Understanding Stratified Sampling

Stratified sampling is a method used in statistical analysis where the population is divided into distinct subgroups, or strata, that share similar characteristics. This technique improves the accuracy of the results by ensuring that each subgroup is adequately represented in the final sample. Here are three diverse, practical examples of stratified sampling that illustrate its application in real-world scenarios.

Example 1: Educational Research in a University

In a university setting, a researcher wants to study student satisfaction across different departments. The university has five departments: Arts, Science, Engineering, Business, and Education. To ensure that the sample accurately reflects the diversity of the student body, the researcher uses stratified sampling.

The researcher first identifies the number of students in each department:

• Arts: 200 students
• Science: 300 students
• Engineering: 250 students
• Business: 150 students
• Education: 100 students

Next, the researcher decides to sample 10% of students from each department. The calculation for each department is as follows:

• Arts: 20 students (200 * 0.10)
• Science: 30 students (300 * 0.10)
• Engineering: 25 students (250 * 0.10)
• Business: 15 students (150 * 0.10)
• Education: 10 students (100 * 0.10)

The total sample size is 110 students, ensuring that each department is proportionately represented in the study. This method allows the researcher to gather insights into student satisfaction that are reflective of the entire university population.

Notes:

• Variations can include oversampling certain groups to ensure sufficient data for minority departments.

Example 2: Public Health Survey

A public health organization is conducting a survey to assess the health needs of a community. The community consists of different age groups: children (0-14 years), adults (15-64 years), and seniors (65 years and older). To accurately represent the health needs of each age group, stratified sampling is employed.

The population distribution is as follows:

• Children: 1,000 individuals
• Adults: 3,000 individuals
• Seniors: 500 individuals

The organization plans to survey 200 individuals across all age groups, with the sampling proportionate to the population size:

• Children: 50 individuals (1,000/4,500 * 200)
• Adults: 133 individuals (3,000/4,500 * 200)
• Seniors: 17 individuals (500/4,500 * 200)

This approach ensures that the health needs of each age group are adequately represented, allowing for targeted health interventions and resource allocation based on the survey results.

Notes:

• Researchers may adjust the sample size based on specific health concerns relevant to particular age groups.

Example 3: Market Research for a New Product

A company is planning to launch a new product and wants to conduct market research to understand consumer preferences. The market is segmented by income levels: low, medium, and high income. To ensure that opinions from each income group are included, the company uses stratified sampling.

The income distribution in the target market is as follows:

• Low income: 1,200 individuals
• Medium income: 2,500 individuals
• High income: 800 individuals

The company decides to survey a total of 300 individuals:

• Low income: 80 individuals (1,200/4,500 * 300)
• Medium income: 200 individuals (2,500/4,500 * 300)
• High income: 20 individuals (800/4,500 * 300)

By utilizing stratified sampling, the company gathers a comprehensive understanding of consumer preferences across different income levels, which will inform their marketing strategies and product features.

Notes:

• Variations can include qualitative interviews in addition to quantitative surveys for deeper insights.