COMM 291 Study Guide - Midterm Guide: Cluster Sampling, Standard Deviation, Confounding
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COMM 291 Full Course Notes
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Categorical data: (also called discrete, or count data) binary, nominal(unordered 2+), ordinal (order 2+) Quantitative data (also called measurement, continuous, or interval) have units. Cross-sectional: data are collected at one point in time (e. g. surveys) Time series: data are collected longitudinally at various time points (e. g. sales records) Example 1: suppose you are interested in the mean household income of all canadian households. The population is all canadian households; the parameter is the mean household income. However, a survey research firm contacts 500 households at random. These 500 households form the sample; the mean household income of the 500 households is the statistic/estimate. 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.
