What sampling method selects participants based on their characteristics proportionate to the population?

Stratified sampling is a method that ensures participants are selected based on specific characteristics that are represented in proportion to the whole population. This approach involves dividing the population into distinct subgroups or strata, which can be based on characteristics such as age, gender, income level, or any other relevant demographic factor. By doing so, researchers can ensure that each subgroup is adequately represented in the sample, thus enhancing the generalizability of the findings to the broader population.

For example, if a population consists of 60% females and 40% males, stratified sampling would involve selecting a proportionate number of female and male participants to mirror these population characteristics. This method is particularly valuable in social research, as it allows for more nuanced insights into different segments of the population and helps to minimize bias.

In contrast, random sampling involves selecting participants entirely by chance, which may not account for population characteristics. Systematic sampling involves selecting every nth participant from a list, which does not necessarily ensure proportional representation. Convenience sampling relies on selecting participants who are easily accessible, leading to potential bias and lack of representation. Thus, stratified sampling stands out as a robust approach for ensuring that all relevant characteristics of the population are accurately reflected in the sample.