Chapter 3 Producing Data

1. Chapter 3 Producing Data

2. Observational study: observes individuals and measures variables of interest but does not attempt to influence the responses (ex. surveys/polling) • Experiment: deliberately imposes some treatment on individuals in order to observe their responses (coke vs pepsi, testing new drugs) • Confounding: lurking variables

3. 3.1 Designing Samples

4. voluntary response sample • consists of people who choose themselves by responding to a general appeal • Example: call in response; internet voting • people with negative opinions tend to respond much more often than positive opinions

5. Convenience sampling • chooses individuals easiest to reach • Example: in mall • Not a wide variety of people for sample

6. Bias • The design of a study is biased if it systematically favors certain outcomes • Pg 170 #4, 5, 6

7. Simple random samples • sample chosen so that every individual has an equal chance of being selected • To choose a simple random sample: • assign a number to each individual in the population (all numbers used must have the same # of digits) • use table of random digits (table B) or calculator to select random numbers • Pg 173 # 7, 8, 9

8. stratified random sample • divide the population into groups of similar individuals called strata • male & female; urban, suburban, rural; age groups; grades in school • choose a separate simple random sample in each strata • combine the SRS’s to form the full sample

9. multistage samples • choosing the sample in stages • example: divide into grade level, then male & female, then randomly pick

10. Cautions about sample surveys • undercoverage – an accurate and complete list of population is not usually available (ex. using telephone book) • nonresponse – when a selected individual cannot be contacted or refuses to cooperate • wording of questions • most important influence on the answers • confusing or leading questions can introduce strong bias

11. response bias • respondents may lie • interviewers attitude may suggest one answer is more desirable than others • sex or race may influence answers • answers to questions that ask respondents to recall past events • Pg 179 # 13, 14, 15

12. *** larger random samples give more accurate results than smaller samples

13. 3.2 Designing Experiments

14. Experiments • When we do something to people, animals, or objects in order to observe the response • Experimental units – individuals in which the experiment is done • Subjects – human experimental units • Factors – explanatory variables in an experiment • Treatment – specific experimental condition applied to the units

15. Researchers studying the absorption of a drug into the bloodstream inject the drug into 25 people. • Subjects • 25 people • Factor • Dosage of the drug • Treatment • 1 level / 1 injection of the drug • Response variable • Concentration of the drug in a subject’s blood

16. What are the effects of repeated exposure to an advertising message? • An experiment investigated this question using undergraduate students as subjects. All subjects viewed a 40 minute TV program that included ads for a 35 mm camera. Some subjects saw a 30 second commercial; others, a 90 second version. The same commercial was repeated either 1, 3, or 5 times during the program. After viewing, all subjects were asked questions about their recall of the ad, their attitude toward the camera, and their intention to purchase it.

17. Subjects • Undergraduate students • Factors • Length of commercial – 2 levels • Repetitions – 3 levels • Treatments • 6 combinations length of commercial with repetitions • Response variables • Recall of ad, attitude, intent to purchase

18. Advantages of experiments over observational studies • allows us to study the effects of the specific treatments we are interested in • can control the environment of the experimental units to hold constant factors that are of no interest to us • can give good evidence for causation • can study the combined effects of several factors simultaneously • Pg 187 # 32, 33, 34

19. Principles of experimental design • Control of the effects of lurking variables • Randomization • Replication of experiment on many units to reduce chance variation in the results

20. statistically significant • an observed effect so large that it would rarely occur by chance

21. lack of realism • the subjects (using rats instead of humans), treatments, or setting of an experiment (in lab instead of at home) may not duplicate the conditions we really want to study • Examples pg 195 • Pg 196 # 41, 42

22. Comparative experiments • experiments should compare treatments rather than attempt to assess a single treatment • Placebo effect – giving a person a “dummy” drug and they react to it anyway. Probably because they trust that the medicine will work. “The power of positive thinking” • the placebo effect and other lurking variables operate on both groups • Control group – the group of people receiving a “fake” treatment

23. Randomized comparative experiments • an experiment that uses both comparison and randomization • can have one or more than one factor to compare • Examples pg 190 & 191 • Pg 192 # 37

24. logic of randomized comparative experiments • random assignment of subjects forms groups that should be similar in all respects before the treatments are applied • comparative design ensures that influences other than the experimental treatments operate equally in all groups • differences in average response must be due either to the treatments or to the play of chance in the random assignment of subjects to the treatments

25. Advantage of Randomized comparative experiments • produces data that gives good evidence for a cause-and-effect relationship between the explanatory and response variables • Pg 194 # 39, 40

26. double blind experiment • when neither the patient nor the doctor knows what form of the pill they are getting

27. Matched pair design • compares just two treatments • choose subjects as completely matched as possible and each receive one treatment • Coke vs Pepsi • each subject serves as his or her own control, each subject receives both treatments in random order • order of treatments can influence the subjects response so randomize (flip a coin, roll a die) the order

28. Block design • Block separating into groups before you start (male/female) • random assignment of units to treatments within the block • allows us to draw separate conclusions about each block • allows for more precise overall conclusions • Pg 198 # 43, 45

29. Observational study issues to be addressed: • What is the population of interest and what is the sampled population? • Careful of undercoverage or overcoverage • How were the individuals or objects in the sample actually selected? • What are the potential sources of bias • Increasing the sample size does not reduce bias

30. Experiment issues to be addressed: • Should be clear about how random assignment was incorporated into design of experiment • Any factors held constant throughout the experiment • Was blocking used? If so, how were the blocks created?