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The Logic of Sampling

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Political Polls and Survey Sampling In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating the votes of 100 million people. To gather this information, they interviewed fewer than 2,000 people.

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Election Eve Polls - U.S. Presidential Candidates, 2000 DateAgencyGoreBushNaderBuchanan 11/6IDB/CSM474940 11/6CBS484741 11/6CNN/USA Today] 464841 11/6Reuters/ MSNBC 484651 11/6Voter. com 455140 11/7Results48 31

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Observation and Sampling Polls and other forms of social research, rest on observations. The task of researchers is to select the key aspects to observe, or sampling. Generalizing from a sample to a larger population is called probability sampling and involves random selection.

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Probability Sampling Used when researchers want precise, statistical descriptions of large populations. A sample of individuals from a population must contain the same variations that exist in the population.

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Probability Sampling Most effective method for selection of study elements. Avoids researchers biases in element selection. Permits estimates of sampling error.

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Populations and Sampling Frames Findings based on a sample of elements that compose a sampling frame. Sampling frames do not always include all the elements their names imply. All elements must have equal representation in the frame.

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Types of Sampling Designs Simple random sampling (SRS) Systematic sampling Stratified sampling

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Simple Random Sampling Every member of a population has an equal (non-zero) chance of being selected. Feasible only with the simplest sampling frame. Not always the most accurate method available.

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Systematic Sampling Requires a good sampling frame (list of elements) Arrangement of elements in the list can result in a biased sample.

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Stratified Sampling Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population. –Urban/rural; male/female; race; geographic Results in a greater degree of representativeness by decreasing the probable sampling error.

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Multistage Cluster Sampling Used when it's not possible or practical to create a list of all the elements that compose the target population. Involves repetition of two basic steps: listing and sampling. Highly efficient but less accurate.

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Probability Proportionate to Size Sampling Used when clusters sampled are of greatly differing sizes (picking a small cluster and missing a large one) For example: If we wanted to do a Multistage cluster sample of Nevada we wouldn’t want to randomly select 50 residents from White Pine and 50 from Clark (people in Clark should have a higher probability of being selected)

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Disproportionate Sampling & Weighting Oversampling population to get a large enough N Weighing the sample subgroup to approximate the true population (i.e. multiplying all blacks by 4) Problem is that if the small sample of African-Americans is not representative of the total population, weighing is not going to help

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Types of Nonprobability Sampling Reliance on available subjects: Only justified if less risky sampling methods are not possible. Researchers must exercise caution in generalizing from their data when this method is used.

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Types of Nonprobability Sampling Purposive or judgmental sampling Selecting a sample based on knowledge of a population, its elements, and the purpose of the study. Can be used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors

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Types of Nonprobability Sampling Snowball sampling Appropriate when members of a population are difficult to locate. Researcher collects data on members of the target population she can locate, then asks them to help locate other members of that population.

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Types of Nonprobability Sampling Quota sampling Begin with a matrix of the population. Data is collected from people with the characteristics of a given cell. Each group is assigned a weight appropriate to their portion of the population. Data should provide a representation of the total population.

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