1. 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. How good is a census? Do frog fairy tale . . .
The answer is 83!

6. 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
Look at the U.S. census – it has a huge amount of error in it; plus it takes a long to compile the data making the data obsolete by the time we get it!
Since taking a census of any population takes time, censuses are VERY costly to do!
Suppose you wanted to know the average weight of the white-tail deer population in Texas – would it be feasible to do a census?

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

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

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

10. Simple Random Sample (SRS)
Suppose we were to take an SRS of 100 SWH students – put each students’ name in a hat. Then randomly select 100 names from the hat. Each student has the same chance to be selected!
Not only does each student has the same chance to be selected – but every possible group of 100 students has the same chance to be selected! Therefore, it has to be possible for all 100 students to be seniors in order for it to be an 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

11. Stratified random sample
Homogeneous groups are groups that are alike based upon some characteristic of the group members.
Suppose we were to take a stratified random sample of 100 SWH students. Since students are already divided by grade level, grade level can be our strata. Then randomly select 50 seniors and randomly select 50 juniors.
population is divided into homogeneous groups called strata
SRS’s are pulled from each strata

12. Systematic random sample
Suppose we want to do a systematic random sample of SWH students - number a list of students
(There are approximately 2000 students – if we want a sample of 100, 2000/100 = 20)
Select a number between 1 and 20 at random. That student will be the first student chosen, then choose every 20th student from there.
Systematic random sample
select sample by following a systematic approach
randomly select where to begin

13. Cluster Sample based upon location
Suppose we want to do a cluster sample of SWH students. One way to do this would be to randomly select 10 classrooms during 2nd period. Sample all students in those rooms!
Cluster Sample
based upon location
randomly pick a location & sample all there

14. Multistage sample
To use a multistage approach to sampling SWH students, we could first divide 2nd period classes by level (AP, Honors, Regular, etc.) and randomly select 4 second period classes from each group. Then we could randomly select 5 students from each of those classes. The selection process is done in stages!
select successively smaller groups within the population in stages
SRS used at each stage

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

16. 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

17. Systematic Random Sample
Advantages
Unbiased
Ensure that the sample is distributed across population
More efficient, cheaper, etc.
Disadvantages
Large variance
Can be confounded by trend or cycle
Formulas are complicated

18. 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

19. 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

20. 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

21. 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

22. Numbers can be read across.
Random digit table
Numbers can be read vertically.
The following is part of the random digit table found on page 847 of your textbook:
Row
Numbers can be read diagonally.
each entry is equally likely to be any of the 10 digits
digits are independent of each other

23. 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

24. Bias ERROR 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!

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

26. Remember – the way to determine voluntary response is:
People chose to respond
Usually only people with very strong opinions respond
An example would be the surveys in magazines that ask readers to mail in the survey. Other examples are call-in shows, American Idol, etc.
Remember, the respondent selects themselves to participate in the survey!
Remember – the way to determine voluntary response is:
Self-selection!!

27. Convenience sampling Ask people who are easy to ask
The data obtained by a convenience sample will be biased – however this method is often used for surveys & results reported in newspapers and magazines!
Ask people who are easy to ask
Produces bias results
An example would be stopping friendly-looking people in the mall to survey. Another example is the surveys left on tables at restaurants - a convenient method!

28. People with unlisted phone numbers – usually high-income families
Undercoverage
some groups of population are left out of the sampling process
People without phone numbers –usually low-income families
Suppose you take a sample by randomly selecting names from the phone book – some groups will not have the opportunity of being selected!
People with ONLY cell phones – usually young adults

29. Nonresponse
Because of huge telemarketing efforts in the past few years, telephone surveys have a MAJOR problem with nonresponse!
occurs when an individual chosen for the sample can’t be contacted or refuses to cooperate
telephone surveys 70% nonresponse
People are chosen by the researchers, BUT refuse to participate.
NOT self-selected!
This is often confused with voluntary response!
One way to help with the problem of nonresponse is to make follow contact with the people who are not home when you first contact them.

30. Suppose we wanted to survey high school students on drug abuse and we used a uniformed police officer to interview each student in our sample – would we get honest answers?
Response bias
occurs when the behavior of respondent or interviewer causes bias in the sample
wrong answers
Response bias occurs when for some reason (interviewer’s or respondent’s fault) you get incorrect answers.

31. Wording of the Questions
The level of vocabulary should be appropriate for the population you are surveying
Questions must be worded as neutral as possible to avoid influencing the response.
wording can influence the answers that are given
connotation of words
use of “big” words or technical words
– if surveying Podunk, TX, then you should avoid complex vocabulary.
– if surveying doctors, then use more complex, technical wording.

32. 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 10 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.
Undercoverage – since the Digest’s survey comes from car owners, etc., the people selected were mostly from high-income families and thus mostly Republican! (other answers are possible)

33. Convenience sampling – easy way to collect data
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.
Convenience sampling – easy way to collect data or
Undercoverage – students who buy books from on-line bookstores are excluded.

34. (other answers are possible)
3) To find the average value of a home in Memorial, one averages the price of homes that are listed for sale with a realtor.
Undercoverage – leaves out homes that are not for sale or homes that are listed with different realtors.
(other answers are possible)

35. Designing Experiments
The individuals on which the experiment is done are the experimental units.
If units are humans, they are called subjects.
The experimental condition applied to the units (aka the thing we ‘do’ to the people participating) is called a treatment.
Goal of research is to establish a causal link between a particular treatment and a response.

36. Factors & levels
Factors: number of variables interested in (example: Study differences of gender and alcohol preference. 2 factors: Gender, alcohol preference)
Levels: number of ‘categories’ for each: (gender has 2 levels…M/F, Alcohol lets say has 3 levels…hard liquor/beer/wine)
This is an example of a 2x3 study

37. Control
We use lab experiments often to protect us from lurking variables which may happen when conducting experiments ‘in the field’

38. Replication
Even w/control, natural variability occurs among experimental units.
If we assign many individuals to each treatment group, the effects of chance (and individual differences) will average out.

39. Randomized Comparative Experiments

40. Principles of Experimental Design
1. Control the effects of lurking variables on the response, most simply by comparing 2 or more treatments
2. Replicate each treatment on many units to reduce chance variation in results
3. Randomize – use impersonal chance to assign experimental units to treatments

41. Blocking/block design
A block is a group of experimental units that are known before the experiment to be similar in some way that is expected to systematically affect the response to treatments (ex: Testing the effect of weight lifting on a group of people- men/women will have obvious differences).
Separate into “blocks” of similar subjects to reduce the effect of variation

42. Cautions about experimentation
Double-blind: neither subject nor experimenter knows which treatment is assigned
Lack of realism: subjects or treatments of an experiment may not realistically duplicate the conditions we really want to study.