What type of sampling requires choosing representative individuals from a specific subgroup?

The concept of quota sampling is centered around selecting a specific number of participants from identified subgroups within a larger population, ensuring that the sample reflects the diversity of the whole group. This approach is particularly useful when researchers want to ensure that particular characteristics—such as age, gender, or socioeconomic status—are represented in the correct proportions within the sample.

In quota sampling, researchers set quotas for each subgroup based on certain attributes or criteria. Once those quotas are met for each subgroup, the sample collection is complete. This method allows researchers to focus on obtaining a diversity of opinions and experiences while ensuring that specific characteristics are captured.

Understanding this, it becomes clear that quota sampling is distinct from other sampling methods. For instance, random sampling does not specifically account for subgroups, as participants are chosen entirely by chance without regard for their characteristics. Systematic sampling involves selecting individuals based on a random starting point and then a fixed interval, rather than focusing on specific subgroups. Stratified sampling, although it also involves subgroups, requires that the sample is proportional to the size of those subgroups within the population, which is a slightly different approach than setting predetermined quotas.