- Cluster Sampling- takes the entire population and divides them into clusters based on things such as age, gender, geographical locations instead of just picking from a general population. They can then pick every individual from within a specific cluster or random people from each cluster.
Example- Cluster sampling may be used when trying to determine the vaccination rate within the Inland Empire located in southern California, each city within the Inland Empire would be considered a cluster.
- Convenience Sampling- instead of carefully selecting what members of the population are selected, in convenience sampling the most convenient people are selected, whatever is the most accessible means is what will be chosen
Example- Using the neighbors in your own neighborhood instead of using a neighborhood that is in a different city would be convenient sampling.
- Random Sampling- everyone who is part of the population has an equal chance of being selected
Example- All names are placed in a jar to form teams for a relay race each name will be randomly assigned to a team
- Simple Random Sampling- each individual selected is selected from a larger population randomly, leaving each person an equal opportunity to be a part of the selected group
Example- Working in a place with 500 employees and being chosen as a group of 25 to take a survey regarding the business
- Stratified Sampling- The overall population is first divided into smaller groups referred to as strata, then the individuals are selected out of each strata
Example- Dividing a group of people into age groups such as 18-21, 22-25 26-30 then randomly selecting from each of those specific age groups
- Systematic Sampling- creating a starting point and then selecting a subject based on the place in which they occur keeping the same pattern
Example- selecting every 12 th person in a group of 400 subjects
