Stratified Sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, known as strata, and then randomly selecting samples from each stratum. This technique ensures that different segments of the population are represented in the sample, which helps improve the accuracy and reliability of survey results. By addressing the diversity within the population, stratified sampling can yield insights that may be overlooked in simpler random sampling methods.

5 Must Know Facts For Your Next Test

1. Stratified sampling can lead to more precise estimates compared to simple random sampling because it reduces variability within each subgroup.
2. Each stratum can be based on specific characteristics such as age, income level, education, or any other relevant factor that might affect survey outcomes.
3. Researchers often determine the sample size for each stratum proportionally to its size within the overall population to maintain representativeness.
4. This sampling method is especially useful in surveys that aim to compare different groups within a population or assess issues that may affect those groups differently.
5. Stratified sampling can be either proportionate, where samples are taken in proportion to each stratum's size, or disproportionate, where certain strata are oversampled for more detailed analysis.

Review Questions

• How does stratified sampling improve the accuracy of survey 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 distinct strata and randomly selecting samples from each, researchers can capture variations across different segments. This reduces the chances of bias and provides a more comprehensive view of the population, leading to better-informed conclusions.
• Discuss how researchers determine the appropriate stratification criteria when using stratified sampling.
  • Researchers choose stratification criteria based on characteristics that are relevant to the study's objectives. These criteria might include demographics such as age, gender, socioeconomic status, or other factors that could influence survey responses. The goal is to create strata that will yield insights into differences between groups while ensuring that all segments of the population are adequately represented in the final sample.
• Evaluate the impact of using disproportionate stratified sampling on data interpretation and conclusions drawn from survey results.
  • Using disproportionate stratified sampling can significantly impact data interpretation by allowing researchers to focus on specific subgroups that may be underrepresented in a proportional approach. While this can lead to more detailed insights about those particular strata, it also risks introducing bias if not handled carefully. The findings may not generalize well to the overall population since certain groups are overrepresented, which can skew conclusions and affect policy decisions based on those results.

Related terms

Random Sampling: A sampling method where each member of the population has an equal chance of being selected, ensuring unbiased representation.

Population: The entire group of individuals or instances about whom we want to learn or draw conclusions.

Sampling Error: The error that occurs when a sample does not accurately represent the population from which it was drawn.