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Sampling Probability Sampling Nonprobability Sampling.

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Presentation on theme: "Sampling Probability Sampling Nonprobability Sampling."— Presentation transcript:

1 Sampling Probability Sampling Nonprobability Sampling

2 Probability Sampling Sampling element Population Target population Sampling frame Sampling ratio

3 There is a classic Jimmy Stewart movie, Magic Town, about "Grandview," a small town in the Midwest that is a perfect statistical microcosm of the United States, a place where the citizens' opinions match perfectly with Gallup polls of the entire nation. A pollster (Jimmy Stewart), secretly uses surveys from this "mathematical miracle" as a shortcut to predicting public opinion. Instead of collecting a national sample, he can more quickly and cheaply collect surveys from this single small town. The character played by Jane Wyman, a newspaper editor, finds out what is going on and publishes her discovery. As a result the national media descend upon the town, which becomes, overnight, "the public opinion capital of the U.S."

4 Probability Sampling

5 Sampling Distribution

6 Probability Sampling Random sample Sampling error Four Ways to Sample Randomly – Simple Random – Systematic – Stratified Sampling – Cluster Sampling

7 Random Sample Sampling Error: Variation Component Sample size Component

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9 R Session data=c(1,1,0,0,0,0,1,1,0,1,1,0,1,1,1,0) population.mean=mean(data) #samples of size 5 a.sample=sample(x=data,size=5,replace=FALSE) a.mean=mean(a.sample) #another sample b.sample=sample(data,5,FALSE) b.mean=mean(b.sample) #Distribution of sample mean #We need to sample lots of times sim.runs=100 mean.sample=NA for (i in 1:sim.runs){ sample.data=sample(data,5,FALSE) mean.sample[i]=mean(sample.data) } hist(mean.sample,breaks=4)

10 Sampling Distribution and Sampling Error

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12 Sampling and Confidence x

13 Important Concepts in Sampling Margin of Error Finite Population Correction Factor Sampling error Next: Sample size

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15 Other Probability Sampling Designs


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