1. C1, L3-4, S1 Types of Studies

2. C1, L3-4, S2 Research classifications Observational vs. Experimental Observational – researcher collects info on attributes or measurements of interest, but does not influence results. Experimental – researcher deliberately influences events and investigates the effects of the intervention, e.g. clinical trials and laboratory experiments. We often use these when we are interested in studying the effect of a treatment on individuals or experimental units.

3. C1, L3-4, S3 Experiments & Observational Studies We conduct an experiment when it is (ethically, physically etc) possible for the experimenter to determine which experimental units receive which treatment.

4. C1, L3-4, S4 Experiments & Observational Studies Experiment Terminology Experimental Unit Treatment Response patient drug cholesterol heroin addict rehab program relapse student prior knowledge instructor rating of lecture patient magnets foot pain

5. C1, L3-4, S5 Experiments & Observational Studies In an observational study, we compare the units that happen to have received each of the treatments.

6. C1, L3-4, S6 e.g. You cannot set up a control (non-smoking) group and treatment (smoking) group. Observational Study patientsmokinglung cancer inmate race Sentence Length subject genderopinion Experiments & Observational Studies Unit“Treatment”Response

7. C1, L3-4, S7 Experiments & Observational Studies Note: Only a well-designed and well-executed experiment can reliably establish causation. An observational study is useful for identifying possible causes of effects, but it cannot reliably establish causation.

8. C1, L3-4, S8 Basic Experimental Design

9. C1, L3-4, S9 1. Completely Randomized Design The treatments are allocated entirely by chance to the experimental units.

10. C1, L3-4, S10 1. Completely Randomized Design Example: Which of two varieties of tomatoes (A & B) yield a greater quantity of market quality fruit? Factors that may affect yield: different soil fertility levels exposure to wind/sun soil pH levels soil water content etc.

11. C1, L3-4, S11 Divide the field into plots and randomly allocate the tomato varieties (treatments) to each plot (unit). 8 plots – 4 get variety A (A) (B) 1. Completely Randomized Design What if the field sloped upward from left to right? UPHILL Discuss for ½ Minute (B) (A) Randomly assign A & B varieties in each strip of similar elevation.

12. C1, L3-4, S12 1. Completely Randomized Design Note: Randomization is an attempt to make the treatment groups as similar as possible — we can only expect to achieve this when there is a large number of plots or experimental units.

13. C1, L3-4, S13 2. Blocking Group (block) experimental units by some known factor and then randomize within each block in an attempt to balance out the unknown factors. Use: blocking for known factors (e.g. slope of field in previous example) and randomization for unknown factors to try to “balance things out”.

14. C1, L3-4, S14 2. Blocking Example continued: It is recognized that there are two areas in the field – well drained and poorly drained. Partition the field into two blocks and then randomly allocate the tomato varieties to plots within each block.

15. C1, L3-4, S15 Well drained Poorly drained 2. Blocking How should we allocate varieties to plots? Discuss in groups for 1/2 minute. 7 (B) 2 (A) 3 (A) 5 (A) 6 (A) 1 (A) 2 (B) 3 (A) 4 (B) 8 (B) 4 (B) 1 (B) Randomly assign types to 4 well drained plots and then to the 8 poorly drained plots.

16. C1, L3-4, S16 2. Blocking Example 2: Comparing Three Pain Relievers for Headache Sufferers How could blocking be used to increase precision of a designed experiment to compare the three pain relievers? What are some other design issues?

17. C1, L3-4, S17 Example 3: Comparing 17 Different Leg Wraps/Boots Used on Race Horses 17 “boots” tested & each boot is tested n = 5 times. Why? Because of the time constraints all boots were not tested on the same day. 8 tested 1 st day, 5 tested 2 nd day, 4 tested 3 rd day. Leg was placed in freezer and thawed before the 2 nd and 3 rd days of testing. Days 1 and 2 were about a week apart and days 2 and 3 were a few days apart.

18. C1, L3-4, S18 Horse Boots (cont’d) What problems do you foresee with this experimental design? Discuss What actually happened? What are the implications of these results? Discuss Forces readings obtained from cadaver leg when no boot was used.

19. C1, L3-4, S19 Horse Leg Wraps (cont’d) FINAL BOOT COMPARISONS

20. C1, L3-4, S20 Horse Legs Wraps (cont’d) What should have been done? Discuss

21. C1, L3-4, S21 3. People as Experimental Units Example: Cholesterol Drug Study – Suppose we wish to determine whether a drug will help lower the cholesterol level of patients who take it. How should we design our study? Discuss for two minutes in groups.

22. C1, L3-4, S22 Polio Vaccine Example

23. C1, L3-4, S23 Polio Vaccine Example Dr. Jonas Salk, vaccine pioneer 1914-95 Iron Lung

24. C1, L3-4, S24 The Salk Vaccine Field Trial 1954 Public Health Service organized an experiment to test the effectiveness of Salk’s vaccine. Need for experiment: –Polio, an epidemic disease with cases varying considerably from year to year. A drop in polio after vaccination could mean either: Vaccine effective No epidemic that year

25. C1, L3-4, S25 The Salk Vaccine Field Trial Subjects: 2 million, Grades 1, 2, and 3 500,000 were vaccinated –(Treatment Group) 1 million deliberately not vaccinated –(Control Group) 500,000 not vaccinated - parental permission denied

26. C1, L3-4, S26 The Salk Vaccine Field Trial NFIP Design Treatment Group: Grade 2 Control Group: Grades 1 and 3 + No Permission Flaws ? Discuss for 30 seconds. Polio contagious, spreading through contact. i.e. incidence could be greater in Grade 2 (bias against vaccine), or vice-versa. Control group included children without parental permission (usually children from lower income families) whereas Treatment group could not (bias against the vaccine).

27. C1, L3-4, S27 The Salk Vaccine Field Trial Double-Blinded Randomized Controlled Experimental Design Control group only chosen from those with parental permission for vaccination Random assignment to treatment or control group Use of placebo (control group given injection of salted water) Diagnosticians not told which group the subject came from (polio can be difficult to diagnose) i.e., a double-blind randomized controlled experiment

28. C1, L3-4, S28 (NFIP rate) (25) Grade 2 (54) Grade 1/3 (44) Grade 2 The Salk Vaccine Field Trial The double-blind randomized controlled experiment (and NFIP) results Size of group Rate per 100,000 Treatment200,00028 Control200,00071 No consent350,00046

29. C1, L3-4, S29 3. People as Experimental Units control group: –Receive no treatment or an existing treatment blinding: –Subjects don’t know which treatment they receive double blind: –Subjects and administers / diagnosticians are blinded placebo: –Inert dummy treatment

30. C1, L3-4, S30 3. People as Experimental Units placebo effect: –A common response in humans when they believe they have been treated. –Approximately 35% of people respond positively to dummy treatments - the placebo effect

31. C1, L3-4, S31 Observational Studies There are two major types of observational studies: prospectiveand retrospective studies

32. C1, L3-4, S32 Observational Studies 1. Prospective Studies –(looking forward) –Choose samples now, measure variables and follow up in the future. –E.g., choose a group of smokers and non-smokers now and observe their health in the future.

33. C1, L3-4, S33 Observational Studies –Looks back at the past. –E.g., a case-control study Separate samples for cases and controls (non-cases). Look back into the past and compare histories. E.g. choose two groups: lung cancer patients and non-lung cancer patients. Compare their smoking histories. 2. Retrospective Studies –(looking back)

34. C1, L3-4, S34 Observational Studies Important Note: 1. Observational studies should use some form of random sampling to obtain representative samples. 2.Observational studies cannot reliably establish causation.

35. C1, L3-4, S35 Controlling for various factors A prospective study was carried out over 11 years on a group of smokers and non- smokers showed that there were 7 lung cancer deaths per 100,000 in the non- smoker sample, but 166 lung cancer deaths per 100,000 in the smoker sample. This still does not show smoking causes lung cancer because it could be that smokers smoke because of stress and that this stress causes lung cancer.

36. C1, L3-4, S36 Controlling for various factors To control for this factor we might divide our samples into different stress categories. We then compare smokers and non-smokers who are in the same stress category. This is called controlling for a confounding factor.

37. C1, L3-4, S37 Example 1 “Home births give babies a good chance” NZ Herald, 1990 –An Australian report was stated to have said that babies are twice as likely to die during or soon after a hospital delivery than those from a home birth. –The report was based upon simple random samples of home births and hospital births. Q: Does this mean hospitals are dangerous places to have babies in Australia? Why or why not? Discuss for 1 minute in groups.

38. C1, L3-4, S38 Example 2 “Lead Exposure Linked to Bad Teeth in Children” ~ USA Today The study involved 24,901 children ages 2 and older. It showed that the greater the child’s exposure to lead, the more decayed or missing teeth. Q: Does this show lead exposure causes tooth decay in children? Why or why not? Discuss for 1 minute.

39. C1, L3-4, S39 Example 2 ~ cont’d “Lead Exposure Linked to Bad Teeth in Children” ~ USA Today Researcher: “We controlled for income level, the proportion of diet due to carbohydrates, calcium in the diet and the number of days since the last dental visit.”

40. C1, L3-4, S40 Figure 1.5 pg. 9

41. C1, L3-4, S41 Additional Example 1 – Determine Whether Age at 1 st Pregnancy is a Risk Factor for Cervical Cancer How might we proceed? Discuss

42. C1, L3-4, S42 Additional Example 2 – Determine whether prior knowledge about an instructor effects the rating given to a lecture presentation. How might we proceed? Discuss

43. C1, L3-4, S43 Additional Example 3 – Identify factors related to fall-to-fall retention of WSU students. How might we proceed? Discuss

44. C1, L3-4, S44 Surveys and Polls (and the errors inherent in them)

45. C1, L3-4, S45 Nonsampling Errors Sampling/Chance/ Random Errors Selection bias Interviewer effects Non-response bias Behavioural considerations Self selection Transfer findings Question effects Survey-format effects Sampling

46. C1, L3-4, S46 Sources of Nonsampling Errors Selection bias Population sampled is not exactly the population of interest. e.g. KARE 11 poll, telephone interviews population sample

47. C1, L3-4, S47 Sources of Nonsampling Errors Non-response bias People who have been targeted to be surveyed do not respond. Non-respondents tend to behave differently to respondents with respect to the question being asked.

48. C1, L3-4, S48 1936 U.S. Election Country struggling to recover from the Great Depression 9 million unemployed 1929-1933 real income dropped by 1/3

49. C1, L3-4, S49 1936 U.S. Election Candidates: –Albert Landon (Republican) “The spenders must go!” – Franklin D Roosevelt (Democrat) Deficit financing - “Balance the budget of the people before balancing the budget of the Nation”

50. C1, L3-4, S50 1936 U.S. Election Roosevelt’s percentage –Digest prediction of the election result –Gallup’s prediction of the Digest prediction –Gallup’s prediction of the election result –Actual election result 43% 44% 56% 62% Digest sent out 10 million questionnaires to people on club membership lists, telephone directories etc. – received 2.4 million responses Gallup Poll used another sample of 50,000 Gallup used a random sample of 3,000 from the Digest lists to predict Digest outcome

51. C1, L3-4, S51 Sources of Nonsampling Errors Self-selection bias People decide themselves whether to be surveyed or not. Much behavioural research can only use volunteers.

52. C1, L3-4, S52 Sources of Nonsampling Errors

53. C1, L3-4, S53 Sources of Nonsampling Errors This poll is not scientific and reflects the opinions of only those Internet users who have chosen to participate. The results cannot be assumed to represent the opinions of Internet users in general, nor the public as a whole. The QuickVote sponsor is not responsible for poll content, functionality or the opinions expressed therein.

54. C1, L3-4, S54 Sources of Nonsampling Errors Question effects Subtle variations in wording can have an effect on responses. Eg “Should euthanasia be legal?” vs “Should voluntary euthanasia be legal?”

55. C1, L3-4, S55 New York Times/CBS News Poll (8/18/80) “Do you think there should be an amendment to the constitution prohibiting abortions?” Yes 29%No62% Later the same people were asked: “Do you think there should be an amendment to the constitution protecting the life of the unborn child?” Yes50%No39%

56. C1, L3-4, S56 Sources of Nonsampling Errors Interviewer effects Different interviewers asking the same question can obtain different results. Eg sex, race, religion of the interviewer

57. C1, L3-4, S57 Interviewer Effects in Racial Questions In 1968, one year after a major racial disturbance in Detroit, a sample of black residents were asked: “Do you personally feel that you trust most white people, some white people or none at all?” White interviewer: 35% answered “most” Black interviewer: 7% answered “most”

58. C1, L3-4, S58 Sources of Nonsampling Errors Behavioural considerations People tend to answer questions in a way they consider to be socially desirable. e.g. pregnant women being asked about their drinking habits

59. C1, L3-4, S59 Behavioural Considerations in Election Official vote counts show that 86.5 million people voted in the 1980 U.S. presidential elections. A census bureau survey of 64,000 households some weeks later estimated 93.1 million people voted.

60. C1, L3-4, S60 Sources of Nonsampling Errors Transferring findings Taking the data from one population and transferring the results to another. e.g. Twin Cities opinions may not be a good indication of opinions in Winona. Twin Cities sample Winona

61. C1, L3-4, S61 Sources of Nonsampling Errors Survey-format effects Eg question order, survey layout, interviewed by phone or in- person or mail.

62. C1, L3-4, S62 Nonsampling Errors Sampling/Chance/ Random Errors Selection bias Interviewer effects Non-response bias Behavioural considerations Self selection Transfer findings Question effects Survey-format effects Sampling

63. C1, L3-4, S63 Survey Errors Sampling/Chance/ Random Errors Nonsampling Errors

64. C1, L3-4, S64 Sampling / Chance / Random Errors errors caused by the act of taking a sample have the potential to be bigger in smaller samples than in larger ones possible to determine how large they can be unavoidable (price of sampling)

65. C1, L3-4, S65 Nonsampling Errors can be much larger than sampling errors are always present can be virtually impossible to correct for after the completion of survey virtually impossible to determine how badly they will affect the result must try to minimize in design of survey (use a pilot survey etc.)

66. C1, L3-4, S66 Surveys / Polls A pilot survey is a small survey that is carried out before the main survey and is often used to identify any problems with the survey design (such as potential sources of non-sampling errors).

67. C1, L3-4, S67 Surveys / Polls A report on a sample survey/poll should include: –target population (population of interest) –sample selection method –the sample size and the margin of error –the date of the survey –the exact question(s)