Do something to:
"Experimental Units" = "Subjects"
Treatment: Specific experimental condition.
Factor: Explanatory Variable we manipulate.
Levels: Specific values of a factor that
we set.
Response variable(s)
E.g. 2 headache medications, in combination?
A two-factor experiment, each with 3 levels. 9 possible treatments.
Factor A: Aspirin: levels None, 500 mg,
1000 mg
Factor B: Caffeine: levels None, 50 mg, 100 mg
Response variable: reported pain relief
| Aspirin | ||||
| None | 500 mg | 1000 mg | ||
| None | Treatment 1 | Treatment 2 | Treatment 3 | |
| Caffeine | 50 mg | Treatment 4 | Treatment 5 | Treatment 6 |
| 100 mg | Treatment 7 | Treatment 8 | Treatment 9 |
Lurking variables: control--how?
Nothing except experimental treatment should affect response.
Compare responses under several treatments,
look at differences.
Placebo effect: a positive response to a "sham" medical treatment--if
you believe it will work, it very likely will. (Tinkerbell?)
A medical treatment must be shown to be better than
a placebo (at least) to be approved by the FDA.
(Cf. "Claritin,"
Sunday NYTimes magazine, March 11, '01)
placebo="I
shall please" (Latin)
To control for the placebo effect, All treatments should "look alike".
Treatment 1 above should be a pill with no medicine--a "placebo".
(Some experiments even try to duplicate side effects of actual medication.)
"Control group" Group that gets the "none" or "placebo" level
of the factor. Should be "just like" the group(s) that get the "treatment"
("real" levels of the factor). So Treatment 1 above will go to the
"control group", the other 8 will go to "experimental" or "treatment groups."
Murky language here: "Experimental vs. control" or "Treatment
vs. control" is different usage from "Treatments", one of which is the
"control"="none"/"placebo".
How to get groups "just like" one another? Randomize
who
goes into which group. (Usually our batch of experimental units
is not a random sample from the population of all individuals--volunteers,
etc.)
Randomized comparative experiment : Diagrams of design pp. 140-141
Completely randomized: all exp. units allocated at random among
the treatments.
E.g. does acupuncture work for PMS? Response: report of symptoms.
One factor, 3 Levels: None (music?),
Acupuncture
(wrong places), Acupuncture (right places). 3 treatments.
(control(s)?)
30 subjects with PMS: Randomize,
10 each treatment. Administer treatments. Compare symptoms.
(Do diagram)
Picking groups with random number table: Pick "sample" of size 10 from the 30 for first treatment. Pick another "sample" of size 10 for 2nd treatment, from the remainder. The 10 remaining get the 3rd treatment.
Why 10 each, not just 1 each? Replication of the experimental treatments on many units allows "averaging out" chance variation in the units. (Don't confuse the replication needed within one experiment, with "replication" of the whole experiment in a different time and place to confirm its results.)
Principles of designing an experiment (p. 143) See above
More problems, cautions:
Placebo and biasing effects can result from expectations of medical
staff. "Blind" means subject doesn't know who's getting "real" treatment.
"Double blind" means neither subject, nor staff administering treatment,
nor people recording response variables know who's getting which treatment.
(That should really be triple blind?) Most convincing
for cause/effect.
Lack of realism Do sociology, psychology experiments generalize to "real life?" Ethical questions...
Statistical Significance
p.194: An observed effect so large that it would rarely occur by chance
(assuming no real difference in treatments) is called "statistically significant".
"So large", "rarely", "by chance" will be defined and quantified in Ch.
6.
Example: Suppose 95% of the subjects
had their headaches cured by treatment 9 and only 25% by treatment
1 (placebo). IF the medicine in fact did "no good" that would be
an unlikely outcome. So we will say the difference in headache cures
between treatment 1 and treatment 9 is "statistically significant" and
be inclined to believe the medicine "works".
HWDay19, Covering 3.2 up to Matched Pairs, p. 196 (Matched Pairs and Block design next)
| Hand in:
Hand in answers to these questions on the "Placebo Effect" article (outside my door/on reserve): a) Give two examples of the placebo effect b) What do researchers believe causes the placebo effect? Design of experiment: p. 200, 3.46 experiment? p. 187, 3.32 sickle cell p. 188, 3.34& p. 209 3.70 chemical reaction (randomize order) p. 192 3.37 child care, recruitment(randomize) Control, randomization, replication p. 194 3.38 With Day 20 Significance p. 195 3.40 Cautions (blinding, lack of realism) p. 196 3.42 pain reliever p. 202 3.55 placebo effect p. 208 3.75 reading medical jl |
Read, to discuss
p. 194 3.39 exercise/heart
p. 209 3.74
3.41meditation/anxiety p. 202, 3.52 sickle cell |
Optional
p. 187, 3.33 sealing food p. 192, 3.36 ditto |
| Sievers home | Math151-Sp01/Day19.htm | 3/12/01 |