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## Chapter 7

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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 • Probability Sampling in Review**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.**Presidential Elections**• 1936 Dewey vs. Roosevelt—what happened? (Poor?) • Gallup—used Quota Sampling—while previously effective—what happened?**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.**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.**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.**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**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.**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.**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**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)**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.**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.**Advantages of Probability Sampling**• Representativeness (more than quota) • Allows us to estimate probability of sample error.**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)**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)**The Normal Curve**• Has a mean of 0 and a standard deviation of 1. • Unimodal • Symmetrical • Areas under curve (probability) (STOP HERE FOR TODAY)**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.**Types of Sampling Designs**• Simple random sampling (SRS) • Systematic sampling • Stratified sampling • Cluster sampling/Multi-stage cluster**Simple Random Sampling**• Feasible only with the simplest sampling frame. • Not the most accurate method available.**Systematic Sampling**• Slightly more accurate than simple random sampling. • Arrangement of elements in the list can result in a biased sample.**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.**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.**Probability Proportionate to Size (PPS) Sampling**• Sophisticated form of cluster sampling. • Used in many large scale survey sampling projects.**Probability Sampling**• Most effective method for selection of study elements. • Avoids researchers biases in element selection. • Permits estimates of sampling error.