Stratified Random Sampling

Definition

Stratified random sampling is a sampling method where the population is divided into distinct subgroups, or strata, that share similar characteristics. This approach ensures that each subgroup is adequately represented in the sample, allowing for more accurate and reliable estimates of the population parameters. By focusing on specific strata, this method reduces variability within groups and enhances the precision of overall results.

5 Must Know Facts For Your Next Test

1. Stratified random sampling is particularly useful when researchers want to ensure representation from specific subgroups, such as age, gender, or income levels.
2. The process involves first dividing the population into strata based on shared characteristics, and then performing random sampling within each stratum.
3. This method can lead to increased statistical efficiency compared to simple random sampling, especially when the variability within strata is less than the variability across the entire population.
4. Stratified sampling can be proportionate or disproportionate, meaning that the sample sizes from each stratum can reflect their size in the population or be adjusted for specific research needs.
5. This approach helps minimize sampling bias and provides more precise estimates, making it a preferred method in survey research and experiments.

Review Questions

• How does stratified random sampling improve the accuracy of estimates compared to simple random sampling?
  • Stratified random sampling improves accuracy by ensuring that specific subgroups within the population are represented according to their actual proportions. This reduces variability and allows for more precise estimates of population parameters since the sample reflects the diversity of characteristics present in the population. In contrast, simple random sampling may overlook some subgroups entirely or misrepresent their proportions, leading to less reliable results.
• Discuss a scenario where stratified random sampling would be more beneficial than simple random sampling and explain why.
  • In a study analyzing the voting behavior of different age groups in a city, stratified random sampling would be beneficial because age is a critical factor influencing voting preferences. By dividing the population into strata based on age groups (e.g., 18-24, 25-34, etc.), researchers can ensure each group is adequately represented in the sample. This method allows for more tailored analysis and understanding of how different age demographics may impact overall voting trends, something simple random sampling might miss due to potential underrepresentation of certain age groups.
• Evaluate how stratified random sampling can contribute to minimizing bias in research studies focused on public health interventions.
  • Stratified random sampling significantly minimizes bias in public health studies by ensuring that diverse populations are represented in the research. For instance, if a study aims to evaluate a health intervention's effectiveness across different socioeconomic statuses, stratifying the sample allows researchers to capture varied health outcomes associated with those statuses. This targeted approach helps identify specific barriers or facilitators related to health interventions among different strata, leading to more relevant conclusions and actionable recommendations while avoiding generalizations that could arise from biased samples.

Related terms

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

Sample: A subset of individuals selected from a larger population, used to estimate characteristics of the whole population.

Random Sampling: A sampling technique where each member of the population has an equal chance of being selected, ensuring that the sample is representative of the population.