# Sampling Probability Sampling Nonprobability Sampling.

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

Sampling Probability Sampling Nonprobability Sampling

Probability Sampling Sampling element Population Target population Sampling frame Sampling ratio

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."

Probability Sampling

Sampling Distribution

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

Random Sample Sampling Error: Variation Component Sample size Component

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)

Sampling Distribution and Sampling Error

Sampling and Confidence x

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

Other Probability Sampling Designs