterogeneous but internally homogeneous. For example, in stratified sampling, a researcher may divide the population into two groups: males vs. females. Conversely, in cluster sampling, the clusters are similar to each other but with different internal composition. Advantages of Cluster SamplingThe cluster method comes with a number of advantages over simple random sampling and stratified sampling. The advantages include: 1. Requires fewer resourcesSince 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. 2. More feasibleThe division of the entire population into homogenous groups increases the feasibility of the sampling. Additionally, since each cluster represents the entire population, more subjects can be included in the study. Disadvantages of Cluster SamplingDespite its benefits, this method still comes with a few drawbacks, including: 1. Biased samplesThe method is prone to biases. If the clusters representing the entire population were formed under a biased opinion, the inferences about the entire population would be biased as well. 2. High sampling errorGenerally, the samples drawn using the cluster method are prone to higher sampling error than the samples formed using other sampling methods. Related ReadingsThank you for reading CFI’s guide to Cluster Sampling. To keep learning and advancing your career, the additional CFI resources below will be useful:
In probability sampling, it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected. The following sampling methods are examples of probability sampling:
Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling. Stratified Sampling is possible when it makes sense to partition the population into groups based on a factor that may influence the variable that is being measured. These groups are then called strata. An individual group is called a stratum. With stratified sampling one should:
Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups. Under these conditions, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample. Table 2.2 shows some examples of ways to obtain a stratified sample. Table 2.2. Examples of Stratified Samples
Cluster Sampling is very different from Stratified Sampling. With cluster sampling, one should
It is important to note that, unlike with the strata in stratified sampling, the clusters should be microcosms, rather than subsections, of the population. Each cluster should be heterogeneous. Additionally, the statistical analysis used with cluster sampling is not only different but also more complicated than that used with stratified sampling. Table 2.3. Examples of Cluster Samples
Each of the three examples that are found in Tables 2.2 and 2.3 was used to illustrate how both stratified and cluster sampling could be accomplished. However, there are obviously times when one sampling method is preferred over the other. The following explanations add some clarification about when to use which method.
The most common method of carrying out a poll today is using Random Digit Dialing in which a machine random dials phone numbers. Some polls go even farther and have a machine conduct the interview itself rather than just dialing the number! Such "robocall polls" can be very biased because they have extremely low response rates (most people don't like speaking to a machine) and because federal law prevents such calls to cell phones. Since the people who have landline phone service tend to be older than people who have cell phone service only, another potential source of bias is introduced. National polling organizations that use random digit dialing in conducting interviewer based polls are very careful to match the number of landline versus cell phones to the population they are trying to survey. What is twoIn the two-stage sampling design the population is partitioned into groups, like cluster sampling, but in this design new samples are taken from each cluster sampled. The clusters are the first stage units to be sampled, called primary or first sampling units and denoted by SU1.
What type of sampling design is cluster sampling?Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. The clusters should ideally each be mini-representations of the population as a whole.
Which of the following is a sampling method in which the whole population is subdivided into?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.
Which of the following is a sampling method in which the population is first divided into strata and then samples are randomly selected from each stratum Brainly?Stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). Random samples are then selected from each stratum.
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