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In stratified sampling, the sampling is done on elements within each strata. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are sampled. A common motivation of cluster sampling is to reduce costs by increasing sampling efficiency.

This contrasts with stratified sampling where the motivation is to increase precision. There is also multistage cluster sampling , where at least two stages are taken in selecting elements from clusters.

Without modifying the estimated parameter, cluster sampling is unbiased when the clusters are approximately the same size.

In this case, the parameter is computed by combining all the selected clusters. When the clusters are of different sizes, probability proportionate to size sampling is used. In this sampling plan, the probability of selecting a cluster is proportional to its size, so that a large clusters has a greater probability of selection than a small cluster.

However, when clusters are selected with probability proportionate to size, the same number of interviews should be carried out in each sampled cluster so that each unit sampled has the same probability of selection.

An example of cluster sampling is area sampling or geographical cluster sampling. Each cluster is a geographical area. Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster. It is usually necessary to increase the total sample size to achieve equivalent precision in the estimators , but cost savings may make such an increase in sample size feasible.

Cluster sampling is used to estimate high mortalities in cases such as wars , famines and natural disasters. The other probabilistic methods give fewer errors than this method. For this reason, it is discouraged for beginners. Two-stage cluster sampling, a simple case of multistage sampling , is obtained by selecting cluster samples in the first stage and then selecting sample of elements from every sampled cluster.

Consider a population of N clusters in total. In the first stage, n clusters are selected using ordinary cluster sampling method. In the second stage, simple random sampling is usually used.

The total number of clusters N , number of clusters selected n , and numbers of elements from selected clusters need to be pre-determined by the survey designer. Two-stage cluster sampling aims at minimizing survey costs and at the same time controlling the uncertainty related to estimates of interest. For instance, researchers used two-stage cluster sampling to generate a representative sample of the Iraqi population to conduct mortality surveys.

Cluster sampling methods can lead to significant bias when working with a small number of clusters. For instance, it can be necessary to cluster at the state or city level, units that may be small and fixed in number. Microeconometrics methods for panel data often use short panels, which is analogous to having few observations per clusters and many clusters.

The small cluster problem can be viewed as an incidental parameter problem. If the number of clusters is low the estimated covariance matrix can be downward biased. Small numbers of clusters is a risk when there is serial correlation or when there is intraclass correlation as in the Moulton context.

When having few clusters, we tend to underestimate serial correlation across observations when a random shock occurs, or the intraclass correlation in a Moulton setting. In the framework of the Moulton factor, an intuitive explanation of the small cluster problem can be derived from the formula for the Moulton factor.

Assume for simplicity that the number of observation per cluster is fixed at n. The ratio on the left-hand side provides an indication of how much the unadjusted scenario overestimates the precision. Therefore, a high number means a strong downward bias of the estimated covariance matrix.

A small cluster problem can be interpreted as a large n: It follows that inference when the number of clusters is small will not have correct coverage. Several solutions for the small cluster problem have been proposed. One can use a bias-corrected cluster-robust variance matrix, make T-distribution adjustments, or use bootstrap methods with asymptotic refinements, such as the percentile-t or wild bootstrap, that can lead to improved finite sample inference.

From Wikipedia, the free encyclopedia. Retrieved September 14, The intracluster correlation coefficient in cluster randomization. British Medical Journal , , — The sample will be representative of the population if the researcher uses a random selection procedure to choose participants. The group of units or individuals who have a legitimate chance of being selected are sometimes referred to as the sampling frame. If a researcher studied developmental milestones of preschool children and target licensed preschools to collect the data, the sampling frame would be all preschool aged children in those preschools.

Students in those preschools could then be selected at random through a systematic method to participate in the study. This does, however, lead to a discussion of biases in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study. Extra care has to be taken to control biases when determining sampling techniques. There are two main types of sampling: The difference between the two types is whether or not the sampling selection involves randomization.

Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each. Probability Sampling — Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:.

Non-probability Sampling — Does not rely on the use of randomization techniques to select members. This is typically done in studies where randomization is not possible in order to obtain a representative sample. Bias is more of a concern with this type of sampling. The different types of non-probability sampling are as follows:. The following Slideshare presentation, Sampling in Quantitative and Qualitative Research — A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.

Examples of Data Collection Methods — Following is a link to a chart of data collection methods that examines types of data collection, advantages and challenges. Qualitative and Quantitative Data Collection Methods - The link below provides specific example of instruments and methods used to collect quantitative data. Sampling and Measurement - The link below defines sampling and discusses types of probability and nonprobability sampling. Principles of Sociological Inquiry — Qualitative and Quantitative Methods — The following resources provides a discussion of sampling methods and provides examples.

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Cluster sampling (also known as one-stage cluster sampling) is a technique in which clusters of participants that represent the population are identified and included in the sample. Cluster sampling involves identification of cluster of participants representing the population and their inclusion in the sample group.

Cluster sampling analyzes a particular cluster of data in which the sample consists of multiple elements like city, family, university or school. Learn about cluster sampling definition, methods with examples, advantages, and applications.

Another form of cluster sampling is two-way cluster sampling, which is a sampling method that involves separating the population into clusters, then selecting random samples from those clusters. Cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected.

The main difference between cluster sampling and stratified sampling lies with the inclusion of the cluster or strata. In stratified random sampling, all the strata of the population is sampled while in cluster sampling, the researcher only randomly selects a number of clusters from the collection of clusters of the entire population. Therefore, . With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters.