C1, L3-4, S1

Types of Studies

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.

C1, L3-4, S3

Experiments & Observational StudiesWe conduct an experiment when it is (ethically, physically etc) possible for the experimenter to determine which experimental units receive which treatment.

C1, L3-4, S4

Experiments & Observational StudiesExperiment Terminology

Experimental Unit Treatment Response

patient drug cholesterolheroin addict rehab program relapsestudent prior knowledge instructor rating of lecture

patient magnets foot pain

C1, L3-4, S5

Experiments & Observational StudiesIn an observational study, we compare the units that happen to have received each of the treatments.

C1, L3-4, S6

e.g. You cannot set up a control (non-smoking) group and treatment (smoking) group.

Observational Study

patient smoking lung cancerinmate race Sentence Length subject gender opinion

Experiments & Observational Studies

Unit “Treatment” Response

C1, L3-4, S7

Experiments & Observational StudiesNote:

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.

C1, L3-4, S8

Basic Experimental Design

C1, L3-4, S9

1. Completely Randomized Design

The treatments are allocated entirely by chance to the experimental units.

C1, L3-4, S10

1. Completely Randomized DesignExample:

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.

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) (A) (A)(A)(B) (B)

(B)(B)

1. Completely Randomized Design

What if the field sloped upward from left to right?

UPHILL

Discuss for ½ Minute

(B)

(B)

(B) (B

)(A)

(A)

Randomly assign A & B varieties in each strip of similar elevation.

C1, L3-4, S12

1. Completely Randomized DesignNote:

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.

C1, L3-4, S13

2. BlockingGroup (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”.

C1, L3-4, S14

2. BlockingExample 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.

C1, L3-4, S15

Well drained Poorly drained2. 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.

C1, L3-4, S16

2. BlockingExample 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?

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 1st day, 5 tested 2nd day, 4 tested 3rd

day.• Leg was placed in freezer and thawed before

the 2nd and 3rd days of testing. Days 1 and 2 were about a week apart and days 2 and 3 were a few days apart.

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.

C1, L3-4, S19

Horse Leg Wraps (cont’d)

FINAL BOOT COMPARISONS

C1, L3-4, S20

Horse Legs Wraps (cont’d)

• What should have been done? Discuss

C1, L3-4, S21

3. People as Experimental UnitsExample: 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.

C1, L3-4, S22

Polio Vaccine Example

C1, L3-4, S23

Polio Vaccine Example

Dr. Jonas Salk, vaccine pioneer 1914-95

Iron Lung

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

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

C1, L3-4, S26

The Salk Vaccine Field TrialNFIP 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).

C1, L3-4, S27

The Salk Vaccine Field TrialDouble-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

C1, L3-4, S28

(NFIP rate)

(25) Grade 2

(54) Grade 1/3

(44) Grade 2

The Salk Vaccine Field TrialThe double-blind randomized controlled experiment (and NFIP) results

Size ofgroup

Rate per 100,000

Treatment 200,000 28

Control 200,000 71

No consent 350,000 46

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

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

C1, L3-4, S31

Observational Studies • There are two major types of

observational studies:

prospective and retrospective studies

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.

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)

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.

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.

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.

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.

C1, L3-4, S38

Example 2• “Lead Exposure Linked to Bad Teeth in

Children” ~ USA TodayThe 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.

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.”

C1, L3-4, S40

Figure 1.5 pg. 9

C1, L3-4, S41

Additional Example 1 – Determine Whether Age at 1st Pregnancy is a Risk Factor for Cervical Cancer

How might we proceed?Discuss

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

C1, L3-4, S43

Additional Example 3 – Identify factors related to fall-to-fall retention of WSU students.

How might we proceed? Discuss

C1, L3-4, S44

Surveys and Polls(and the errors inherent in them)

C1, L3-4, S45

NonsamplingErrors

Sampling/Chance/Random Errors

Selection bias Interviewer effects

Non-response bias Behavioural considerations

Self selection Transfer findings

Question effects Survey-format effects

Sampling

C1, L3-4, S46

Sources of Nonsampling ErrorsSelection biasPopulation sampled is not exactly the population of interest.

e.g. KARE 11 poll, telephone interviews

population

sample

C1, L3-4, S47

Sources of Nonsampling ErrorsNon-response biasPeople 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.

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

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”

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

C1, L3-4, S51

Sources of Nonsampling ErrorsSelf-selection biasPeople decide themselves whether to be surveyed or not.

Much behavioural research can only use volunteers.

C1, L3-4, S52

Sources of Nonsampling Errors

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.

C1, L3-4, S54

Sources of Nonsampling ErrorsQuestion effectsSubtle variations in wording can have an effect on responses.Eg “Should euthanasia be legal?”vs “Should voluntary euthanasia be legal?”

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% No 62%

Later the same people were asked:

“Do you think there should be an amendment to the constitution protecting the life of the unborn child?”

Yes 50% No 39%

C1, L3-4, S56

Sources of Nonsampling ErrorsInterviewer effectsDifferent interviewers asking the same question can obtain different results.

Eg sex, race, religion of the interviewer

C1, L3-4, S57

Interviewer Effects in Racial QuestionsIn 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”

C1, L3-4, S58

Sources of Nonsampling ErrorsBehavioural considerationsPeople tend to answer questions in a way they consider to be socially desirable.

e.g. pregnant women being asked about their drinking habits

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.

C1, L3-4, S60

Sources of Nonsampling ErrorsTransferring findingsTaking 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

C1, L3-4, S61

Sources of Nonsampling ErrorsSurvey-format effectsEg question order, survey

layout, interviewed by phone or in- person or mail.

C1, L3-4, S62

NonsamplingErrors

Sampling/Chance/Random Errors

Selection bias Interviewer effects

Non-response bias Behavioural considerations

Self selection Transfer findings

Question effects Survey-format effects

Sampling

C1, L3-4, S63

SurveyErrors

Sampling/Chance/

Random Errors

Nonsampling

Errors

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)

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.)

C1, L3-4, S66

Surveys / PollsA 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).

C1, L3-4, S67

Surveys / PollsA 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)