BU422 Chapter Notes - Chapter 9: Nonprobability Sampling, Simple Random Sample, Stratified Sampling
Document Summary
Reasons for taking a sample: cost and population size makes sampling more desirable than a census, hard to analyze the huge amounts of data in a census. Informal cost-benefit analysis: there could be very little additional information gained by surveying a slight incremental in sample size. Probability sampling methods: 1) simple random sampling: probability of selection into the sample is known and equal for all members of the population, probability of selection = sample size/population size, ex. Rolling a dice: random numbers method: numbers whose chance nature is assured, ex. Using a computer to generate numbers without any systematic pattern: ex. If skip interval = 250, every 250th name would be selected: this is fair because there is a random starting point, disadvantages, lists may not be current, ex. Telephone directories may have many unlisted numbers: 3) cluster sampling: divides population into groups, any of which can be considered a representative sample, clusters are theoretically identical.
