Which sampling method ensures a balanced representation based on specific criteria?

The stratified sampling method is designed to ensure balanced representation from different segments or strata of a population based on specific criteria. In this approach, the population is divided into distinct subgroups that share common characteristics, such as age, gender, income level, or any other relevant attribute. After categorizing the population into these strata, researchers then select samples from each subgroup, typically in proportion to their presence within the whole population.

This method is particularly beneficial when researchers want to ensure that all relevant subgroups are represented in the sample, which enhances the reliability and validity of the results. For instance, if a study aims to analyze consumer preferences across different age groups, stratified sampling would ensure that each age group is adequately represented, preventing any bias that might result from over-representing or under-representing particular segments.

In comparison, random sampling selects individuals at random without regard to any specific characteristics, which may lead to unequal representation of significant subgroups. Purposive sampling, on the other hand, targets specific individuals based on predetermined criteria, but does not ensure a systematic representation across all relevant segments of the population. Cluster sampling involves dividing the population into clusters (often geographically) and randomly selecting entire clusters, which may not reflect the diversity of the