Stratified Sampling

Definition

Stratified sampling is a probability sampling method where the population is divided into distinct subgroups, or strata, that share similar characteristics. This technique ensures that each subgroup is adequately represented in the final sample, enhancing the accuracy and reliability of survey results by capturing the diversity within the population.

5 Must Know Facts For Your Next Test

1. Stratified sampling is particularly useful when researchers want to ensure that specific subgroups are well represented, improving the generalizability of the findings.
2. The strata can be based on various factors such as age, gender, income level, or education, depending on the research goals.
3. In stratified sampling, samples can be selected proportionally, where each stratum is represented in the same proportion as in the population, or equally, where each stratum is given an equal number of samples regardless of its size.
4. This method helps reduce sampling error since it accounts for variability within different subgroups of the population.
5. Using stratified sampling can lead to more accurate insights and analyses compared to simple random sampling, especially in heterogeneous populations.

Review Questions

• How does stratified sampling improve the representativeness of a sample compared to simple random sampling?
  • Stratified sampling improves representativeness by ensuring that all relevant subgroups within a population are included in the sample. Unlike simple random sampling, which may overlook certain segments, stratified sampling divides the population into strata based on shared characteristics. By doing this, researchers can ensure that each subgroup is appropriately represented, leading to more accurate and reliable results that reflect the diversity of the entire population.
• Discuss the advantages of using stratified sampling over other non-probability methods like quota sampling.
  • Stratified sampling offers significant advantages over non-probability methods like quota sampling by providing a framework for obtaining a representative sample based on probability theory. While quota sampling relies on subjective selection and may introduce bias if not carefully managed, stratified sampling employs random selection within each stratum. This randomness reduces the risk of bias and allows for statistical inference about the entire population, making results more reliable and valid.
• Evaluate how stratified sampling can affect data quality and validity in marketing research studies.
  • Stratified sampling can greatly enhance data quality and validity in marketing research by ensuring that insights are drawn from a well-represented sample that reflects the target audience's diversity. By carefully defining strata and applying random sampling methods within each group, researchers minimize potential biases and improve response accuracy. This leads to stronger conclusions about consumer behavior and preferences, allowing businesses to make informed decisions based on reliable data that reflects their market effectively.

Related terms

Population:

The entire group of individuals or instances about whom we want to draw conclusions.

Sampling Frame: A list or database from which a sample is drawn, representing the population as a whole.

Quota Sampling: A non-probability sampling technique where researchers ensure equal representation of various segments within the population based on pre-set quotas.