Stratified Sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups or strata, and then selecting samples from each stratum to ensure that the sample accurately reflects the diversity within the population. This technique helps enhance the precision of estimates and ensures representation across different segments of the population, making it a crucial tool in various research contexts.

5 Must Know Facts For Your Next Test

1. Stratified sampling can increase the statistical efficiency of the sample by ensuring that all relevant subgroups are represented.
2. Researchers often use stratified sampling when they believe that different subgroups in the population might behave differently regarding the variable being studied.
3. In stratified sampling, strata can be based on characteristics such as age, income, education level, or any other relevant factor.
4. This method allows for better comparisons between strata and can improve the accuracy of results by reducing sampling error.
5. Stratified sampling can be implemented using either proportional allocation, where the sample size from each stratum is proportionate to its size in the population, or equal allocation, where each stratum is sampled equally.

Review Questions

• How does stratified sampling improve the accuracy and reliability of research findings?
  • Stratified sampling improves accuracy and reliability by ensuring that all significant subgroups within a population are represented in the sample. By dividing the population into strata based on relevant characteristics, researchers can collect data that reflects the diversity within the population. This representation reduces bias and enhances the precision of estimates, making findings more generalizable to the entire population.
• Compare and contrast stratified sampling with random sampling. What are the advantages of using stratified sampling?
  • While both stratified and random sampling aim to create a representative sample, stratified sampling involves dividing the population into distinct subgroups before selection. The advantage of stratified sampling lies in its ability to ensure representation from key subgroups that may behave differently, leading to more accurate estimates. Random sampling may overlook smaller or less represented groups, potentially skewing results.
• Evaluate the impact of stratified sampling on survey administration and response rates. How does it address potential biases?
  • Stratified sampling positively impacts survey administration by ensuring diverse representation which can enhance engagement and response rates from various groups. By targeting specific strata, researchers can tailor their approach to appeal to different demographics, reducing non-response bias. This method addresses potential biases by capturing perspectives from all segments of the population, resulting in more balanced and reliable data.

Related terms

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

Sample: A subset of the population selected for participation in a study, which is used to draw conclusions about the entire population.

Random Sampling: A sampling method where every individual in the population has an equal chance of being selected, ensuring that the sample is unbiased.