Stratified Sampling

Definition

Stratified sampling is a sampling technique that involves dividing a population into distinct subgroups, or strata, based on specific characteristics, and then selecting samples from each stratum. This method ensures that different segments of the population are represented in the sample, which helps reduce sampling error and increases the accuracy of the results.

5 Must Know Facts For Your Next Test

1. Stratified sampling helps ensure that each subgroup within the population is adequately represented in the final sample, which can improve the reliability of survey results.
2. This technique can be particularly useful when certain strata are smaller but still important for analysis, as it guarantees their inclusion.
3. When implementing stratified sampling, researchers must decide how many samples to take from each stratum, often based on their size relative to the overall population.
4. Stratified sampling reduces variability in estimates because it accounts for known differences among subgroups, leading to lower sampling error compared to simple random sampling.
5. To effectively conduct stratified sampling, accurate identification of relevant strata is crucial, as poorly defined strata can lead to biased results.

Review Questions

• How does stratified sampling improve the accuracy of survey results compared to simple random sampling?
  • Stratified sampling improves accuracy by ensuring that different segments of the population are represented proportionally in the sample. By dividing the population into strata based on key characteristics, researchers can capture the diversity within the population and reduce variability in estimates. This leads to more precise and reliable results than those obtained through simple random sampling, where certain groups might be underrepresented or overrepresented by chance.
• What factors should researchers consider when determining the appropriate number of samples to take from each stratum during stratified sampling?
  • Researchers should consider factors such as the size of each stratum relative to the overall population, the variability within each stratum, and the specific research objectives. By allocating samples based on these factors, they can ensure that smaller but significant strata receive adequate representation while also managing resources effectively. Balancing representation and resource constraints is key to effective stratified sampling.
• Evaluate the potential impact of improperly defined strata on the outcomes of a study using stratified sampling.
  • Improperly defined strata can lead to biased results in a study using stratified sampling because they may not accurately reflect the characteristics of the population. If strata are too broad or not based on relevant characteristics, important differences between subgroups may be overlooked. This can result in misleading conclusions and decreased generalizability of findings, ultimately undermining the advantages of stratified sampling and leading to inaccurate interpretations of data.

Related terms

Population: The entire group of individuals or items that researchers want to study.

Sampling Error: The difference between the results obtained from a sample and the actual values in the population.

Cluster Sampling: A sampling technique where the population is divided into clusters, and entire clusters are randomly selected to form the sample.