What sampling method ensures that a norm group is representative of the population by classifying along certain variables before selection?

Stratified random sampling is a method designed to ensure that a sample reflects the characteristics of the population it represents by dividing the population into distinct subgroups, known as strata, before sampling. These strata are based on specific variables that are relevant to the research, such as age, gender, income level, ethnicity, or other factors that may influence the outcomes of the study.

In stratified random sampling, researchers first identify these key characteristics and then randomly select participants from each stratum proportional to their representation in the overall population. This approach increases the likelihood that the sample mirrors the broader population's diversity, thus enhancing the validity and reliability of the research findings.

For example, if a population has varying percentages of different ethnic groups and a researcher wants to ensure representation from each group, they would apply stratified sampling to create a more balanced sample that reflects those proportions. This method is particularly useful when certain subgroups within a population might be underrepresented in a simple random sample, making stratified sampling a powerful technique for gathering data that accurately reflects the population of interest.