Probability sampling ensures every member of your target population has a known chance of being selected for your sample. This allows for more generalizable results compared to non-probability sampling. Several key methods exist:
Types of Probability Sampling
-
Simple Random Sampling: Every individual in the population has an equal and independent chance of being selected. Imagine drawing names from a hat – that's a simple random sample. This method is straightforward but can be challenging with large populations.
-
Systematic Sampling: You select individuals at a fixed interval from a randomly ordered list. For example, choosing every 10th person from an alphabetized list. This is efficient but susceptible to bias if the list has a hidden pattern.
-
Stratified Sampling: You divide the population into subgroups (strata) based on relevant characteristics (e.g., age, gender, location) and then randomly sample from each stratum. This ensures representation from all subgroups and allows for comparisons between them. It's more complex than simple random sampling but offers greater precision.
-
Cluster Sampling: You divide the population into clusters (e.g., geographic areas, schools) and randomly select entire clusters to sample. This is cost-effective for large, geographically dispersed populations, but individual cluster characteristics might not perfectly represent the whole population.
Example: Imagine you want to survey student opinions at a university.
- Simple Random: Assign each student a number and randomly select numbers using a computer program.
- Systematic: Obtain a list of all students and select every 50th student.
- Stratified: Divide students into year groups (freshmen, sophomores, etc.) and randomly sample from each year.
- Cluster: Randomly select several departments within the university and survey all students in those departments.
The reference states that probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. These are the foundational techniques ensuring each member of the population has a quantifiable chance of inclusion in the sample.
