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1 COMM 301: Empirical Research in Communication Kwan M Lee Lect5_1

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2 Sampling Things to know by the end of the lecture: –What are the key concepts in sampling? –What are the main types of probability sampling? –Know how to do the main types of probability sampling

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3 Sampling Moving from issues about internal validity to those about external validity External validity: –How accurately the findings of a study may be generalized to other groups Depends on how representative are the subjects studied Key to selecting representative subjects is good sampling

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4 Sampling Key concepts in sampling –population (universe): group of people (non-human elements) with particular characteristics of interest to the study –parameters: specific characteristics that must be present for an individual (or for an object) to fit the population e.g. ages between 10-19, income more than 10K, couples married within last 2 years, etc –census: study of every element in the population (universe)

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5 Sampling Key concepts in sampling (continued): –Sample: representative subset of population (universe) –Representativeness: how closely a sample matches its population in terms of the characteristics we want to study –Sampling error: degree to which a sample’s characteristics differ from the population’s

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6 Sampling Sampling error depends on –sample size larger sample size cuts sampling error, but at a decreasing rate Example (see lect6_2 sampling error table) –homogeneity of population (universe) higher homogeneity (the more the population members are alike) cuts sampling error Example: Also see lect6_2 sampling error table

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7 Sampling Probability Sampling Sampling methods: probability vs. non-probability Probability sampling –selects elements from population (universe) guided by a set of mathematical rules –allow calculation of sampling error (crucial advantage over non probability error)

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8 Probability Sampling Methods of probability sampling –simple random sampling –systematic sampling –stratified sampling –multistage cluster sampling Common to all is the sampling frame, a list of all elements in the population (universe)

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9 Probability Sampling: Simple random sampling Simple random sampling –basic form –each element in population (universe) is given equal chance to be selected –use lottery or random numbers Example

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10 Probability Sampling: Systematic sampling Systematic sampling Example Caution: make sure that the elements in the sampling frame are not organized in some pattern

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11 Probability Sampling: Stratified sampling Stratified sampling –proportional representation on a certain variable Example

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12 Probability Sampling: Multistage cluster sampling Multistage cluster sampling –simple in concept, complicated in execution –used to deal with very large populations, when using a sampling frame is not feasible

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13 Probability Sampling: Multistage cluster sampling (cont.) Multistage cluster sampling example –Sample 1000 people from US population –Using geographic boundaries from 50 states, select randomly 5 states from each state, select randomly 5 counties (25 counties total) from each county, select randomly 5 cities or equivalent (125 cities total) from each city, select 8 individuals (1000 individuals total)

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14 Probability Sampling: Multistage cluster sampling (cont.) Multistage cluster sampling problems –over- or under-representation at each layer –homogeneity of each cluster Statistical adjustments possible

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15 Tips in Probability Sampling Tips in practice –coping with the amount of information be careful and thorough keep things neat do a small batch and review Response rate –Another VERY important issue for survey research!

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