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The Logic of Sampling

The Logic of Sampling. 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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The Logic of Sampling

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  1. The Logic of Sampling

  2. 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.

  3. Election Eve Polls - U.S. Presidential Candidates, 2000

  4. 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.

  5. 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.

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

  7. 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.

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

  9. 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.

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

  11. 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.

  12. 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.

  13. 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)

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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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