Which sampling technique involves dividing the

Solution

Stratified sampling is a technique in which the population is first divided into subgroups, or "strata," that share similar characteristics (e.g., age, gender, income). After these strata are defined, samples are randomly selected from each subgroup. This method ensures that every subgroup is represented in the final sample, which is especially important when the researcher wants to ensure that certain groups (often minority groups) are included in the sample. Stratified sampling can lead to more precise and accurate results than simple random sampling when the subgroups are heterogeneous and the researcher wants to make sure each subgroup’s characteristics are properly represented in the analysis. Why Other Options Are Incorrect: • A: Simple random sampling involves selecting individuals completely at random from the entire population, without the stratification step. • C: Systematic sampling involves selecting every nth individual from a list, not dividing the population into subgroups. • D: Cluster sampling involves dividing the population into clusters and randomly selecting entire clusters, rather than sampling from within the clusters. • E: Judgmental sampling is a non-probability technique where the researcher uses their judgment to select individuals, not based on any subgroup characteristics.