1. Surveys, Experiments, and Simulations Unit 3 Part 3 Experimental Design

2. More Complex Experimental Design Experimental Design Multiple Factors Blind Double Blind Block Design Matched Pairs Design

3. Multiple Factors Experimental Design Experimental Units or Subjects Treatment Applied Observed Response Group A: SRS of 40 individuals 1 hour of exercise daily 200 mg of caffeine daily Measured Response: Weight Loss Observed Rate: 3 lbs / month Population of Interest Individuals 40 to 60 years of age. Group C: SRS of 40 individuals 0 hours of exercise daily 200 mg of caffeine daily Measured Response: Weight Loss Observed Rate: -3 lbs / month Group B: SRS of 40 individuals 1 hour of exercise daily 0 mg of caffeine daily Measured Response: Weight Loss Observed Rate: 2 lbs / month 2 factors, 2 levels of each = 4 treatment groups Group D: SRS of 40 individuals 0 hours of exercise daily 0 mg of caffeine daily Measured Response: Weight Loss Observed Rate: -2 lbs / month

4. Single Blind Design Experimental Design In a blind experiment the participants do not know which treatment they are receiving and often don’t know the response being measured. The purpose is to remove participant response bias which might be either intentional or unconscious. Ex. A taste test is a classic example of a single blind design where a tester prepares two sets of cups of cola labeled "A" and "B". For example, one set of cups is filled with Pepsi, while the other is filled with Coca-Cola. The tester knows which soda is in which cup but is not supposed to reveal that information to the subjects. Volunteer subjects are encouraged to try the two cups of soda and polled for which ones they prefer.

5. Problems with a Single Blind Design Experimental Design In some cases this simply might not be possible. In the case of the taste test, the tester might be biased and give unconscious hints – for example, overfilling one glass, pushing it closer to the taster, etc.

6. Double Blind Design Experimental Design In a double blind experiment the participants do not know which treatment they are receiving or the response being measured. Additionally, the experimenter doesn’t know which treatment is being applied. The purpose is to remove response bias which might be either intentional or unconscious by both the participant AND experimenter. Ex. Expanding on the taste test, the two cups can be provided by an independent 3 rd party who labels the cups on the bottom w/ the contents. After the testing and rating, the cups are turned over to reveal which is which.

7. Experimental Design Sometimes this is even more difficult to conduct (or impossible). Random assignment by a computer, processing of treatments by 3 rd parties (ie. Filling and labeling pill containers, etc.) is required which can be difficult. In the most well designed cases, the results will be completely gathered AND analyzed before the revelation is ever made to the tester. For example, in the taste test, the final result might be that Soda A had 120 people prefer the soda and Soda B had 30. Only after the conclusion of data collection and analysis would the tester learn that Soda A = Pepsi and Soda B = Coca Cola. That way they wouldn’t apply their bias at any point during the experiment or analysis. Problems with Double Blind Design

8. Block Design Experimental Design A block design is simply a stratified random sample, but for EXPERIMENTS. block design: experiment stratified random sample: observational study In both cases you are first dividing potential participants by some strata (characteristic) of the participants before either measuring something or applying treatments.

9. Matched Pairs Design Experimental Design This is a very special style of experiment where you try to make sure that every participant in treatment group A has a corresponding and congruent (yay geometry) participant in treatment group B (and other treatment groups if there are 3+). Example: Experiment to measure the impact of Drug 23 vs Placebo Treatment Group A ParticipantsTreatment Group B Participants NameAgeGenderOccupation Henry22MPoliceman Tabitha23FMilitary Mick35MPolitician Matthew31MSales Person Samantha27FTeacher NameAgeGenderOccupation Rick22MPoliceman Tammi23FMilitary Carl35MPolitician Hank31MSales Person Christina27FTeacher

10. Double Treatment Design Experimental Design This is a very special style of experiment where you try to make sure that every participant in treatment group A has a corresponding and congruent (yay geometry) participant in treatment group B (and other treatment groups if there are 3+). Example: Experiment to measure the impact of Drug 23 vs Placebo NameAgeGenderOccupation Henry22MPoliceman Tabitha23FMilitary Mick35MPolitician Matthew31MSales Person Samantha27FTeacher 20 mg Drug 23 daily Measured Response: Cholesterol Level Observed Rate: 20% improvement Placebo daily Measured Response: Cholesterol Level Observed Rate: 5% improvement ParticipantsSecond TreatmentFirst Treatment Wait a few months