Which sampling method selects subgroups based on specific characteristics to ensure representation?

Stratified sampling is designed to ensure that specific subgroups within a population are represented in the sample based on characteristics such as age, gender, income, education level, or other relevant factors. In this method, the population is divided into distinct subgroups, or strata, and then samples are drawn from each stratum in proportion to their size in the overall population or evenly across strata. This approach helps to achieve a more accurate reflection of the diversity within the population, which ultimately enhances the validity of research findings.

The focus on representing specific characteristics within the sample makes stratified sampling particularly useful in psychological research, where variations in responses can be significant across different demographic groups. This method stands apart from convenience sampling, which relies on readily available participants without ensuring diversity; purposive sampling, which targets specific individuals based on their knowledge or experience rather than ensuring proportional representation; and random sampling, which involves selecting individuals randomly from the population without regard to subgroups.