An example of probability sampling is Stratified Random Sampling.
Example of Stratified Random Sampling:
Suppose a researcher wants to study the academic performance of students in a large university that has a diverse student body with different majors (e.g., Engineering, Arts, Business, and Sciences). To ensure that each major is adequately represented in the study, the researcher divides the population into strata based on the students' majors.
- Define the strata: The researcher identifies the different majors as strata: Engineering, Arts, Business, and Sciences.
- Determine sample size: The researcher decides how many students they want to survey. For example, they choose to survey 200 students.
- Random sampling within strata: The researcher then randomly selects students from each major proportionally based on the total number of students in each major. For instance, if 40% of the students are in Engineering, 20% in Arts, 25% in Business, and 15% in Sciences, the researcher would select:
- 80 Engineering students (40% of 200)
- 40 Arts students (20% of 200)
- 50 Business students (25% of 200)
- 30 Sciences students (15% of 200)
- Conduct the study: The researcher then collects data from the randomly selected students in each major.
By using stratified random sampling, the researcher ensures that all major groups are represented in the sample, leading to more generalizable results across the population of the university.
Clarification on Other Sampling Types:
- Convenience or Accidental Sampling: Selecting a sample based on ease of access (e.g., surveying students in a nearby cafeteria).
- Quota Sampling: Selecting a specific number (quota) of samples from identified segments without randomization (e.g., interviewing 50 Engineering students and 50 Arts students regardless of their distribution).
- Purposive or Judgmental Sampling: Selecting samples based on the judgment of the researcher (e.g., choosing students who have a high GPA to study academic performance).
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