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# chapter 7 - PowerPoint PPT Presentation

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

• Introduction

• A Brief History of Sampling

• Nonprobability Sampling

• The Theory and Logic of Probability Sampling

• Populations and Sampling Frames

• Types of Sampling Designs

• Multistage Cluster Sampling

• Probability Sampling in Review

• 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.

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

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

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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)

• 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.

• 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.

• Representativeness (more than quota)

• Allows us to estimate probability of sample error.

• 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)

• 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)

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

• Unimodal

• Symmetrical

• Areas under curve (probability)

(STOP HERE FOR TODAY)

• 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.

• Simple random sampling (SRS)

• Systematic sampling

• Stratified sampling

• Cluster sampling/Multi-stage cluster

• Feasible only with the simplest sampling frame.

• Not the most accurate method available.

• Slightly more accurate than simple random sampling.

• Arrangement of elements in the list can result in a biased sample.

• 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.

• 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.

• Sophisticated form of cluster sampling.

• Used in many large scale survey sampling projects.

• Most effective method for selection of study elements.

• Avoids researchers biases in element selection.

• Permits estimates of sampling error.