Stratified Sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, known as strata, and then randomly selecting samples from each stratum. This technique ensures that each subgroup is adequately represented in the final sample, which can lead to more accurate and reliable statistical results. By focusing on specific segments of the population, stratified sampling enhances the precision of estimates and reduces sampling error compared to simple random sampling.

5 Must Know Facts For Your Next Test

1. Stratified sampling is particularly useful when researchers want to ensure representation from key subgroups within a population, such as age, gender, or income level.
2. This method can improve the efficiency of data collection by reducing variability within each stratum, leading to more accurate estimates.
3. When conducting stratified sampling, the proportion of each stratum in the sample should reflect its proportion in the overall population for optimal results.
4. Stratified sampling can be either proportional (where the size of each stratum in the sample is proportional to its size in the population) or disproportionate (where specific strata are over- or under-sampled intentionally).
5. This technique is often used in social science research, market research, and health studies to gather comprehensive data while controlling for potential confounding variables.

Review Questions

• How does stratified sampling improve the accuracy of statistical estimates compared to simple random sampling?
  • Stratified sampling improves accuracy by ensuring that key subgroups within a population are represented in the sample. By dividing the population into strata based on relevant characteristics and then randomly selecting samples from each stratum, this method minimizes sampling error and reduces variability within groups. This results in more precise estimates that better reflect the true characteristics of the overall population.
• Evaluate the advantages and disadvantages of using stratified sampling in research design.
  • The advantages of stratified sampling include improved representation of key subgroups and increased precision in estimates due to reduced variability within strata. However, it can also introduce complexity in the design and implementation process, requiring researchers to accurately identify strata and ensure proper sampling techniques. Additionally, if strata are not well-defined or representative, it may lead to biased results or complicate data analysis.
• Design an experiment that utilizes stratified sampling to investigate consumer preferences among different age groups for a new product.
  • To investigate consumer preferences for a new product among different age groups, first define the target population and identify relevant age strata, such as 18-24, 25-34, 35-44, etc. Create a sampling frame that includes individuals from each age group. Then, use stratified sampling to randomly select participants from each stratum in proportion to their representation in the overall population. Conduct surveys or focus groups with these participants to gather insights on their preferences. This method will ensure diverse input across age demographics and enhance the reliability of the findings.

Related terms

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

Sampling Frame:

A list or database of all the elements in a population from which a sample is drawn.

Systematic Sampling: A method where samples are selected at regular intervals from a randomly ordered population.