Which sampling technique involves selecting individuals based on major subgroups within the population?

Stratified sampling is focused on dividing the population into distinct subgroups, known as strata, that share specific characteristics. This technique ensures that each subgroup is represented in the sample, which is particularly useful when there are significant differences among these subgroups that are relevant to the research.

By employing stratified sampling, researchers can enhance the precision of their estimates and ensure that variations within the population are accounted for. For instance, if a study is examining educational outcomes across different age groups, stratifying by age allows the researcher to ensure that each age group is represented, yielding more reliable and valid results. The process typically involves identifying the relevant strata, determining the sample size for each group, and then randomly selecting individuals from each subgroup.

In contrast, other sampling techniques such as cluster sampling involve dividing the population into groups (clusters) but then randomly selecting entire groups rather than individuals within those groups. Random sampling selects individuals entirely at random from the population without consideration of any stratification, while systematic sampling selects individuals based on a fixed interval from a randomly ordered list. These methods do not focus on ensuring representation from major subgroups, which is the distinguishing feature of stratified sampling.