Slide 1Sampling_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 2Why Sample?• Why not study everyone?• Debate about Census vs. sampling_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 3Problems in Sampling?• What problems do you know about?• What issues are you aware of?• What questions do you have?_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 4Key Sampling ConceptsCopyright ©2002, William M.K. Trochim, All Rights Reserved_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 5Sampling ProcessList of Target SampleUnits of Analysis (people)Actual Population to Which Generalizations Are MadeDefined/Listed by Sampling FrameSampling FrameList or Rule Defining the PopulationSampleThe people actually studiedTarget PopulationPopulation of InterestTarget SampleMethod of selectionResponseRateGeneralizationList or Procedure_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 6Key Ideas• Distinction between the population of interest and the actual population defined by the sampling frame• Generalizations can be made only to the actual population• Understand crucial role of the sampling frame_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 7Sampling Frame• The list or procedure defining the POPULATION. (From which the sample will be drawn.)• Distinguish sampling frame from sample.• Examples:– Telephone book– Voter list– Random digit dialing• Essential for probability sampling, but can be defined for nonprobability sampling_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 8Types of SamplesProbabilityNon-ProbabilityConveniencePurposiveSimple RandomSystematic RandomStratified RandomRandom ClusterComplex Multi-stage Random (various kinds)QuotaStratified Cluster_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 9Probability Samples• A probability sample is one in which each element of the population has a known non-zero probability of selection.• Not a probability sample of some elements of population cannot be selected (have zero probability)• Not a probability sample if probabilities of selection are not known._____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 10Probability Sampling• Cannot guarantee “representativeness” on all traits of interest• A sampling plan with known statistical properties• Permits statements like: “The probability is .99 that the true population correlation falls between .46 and .56.”_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 11Sampling Frame is Crucial in Probability Sampling• If the sampling frame is a poor fit to the population of interest, random sampling from that frame cannot fix the problem• The sampling frame is non-randomly chosen. Elements not in the sampling frame have zero probability of selection.• Generalizations can be made ONLY to the actual population defined by the sampling frame_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 12Types of Probability SamplesSimple Random Systematic RandomStratified RandomRandom ClusterComplex Multi-stage Random (various kinds)Stratified Cluster_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 13Simple Random Sampling• Each element in the population has an equal probability of selection AND each combination of elements has an equal probability of selection• Names drawn out of a hat• Random numbers to select elements from an ordered list_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 14Stratified Random Sampling-1• Divide population into groups that differ in important ways• Basis for grouping must be known before sampling• Select random sample from within each group_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Slide 15Stratified Random Sampling-2• For a given sample size, reduces
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