1. Chapter One Data Collection
Observational Studies versus Designed Experiments

2. Objectives
Distinguish between an observational study and an experiment.
Explain the various types of observational studies.

3. Read Example 1 and Example 2 on pages 15-6
Warm-up
Read Example 1 and Example 2 on pages 15-6
Read the paragraph following them that describes an explanatory variable and a response variable.
In your own words, explain the difference between an explanatory variable and response variable.

4. Warm-up Response Variable The outcome of a study.
A variable you would be interested in predicting or forecasting.
Often called a dependent variable or predicted variable.
Explanatory Variable
Any variable that explains the response variable.
Often called an independent variable or predictor variable.

5. Observational study
Measures the characteristics of a population by studying individuals in a sample
Does not attempt to manipulate or influence the individuals.

6. Various Types of Observational Studies
Cross-sectional studies
Collect information at a specific point in time or over a short period of time (snapshot)
Case-control studies
Looks back in time or look at existing records (retrospective)
Cohort studies
Collect information over a long period of time (prospective)

7. Designed experiment
Investigators apply a treatment to experimental units (people, animals, plots of land, etc.) and observe the effects (response) of the treatment on the experimental units.

8. For example, suppose we want to study the effect of smoking on lung capacity in women.

9. Example of Observational Study
Find 100 women age 30 of which 50 have been smoking a pack a day for 10 years while the other 50 have been smoke free for 10 years.
Measure lung capacity for each of the 100 women.
Analyze, interpret, and draw conclusions from data.

10. Example of Experiment
Find 100 women age 20 who do not currently smoke.
Randomly assign 50 of the 100 women to the smoking treatment and the other 50 to the no smoking treatment.
Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke free for 10 years.
Measure lung capacity for each of the 100 women.
Analyze, interpret, and draw conclusions from data.

11. Confounding Variable
Suppose there is a gene that causes smoking to appear to be a very pleasurable experience.
Suppose the same gene also causes emphysema, lung cancer, throat cancer, etc.

12. Confounding Variable
• People who have the gene will be more likely to smoke than people who do not have the gene.
• People who have the gene will be more likely to get emphysema, lung cancer, throat cancer, etc.
So is it really smoking that causes health
problems? Maybe it is just the gene?

13. Confounding Variable
Related both to group membership and to the outcome of interest.
Its presence makes it hard to establish the outcome as being a direct consequence of group membership.

14. Lurking Variable
A variable that was not identified in a study, but affects the value of the response variable in the study

15. What could be a lurking variable in these examples?
• There is a strong positive correlation between the foot length of K-12 students and reading scores.

16. What could be a lurking variable in these examples?
• Students who use tutors have lower test scores than students who don’t.

17. What could be a lurking variable in these examples?
• A survey shows a strong positive correlation between the percentage of a country's inhabitants that use cell phones and the life expectancy in that country.

18. Observational studies
Seek to learn the characteristics of a population.
Do not allow a researcher to claim causation, only association.
May help to determine if there is a relationship between two variables, but it requires an experiment to isolate the cause of the relation.

19. Four Sources of Data
A census

20. A census is a list of all individuals in a population along with certain characteristics of each individual.

21. ..\2010_Nighttime_PopDist_Wall_Map.pdf
2010 POPULATION DISTRIBUTION IN THE UNITED STATES AND PUERTO RICO
One dot represents 1000 people
Prepared by Geography Division, U.S. Department of Commerce Economics and Statistics Administration U.S. Census Bureau 2010 POPULATION DISTRIBUTION IN THE UNITED STATES AND PUERTO RICO
Map Scales - Contiguous United States and Hawaii 1:7,500,000; Alaska 1:18,000,000; Puerto Rico 1:4,500,000

22. Four Sources of Data A census Existing sources
Centers for Disease Control
Internal Revenue Service
Department of Justice

23. Four Sources of Data
A census
Existing sources
Survey sampling

24. Four Sources of Data A census Existing sources Survey sampling
Designed experiments

25. Objectives
Distinguish between an observational study and an experiment.
Explain the various types of observational studies.