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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 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.