| Hand in Wednesday yes,all p. 199 8.9 Apartment living, SRS. Use Table B. p. 209, 8.36 Area code sample, SRS Use Table B. p. 211, 8.45 random digit dialing p. 210, 8.41 random digit characteristics p.209-10, 8.38 b only Traffic lights p. 208, 8.30 movie viewing p. 205, 8.16, Ask more people p. 212, 8.50, Polling Hispanics Chapter 9 ------ ------ ----- p. 229 9.25, 9.26 obsn/expt p. 215, 9.1, 9.2, 9.3 treatments, factors, response, etc. p. 216, 9.4 unemployment (confounding) - - - - - - - - - - Hand in Monday: Hand in answers to these questions on the "Placebo Effect" articles (outside my door/on reserve): a) Give two examples of the placebo effect (from the article!) b) What do researchers believe causes the placebo effect? c) In the separate article: "Pill will make you feel better...," what country was surveyed? |
Read, to discuss
p. 211, 8.47 guns ------ ------ -----
|
Optional p. 209, 8.35 Use table B (more practice) p. 209, 8.34 seat belt use ------ ------ ----- Think about this one... p. 230, 9.29 red wine. This is a complex experiment with different amounts of polyphenols in different kinds of liquids. Doesn't fall neatly into our Factors-value structures? Think about it a bit... |
(SAMPLING) BIAS: The design of a study is biased
if
it systematically favors certain outcomes.
Non-probability samples (sampling badly): Voluntary
response sample , Convenience
sample
Do something to:
"Experimental Units" = "Subjects"
= individuals.
Treatment: Specific experimental condition we impose
on one or more subjects.
Factor: Explanatory Variable we manipulate.
There will be Specific values of a
factor that
we set.
(Sometimes called "levels")
Response variable(s) Results that we measure.
E.g. Corn planting (HW
day 15(?), p. 110, 4.28) 1
factor = planting rate. 5 different values (levels). 16
individuals (plots of ground). Response: yield per acre.
E.g. 2 headache medications, in combination?
A two-factor experiment, each with 3 values (levels).
9 possible treatments.
Factor A: Aspirin: values: None, 500 mg,
1000 mg
Factor B: Caffeine: values: 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 |
Start about here Wed.
Lurking variables:
Control--how?
Nothing except experimental treatment should differentially 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.
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 "baseline"--"null"--
"none" or "placebo" value of the factor. Should be "just like"
the
group(s) that get the "treatment" ("real" values 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".
*Sometimes the Control is the current
"best practice" treatment, rather than none.
Sometimes (especially in bio, physics, chem experiments)
there is no "control group" --no baseline--just a sequence of
different values (like corn planting experiment.) Moore
says "uncontrolled" --which doesn't mean "out of control" :-)
In these environments also, we make everything else "the same"
to try to eliminate confounding/lurking variable effects.
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