1. Deciding what and how to measure
Planning a Study
Deciding what
and how to measure

2. Vocabulary Measuring What? Units Experimental Units Subjects
Participants
Various Variables
Explanatory (independent) variable
Response (dependent) variable
Confounding variable
Lurking variable

3. Experiment
Subjecting the sample to a controlled treatment where one variable is altered.
The objects on which the treatment is imposed on are called experimental units (human subjects).
Explanatory variables are called factors and specific values of the explanatory variable are levels.

4. Designing a Good Experiment
Randomization--randomly assign subjects to treatment and control groups
Control
Replication--consistency
“Differences in the response variable between groups, if enough to rule out natural chance variability, can then be attributed to the manipulation of the explanatory variable.” This will allow determination of cause and effect.

5. Randomization--Crucial
“Researchers do experiments to reduce the likelihood that the results will be affected by confounding variables and other sources of bias.”
Randomize Type of Treatment
Randomize Order of Treatment

6. These control for UNKNOWN variability
Control Groups
Control group--receives standard treatment OR
Placebo (sham) group--receives no treatment
Single-Blind
Double-Blind
These control for UNKNOWN variability

7. This controls for KNOWN variability.
Designing Control
Block Design--”divide units into homogeneous groups (called blocks) and each treatment is randomly assigned to one or more units in each block.”
Matched-Pair Design--”assigned either two matched individuals (identical twins) OR the same individual (repeated measure) to receive the two treatments”
This controls for KNOWN variability.

8. Quitting Smoking w/Nicotine Patches
Recruited 240 smokers (volunteers) at Mayo Clinic from 3 large cities
Randomly assigned 22-mg nicotine
patch or placebo patch for 8 weeks.
All attended counseling before, during, and after.
Double-blind (neither volunteers nor nurses taking measurements knew type of patch)
After 8-wk (1 yr), 46% (27.5%) of nicotine patch group quit smoking and 20% (14.2%) of placebo group quit.

9. Observational Study
Observing the behaviors of a sample from a population.
The observer does not impose active treatments on units/subjects.
Or using previously collected data to do statistical analysis.

10. Census--Observational Study
The systematical collection of data on the entire population.
When the population is large, it will become time consuming and expensive.

11. Sample Survey-- Observational Study
A portion of the population is asked a question and the study is done based on their voluntary answers.

12. Newsweek announced “A Really Bad Hair Day: Researchers link baldness and heart attacks.” The article reported that “men with typical male pattern baldness…are anywhere from 30 to 300 percent more likely to suffer a heart attack than men with little or no hair loss at all.”
The report was based on an observational study conducted by researchers at Boston Univ. School of Medicine. They compared 665 men who had been admitted to the hospital with their 1st heart attack to 772 men in the same age group (21- to 54-years old) who had been admitted to the same hospital for other reasons.

13. Case Control Studies--Observational Study
“Cases” who have a specific attribute/condition are compared to “Controls” who don’t.
Efficiency
Reduces potential confounding variables
Retrospective vs. Prospective

14. Characteristics of a well-designed and well-conducted survey
Trained interviewers must be consistent with asking neutral, non-leading questions.
An unbiased sampling should represent the population of interest.

15. Populations  Random Selections  Samples

16. Sampling Methods Random Digit Dialing Self-Selected Sample
Simple Random Sample (SRS)
Stratified Random Sampling
Cluster Sampling
Systematic Sampling
Multi-Stage Sampling
Random Digit Dialing
Self-Selected Sample
Convenience Sample
“Quickie Polls”

17. Simple Random Sampling
From the entire population every possible grouping of specified size has same chance of being selected.

18. Stratified RS vs Cluster S
1st divide population into groups (strata), then take a Simple Random Sample from each strata
1st divide population into groups (cluster), then randomly select some clusters and sample everyone in that cluster

19. Systematic Sampling & Random Digit Dialing
From a list, divide into consecutive segments (every 50 names), randomly choose starting point (21st entry), then sample at that same point in each segment (21, 71, 121, 171, …)
Sample that approximates a SRS of all households in US that have telephones with a specific exchange
( )

20. Multi-Stage Sampling
“survey designers might stratify population by region of country, then stratify by urban, suburban, or rural, then choose a random sample of communities within those strata. They would continue to divide communities into city blocks (fixed areas) as clusters, and sample from the selected clusters.”

21. Self-Selected Sample--radio station call-in Convenience Sample--surveying folks in a mall who appear willing to talk to you “Quickie Polls”--hastily designed, poorly pre-tested, one night survey sample for evening news show

22. Sources of bias in surveys
If a selection process consistently obtains values too high or too low, then BIAS exists.
Selection Bias
Non-response Bias
Response Bias

23. Survey Questions Unnecessary complexity to question
Misleading question
Ordering of questions
Ensuring confidentiality
Anonymous survey

24. Gathering Data
Experimental Design
Observational Study