Which sampling technique involves dividing the population into groups and selecting samples from each group?

Stratified sampling is a technique that involves dividing the population into distinct subgroups or strata that share similar characteristics. The goal of this method is to ensure that the sample accurately reflects the diversity within the population by considering these subgroups. After the population has been divided into these groups, researchers then randomly select samples from each group. This approach allows for better representation of the various segments within the population, leading to more reliable and valid results.

In contrast, quota sampling involves selecting a predetermined number of subjects from each subgroup but does not employ random selection, which can introduce bias. Cluster sampling involves dividing the population into clusters and then randomly selecting entire clusters rather than individuals from each cluster, which differs from the stratified method. Systematic sampling uses a fixed interval to select participants, rather than grouping them by shared characteristics. Each of these methods is valid but serves different research needs and objectives.