| Hand in Wednesday 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) p. 229 9.27 wine, beer, spirits , diagram design. p. 230 9.32 a only headache prevention design p. 231 9.33 fabric finishing, design Postpone 9.28 and 9.30 p. 230, 9.28 marijuana Use the Simple Random Sample Applet, see below for details, to find who to put in the two groups. Also: pick just the first 3 people for the "weak" group using Table B at line 131. p230, 9.30 TV ads Use the Simple Random Sample Applet, see below for details. - - - - - - - - - - Hand in Friday: 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. 212, 8.49 Canada healthcare ------ ------ -----
|
Optional ------ ------ ----- 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 14(?), 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 |
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.
..
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, Moore pp. 218-19: shows where randomizing
happens, how many to each treatment, what the treatments are.
Completely randomized: all exp. units allocated at random among the
treatments.
E.g. does acupuncture work for Backache?? Response: report of
symptoms.
One factor, 3 values: None (music??(but
might work...!). "Usual treatment"), Acupuncture (wrong
places), Acupuncture (right places). 3 treatments.
(control(s)?)
30 subjects with Backache:
Randomize, 10 each treatment. Administer treatments.
Compare symptoms. (Do diagram)
Got to here Monday...
(I'm not making this up.... Backache(1)
(2) )
Picking groups with random number
table: Table B. Pick "sample" of
size 10 from the 30 for first treatment group. Pick another
"sample" of size 10 for 2nd treatment group, from the remainder.
The 10 remaining get the 3rd treatment.
Easier with Simple
Random Sample Applet. Enter total number of subjects in
"Population size", enter size of first group in "sample size", hit
Reset, then Sample. Write down the numbers for this sample, it's
group 1. Hit Sample again (DON'T reset) to choose the second
group. Write down the numbers, continue...
Why 10 each, not just 1 each? Use enough subjects for each treatment so
that you can
"average out" (and measure) chance variation in the subjects.
Principles of designing an experiment (p. 221) See above
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