What is an example of a stratified sample?

A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above.

Read, more elaboration about it is given here. Similarly, what is meant by stratified sampling?

Stratified sampling refers to a type of sampling method . With stratified sampling, the researcher divides the population into separate groups, called strata. Then, a probability sample (often a simple random sample ) is drawn from each group. Stratified sampling has several advantages over simple random sampling.

Secondly, what is an example of a cluster sample? The most common cluster used in research is a geographical cluster. For example, a researcher wants to survey academic performance of high school students in Spain. He can divide the entire population (population of Spain) into different clusters (cities).

Furthermore, how do you calculate a stratified sample?

Step 1: Divide the population into smaller subgroups, or strata, based on the members' shared attributes and characteristics. Step 2: Take a random sample from each stratum in a number that is proportional to the size of the stratum. Step 3: Pool the subsets of the strata together to form a random sample.

How do you determine sample size in stratified sampling?

The sample size for each strata (layer) is proportional to the size of the layer: Sample size of the strata = size of entire sample / population size * layer size.