Stratified Sampling

What is Stratified Sampling in Surveys?

Stratified sampling is a survey sampling technique where the population is divided into distinct subgroups or 'strata' based on specific characteristics, and samples are drawn from each subgroup. This method ensures that each subgroup is adequately represented in the survey results.

Synonyms: stratified random sampling, layered sampling, stratum sampling, proportional sampling

Why Stratified Sampling is Important

Stratified sampling improves the accuracy and representativeness of survey results by ensuring that all key subgroups within a population are included. This reduces sampling bias and allows for more precise insights about different segments of the population.

How Stratified Sampling is Used in Surveys

Researchers first identify relevant strata such as age, gender, income level, or geographic location. Then, they randomly select samples from each stratum proportional to its size or importance. This approach helps in capturing diverse perspectives and making comparisons across groups.

Examples of Stratified Sampling

For example, in a national health survey, the population might be divided into strata based on age groups (e.g., 18-29, 30-49, 50+). Samples are then drawn from each age group to ensure the survey reflects the health status of all age categories.

Frequently Asked Questions

What is the main advantage of stratified sampling? It ensures all important subgroups are represented, leading to more accurate and reliable survey results.
How is stratified sampling different from simple random sampling? Stratified sampling divides the population into subgroups before sampling, while simple random sampling selects individuals randomly from the entire population without subgroup consideration.
Can stratified sampling be used for any type of survey? Yes, it is especially useful when the population has distinct subgroups that need to be represented proportionally.