Stratified sampling involves splitting a sample based on which criteria?

Stratified sampling is a method used in research where the population is divided into sub-groups, or strata, based on specific characteristics that are relevant to the research study. These characteristics can include variables such as gender, age, socio-economic status, ethnicity, or any other criteria that could have an impact on the data being collected. The primary goal of stratified sampling is to ensure that different segments of the population are adequately represented in the sample, enhancing the validity and reliability of the research findings.

By focusing on specific characteristics, researchers can ensure that the sample reflects the diversity of the population, which can be particularly important in studies where these factors might influence the outcomes. This approach reduces sampling bias and allows for more precise comparisons between different groups within the study.

In contrast, the other options involve methods or criteria that do not align with the fundamental principles of stratified sampling. For instance, relying on availability and opportunity does not ensure representation of specific groups, and using personal connections can introduce bias and limit diversity. Random selection pertains more to techniques like simple random sampling, where each individual has an equal chance of being selected, which does not inherently account for subgroup representation.