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Chapter 7

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Chapter 7

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  1. Chapter 7 The Logic Of Sampling

  2. Chapter Outline • Introduction • A Brief History of Sampling • Nonprobability Sampling • The Theory and Logic of Probability Sampling

  3. Chapter Outline • Populations and Sampling Frames • Types of Sampling Designs • Multistage Cluster Sampling • Probability Sampling in Review

  4. 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. (Polling is amazingly consistent) To gather this information, they interviewed fewer than 2,000 people.

  5. Presidential Elections • 1936 Dewey vs. Roosevelt—what happened? (Poor?) • Gallup—used Quota Sampling—while previously effective—what happened?

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

  7. NON-PROBABLILTY SAMPLING • NON-PROBABILITY SAMPLING— When samples are selected using pragmatic means to obtain people. We use these because they work—not because they are accurate—just useful.

  8. 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.

  9. 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.

  10. 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. (Use judgement—which ones is most representative!) • Used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors

  11. 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.

  12. 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.

  13. Selecting Informants • Informants—a member of the group who can talk directly about the group per se. (Well-versed member of group.) • Informants usually are representative of the group. • Respondents—respond about themselves

  14. 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. (OVERALL GOAL—richest possible data/representativeness)

  15. Probability Sampling • Every member of the population—must have an equal chance of being selected. • Bias in sampling means those selected are not representative of the characteristics of the population.

  16. Variation • See Chart—p.193—the population of 100 folks varies by race and gender, among other things unnamed. • We are trying to explain this variety. • Show standard deviation—how it measures variety.

  17. Advantages of Probability Sampling • Representativeness (more than quota) • Allows us to estimate probability of sample error.

  18. Population Definition • Population—the theoretically specified aggregation of study elements. • Study population—the aggregate of elements from which the sample is actually drawn. (Someone left off list)

  19. Ten Cases • Parameter—the summary description of a given variable. (μ=mean of population) (σ of population) Explain the Theoretical Sampling Distribution (see page 200)

  20. The Normal Curve • Has a mean of 0 and a standard deviation of 1. • Unimodal • Symmetrical • Areas under curve (probability) (STOP HERE FOR TODAY)

  21. Populations and Sampling Frames • Findings based on a sample represent the aggregation of elements that compose the sampling frame. • Sampling frames do not always include all the elements their names imply. (PROBLEMS WITH LISTS) • All elements must have equal representation in the frame.

  22. Types of Sampling Designs • Simple random sampling (SRS) • Systematic sampling • Stratified sampling • Cluster sampling/Multi-stage cluster

  23. Simple Random Sampling • Feasible only with the simplest sampling frame. • Not the most accurate method available.

  24. Systematic Sampling • Slightly more accurate than simple random sampling. • Arrangement of elements in the list can result in a biased sample.

  25. Stratified Sampling • Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population. (WHY—fear of sample bias) • Results in a greater degree of representativeness by decreasing the probable sampling error.

  26. A Stratified, Systematic Sample with a Random Start

  27. 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.

  28. Probability Proportionate to Size (PPS) Sampling • Sophisticated form of cluster sampling. • Used in many large scale survey sampling projects.

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