COMM 291 Lecture Notes - Lecture 2: Marginal Distribution, Bar Chart, Cluster Sampling
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COMM 291 Full Course Notes
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Three principles of sampling: examine part of the whole, randomize, the size of the sample is what matters. Population entire class of individuals which have a common characteristic of interest that the investigator would like to generalize about. Sampling frame the portion of the population you can access, and can sample from. Sample a subset which will represent the population. Parameter a numerical fact or characteristic about the population of interest. Statistic/estimate a number computed from the sample, which is used to estimate a parameter. This has the benefit of reducing sampling variability: cluster random sampling divide the population into parts or clusters each of which represents the population. Select a few clusters at random and do a census within each one. If each cluster is fairly representative, the sample will be unbiased: multi-stage cluster sampling repeatedly divide the population into smaller and smaller subgroups and, at each stage, use a chance procedure to pick the sample.
