In which sampling method are samples selected from each identified subgroup?

Stratified random sampling is the correct choice here because it involves dividing the population into distinct subgroups, or strata, based on specific characteristics that are relevant to the research. Each subgroup is then represented in the sample, ensuring that the sample reflects the diversity of the entire population. This method is particularly useful when researchers want to ensure that certain characteristics are adequately represented in their analysis, making the findings more reliable and generalizable. By sampling from each identified subgroup, stratified random sampling helps capture variations within the population, leading to more precise estimates and insights regarding the overall group being studied.

The other sampling methods mentioned do not involve selecting samples from each identified subgroup in the same manner as stratified random sampling. For example, systematic random sampling typically involves selecting samples at regular intervals from a random starting point; snowball sampling relies on referrals from initial subjects to find additional participants, which may not ensure representation across subgroups; and judgmental sampling is based on the researcher's discretion to select subjects, lacking a systematic approach to subgroups.