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PNB 2XE3 Chapter Notes -Descriptive Statistics, Winsorizing, Random Number Table


Department
Psychology, Neuroscience & Behaviour
Course Code
PNB 2XE3
Professor
Brett Beston

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Answers to Exercises
(I have not provided graphs where those are called for
because of the amount of space they require and the
cost of preparation. I have also left out a few answers
that would require an inordinate amount of space.
These items are included in both the Instructor’s
Manual and the Student’s Manual. The Student’s
Manual is available at
www.uvm.edu/~dhowell/fundamentals7/
In early chapters there is often close correspondence
between these answers and the answers in the
Student’s Manual. This is much less true in later chap-
ters, where the problems are more computational.)
Chapter 1
1.1 A good example is the development of toler-
ance to caffeine. People who do not normally drink
caffeinated coffee are often startled by the effect of
one or two cups of regular coffee, whereas those who
normally drink regular coffee see no such effect. To
test for a context effect of caffeine, you would first
need to develop a dependent variable measuring the
alerting effect of caffeine, which could be a vigilance
task. You could test for a context effect by serving a
group of users of decaffeinated coffee two cups of reg-
ular coffee every morning in their office for a month,
but have them drink decaf the rest of the time. The
vigilance test would be given shortly after the coffee,
and tolerance would be seen by an increase in errors
over days. At the end of the month they would be
tested after drinking caffeinated coffee in the same
and in a different setting.
1.3 Context affects people’s response to alcohol, to
off-color jokes, or to observed aggressive behavior.
1.5 The sample would be the addicts that we observe.
1.7 Not all people in the city are listed in the phone
book. In particular, women and children are under-
represented. A phone book is particularly out of date
as a random selection device with the increase in the
use of cell phones.
1.9 In the tolerance study discussed in the text we
really do not care what the mean length of paw-lick
latency is. No one would be excited to know that a
mouse can stand on a surface at for 3.2 seconds
without licking its paws. But we do very much care
that the population mean of paw-lick latencies for
morphine-tolerant mice is longer in one context than
in another.
1.11 I would expect that my mother would con-
tinue to wander around in a daze, wondering what
happened.
1.13 Three examples of measurement data:
Performance on a vigilance task, typing speed, blood
alcohol level.
1.15 Relationship: the relationship between stress
and susceptibility to disease, the relationship between
driving speed and accident rate.
1.17 You could have one group of mice trained and
tested in the same condition, one group trained in
one condition and tested in the other, and a third
group given a placebo in the training context but
given morphine in the testing condition.
1.19 This is an Internet search exercise with no
fixed answer.
Chapter 2
2.1 Nominal: names of students in the class;
Ordinal: the order in which students hand in their
first exam; Interval: a student’s grade on that first
exam; Ratio: the amount of time that a student spent
taking the exam.
2.3 If the rat lies down to sleep in the maze after
performing successfully for several trials, this probably
says little about what the animal has learned in the
task, but more about motivation.
105°
620

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Answers to Exercises 621
2.5 We have assumed the following at the very least
(and I’m sure that I left some out):
1. Mice are adequate models for human
behavior.
2. Morphine tolerance effects in mice are like
heroin effects in humans.
3. Time on a warm surface is in some way
analogous to a human response to heroin.
4. A context shift for mice is analogous to a
context shift for humans.
5. A drug overdose is analogous to pain toler-
ance.
2.7 The independent variables are the gender of the
subject and the gender of the other person.
2.9 The experimenter expected to find that women
would eat less in the presence of a male partner than
in the presence of a female partner. Men, on the
other hand, were not expected to vary the amount
that they ate as a function of gender of their partner.
2.11 We would treat a discrete variable as if it were
continuous if it had many different levels and was at
least ordinal.
2.13 When I drew 50 numbers three times I
obtained 29, 26, and 19 even numbers, respectively.
The last time only of my numbers were even,
which is probably less than I might have expected—
especially if I didnt have a fair amount of experience
with similar exercises.
2.15 Eyes level condition:
(a)
(b)
(c)
2.17 Eyes level condition:
(a)
(b)
(c) This is the mean, a type of average.
2.19 Putting the two sets of data together:
(a) XY 2.854 1.06 4.121 1.750 0.998
1.153 2.355 3.218 2.543 2.699
(b)
(c)
(d)
(e) 0.1187
22.7496 ?216.82
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5
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1©X225114.82225219.63;
a
10
i51
Xi514.82
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X352.03; X551.05; X851.86
38%
2.21 Show
5 7363
9 11 7 10 7
2.23 In the text I spoke about room temperature as
an ordinal scale of comfort (at least up to some
point).
2.25 Beth Peres
(a) In the Beth Peres story the dependent
variable is the weekly allowance, and the inde-
pendent variable is the gender of the child.
(b) We are dealing with a selected sample—
the children in her class.
(c) The age of the students would influence
the overall mean. The fact that these children
are classmates could easily lead to socially
appropriate responses—or what the children
deem to be socially appropriate in their setting.
(d) At least within her school Beth could
randomly sample by taking a student roster,
assigning each student a number, and
matching those with the numbers drawn
from a random number table. Random
assignment to Gender would obviously be
impossible.
(e) I dont see negative aspects of the lack of
random assignment here because that is the
nature of the variable under consideration. It
might be better if we could randomly assign a
child to a gender and see the result, but we
clearly cant.
(f ) The outcome of the study could be influ-
enced by the desire of some children to exag-
gerate their allowance or to minimize it so as
not to appear too different from their peers.
I would suspect that boys would be likely to
exaggerate.
(g) The descriptive features of the study are
her statements that the boys in her class
received $3.18 per week in allowance, on
average, whereas the girls received an average
of $2.73. The inferential aspects are the infer-
ences to the population of all children, con-
cluding that boys get more than girls.
2.27 I would record the sequence number of each
song that is played and then plot them on a graph.
I cant tell if they are truly random, but if I see a
pattern to the points I can be quite sure that they are
not random.
©1X142544 5124 15342X14
©X524X
©1X1C25©X1NC

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

622 Answers to Exercises
Chapter 3
3.1 (b) There is too little data to say much about
the shape of this distribution.
3.3 I would use stems of 3*, 3., 4*, 4., 5*, and 5. for
this display.
3.5 Compared to those who read the passages:
(a) Almost everyone who read the passages did
better than the best person who did not read them.
Certainly knowing what you are talking about is a
good thing (though not always practiced).
(c) It is obvious that the two groups are very
different in their performance. We would be
worried if they were not.
(d) This is an Internet question with no fixed
answer.
3.7 The following is a plot (as a histogram) of reac-
tion times collapsed across all variables.
3.9 Histogram of GPA:
20
40
60
80
100
00 1000 2000 3000 4000 5000
RTmsec
Frequency
Std. Dev. = 637.41
Mean = 1625
N = 600
3.11 (1) Mexico has very many young people and
very few old people, whereas Spain has a more even
distribution. (2) The difference between males and
females is more pronounced at most ages in Spain
than it is in Mexico. (3) You can see the high infant
mortality rate in Mexico.
3.13 The distribution of those whose attendance
is poor is far more spread out than the distribution
of normal attendees. This is expected because a
few very good students can score well on tests even
when they dont attend, but most of the poor
attendees are generally poor students who would
score badly no matter what. The difference
between the average grades of these two groups is
obvious.
3.15 As the degree of rotation increases, the distri-
bution of reaction time scores appears to move from
left to right—which is also an increase.
3.17 The data points are probably not independent
in that dataset. At first the subject might get better
with practice, but then fatigue would start to set in.
Data nearer in time should be more similar than data
further apart in time.
3.19 The amount of shock that a subject delivers to
a white participant does not vary as a function of
whether than subject has been insulted by the exper-
imenter. However, black participants do suffer when
the subject has been insulted.
4
2
0
6
14
.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00
Std. Dev. = .86
Mean = 2.46
N = 88.00
GPA
Frequency
3.25 3.50 3.75 4.00
12
10
8
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