How do you know what
you know?
How do you know it?
What don’t you know?
Why don’t you know it?
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The scientific revolution and the subsequent
discoveries made us smarter!
So, how does that research work for Psychology…what
are the different methodologies?
Here is an example of Psychological research…
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•
There are various Methodologies?
- According to the text,
methodology simply refers to the
methods that we use to conduct an
investigation.
The goals of research are to
describe behavior, to explain its
causes, to predict the
circumstances under which certain
behaviors may occur again, and to
control certain behaviors.
Psychologists use various methods
of research to accomplish each of
these goals.
As we go through
this, try to figure out
what kind of
research you would
like to do…
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
Milgrim Short
Version
Milgrim Full Version
•
Naturalistic Observation: A research method in
which the psychologist observes the subject in
a natural setting without interfering. (In other
words…Watch, but do not touch!)

Participant Observation: A research method in which
the psychologist observes the subject in a natural
setting but gets involved. (In other words…Watch AND
interact
•
Surveys: A research method in which information is
obtained by asking many individuals a fixed set of
questions. This is usually used to get an idea of the
population’s attitudes toward something.
- Note: These are only accurate if they are
representative of the population as a whole.
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•
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Longitudinal Study: A research method in which data
is collected about a group of participants over a
number of years to assess how certain change or
remain the same during development.
Cross Sectional Studies: A research method in which
data is collected from groups of participants of
different ages and compared so that conclusions can
be drawn about differences due to age.
Experiments : A research method in which variables
are manipulated in order to determine causation.
• While surveys and other methods are good at determining
Correlation…experiments are good at determining
Causation
•
Case Studies: A research method that involves an
intensive investigation of one or more participants.
(long term, very involved.)
- Note: By itself, a case study does not prove or
disprove anything. The researchers conclusions may
not be correct. This was the technique used by Freud.

Case studies may be
used to describe
particularly rare
phenomena.
• The study of presidential
assassins is limited to
case studies of a few
people who have killed
or tried to kill U.S.
Presidents.
• Investigations of mass
murderers are also
limited to case studies.
Study things that
are difficult to
test.
-ethically, there are
limits to what we
can study…or is
there?

Video 4 of 5 2:00-7:00
 Case
studies provide illustrative
anecdotes.
• Researchers and teachers often use case studies
to illustrate general principles to students.
 There
are also at least 2 major limitations
to case studies:
• 1.) They are virtually useless in providing
evidence to test behavioral theories or
treatments.
 The lives and events studied often occur in an
uncontrolled fashion and without comparison
information.
 No matter how reasonable a researchers explanations
may be, you cannot rule out alternative explanations.
• 2.) Most case studies rely on the observations
of a single investigator.
 We often have no way of assessing the reliability of that
single researcher’s observations and interpretations.
 Because the researcher may have some sort of vested
interest in the outcome of the study, one must always be
concerned about the self-fulfilling prophecy.
2. Surveys
 Surveys
are very
appealing.
• Seems like an efficient way
to do research.
 What
are some of the
flaws that you can think of
with surveys?
•
•
•
•
Wording
Audience
Order
Who is asking?
 Wording:
• How can the wording of a
survey influence the
responses given?
 “Sex” vs. “Relations”
 “Hate” vs. “Dislike”
 “Love” vs. “Like”
 Audience:
• How can the audience
to which the survey is
given influence the
responses?
 Location and topic
 How are the questions
asked?
 Order:
• How can the order
in which the
questions are asked
influence the
responses?
-1.) Do you think the actions
of the Sept. 11th attacks were
wrong?
-2.) Does is anger you that
American soldiers are dying
in Iraq?
-3.) Do you think people in
the Middle East are
generally violent?
 Who
is asking?
• How could the person
asking influence the
answers given?
 Race
 Gender
 Background
 Inflection
 Why
is it important to have random samples in
survey research?
• Some fun statistics:
 68% of people roll toilet paper over the spool
 79% squeeze toothpaste from the top
 7% look behind the shower curtain when using someone
else’s bathroom
 80% of people eat corn on the cob in circles instead of rows
 10% of people have seen a ghost
 7% of people have flossed their teeth with their own hair
 What
was wrong
with each one of
those statistics?
• Those responses
were the results of
only about 7,000
people out of 25,000
surveyed.
 Obtaining
random samples for any purpose is
difficult. The government has had no more
success than the private sector.
• The 1970 lottery to determine the order of the
military draft was almost certainly unfair.
• The 31 capsules for January were placed in the bin
first, then the 29 for February, and so on until the 31
for the next December.
• What do you think happened when the bin was not
turned enough?
 The December Birthdays were drawn much earlier.
 You
need to be aware of the difference
between correlation and causation
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Correlation: The measure of a relationship between two variables or sets
of data.
Whaaaaaaaaaaaaaa?
There are 2 types of Correlation. Positive and Negative.
For example, there is a positive correlation between IQ scores and
academic success. Low IQ scores tend to go with low grades.
Another example, there is a negative correlation between the number of
times you go down a slide and the number of times you get hurt from it. In
other words, the more you practice, the less you will get hurt! (High score
= Low score, Low score = High score)
It is important to remember that while the word correlation describes the
relationship between 2 things, it does not always mean that one thing
causes the other. Why?
 RELATIONSHIP….Not
cause!
 It is very easy to misinterpret correlation
studies as a cause an effect issue, but this is not
accurate.
A Story;
In the early twentieth century,
thousands of Americans in
the South died from Pellagra,
a disease marked by
dizziness, lethargy, running
sores, and vomiting.
Finding that families struck with the
disease often had poor plumbing and
sewage, many physicians concluded
that pellagra was transmitted by poor
sanitary conditions.
In contrast, Surgeon General Joseph
Goldberger thought that the illness
was caused by an inadequate diet.
He felt that the correlation between
sewage conditions and pellagra did
not reflect a casual relationship, but
that the correlation arose because the
economically disadvantaged were
likely to have poor diets, as well as
poor plumbing.
So, how was the controversy resolved?
Well, the answer demonstrates the
importance of the scientific
method.
To prove he was right, Goldberger
not only had himself injected
with the blood of a victim with
sores all over his body, he found
a victim with diarrhea and….ate
his excrement!
He did NOT come down with
pellagra!
To further make his case, Goldberger
asked two groups from a Mississippi
state prison farm to volunteer for an
experiment. One group was given a high
carbohydrate, low protein diet that
Goldberger suspected to be the culprit,
while the other received a balanced diet.
Within months, the first group was
ravaged by pellagra, while the second
showed no signs of the disease.
 Causation
refers to 1 variable causing
another variable to behave a certain way.
 While
surveys can highlight correlations,
experiments can show causations
 In experiments there are multiple
variables
• Independent variable-variable that causes something to happen to DV
• Dependent variable-variable that shows the effect of changing the IV
• Confounding variable-variable other than IV that can produce a change in
DV
 Experimental
group-participants are exposed to treatment
 Control group-participants are NOT exposed to treatment
 Double-Blind procedure-when the researchers and
participants don’t know the procedure being used.
 Now, try
to go back to Milgram’s
Obedience experiment and figure out
the following:
• Independent Variable
• Dependent Variable
• Experimental Group
• Control
 In
order for experiments to be
considered valid, they must be
repeatable.
 How do we really know if something is
legit if it is not repeatable?
• Could be a one-time fluke.
 There
are different types of bias:
• Confirmation bias
 A tendency to search for information that confirms a preconception.
 THIS IS DANGEROUS!!!!
 WE ALL DO IT
 Check out these videos…1
2
3
 Now try this activity…2, 4, 6…can you figure out the rule?
Write 3 numbers down that match the rule and I will tell you if
it is correct or not. Then write down the rule and check it with
me.
• Participant Bias
 Research participants behave in a certain way because they
know they are being observed or part of an experiment.
 MEAN:
The MEAN is the arithmetic
average, the average you are probably
used to finding for a set of numbers - add
up the numbers and divide by how many
there are: (80 + 90 + 90 + 100 + 85 + 90) /
6 = 89 1/6.
 MEDIAN:
The MEDIAN is the number in the
middle. In order to find the median, you have to
put the values in order from lowest to highest,
then find the number that is exactly in the
middle:
 80 85 90 90 90 100
^
since there is an even number of values, the
MEDIAN is between these two, or it is 90. Notice
that there is exactly the same number of values
ABOVE the median as BELOW it!
 MODE:
The MODE is the value that
occurs most often. In this case, since
there are 3 90's, the mode is 90. A set of
data can have more than one mode.
 80
85 90 90 90 100
 RANGE:
The RANGE is the difference
between the lowest and highest values. In
this case 100 - 80 = 20, so the range is 20.
 The range tells you something about how
spread out the data are. Data with large
ranges tend to be more spread out.