Stratified Sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, that share similar characteristics before randomly selecting samples from each stratum. This technique ensures that different segments of a population are adequately represented in the sample, which can improve the precision and relevance of research findings. It is particularly useful when researchers want to analyze specific characteristics or behaviors within distinct groups in the population.

5 Must Know Facts For Your Next Test

1. Stratified sampling can increase the statistical efficiency of a study by reducing variability within strata and ensuring better representation of minority groups.
2. In stratified sampling, researchers determine the number of samples to draw from each stratum based on its proportion to the overall population.
3. This sampling technique is particularly effective in studies where the researcher is interested in specific subgroups, such as age, gender, or income level.
4. Stratified sampling can be conducted using proportional allocation (where samples are drawn based on the size of each stratum) or equal allocation (where equal numbers are taken from each stratum regardless of size).
5. When conducting power calculations, stratified sampling can influence the required sample size by increasing the precision of estimates for each subgroup.

Review Questions

• How does stratified sampling enhance the representativeness of a sample compared to simple random sampling?
  • Stratified sampling enhances representativeness by ensuring that specific subgroups within a population are included in proportion to their presence in the population. Unlike simple random sampling, which may overlook smaller or marginalized groups, stratified sampling targets these groups by dividing the population into strata and sampling from each one. This approach leads to more reliable insights about various segments and minimizes sampling bias.
• Discuss the importance of determining strata when using stratified sampling in research studies.
  • Determining strata is crucial because it directly affects how well the sample reflects the diversity within the population. Identifying relevant characteristics for stratification helps researchers ensure that important segments are not underrepresented. For example, if studying health outcomes, stratifying by age or socioeconomic status can reveal significant differences across groups that may inform policy decisions or targeted interventions.
• Evaluate how stratified sampling could impact power calculations in research design and its implications for study outcomes.
  • Stratified sampling can significantly impact power calculations because it enhances the precision of estimates by reducing variability within strata. This means researchers can achieve the same level of statistical power with a smaller overall sample size compared to using simple random sampling. Consequently, studies employing stratified sampling might provide more accurate conclusions about subgroup effects and relationships, thus leading to more informed decision-making in applied fields like public health or education.

Related terms

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

Sampling Frame: A list or database from which a sample is drawn, containing all the members of the population.

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