Cluster sampling helps us when it is impossible or impractical to create a sampling frame from a target population because it is widely dispersed geographically and the cost of data collection is relatively high.
Cluster sampling, also known as cluster sampling , is a probability sampling procedure in which elements of the population are randomly selected in a natural way by groupings (clusters). The sample elements are selected from the population individually, one at a time.
Sampling units or groups may be spaced, as occurs naturally in geographic or physical units (e.g., states, districts); based on organization such as schools, grade levels; or telephone service such as area codes or the change in the area codes of telephone numbers.
Group heterogeneity is essential for a good cluster sampling design. Moreover, the elements within each group should be as heterogeneous as the target population.
Steps to select a cluster sample
Define the target population.
Determine the desired sample size .
Identify an existing sampling frame or develop a new sampling frame jordan phone number target population groups.
Assess the sampling frame for undercoverage, overcoverage, multiple coverage, and clustering, and make adjustments where necessary. Ideally, groups would be as heterogeneous as the population, mutually exclusive, and collectively exhaustive. Duplication of sample elements may occur if population elements belong to more than one group. Omission will result in coverage bias.
Determine the number of groups to select. This can be done by dividing the sample size by the estimated average number of population elements in each group. As the homogeneity and heterogeneity of the groups differ from that of the population, the group number increases and increases precision. On the other hand, if the differences increase, precision decreases.
Randomly select the expected number of clusters.
Also learn about the characteristics of a cluster analysis.
Subtypes of cluster sampling
Two broad dimensions are used to classify the different types of cluster sampling. One is based on the number of stages in the sample design, and the other on the proportional representation of the groups in the total sample.
Subtype based on the number of stages : Often, cluster sampling is carried out in more than one “stage.” A stage is a step within the sampling process at which a sample is taken. Considering the number of stages in the design, there are three main subtypes of this sampling: single-stage, two-stage, and multistage sampling.
One-stage cluster sampling . Sampling is done only once. An example would be, let's say we are interested in studying homeless people living in shelters.
If there are five shelters in a city, a researcher will randomly select one of the shelters and then include in the study all homeless people residing in the selected shelter. A market researcher might choose to use a single-stage cluster sampling design.
Let's say a researcher is interested in test marketing a product. The researcher can randomly select zip codes; send samples of the product along with an evaluation questionnaire to each address within the selected group.