Stratified Sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, and then selecting samples from each stratum. This approach ensures that specific characteristics of the population are represented in the sample, allowing for more accurate and reliable statistical analysis. By focusing on these subgroups, journalists can uncover insights and trends that might be missed using other sampling techniques.

5 Must Know Facts For Your Next Test

1. Stratified sampling helps to ensure that all relevant subgroups within a population are represented, improving the precision of estimates derived from the sample.
2. This method can reduce sampling error by providing a clearer picture of the differences and similarities between strata.
3. Strata can be based on various characteristics, such as age, gender, income level, or geographic location, depending on the research objectives.
4. Stratified sampling can be either proportional or disproportional, with proportional sampling reflecting the actual proportions of strata in the population, while disproportional sampling might emphasize certain strata for specific analysis.
5. Using stratified sampling often leads to more efficient data collection since researchers can focus on targeted groups, which can save time and resources.

Review Questions

• How does stratified sampling improve the accuracy of statistical results compared to simple random sampling?
  • Stratified sampling improves accuracy by ensuring that all relevant subgroups within a population are represented in the sample. In simple random sampling, there is a risk that some subgroups may be underrepresented or overlooked entirely, leading to skewed results. By deliberately dividing the population into strata based on key characteristics and sampling from each stratum, researchers can obtain a more balanced and representative sample, ultimately yielding more reliable insights.
• Discuss the advantages and disadvantages of using stratified sampling in journalistic research.
  • The advantages of using stratified sampling include enhanced representativeness of diverse groups and increased accuracy in statistical estimates. It allows journalists to focus on specific demographics and obtain more nuanced insights. However, one disadvantage is that it can be more complex and time-consuming to implement compared to simpler methods like random sampling. Additionally, defining appropriate strata requires careful consideration to avoid misclassification that could lead to biased results.
• Evaluate the impact of stratified sampling on understanding societal trends and issues in journalism.
  • Stratified sampling plays a critical role in journalism by enabling reporters to capture the diversity within populations when investigating societal trends and issues. This approach allows for a nuanced analysis that reflects the varied experiences and perspectives of different demographic groups. By focusing on specific strata, journalists can uncover hidden stories and trends that might otherwise be overlooked, contributing to more informed public discourse and better representation in news coverage.

Related terms

Population: The entire group of individuals or instances about whom we seek to learn or draw conclusions.

Sample Size: The number of individuals or observations selected from a population for analysis, which can affect the accuracy and reliability of statistical results.

Random Sampling: A sampling technique where each member of a population has an equal chance of being selected, often used to avoid bias in research.