1. Chapter 12
Sample Surveys

2. Topics Three Central Ideas to Sampling Population v. Sample
Parameter v. Statistic
Types of Sampling

3. Three Central Ideas to Sampling
Examine a Part of the Whole
Randomize
It’s the Sample Size

4. What did the Survey Accomplish?
Be able to identify the following:
The population
The population parameter of interest
The sampling frame
The sample
The sampling method, including whether or not randomization was employed
Any potential sources of bias or lack of generalization to the population

5. Populations and Samples
Population: The group of interest in a study.
Sample: A subgroup of the population of interest.
Sampling Frame: The list of individuals from who the sample is drawn.

6. Example Identify the population, sampling frame and sample.
A question posted on a web site asked visitors to the site to say whether they thought marijuana should be legally available for medicinal purposes.

7. Representative Sample
A sample which reflects as closely as possible the relevant characteristics of the population under consideration.

8. Representative or Not?
Are the following procedures likely to produce a representative sample for the population of interest?

9. Sample #1 Population: Whatcom County Residents
Sampling Method: 2 students stand outside a grocery store at 10:00 AM to ask shoppers about their opinions regarding the Bellingham waterfront redevelopment.

10. Sample #2 Population: United States homeowners.
Sampling: A market research company numbers all homeowners in the U.S. in a database and calls 5000 homeowners chosen using a random number generator during different parts of the day to ask their political preference.

11. Sample #3 Population: WCC Students
Sampling Method: 2 students are collecting data regarding class preference at WCC by asking random students sitting eating lunch in the courtyard.

12. Sample #4 Population: U.S. Voters in 1936
Literary Digest sent surveys to 10 million people who were either listed in telephone books or found in automobile registration lists million people responded. They predicted Alf Landon would win the presidential election handily. For comparison, most national polls today collect a sample size around

13. Different Methods for Obtaining a Representative Sample
Simple Random Sampling
Systematic Random Sampling
Cluster Sampling
Stratified Sampling

14. (Simple) Random Sampling
A sampling procedure for which each possible sample of a given size is equally likely to be the one obtained (assumed to be done without replacement, unless specified otherwise.)
A simple random sample is a sample obtained by simple random sampling.

15. Systematic Random Sampling
Divide the population size by the sample size and round the result down to the nearest whole number, m.
Use a random number table (or similar device) to obtain a number ,k, between 1 and m.
Select for the sample those members of the population that are numbered k, k + m, k +2m. . .

16. Systematic Random Sampling example
Systematically create a sample of 125 students of the 7000 that are currently attending WCC .

17. Cluster sampling Divide the population into groups (clusters).
Obtain a simple random sample of the clusters.
Use all of the members of each of the clusters obtained.

18. Example Explain how cluster sampling would work here.
A political candidate is trying to take a poll on a big issue in the upcoming campaign. A mail questionnaire produces responses from only those who feel passionately in one way or another. There are approximately 50,000 people in town. The crew decides to poll door to door. A sample of 500 is deemed large enough to provide accurate responses if the sample is random enough.
Explain how cluster sampling would work here.

19. Stratified Sampling
Divide the population into subpopulations called strata.
Sample from these strata.
These are sampled in proportion to the strata size.

20. Example
Suppose a town is voting on ballot measure to offer a tax break to people in the middle tax bracket. The income of the town is split into three tax brackets: 30% are in the lowest tax bracket, 60% are in the middle tax bracket and 10% are in the upper tax bracket.
Explain how stratified sampling would work here.

21. 3 Cautionary Remarks
1. Even with large samples, you may get the occasional “unlucky” sample which is not close to the population results.
2. The sample size means almost nothing if the sample is not random.
3. If the population size is at least 10 times as big as the sample size, the precision of the sample results depends on the sample size, not the population size.