Stratified vs. Cluster Sampling

Cluster vs Strata:

A cluster is a group of objects that are similar in some way. For example, a cluster of people who have similar interests, hobbies, or occupations.

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Strata is a term used in geology to describe layers of sedimentary rocks that have been deposited over time. Stratification is the separation of layers in sedimentary rocks.

Cluster Sampling:

Cluster sampling is a method of choosing a sample by randomly selecting units from a cluster of units. It is based on an assumption that the units within a cluster are homogeneous with respect to the characteristics being measured. Sometimes it is more cost-effective to select respondents in groups ('clusters'). Sampling is often clustered by geography, or by time periods.

Examples:

  • Survey all customers visiting particular stores on particular days.
  • Randomly select one school from the district and survey all students in that school.

Stratified Sampling:

Stratified sampling is a method of selecting samples from a population that are representative of the whole population. It is based on the assumption that the distribution of the characteristics of interest within each stratum is similar to the overall distribution.

It is used to ensure that sub-groups within a population are represented proportionally in the sample. You would use stratified sampling, if the population characteristics are diverse (male/female, young/old, sick/healthy etc.), and you want to make sure that each characteristic is properly represented.

Examples:

  • 10 people are randomly drawn to represent a country, 5 of them are male and 5 females to avoid the sex bias.
  • One random student is selected from each age group.

 In Summary:

In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling, only the selected clusters are sampled.

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