| Hand
in Wednesday Chapter 13 p257ff. 1,2,4,5,6,10,11,12,17,18 Do a: Decide if it is an observational study or an experiment. If it was an observational = "investigative" study answer that b,c,d,e. (We'll complete the experiment ones later; start the experiment ones on a separate page and keep it, answering the questions you can so far. 1, 6, 17 are (completely randomized) experiments: do b,c,d,e now for these. (answer for 17) 25 Wine 24 Full moon - - - - - - - - - Hand in answers to these questions on the "Placebo Effect" articles (outside my door/on reserve) Hand in WEDNESDAY: 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? - - - - - - - - - - - 31 Weekend deaths 21 Mozart do a, read b, do c, d 26 Swimming do a, b, c (Check your answers to the first problems: 4,5,10,11,12 are observational.) x x x x x x x x x x x x x x x Postpone the rest 21 Mozart do b 26 Swimming do d (be clear about what state re depression your subjects should be in to start with) 33 Beetles (+ make diagram ) 32 a, b, c Shingles On the separate page and keep it: 1,2,4,5,6,10,11,12,17,18 . Work on the experiment ones now: Do b,c,d,e, g, h, and see if you can pick out which ones are completely randomized (part of f) We'll finish it when we look at the other designs. |
Read,
to discuss
(review, ch.12) p. 239 #13 Wording
|
Optional |
Fay's hours:
Fay writes: " An emergency has come up and I will have to reschedule
myWednesday hours. I will be here from 1 (maybe 1.30) until 3." [SRS
says: I can work with you 12:30 on]
Fay's Review Session - a reminder. "There will be a session on Thursday
night at 7pm in the Math Clinic. Make sure to go through the practice
exam and bring questions."
Homework questions? Day
21 Circulate
your random
samples from Old Faithful data? Lines for n = 20 pretty close to
population regression line? For n = 5 only roughly similar.
Sampling, finished on Friday
Sources of Bias in
sampling: any systematic failure of a sample (or its method) to
represent its population. (E.g. sampling
frame excludes "different" part of population.) see
Day 21
HW #21: Are we running a risk if we take the same (place) bottle from each case? (Maybe. Environment effects? Deliberate "stacking" of bottles?)
Using Random Number Table to sample (p. A-49) Every digit, every sequence of digits, is equally likely to be "next" in any direction. see Day 21
= = =
= = = = = = = = = = = = = = = = = = = = = = =
D&V Ch13 Goal:
show cause-and-effect. Predictor-->Response
Observational Study: Observe
individuals; don't do anything to them;
do not influence the responses.
Can indicate
strength of relationship, differences, but not cause and
effect.
(Often not with samples, but with selected group(s).) Lurking
variables?!?
(Fisher: Smokers smoke to soothe irritabilities that may
cause
cancer.)
Retrospective:
gather data after the fact (observe that x% of men hospitalized
with heart disease were/are smokers)
Prospective:
choose individuals in advance. Measure them; or follow them as
events
happen. (Framingham Heart Study: 5,209
(2,873
women and 2,336 men) healthy residents between 30 and 60 years of
age.
Followed from 1948 to now. A second-generation cohort recruited 1971,
Minority
group 1995 http://www.framingham.com/heart/)
Experiment: Impose
treatments
on individuals, to see how the treatment
influences the response.
Compare treatments' effects.
Do something to: "Experimental Units" =
"Subjects"
Treatment: A Specific experimental condition.
Factor: = Explanatory (Predictor) Variable that 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 |
E.g. (Day 14, MRA-95-13 )Corn
yield=
response variable. One Factor = Planting rate. 5 Levels=the
rates.
Principles of designing a comparative
experiment
(p. 243)
Completely randomized: all
experimental
units allocated at random among the treatments.
Got to here today (Monday).
Diagrams p. 248: show sequence: random allocation,
groups:
counts and labeled treatments, compare results.
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.
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.
(Equal numbers to each treatment group is usually desirable,
or roughly equal....)
Bias: issues,
how to avoid...
--Subjects are not (usually) a random sample from the population;
generalize
with care. (Most psychology "facts" were based on studies of
Ivy
League males, before 1970's.) But random assignment to
treatment
groups should "equalize" some biases, differences cancel out.
--"Control" treatment is done to "control" group: baseline
or zero-level treatment to compare to. (Contrast with
"control"
of extraneous sources of variation.
)
--Blinding participants to treatment to prevent prejudgments,
expectations, subtle changes. Don't know which treatment.
+Those who can influence results
(subjects,
treatment administrators, technicians, nurses, etc.)
+Those who evaluate results
(judges,
physicans, etc.)
Single blind: everyone in one
category.
Double blind: everyone in both categories. (Drug:
bottle
labeled by number. Which is which is not revealed till the
results are
in.)
--Placebo effect: a real improvement in symptoms and/or
disease, resulting from a treatment that "should" have no medicinal
effect.
Placebo
("I
shall please") mimicking real treatment is used as control.
--Confounded variables (p.253): are usually either experiment
factors, or one(s) we didn't think about or control for
(lurking).
If the levels of two variables "travel together" (so we can't sort out
which one an effect is due to) they are "confounded".
Usually an experiment treats the placebo effect as a
potentially
confounding
variable, and is designed so placebo effect will work
equally
on all groups. There is no attempt to measure
the
placebo effect. ("All" drug studies.)
PMS/acupuncture:
Acupuncture (wrong) vs. Acupuncture (right).
&&Sometimes an experiment deliberately tries to measure
the placebo effect (as in the articles).
Acupuncture (wrong) vs.
Music.
Next--designs other than completely randomized
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
Good practice: Beware confounding; record everything you
can in case it turns out to be important; do pilot experiment.
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