What is the method of selecting a sample that involves grouping individuals based on certain characteristics?

The method of selecting a sample that involves grouping individuals based on certain characteristics is known as stratified sampling. This approach is utilized to ensure that specific subgroups within a population are adequately represented in the sample. In stratified sampling, the population is divided into distinct groups or strata based on shared traits—such as age, gender, income level, or education—before individuals are randomly selected from each group.

This method enhances the accuracy and reliability of the sample by allowing researchers to capture the diversity of the population and ensure that key characteristics are reflected in the data collected. For example, if a researcher is studying the impact of a new educational program across different age groups, stratified sampling would ensure that each age group is proportionately represented in the final sample.

In contrast, other sampling methods like cluster sampling involve dividing the population into groups but then randomly selecting entire clusters rather than individuals from each stratum. Random sampling ensures every individual has an equal chance of being chosen but does not take demographic diversity into account. Convenience sampling focuses on selecting individuals who are easiest to reach, often leading to biases. Therefore, stratified sampling is the most effective method for ensuring that certain characteristics are represented within a sample.