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# Research Methods: Thinking Critically With Psychology

Basic overview of scientific method, central tendencies, central variance, and more
by

## Ms Schwinge

on 5 August 2014

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#### Transcript of Research Methods: Thinking Critically With Psychology

Research Methods:
Thinking Critically with Psychological Science

"Life is lived forwards, but understood backwards"
- Philosopher Soren Kierkegaard

The Scientific Method
observation
; something we notice that makes us pause to think.
Measures of
Central Tendency
In order to summarize and evaluate our data, we must know how to measure it.
Measures of Variation
Random Assignment
Variables
In order to test our hypothesis, we must decide what we are testing for. Experiments examine the effect of
manipulated variables
(known as
independent variables
) on some
measurable behavior or outcome
(known as a
dependent variable
).
This is also known as
hindsight bias
: the tendency to believe, after learning an outcome, that one would have
foreseen
it. This is because
"common sense"
more easily
describes what HAS happened than what WILL happen.

Next, we come up with an
inquiry
(or a question) based on that observation that we can test.
The
hypothesis
is our next step; which is an educated guess or a testable prediction we can make.
(a theory =/= a guess or hypothesis)
Once we know what we're testing, we can
experiment
.
Once we've got our data, we need to
analyze
it to see what our experiment tells us.
Finally, we make a
conclusion
where we either accept or reject our hypothesis based on the data.
The
mean
is the arithmetic average (the sum of all the scores divided by the number of scores)
Just like on a highway, the
median
is in the middle (but you must first organize all the numbers from smallest to largest in order to find it)
The
mode
is the most frequently occurring number (the one that shows up the most)
The
range
of scores is the gap between the highest and lowest score (a crude estimate of variation).
The more useful standard for measuring how much scores deviate is the
standard deviation
(which is a computed measure of how much scores vary around the mean score)
Large numbers of data (heights, weights, intelligence) often form a symmetrical, bell shaped distribution. Most cases fall near the mean, and fewer cases fall near either extreme. we call this type of curve the
normal curve.
The
smaller
the standard deviation (SD), the
skinnier
the curve. The
larger
the SD, the
fatter
the curve.
Confounding variables
are factors that can potentially influence the results of the experiment. We try to control for this by doing
random assignment
when dealing with people or animals.
Constants
(like the name implies) are things we keep the same in the experiment to limit the amount of confounding variables.
No single experiment is conclusive.
Random assignment
is when scientists assign participants to experimental and control groups by
chance.
This minimizes preexisting differences between those assigned to the different groups.
The
participants are also uninformed
about what treatment, if any, they are receiving
. This is an example of an experimental group:
one group receives the treatment, and a contrasting control group does not receive the treatment
placebo.
In a
double-blind procedure, neither the participants or the research assistants
collecting the data will
know
what group is receiving treatment (this helps protect against
research bias
).
It's possible
just by thinking
you're getting treatment to boost your spirits, relax your body, and relieve your symptoms. This is known as the
placebo effect
, and is well documented in reducing pain, depression and anxiety.
Observation of Behavior
There are several different ways psychologists observe and describe behavior.
The Case Study:
This is among one of the oldest research methods. Case studies
examine one individual in depth
in hopes of revealing things true of us all. They often suggest directions for further study, and they show us what can happen.
...But individual cases can
us if the individual being studied is
atypical.

The Survey:
The survey method looks at
many cases in less depth.
Researchers do surveys when wanting to estimate, from a
representative sample
of people, the attitudes or reported behaviors of a
whole population.
...But
, and the answers often depend on the ways questions are worded and the way respondents are chosen.
For instance, people are much more
likely
to approve "
not allowing
" certain things rather than "forbidding" or "censoring" them.
Remember: before accepting survey findings, think critically about the
sample and the sample size
(but you cannot compensate for an unrepresentative sample by simply adding more people)
The Naturalistic Observation:
The naturalistic observation method
records behavior in natural environments
. This can range from watching chimpanzee societies in the wild, to unobtrusively videotaping (and later analyzing) parent-child interactions in different cultures.
Like the case study and survey methods,
naturalistic observations
do not EXPLAIN behavior, they just
DESCRIBE
it. Think of them as snapshots of everyday life that do not control for all the factors that may influence behavior.
Correlation
Describing
behavior is the first step to
predicting
it. Surveys and naturalistic observation often show us that
one trait or behavior is related to another
. In such cases, we say the two
correlate
.
A statistical measure (the
correlation coefficient
) helps us figure how closely two things
vary
together, and thus how well either one
predicts
the other).
Scatterplots
illustrate the
range
of possible correlations, which can go from a perfect positive (+1) to a perfect negative (-1). Each dot represents the
scattered values
of two variables.
A correlation is
positive
if two sets of scores (such as height and weight), tend to
rise or fall together
.
A correlation is
negative
if two sets of scores
relate inversely
(one set going up as the other goes down). An example would be temperature and elevation.
A
weak correlation
, indicating
little relationship
, has a coefficient near zero.
REMEMBER:
Correlation does NOT prove causation!
Although correlation indicates the
possibility
of a cause-effect relationship, it does not prove it.
In addition, when we notice random coincidences, we may forget that they are random and instead see them as correlated. This can result in an
illusory correlation.
Knowing the value of an appropriate measure of central tendency is helpful, but it is also important to know about the
amount of variation
in the data; how similar or diverse the numbers are.
“I walk on x, fly on y”
(I = independent variable, therefore I.V. goes on x axis)
Independent variable starts with “I”, it’s what * I * change
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