Stratified sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, based on shared characteristics before selecting samples from each stratum. This technique ensures that each subgroup is adequately represented in the final sample, which can improve the accuracy and reliability of results. By focusing on specific segments of the population, stratified sampling reduces variability and can provide more precise estimates than simple random sampling.

5 Must Know Facts For Your Next Test

1. Stratified sampling is particularly useful when researchers want to ensure that specific subgroups within a population are adequately represented.
2. The strata in stratified sampling can be based on various factors, such as age, income, education level, or geographical location.
3. This method can lead to increased precision in estimates compared to simple random sampling because it reduces variance within each stratum.
4. Stratified sampling is commonly used in social sciences, market research, and health studies to draw more reliable conclusions.
5. Selecting samples from each stratum can be done proportionally or equally, depending on the research objectives and the importance of each subgroup.

Review Questions

• How does stratified sampling enhance the representation of subgroups within a population?
  • Stratified sampling enhances subgroup representation by ensuring that distinct segments of the population are specifically included in the sample. By identifying and dividing the population into strata based on shared characteristics, researchers can select samples from each group. This targeted approach reduces bias and variability within the results, leading to more reliable conclusions about each subgroup's characteristics or behaviors.
• Compare stratified sampling with simple random sampling in terms of accuracy and representation of a diverse population.
  • Stratified sampling generally provides greater accuracy and representation compared to simple random sampling when dealing with diverse populations. In stratified sampling, researchers intentionally include samples from all relevant subgroups, ensuring that no single group is underrepresented. In contrast, simple random sampling may overlook certain subgroups due to chance, leading to potential bias in the results. As a result, stratified sampling is often preferred when precise estimates for different segments are needed.
• Evaluate the impact of using stratified sampling on data analysis and interpretation in a research study.
  • Using stratified sampling can significantly enhance data analysis and interpretation by providing a more nuanced view of how different subgroups behave or respond within a larger population. The detailed insights gained from analyzing data by strata allow researchers to identify trends and differences that may be masked in a simple random sample. This methodological approach can lead to more informed decision-making, targeted interventions, and policy recommendations that account for diversity within the population.

Related terms

Sampling error: The difference between the sample statistic and the actual population parameter, often due to the method used to collect samples.

Population: The entire group of individuals or items that is the subject of a statistical study from which samples are drawn.

Simple random sampling: A basic sampling method where each member of the population has an equal chance of being selected, without consideration of any characteristics.