What Is Difference Between Stratified Sampling And Clustered Sampling?

The Difference Between Stratified and Clusters
Although strata and clusters are both non-overlapping subsets of the population, they differ in several ways.
• All strata are represented in the sample; but only a subset of clusters are in the sample.
• With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous.
Cluster sampling should be used only when it is economically justified - when reduced costs can be used to overcome losses in precision. This is most likely to occur in the following situations.
Constructing a complete list of population elements is difficult, costly, or impossible. For example, it may not be possible to list all of the customers of a chain of hardware stores. However, it would be possible to randomly select a subset of stores (stage 1 of cluster sampling) and then interview a random sample of customers who visit those stores (stage 2 of cluster sampling).
The population is concentrated in "natural" clusters (city blocks, schools, hospitals, etc.). For example, to conduct personal interviews of operating room nurses, it might make sense to randomly select a sample of hospitals (stage 1 of cluster sampling) and then interview all of the operating room nurses at that hospital. Using cluster sampling, the interviewer could conduct many interviews in a single day at a single hospital. Simple random sampling, in contrast, might require the interviewer to spend all day traveling to conduct a single interview at a single hospital.

If the population is not homogeneous or heterogeneous with respect to a variable, then the population is divided into sub-groups known as strata. A stratum is a homogeneous group with respect to variables. If we have to select a sample from the population, then the sample is selected from these strata. This sampling is known as stratified random sampling.

A cluster is a collection of units found in nature. In cluster sampling we first select at random clusters of individual items to make up the over all samples. Clusters are called as primary sampling units. If all the units of the selected clusters are included in the sample, it is called single stage-cluster sampling.

When the population to be studied is not homogeneous with respect to variable or characteristics under study then it is divided into small homogeneous groups called strata. Items with in each stratum are homogeneous with respect to characteristics under study. From each stratum a simple random sample is selected in proportion to the stratum size and overall sample is obtained by combining the samples for all strata. A cluster is a collection of units found in a nature for example, if we are considering industrial establishment, then an industrial factory will be cluster. Clusters are called primary sampling units.