1. Producing Data
Chapter 5

2. Gathering Data Sample survey Observational study Experiment
Before explaining each of these we must be familiar with some vocabulary.

3. Vocabulary
Treatments/Explanatory Variables
Units
Random Assignment

4. Treatments/Explanatory variables
Explanatory Variables: Attempts to explain the observed outcome. Explains differences in response variable.
Treatment: Any particular combination of values for the explanatory variables; conditions you want to assign or compare.

5. Units
A unit is a person, animal, plant or thing or groups of people animals, plants or things which is actually being studied by a researcher. The basic objects upon which the study or experiment is carried out.

6. Units
For example, a person; a monkey; a sample of soil; a pot of seedlings; a cage of rats; a day care center, a child in the day care center, a school, a student at the school, a doctor's practice, a person in the doctors practice.

7. Random Selection
Randomization is the process by which experimental units are allocated to treatments; that is, by a random process and not by any subjective and hence possibly biased approach. The treatments should be allocated to units in such a way that each treatment is equally likely to be applied to each unit.

8. Observational study
A type of study in which individuals are observed and certain outcomes are measured. No attempt is made to affect the outcome (for example, no treatment is given).

9. Observational study No treatments
No random selection of units to treatments
No random selection of units from population of interest

10. Example: Observational study
On March 8, 1983, Newsweek announced, “A really bad Hair Day: Researchers link baldness and heart attacks” (p62) The article reported that “men with typical male pattern baldness…are anywhere from 30 to 300 percent more likely to suffer a heart attack than men with little or no hair loss at all.”

11. Example: Observational study
The report was based on an observational study conducted by researchers at Boston University School Medicine. They compared 665 men who had been admitted to the hospital with their first heart attack to 772 men in the same age group (21 to 54 years old) who had been admitted to the same hospitals for other reasons. There were 35 hospitals involved, all in eastern Massachusetts and Rhode Island.

12. Example: Observational study
The study found that the percentage of men who showed some degree of pattern baldness was substantially higher for those who had had a heart attack (42%) than for those who had not (34%).

13. Example: Observational study
The authors of the study speculated that a third variable, perhaps a male hormone, might simultaneously increate the risk of heart attacks and the propensity for baldness. With an observational study such as this one, scientists can establish a connection, and they can then look for causal mechanisms in future investigations.

14. Sample Survey Units are selected from a population of interest.
Certain outcomes are measured
Makes no random assignment of them to treatments.

15. An Example: Sample Survey
A farmer wishes to estimate the yield of his 100 acre field. He randomly selects 10 of the acres and observes the number of number of “saleable” produces in each of the 10 acres.

16. Experiment
Deliberately imposes some treatments on individuals in order to observe their responses.
Assigns all units randomly to treatments or treatments to trials.
May or may not select units at random from the population

17. How can you differentiate observational studies from sample surveys from experiments?
Are treatments imposed?
Are units randomly assigned to treatments?
Are units randomly selected from population of interest?

18. Random selection of units from population
Sample
Survey
Observational
Study
Experiment
Random selection of units from population
Imposed treatment?
Random assignment of units to treatments
Yes NO
Maybe NO NO
Yes NO NO
Yes

19. Is this an experiment. What is the treatment
Is this an experiment? What is the treatment? Are units randomly assigned to treatments?
Suppose we wanted to study effects of gender on pulse rate for students in our class. Our population is 30 students in the class. We randomly select 10 of them to participate. We compare average pulse rate of males with pulse rate of females.

20. It is not an experiment! Sometimes we cannot assign treatment to units!
Suppose we wanted to study effects of gender on pulse rate. Our population is 30 students in a class. We randomly select 10 of them to participate. Can we randomly assign 5 of them to be males and the others to be females? NO! It is not an experiment. The units of study cannot be randomly assigned to treatments! It is a sample survey!

21. Is this an experiment? Why or why not? What is the treatment?
In 1973 a study was published that compared the purity of intravenous fluids manufactured by three different drug companies. Six different samples were randomly selected from each of the three companies and the number of contaminant particles were measured.

22. Scope of inference
The scope of inference depends on the manner in which the researchers randomized.

23. Results can be used how? Sample Survey
Identify associations among variables for the populations from which the units were randomly selected

24. Results can be used how? Observation Study
Identify associations among variables for the units in the study only.

25. Results can be used how? Experiment
Allows researchers to draw cause-and –effect conclusions for either the study’s units (if the experimental units were not randomly selected) or the population (if the experimental units were randomly selected).

26. Allocation of units to treatment groups At Random Not at Permitted
Inference
Experiment with broad scope of inference
Sample Survey
Inference to population
Experiment with narrow scope of inference
Observational Study
Inference limited to units in study
Permitted Inference
Casual Inference
Association can be made, but no causal inference
Selection of Units

27. Vocabulary
Explanatory Variables: Attempts to explain the observed outcome. Explains differences in response variable.
Treatment: Any particular combination of values for the explanatory variables; conditions you want to assign or compare

28. Example 1
Does regularly taking aspirin help protect people against heart attacks? The physicians Health Study was a medical experiment that helped answer this question. In fact, the Physicians Health study looked at the effects of two drugs: aspirin and beta carotene. The subjects were 21,996 male physicians.

29. How many Treatments are there?
Does regularly taking aspirin help protect people against heart attacks? The 21,996 male physicians took either aspirin and beta carotene, aspirin and a placebo, placebo and a beta carotene or two placebos

30. Factor 2 Beta carotene Yes No Aspirin / Beta Carotene
Aspirin / Placebo
Placebo / Beta
Placebo / Placebo
Factor 1 aspirin

31. There are 4 treatments: What are they?
There are 2 factors:
aspirin or beta carotene
There are 2 levels for each factor:
Yes or NO

32. There are three explanatory variables. What are they?
Aspirin
Beta carotene
Placebo

33. Factorial Design
A factorial design is used to evaluate two or more factors simultaneously. The treatments are combinations of levels of the factors. The advantages of factorial designs over one-factor-at-a-time experiments is that they are more efficient and they allow interactions to be detected.

34. Example 2
Does the type of lighting or the type of music in the dentists waiting room have an effect on the anxiety level of the patient? Consider that there are 3 types of lighting: Low, med and high and 3 types of music: jazz, pop, and classical.

35. Example 2 How many treatments are there? How many factors are there?
How many levels of each factor are there?

36. Two factorial design 3x3 factorial design
Low
Med
High
Jazz
Pop
classical

37. Vocabulary
Response Variable: Data collected is the response variable. Measures outcome of the study

38. What was the response variable?
In the Physicians health study where the effects of aspirin and beta carotene on heart attack rate was studied, what was the response variable?

39. Experimental Units
The smallest object to which a treatment will be applied at random.

40. What are the experimental Units?
A study conducted by researchers exposed one hundred mice at random to cell phone radiation for two half-hour periods each day for eighteen months and fitted another one hundred mice at random with the same type of antennas, which never had the power turned on.

41. What are the experimental Units?
Suppose that instead of randomizing the individual mice to cell phone exposure or no cell phone exposure, the researchers had placed one hundred mice in one cage and then the other hundred mice in another cage. They randomly selected which cage of mice would get the radiation

42. What are the experimental units?
A researcher is interested in comparing 3 different gasoline additives with respect to automobile performance as measured by gas mileage. The experimenter uses a single car with an empty tank. One gallon of gas with one of the additives will be poured into the tank and the car driven until it runs out of gas. This is repeated 30 times – 10 times with each additive.

43. What is the treatment?
A researcher is interested in comparing 3 different gasoline additives with respect to automobile performance as measured by gas mileage. The experimenter uses a single car with an empty tank. One gallon of gas with one of the additives will be poured into the tank and the car driven until it runs out of gas. This is repeated 30 times – 10 times with each additive.

44. Control Randomization Replication
Three Principals of Experimental Design
Control
Randomization
Replication

45. Control
Control the effects of lurking variables on the response, most simply by comparing two or more treatments.

46. Randomize
Use impersonal chance to assign experimental units to treatments

47. Replicate
Replicate each treatment on many units to reduce chance variation in results.

48. For a given experiment, you should be able to:
Identify the response variable and indicate how they propose to measure it.
Determine the experimental units-the individuals or groups of individuals that they will randomly assign to treatments;
Define the scope of inference-the population to which the experiment’s results can be legitimately applied.
Identify the treatments, factors, levels and explanatory variables.