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Chapter 7. The Logic Of Sampling. Chapter Outline. Introduction A Brief History of Sampling Nonprobability Sampling The Theory and Logic of Probability Sampling. Chapter Outline. Populations and Sampling Frames Types of Sampling Designs Multistage Cluster Sampling

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Chapter 7 l.jpg

Chapter 7

The Logic Of Sampling


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Chapter Outline

  • Introduction

  • A Brief History of Sampling

  • Nonprobability Sampling

  • The Theory and Logic of Probability Sampling


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Chapter Outline

  • Populations and Sampling Frames

  • Types of Sampling Designs

  • Multistage Cluster Sampling

  • Probability Sampling in Review


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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. (Polling is amazingly consistent)

    To gather this information, they interviewed fewer than 2,000 people.


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Presidential Elections

  • 1936 Dewey vs. Roosevelt—what happened? (Poor?)

  • Gallup—used Quota Sampling—while previously effective—what happened?



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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.


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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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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. (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


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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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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


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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. (OVERALL GOAL—richest possible data/representativeness)


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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.


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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.


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Advantages of Probability Sampling

  • Representativeness (more than quota)

  • Allows us to estimate probability of sample error.


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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)


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Ten Cases

  • Parameter—the summary description of a given variable. (μ=mean of population)

    (σ=st.dev. of population)

    Explain the Theoretical Sampling Distribution (see page 200)


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The Normal Curve

  • Has a mean of 0 and a standard deviation of 1.

  • Unimodal

  • Symmetrical

  • Areas under curve (probability)

    (STOP HERE FOR TODAY)


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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.


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Types of Sampling Designs

  • Simple random sampling (SRS)

  • Systematic sampling

  • Stratified sampling

  • Cluster sampling/Multi-stage cluster


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Simple Random Sampling

  • Feasible only with the simplest sampling frame.

  • Not the most accurate method available.


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Systematic Sampling

  • Slightly more accurate than simple random sampling.

  • 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. (WHY—fear of sample bias)

  • 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 (PPS) Sampling

  • Sophisticated form of cluster sampling.

  • Used in many large scale survey sampling projects.


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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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