Stratified Sample

Definition

A stratified sample is a sampling method where the population is divided into distinct subgroups, or strata, that share similar characteristics. By selecting samples from each stratum, researchers can ensure that various segments of the population are represented, leading to more accurate and reliable results.

5 Must Know Facts For Your Next Test

1. Stratified sampling is particularly useful when there are significant differences among strata in the population that need to be reflected in the sample.
2. This method can improve the precision of estimates by reducing sampling error compared to simple random sampling, especially when dealing with heterogeneous populations.
3. The selection of samples from each stratum can be proportionate or disproportionate depending on the research objectives.
4. In stratified sampling, researchers must first identify relevant strata before determining how to randomly select individuals from each group.
5. Stratified sampling is commonly used in fields like social sciences, market research, and health studies to ensure diverse representation.

Review Questions

• How does stratified sampling enhance the reliability of survey results compared to simple random sampling?
  • Stratified sampling enhances reliability by ensuring that all relevant subgroups within a population are represented. In contrast to simple random sampling, which may overlook smaller or less prevalent groups, stratified sampling divides the population into strata and samples from each one. This approach helps capture the diversity within the population, leading to more accurate estimates and insights into different segments.
• What factors should be considered when determining how to create strata for a stratified sample?
  • When creating strata for a stratified sample, researchers should consider characteristics that are relevant to the study's objectives, such as demographics (age, gender, income), behaviors, or other variables that may influence outcomes. It's important to ensure that these strata are mutually exclusive and collectively exhaustive so that every member of the population can be classified into one stratum. Additionally, researchers must decide whether to use proportional or disproportional allocation based on the importance of each stratum in relation to the study goals.
• Evaluate the impact of improperly defined strata on the outcomes of a stratified sampling study.
  • Improperly defined strata can lead to biased results and undermine the validity of a stratified sampling study. If strata do not accurately reflect significant differences within the population, certain groups may be overrepresented or underrepresented in the sample. This misrepresentation can distort findings and lead to erroneous conclusions about relationships or trends. Furthermore, if key characteristics that influence outcomes are ignored in stratum creation, it could compromise the study's ability to make reliable predictions and generalizations about the entire population.

Related terms

Population: The entire group of individuals or items that is the subject of study from which a sample may be drawn.

Sampling Frame: A list or database containing all the elements of the population from which a sample is drawn.

Random Sample: A sampling method where each member of the population has an equal chance of being selected, ensuring that the sample is unbiased.