Stratified sampling involves dividing the population into what?

Stratified sampling is a technique in research where the population is divided into distinct subcategories, referred to as strata. This is done to ensure that specific subgroups within a population are adequately represented in the sample. The concept behind stratified sampling is that the researcher recognizes that certain characteristics (such as age, income level, education, etc.) may influence the variable being studied. By dividing the population into strata based on these characteristics, the researcher can then randomly sample from each stratum. This approach enhances the precision and representativeness of the results, providing a more accurate reflection of the entire population.

In contrast, random samples do not take specific subgroups into account, and grouping individuals by different social classes might overlook other critical factors necessary for the study. A single large group does not allow for this nuanced understanding that stratification provides. Therefore, by focusing on subcategories known as strata, researchers can ensure a more balanced and comprehensive representation of the population.