Stratified Sampling

Definition

Stratified sampling is a technique used in statistics where a population is divided into distinct subgroups, or strata, that share similar characteristics. This method ensures that each subgroup is represented in the sample, which helps to improve the accuracy and reliability of statistical analysis. By taking samples from each stratum, researchers can reduce sampling error and increase the generalizability of their findings.

5 Must Know Facts For Your Next Test

1. Stratified sampling is especially useful when researchers want to ensure representation of specific subgroups within a population.
2. This technique can lead to more precise estimates than simple random sampling, especially when the variance within strata is lower than the variance across the entire population.
3. Researchers can choose either proportional or disproportionate stratified sampling based on their study needs and the importance of different strata.
4. Stratified sampling can be applied to both qualitative and quantitative research designs, making it versatile for various fields.
5. It helps in addressing issues of bias by ensuring that all relevant subgroups are included in the sample, leading to more valid conclusions.

Review Questions

• How does stratified sampling differ from simple random sampling in terms of representation and accuracy?
  • Stratified sampling differs from simple random sampling as it ensures that specific subgroups within a population are represented in the sample. In simple random sampling, every member has an equal chance of selection, which may lead to underrepresentation of certain groups. Stratified sampling enhances accuracy by reducing sampling error, as it accounts for variability within subgroups, resulting in more reliable estimates of population parameters.
• Discuss the advantages of using stratified sampling over other sampling methods, particularly in relation to study goals.
  • Stratified sampling offers several advantages, such as improved accuracy and reduced bias, particularly when studying populations with diverse characteristics. By ensuring all relevant subgroups are included, it allows for a more comprehensive understanding of the population. This method can also lead to greater efficiency in data collection because researchers can focus on key strata that are critical to their study goals, enhancing the overall validity of their findings.
• Evaluate how stratified sampling can impact the results and conclusions drawn from a study focused on public health disparities.
  • In a study focused on public health disparities, using stratified sampling can significantly impact results by ensuring that different demographic groups—such as age, gender, or socioeconomic status—are adequately represented. This representation allows researchers to analyze health outcomes with greater precision and draw more informed conclusions about disparities among these groups. By identifying specific needs or risks within strata, policymakers can develop targeted interventions that address inequities, ultimately leading to improved health outcomes for underrepresented populations.

Related terms

Strata: Distinct subgroups within a population that share similar characteristics, used in stratified sampling.

Sampling Error: The difference between the sample statistic and the actual population parameter, which can lead to inaccuracies in statistical conclusions.

Proportional Allocation: A sampling method where the number of observations from each stratum is proportional to the size of that stratum relative to the overall population.