1. Chapter 4
Sampling Design

2. How do we gather data? Surveys Opinion polls Interviews Studies
Observational
Retrospective (past)
Prospective (future)
Experiments

3. Population
the entire group of individuals that we want information about

4. Census
a complete count of the population

5. Why would we not use a census all the time?
Not accurate
Very expensive
Perhaps impossible
If using destructive sampling, you would destroy population
Breaking strength of soda bottles
Lifetime of flashlight batteries
Safety ratings for cars

6. Sample
A part of the population that we actually examine in order to gather information
Use sample to generalize to population

7. Sampling design
refers to the method used to choose the sample from the population

8. Sampling frame
a list of every individual in the population

9. Simple Random Sample (SRS)
consist of n individuals from the population chosen in such a way that
every individual has an equal chance of being selected
every set of n individuals has an equal chance of being selected

10. SRS Advantages Disadvantages Unbiased Easy Large variance
May not be representative
Must have sampling frame (list of population)

11. Stratified random sample
population is divided into homogeneous groups called strata
SRS’s are pulled from each strata

12. Stratified Disadvantages Advantages
More precise unbiased estimator than SRS
Less variability
Cost reduced if strata already exists
Disadvantages
Difficult to do if you must divide stratum
Formulas for SD & confidence intervals are more complicated
Need sampling frame

13. Systematic random sample
select sample by following a systematic approach
randomly select where to begin

14. Systematic Random Sample
Advantages
Unbiased
Don’t need sampling frame
Ensure that the sample is spread across population
More efficient, cheaper, etc.
Disadvantages
Large variance
Can be confounded by trend or cycle
Formulas are complicated

15. Cluster Sample based upon location
randomly pick a location & sample all there

16. Cluster Samples Advantages Disadvantages Unbiased
Cost is reduced
Sampling frame may not be available (not needed)
Disadvantages
Clusters may not be representative of population
Formulas are complicated

17. Multistage sample
select successively smaller groups within the population in stages
SRS used at each stage

18. Identify the sampling design
1)The Educational Testing Service (ETS) needed a sample of colleges. ETS first divided all colleges into groups of similar types (small public, small private, etc.) Then they randomly selected 3 colleges from each group.
Stratified random sample

19. Identify the sampling design
2) A county commissioner wants to survey people in her district to determine their opinions on a particular law up for adoption. She decides to randomly select blocks in her district and then survey all who live on those blocks.
Cluster sampling

20. Identify the sampling design
3) A local restaurant manager wants to survey customers about the service they receive. Each night the manager randomly chooses a number between 1 & 10. He then gives a survey to that customer, and to every 10th customer after them, to fill it out before they leave.
Systematic random sampling

21. Random digit table
The following is part of the random digit table:
Row
each entry is equally likely to be any of the 10 digits
digits are independent of each other

22. Ignore. Ignore. Ignore. Ignore.
Suppose your population consisted of these 20 people:
1) Aidan 6) Fred 11) Kathy 16) Paul
2) Bob 7) Gloria 12) Lori 17) Shawnie
3) Chico 8) Hannah 13) Matthew 18) Tracy
4) Doug 9) Israel 14) Nan 19) Uncle Sam
5) Edward 10) Jung 15) Opus 20) Vernon
Use the following random digits to select a sample of five from these people.
We will need to use double digit random numbers, ignoring any number greater than 20. Start with Row 1 and read across.
1) Aidan
13) Matthew
18) Tracy
5) Edward
15) Opus
Ignore.
Ignore.
Ignore.
Ignore.
Stop when five people are selected. So my sample would consist of :
Aidan, Edward, Matthew, Opus, and Tracy
Row

23. Bias A systematic error in measuring the estimate
favors certain outcomes
Anything that causes the data to be wrong! It might be attributed to the researchers, the respondent, or to the sampling method!

24. Sources of Bias things that can cause bias in your sample
cannot do anything with bad data

25. Voluntary response People chose to respond
Usually only people with very strong opinions respond

26. Convenience sampling Ask people who are easy to ask
Produces bias results

27. Undercoverage
some groups of population are left out of the sampling process

28. Nonresponse
occurs when an individual chosen for the sample can’t be contacted or refuses to cooperate
telephone surveys 70% nonresponse

29. Response bias
occurs when the behavior of respondent or interviewer causes bias in the sample
wrong answers

30. Wording of the Questions
wording can influence the answers that are given
connotation of words
use of “big” words or technical words

31. Source of Bias?
1) Before the presidential election of 1936, FDR against Republican ALF Landon, the magazine Literary Digest predicting Landon winning the election in a 3-to-2 victory. A survey of 2.8 million people. George Gallup surveyed only 50,000 people and predicted that Roosevelt would win. The Digest’s survey came from magazine subscribers, car owners, telephone directories, etc.

32. 2) Suppose that you want to estimate the total amount of money spent by students on textbooks each semester at SMU. You collect register receipts for students as they leave the bookstore during lunch one day.

33. 3) To find the average value of a home in Plano, one averages the price of homes that are listed for sale with a realtor.