Stratified sampling

Definition

Stratified sampling is a statistical technique used to obtain a representative sample by dividing a population into distinct subgroups, known as strata, and then selecting samples from each stratum. This method ensures that each subgroup is adequately represented, which helps improve the accuracy and validity of the overall results. By focusing on specific characteristics of the population, stratified sampling can reduce sampling error and enhance the reliability of conclusions drawn from the data.

5 Must Know Facts For Your Next Test

1. Stratified sampling can lead to more accurate results compared to simple random sampling, especially when there are significant differences between strata.
2. Researchers must clearly define the strata based on relevant characteristics, such as age, income, or education level, to ensure meaningful comparisons.
3. Each stratum may have a different sample size based on its proportion in the overall population or its importance in the study.
4. Stratified sampling helps to minimize bias by ensuring all relevant subgroups are included in the sample.
5. This method is particularly useful in studies where certain subgroups are small or might be overlooked in other sampling techniques.

Review Questions

• How does stratified sampling improve the accuracy of research results compared to other sampling methods?
  • Stratified sampling improves accuracy by ensuring that all relevant subgroups within a population are represented in the sample. By dividing the population into strata based on key characteristics and then sampling from each stratum, researchers can minimize bias and capture the diversity of opinions or traits present. This leads to more reliable data and findings that better reflect the entire population.
• Discuss the process involved in selecting strata for stratified sampling and why it's important to define them accurately.
  • Selecting strata for stratified sampling involves identifying key characteristics that differentiate subgroups within the population. Defining these strata accurately is crucial because it impacts how well the sample represents the overall population. For example, if researchers were studying public opinion on a policy, they might stratify by age or socioeconomic status. If strata are poorly defined or irrelevant, it could lead to skewed results that don't accurately reflect the views of all segments of the population.
• Evaluate the implications of using stratified sampling in research design, particularly regarding data interpretation and generalizability.
  • Using stratified sampling has significant implications for research design as it enhances data interpretation and generalizability. By ensuring that diverse subgroups are adequately represented, researchers can draw more nuanced conclusions about the population as a whole. This approach allows for targeted analysis within strata and fosters a deeper understanding of differences among groups. However, if strata are incorrectly defined or if sample sizes are disproportionate, it may limit generalizability and introduce new biases in data interpretation.

Related terms

Population: The entire group of individuals or items that researchers are interested in studying.

Sample: A subset of individuals or items selected from a population for analysis.

Random sampling: A sampling method where every individual or item in the population has an equal chance of being selected.