Stratified sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, and then randomly selecting samples from each stratum. This technique ensures that the sample represents the entire population accurately by including various segments, allowing researchers to make more precise inferences about the whole group. By focusing on specific characteristics within strata, this approach enhances the reliability and validity of quantitative research findings.

5 Must Know Facts For Your Next Test

1. Stratified sampling helps to ensure that different segments of a population are represented in the sample, which is especially important for diverse groups.
2. This method reduces sampling error and increases statistical efficiency compared to simple random sampling, making it easier to detect differences among subgroups.
3. Researchers can choose between proportionate and disproportionate stratified sampling, depending on whether they want to maintain the same ratio of strata in the sample as in the population.
4. Stratified sampling is particularly useful in studies where certain characteristics, like age or income, are critical to the research question.
5. In quantitative research, using stratified sampling can lead to more accurate public opinion measurements by capturing diverse perspectives within a population.

Review Questions

• How does stratified sampling improve the accuracy of research findings compared to simple random sampling?
  • Stratified sampling improves accuracy by ensuring that all relevant subgroups within a population are represented in the sample. Unlike simple random sampling, which may overlook smaller subgroups, stratified sampling divides the population into strata based on specific characteristics and then randomly samples from each. This way, researchers can gather more representative data that reflects the diversity of opinions and experiences within the population, leading to more valid conclusions.
• Discuss how stratified sampling can be applied in measuring public opinion on a controversial issue.
  • In measuring public opinion on a controversial issue, stratified sampling allows researchers to capture diverse viewpoints by ensuring representation from various demographic segments such as age, gender, income level, or political affiliation. For example, if a study is investigating opinions on climate change policy, researchers might stratify the sample by political party affiliation to understand differing perspectives. By analyzing responses from these distinct groups, researchers can identify trends and nuances that would be overlooked in a non-stratified approach.
• Evaluate the effectiveness of stratified sampling in addressing biases in quantitative research related to public opinion formation.
  • Stratified sampling effectively addresses biases in quantitative research by systematically including diverse segments of the population in the sample. This method allows researchers to counteract potential overrepresentation or underrepresentation of certain groups, thus enhancing the credibility of their findings on public opinion formation. By ensuring that various strata are represented proportionally or intentionally oversampled for critical groups, researchers can obtain a more holistic view of public sentiment and provide insights that reflect true societal dynamics. Ultimately, this leads to more informed decision-making based on reliable data.

Related terms

Population: The entire group of individuals or instances about whom researchers want to draw conclusions.

Sampling Frame: A list or database from which a sample is drawn, representing all the elements in the population.

Random Sampling: A sampling method where every individual in the population has an equal chance of being selected, helping to eliminate bias.