1. Chapter 4 Gathering data
Learn ….
How to gather “good” data
About Experiments and Observational Studies

2. Should We Experiment or Should we Merely Observe?
Section 4.1
Should We Experiment or Should we Merely Observe?

3. Population, Sample and Variables
Population: all the subjects of interest
Sample: subset of the population -
data is collected on the sample
Response variable: measures the outcome of interest
Explanatory variable: the variable that explains the response variable

4. Observational Studies
Types of Studies
Experiments
Observational Studies

5. Experiment
A researcher conducts an experiment by assigning subjects to certain experimental conditions and then observing outcomes on the response variable
The experimental conditions, which correspond to assigned values of the explanatory variable, are called treatments

6. Observational Study
In an observational study, the researcher observes values of the response variable and explanatory variables for the sampled subjects, without anything being done to the subjects (such as imposing a treatment)

7. Example: Does Drug Testing Reduce Students’ Drug Use?
Headline: “Student Drug Testing Not Effective in Reducing Drug Use”
Facts about the study:
76,000 students nationwide
Schools selected for the study included schools that tested for drugs and schools that did not test for drugs
Each student filled out a questionnaire asking about his/her drug use

8. Example: Does Drug Testing Reduce Students’ Drug Use?

9. Example: Does Drug Testing Reduce Students’ Drug Use?
Conclusion: Drug use was similar in schools that tested for drugs and schools that did not test for drugs

10. Example: Does Drug Testing Reduce Students’ Drug Use?
What were the response and explanatory variables?

11. Example: Does Drug Testing Reduce Students’ Drug Use?
Was this an observational study or an experiment?

12. Advantages of Experiments over Observational Studies
We can study the effect of an explanatory variable on a response variable more accurately with an experiment than with an observational study
An experiment reduces the potential for lurking variables to affect the result

13. Experiments vs Observational Studies
When the goal of a study is to establish cause and effect, an experiment is needed
There are many situations (time constraints, ethical issues,..) in which an experiment is not practical

14. Good Practices for Using Data
Beware of anecdotal data
Rely on data collected in reputable research studies

15. Example of a Dataset General Social Survey (GSS):
Observational Data Base
Tracks opinions and behaviors of the American public
A good example of a sample survey
Gathers information by interviewing a sample of subjects from the U.S. adult population
Provides a snapshot of the population

16. What Are Good Ways and Poor Ways to Sample?
Section 4.2
What Are Good Ways and Poor Ways to Sample?

17. Setting Up a Sample Survey
Step 1: Identify the Population
Step 2: Compile a list of subjects in the population from which the sample will be taken. This is called the sampling frame.
Step 3: Specify a method for selecting subjects from the sampling frame. This is called the sampling design.

18. Random Sampling Best way of obtaining a representative sample
The sampling frame should give each subject an equal chance of being selected to be in the sample

19. Simple Random Sampling
A simple random sample of ‘n’ subjects from a population is one in which each possible sample of that size has the same chance of being selected

20. Example: Sampling Club Officers for a New Orleans Trip
The five offices: President, Vice-President, Secretary, Treasurer and Activity Coordinator
The possible samples are:
(P,V) (P,S) (P,T) (P,A) (V,S)
(V,T) (V,A) (S,T) (S,A) (T,A)

21. The possible samples are: (P,V) (P,S) (P,T) (P,A) (V,S) (V,T) (V,A) (S,T) (S,A) (T,A)
What are the chances the President and Activity Coordinator are selected?
1 in 5
1 in 10
1 in 2

22. Selecting a Simple Random Sample
Use a Random Number Table
Use a Random Number Generator

23. Methods of Collecting Data in Sample Surveys
Personal Interview
Telephone Interview
Self-administered Questionnaire

24. How Accurate Are Results from Surveys with Random Sampling?
Sample surveys are commonly used to estimate population percentages
These estimates include a margin of error

25. Example: Margin of Error
A survey result states: “The margin of error is plus or minus 3 percentage points”
This means: “It is very likely that the reported sample percentage is no more than 3% lower or 3% higher than the population percentage”
Margin of error is approximately:

26. Be Wary of Sources of Potential Bias in Sample Surveys
A variety of problems can cause responses from a sample to tend to favor some parts of the population over others

27. Types of Bias in Sample Surveys
Sampling Bias: occurs from using nonrandom samples or having undercoverage
Nonresponse bias: occurs when some sampled subjects cannot be reached or refuse to participate or fail to answer some questions
Response bias: occurs when the subject gives an incorrect response or the question is misleading

28. Poor Ways to Sample
Convenience Sample: a sample that is easy to obtain
Unlikely to be representative of the population
Severe biases my result due to time and location of the interview and judgment of the interviewer about whom to interview

29. Poor Ways to Sample
Volunteer Sample: most common form of convenience sample
Subjects volunteer for the sample
Volunteers are not representative of the entire population

30. A Large Sample Does Not Guarantee An Unbiased Sample
Warning:
A Large Sample Does Not Guarantee An Unbiased Sample

31. What Are Good Ways and Poor Ways to Experiment?
Section 4.3
What Are Good Ways and Poor Ways to Experiment?

32. An Experiment
Assign each subject (called an experimental unit ) to an experimental condition, called a treatment
Observe the outcome on the response variable
Investigate the association – how the treatment affects the response

33. Elements of a Good Experiment
Primary treatment of interest
Secondary treatment for comparison
Comparing the primary treatment results to the secondary treatment results help to analyze the effectiveness of the primary treatment

34. Control Group
Subjects assigned to the secondary treatment are called the control group
The secondary treatment could be a placebo or it could be an actual treatment

35. Randomization in an Experiment
It is important to randomly assign subjects to the primary treatment and to the secondary (control) treatment
Goals of randomization:
Prevent bias
Balance the groups on variables that you know affect the response
Balance the groups on lurking variables that may be unknown to you

36. Blinding the Study
Subjects should not know which group they have been assigned to – the primary treatment group or the control group
Data collectors and experimenters should also be blind to treatment information

37. Example: A Study to Assess Antidepressants for Quitting Smoking
Design:
429 men and women
Subjects had smoked 15 cigarettes or more per day for the previous year
Subjects were highly motivated to quit

38. Example: A Study to Assess Antidepressants for Quitting Smoking
Subjects were randomly assigned to one of two groups:
One group took an antidepressant daily
Second group did not take the antidepressant (this group is called the placebo group)

39. Example: A Study to Assess Antidepressants for Quitting Smoking
The study ran for one year
At the end of the year, the study observed whether each subject had successfully abstained from smoking or had relapsed

40. Example: A Study to Assess Antidepressants for Quitting Smoking
Results after 1 year:
Treatment Group: 55.1% were not smoking
Placebo Group: 42.3% were not smoking
Results after 18 months:
Antidepressant Group: 47.7% not smoking
Placebo Group: 37.7% not smoking
Results after 2 years:
Antidepressant Group: 41.6% not smoking
Placebo Group: 40% not smoking

41. Example: A Study to Assess Antidepressants for Quitting Smoking
Question to Think About: Are the differences between the two groups statistically significant or are these differences due to ordinary variation?

42. What Are Other Ways to Conduct Experimental and Observational Studies?
Section 4.4
What Are Other Ways to Conduct Experimental and Observational Studies?

43. Multifactor Experiments
Multifactor Experiments: have more than one categorical explanatory variable (called a factor).

44. Example: Do Antidepressants and/or Nicotine Patches Help Smokers Quit?

45. Matched-Pairs Design Each subject serves as a block
Both treatments are observed for each subject

46. Subject Drug Placebo 1 Relief No Relief 2 3
Example: A Study to Compare an Oral Drug with a Placebo for Treating Migraine Headaches
Subject Drug Placebo 1
Relief
No Relief
First matched pair 2 3

47. Blocks and Block Designs
Block: collection of experimental units that have the same (or similar) values on a key variable
Block Design: identifies blocks before the start of the experiment and assigns subjects to treatments with in those blocks

48. Experiments vs Observational Studies
An Experiment can measure cause and effect
An observational study can yield useful information when an experiment is not practical
An observational study is a practical way of answering questions that do not involve trying to establish causality

49. Observational Studies
A well-designed and informative observational study can give the researcher very useful data.
Sample surveys that select subjects randomly are good examples of observational studies.

50. Random Sampling Schemes
Simple Random Sample: every possible sample has the same chance of selection

51. Random Sampling Schemes
Cluster Random Sample:
Divide the population into a large number of clusters
Select a sample random sample of the clusters
Use the subjects in those clusters as the sample

52. Random Sampling Schemes
Stratified Random Sample:
Divide the population into separate groups, called strata
Select a simple random sample from each strata

53. Observational Studies
Well-designed observational studies use random sampling schemes

54. Retrospective and Prospective Studies
Retrospective study: looks into the past
Prospective study: follows its subjects into the future

55. Case-Control Study
A case-control study is an observational study in which subjects who have a response outcome of interest (the cases) and subjects who have the other response outcome (the controls) are compared on an explanatory variable

56. Example: Case-Control Study
Response outcome of interest: Lung cancer
The cases have lung cancer
The controls did not have lung cancer
The two groups were compared on the explanatory variable:
Whether the subject had been a smoker