Which sampling method ensures that certain characteristics are represented in the sample?

Stratified sampling is the method that guarantees that specific characteristics are represented within the sample. This technique divides the population into distinct subgroups, or strata, based on shared characteristics, such as age, income, or education level. Each stratum is then sampled, which ensures that the sample reflects the diversity of the population. By doing this, it aims to maintain the proportional representation of each subgroup, leading to more accurate and reliable results in analyses. This method is particularly beneficial when researchers are interested in understanding differences between subgroups or when certain characteristics are crucial for the research objectives.

Other methods mentioned in the question, such as simple random sampling, do not specifically account for subgroup representation and may lead to underrepresentation of certain population characteristics. Convenient sampling lacks a structured approach to ensuring representation, as it relies on easily accessible subjects, which may not reflect the population accurately. Cluster sampling involves dividing a population into clusters and randomly selecting entire clusters, which may not ensure the representation of specific characteristics if the clusters themselves are homogenous. Thus, stratified sampling stands out as the best choice for ensuring that all relevant characteristics are included in the sample.