Finding Sample Sizes (Sampling) > Probability Sampling
What is Probability Sampling?
- Probability sampling uses random sampling techniques to create a sample.
- Non-probability samplingtechniques use non-random processes like researcher judgment or convenience sampling.
Probability sampling is based on the fact that every member of a population has a known and equal chance of being selected. For example, if you had a population of 100 people, each person would have odds of 1 out of 100 of being chosen. With non-probability sampling, those odds are not equal. For example, a person might have a better chance of being chosen if they live close to the researcher or have access to a computer. Probability sampling gives you the best chance to create a sample that is truly representative of the population.
Types of Probability Sampling
- Simple random sampling is a completely random method of selecting subjects. These can include assigning numbers to all subjects and then using a random number generator to choose random numbers. Classic ball and urn experiments are another example of this process (assuming the balls are sufficiently mixed). The members whose numbers are chosen are included in the sample.
- Stratified Random Sampling involves splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.
- Systematic Sampling means that you choose every “nth” participant from a complete list. For example, you could choose every 10th person listed.
- Cluster Random Sampling is a way to randomly select participants from a list that is too large for simple random sampling. For example, if you wanted to choose 1000 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e. cities or counties) and randomly selects from within those boundaries.
- Multi-Stage Random sampling uses a combination of techniques.
Advantages and Disadvantages
Each probability sampling method has its own unique advantages and disadvantages.
- Cluster sampling: convenience and ease of use.
- Simple random sampling: creates samples that are highly representative of the population.
- Stratified random sampling: creates strata or layers that are highly representative of strata or layers in the population.
- Systematic sampling: creates samples that are highly representative of the population, without the need for a random number generator.
- Cluster sampling: might not work well if unit members are not homogeneous (i.e. if they are different from each other).
- Simple random sampling: tedious and time consuming, especially when creating larger samples.
- Stratified random sampling: tedious and time consuming, especially when creating larger samples.
- Systematic sampling: not as random as simple random sampling,
Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. If you'd rather get 1:1 study help, Chegg Tutors offers 30 minutes of free tutoring to new users, so you can try them out before committing to a subscription.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.
Comments? Need to post a correction? Please post a comment on our Facebook page.