Simple Random Sampling
Types of Sampling
In applications:
Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Multi-Stage Sampling * What is each and how is it done? * How do we decide which to use? * How do we analyze the results differently depending on the type of sampling?
Non-probability Sampling: Why don't we use non-probability sampling schemes? Two reasons: * We can't use the mathematics of probability to analyze the results. * In general, we can't count on a non-probability sampling scheme to produce representative samples.
In mathematical statistics books (for courses that assume you have already taken a probability course): * Described as assumptions about random variables * Sampling with replacement versus sampling without replacement
What are the main types of sampling and how is each done?
Simple Random Sampling: A simple random sample (SRS) of size n is produced by a scheme which ensures that each subgroup of the population of size n has an equal probability of being chosen as the sample.
Stratified Random Sampling: Divide the population into "strata". There can be any number of these. Then choose a simple random sample from each stratum. Combine those into the overall sample. That is a stratified random sample. (Example: Church A has 600 women and 400 women as members. One way to get a stratified random sample of size 30 is to take a SRS of 18 women from the 600 women and another SRS of 12 men from the 400 men.)
Multi-Stage Sampling: Sometimes the population is too large and scattered for it to be practical to make a list of the entire population from which to draw a SRS. For instance, when the a polling organization samples US voters, they do not do a SRS. Since voter lists are compiled by counties, they might first do a sample of the counties and then sample within the selected counties. This illustrates two stages. In some instances, they might use even more stages. At each stage, they might do a stratified
In applications:
Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Multi-Stage Sampling * What is each and how is it done? * How do we decide which to use? * How do we analyze the results differently depending on the type of sampling?
Non-probability Sampling: Why don't we use non-probability sampling schemes? Two reasons: * We can't use the mathematics of probability to analyze the results. * In general, we can't count on a non-probability sampling scheme to produce representative samples.
In mathematical statistics books (for courses that assume you have already taken a probability course): * Described as assumptions about random variables * Sampling with replacement versus sampling without replacement
What are the main types of sampling and how is each done?
Simple Random Sampling: A simple random sample (SRS) of size n is produced by a scheme which ensures that each subgroup of the population of size n has an equal probability of being chosen as the sample.
Stratified Random Sampling: Divide the population into "strata". There can be any number of these. Then choose a simple random sample from each stratum. Combine those into the overall sample. That is a stratified random sample. (Example: Church A has 600 women and 400 women as members. One way to get a stratified random sample of size 30 is to take a SRS of 18 women from the 600 women and another SRS of 12 men from the 400 men.)
Multi-Stage Sampling: Sometimes the population is too large and scattered for it to be practical to make a list of the entire population from which to draw a SRS. For instance, when the a polling organization samples US voters, they do not do a SRS. Since voter lists are compiled by counties, they might first do a sample of the counties and then sample within the selected counties. This illustrates two stages. In some instances, they might use even more stages. At each stage, they might do a stratified