In which sampling method is the population divided into strata groups?

Stratified random sampling is a method used in research where the entire population is divided into distinct subgroups, known as strata, based on specific characteristics. These characteristics might include age, gender, income level, education, or any other relevant attribute that is important to the research question. Once the population is divided into these strata, researchers then take random samples from each stratum, which helps ensure that the sample accurately reflects the diversity of the population. This method is particularly useful when the researcher wants to ensure that different segments of the population are adequately represented in the sample, leading to more reliable and valid results.

In contrast, cluster sampling involves dividing the population into clusters and then randomly selecting whole clusters, whereas systematic random sampling involves selecting every nth individual from a list. Convenience sampling, on the other hand, selects individuals based on their easy availability rather than a structured strata-based approach. Thus, stratified random sampling stands out because of its systematic division of the population into meaningful subgroups for a more representative sample.