STAT 110 - Section 5 Lecture 10

1. STAT 110 - Section 5 Lecture 10 Professor Hao Wang University of South Carolina Spring 2012 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA

2. explanatory variable (e.g.,nicotine patch) response variable (e.g, quit smoking) lurking variable (e.g., determination, family background) Last time: Experiment Causes? Solution: Randomized Comparative Experiment

3. Chapter 6 – Experiments in the Real World Experimental Designs (I) completely randomized – all the experimental subjects are allocated at random among all the treatments • more than one explanatory variable can be used • use treatment combinations to conduct the study

4. What are the effects of repeated exposure to an advertising message? Effects of TV ads • Subjects watch 40 minute TV program including ads for a camera • Length of the ads: 30sec or 90 secs • Repetitions of the ads: 1times, 3times or 5times • Answer questions about attitude towards the camera, intention to purchase

5. What are the effects of repeated exposure to an advertising message? Effects of TV ads

6. (II) Matched Pairs Design - Subjects are matched to form pairs, or each subject receives both treatments. Randomization occurs within each pair. • Responses for the pairs are compared. • Not complete randomization • This allows matching to reduce the effect of variation among the subjects.

7. Matched Pairs Design Example 1 • Let’s test the effectiveness of a sunscreen lotion. • We want to study how well it works. • The problem with a randomized comparative experiment is that people have different skin types and body chemistries. • So, what’s a better method? • Test one lotion on one arm and the other lotion on the other arm of each person. • Compare the effects on each arm for all subjects.

8. Matched Pairs Design Example 2 • Some studies have tried to determine how genetics and environmental factors contribute to intelligence, aggression or substance addictions. • Most of the twin’s studies compare identical twins, having 100% genetic similarity

9. (III) Block Design block – a group of experimental subjects that are known before the experiment to be similar in some way that is expected to affect the response to the treatments block design – the random assignment of subjects to treatments is carried out separately within each block

10. Block Design • A separate randomized comparative experiment is performed for each block. • Matched pairs are an example of block designs. • Each pair is a block. • Blocks are another form of control since they control the effects of some outside variables by bringing those variables into the experiments to form the blocks.

11. Block Design Example Which TV commercial is most effective?

12. Suppose we are interested in the effect of a weight loss diet. • There are a total of 2 treatment combinations (diet or not) . • Suppose we have available to us a total of N = 60 individuals to which we are going to apply the different diets based on the 2 treatments. • Prior to the experimentation the individuals were divided into n = 10 homogeneous groups of size 6. • The grouping was based on factors that may affect the effects of diet (age, gender, initial weights) • Within each of the 10 blocks an individual is randomly assigned a treatment combination (diet). a

13. Advantages of Using a Block Design • Block designs are similar to stratified samples. • Blocks allow us to draw separate conclusions about each block. • Blocks reduce confounding. • We can include a potential lurking variable in the design and its effects can now be accounted for.(We call this a blocking variable.)

14. In a randomized block design, where is the randomization performed? • A. When placing subjects into a block. • B. When picking the response of interest • C. When assigning treatments w/in a block. • D. When picking the blocking variable.

15. single blind – an experiment is single blind if the subjects are unaware of the exact treatment being imposed on them  controls for subject bias double blind – an experiment is double blind if the subjects and the experimenter are unaware of the exact treatment being imposed  controls for subject and experimenter bias

16. Example: Blindly Lowering Cholesterol Which lowers cholesterol more? Special diet versus drug? • Details: • Three treatments: drug only, diet and drug, diet with placebo. • The 46 volunteers were randomized by a statistician. • Blinding: researchers and participants both blind as to which drug (or placebo) people in those two groups were taking. • However, participants and dieticians could not be blind to what the participants were eating. Lab staff evaluating cholesterol measurements were blinded to the treatment.

17. Problems with Subjects nonadherers – subjects who participate but do not follow the experimental treatment refusals – some subjects that we want in our study may refuse to participate dropouts – subjects may start in the study and later dropout  especially true for experiments that last over an extended period of time

18. Group task: An Experiment • A taste test is being conducted to compare Dr. K to Dr. Pepper. You have 21 individual participants • Discuss with your group: • Design a completely randomized experiment • Identify lurking variables • Design a matched pairs experiment • How to blind and how to randomize?

19. Having each person try both soda’s makes this a: A – Completely Randomized Design B – Double Blind C – Matched Pairs Design D – Statistically Significant

20. 14 people preferred Dr. K over Dr. Pepper and 7 preferred Dr. Pepper over Dr.K. The percentage of people who preferred Dr. K was: A – 14/7 = 2% B – 14/21 = 66.7% C – 7/14 = 50% D – 7/21 = 33.33%

21. The experiment found that 14 people preferred Dr. K over Dr. Pepper and 7 preferred Dr. Pepper over Dr.K, for a total sample size of 21. Approximately what is the margin of error for 95% confidence? A – 1/14 = 7.1% B – 14/21 = 66.7% C – 1/sqrt(21) = 21.8% D – 3%

22. Flipping a coin to see which soda the subject drank first A – Would hopefully remove confounding with the lurking variable of which you tasted first B – Would hopefully remove confounding with the lurking variable of preferring things just because they are biased towards name brands C – Would hopefully remove confounding with the explanatory variable of which you tasted first D – Would hopefully remove confounding with the explanatory variable of preferring things just because they are biased towards name brands

23. The drinker not seeing which drink they were given made the experiment A – A Block Design B – Double Blind C – Single Blind D – Randomized

24. The drinker not seeing which drink they were given A – Would hopefully remove confounding with the lurking variable of which you tasted first B – Would hopefully remove confounding with the lurking variable of preferring things just because they are biased towards name brands C – Would hopefully remove confounding with the explanatory variable of which you tasted first D – Would hopefully remove confounding with the explanatory variable of preferring things just because they are biased towards name brands