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Chapter 2
Research Methods
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ISBN: 0-131-73180-7
Copyright © Allyn & Bacon 2007
How Do Psychologists
Develop New Knowledge?
Psychologists, like
researchers in all other
sciences, use the scientific
method to test their ideas
empirically
Copyright © Allyn & Bacon 2007
How Do Psychologists
Develop New Knowledge?
Empirical investigation –
An approach to research that relies on
sensory experience and observation as
research data
Scientific method –
A five-step process for empirical investigation
of a hypothesis under conditions designed to
control biases and subjective judgments
Copyright © Allyn & Bacon 2007
The Five Steps of the Scientific Method
Developing a hypothesis
Performing a controlled test
Gathering objective data
Analyzing the results
Publishing, criticizing, and
replicating the results
Copyright © Allyn & Bacon 2007
The Five Steps of the Scientific Method
Developing a
hypothesis
Performing a
controlled test
Gathering
objective data
Analyzing the
results
Publishing,
criticizing, and
replicating the
results
Hypothesis –
A statement predicting the outcome
of a scientific study
Operational definitions –
Exact procedures used in
establishing experimental conditions
and measurement of results
Sample – Identifies who will be
included in the experiment.
Representative Sample – A
representation of the population at large.
Copyright © Allyn & Bacon 2007
The Five Steps of the Scientific Method
Developing a
hypothesis
Performing a
controlled test
Gathering
objective data
Analyzing the
results
Publishing,
criticizing, and
replicating the
results
Independent variable (I.V.) The variable
manipulated by the experimenter
Experimental Group – This is the group
of subjects who get the I.V., or the
variable that is being manipulated or
tested.
Control Group (placebo group)– The
group that is used as a “control” or as a
comparison to make sure that the I.V. is
responsible for the results and not just
chance.
Random Assignment –Using chance
alone to determine which group the
subjects are placed in.
Copyright © Allyn & Bacon 2007
The Five Steps of the Scientific Method
Developing a
hypothesis
Performing a
controlled test
Gathering
objective data
Analyzing the
results
Publishing,
criticizing, and
replicating the
results
Data –
Information gathered by
researcher and used to test
a hypothesis
Dependent variable (D.V.) –
The measured outcome of
a study; the responses of
participants in a study
Copyright © Allyn & Bacon 2007
The Five Steps of the Scientific Method
Developing a
hypothesis
Performing a
controlled test
Gathering
objective data
Analyzing the
results
Publishing,
criticizing, and
replicating the
results
Based on statistical analyses of
results, the hypothesis is accepted or
rejected
In experiments, the researcher
controls all the conditions and
directly manipulates the conditions
Confounding or Extraneous
Variables – Those “extra”
variables that can “confound”
or interfere with the results.
Copyright © Allyn & Bacon 2007
The Five Steps of the Scientific Method
Developing a
hypothesis
Performing a
controlled test
Gathering
objective data
Analyzing the
results
Publishing,
criticizing, and
replicating the
results
Researchers must find out
whether their work can
withstand the scrutiny of the
scientific community
Copyright © Allyn & Bacon 2007
Types of Psychological Research
Non-experimental methods include:
•
•
•
•
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•
•
Correlational studies
Surveys
Naturalistic observation
Longitudinal studies
Cross-sectional studies
Cohort-sequential studies
Ex-Post Facto Design
Copyright © Allyn & Bacon 2007
Correlations: A relationship between
Two Variables
Correlation–
A relationship between two variables, in
which changes in one variable are
reflected in changes in the other variable
Correlation coefficient–
A number between -1.0 and +1.0
expressing the degree of relationship
between two variables
Copyright © Allyn & Bacon 2007
• A + correlation can be just as strong as a – correlation (-.92 is exactly
as strong a correlation as +.92)
•Correlation does not imply causation!
•Positive Correlation: as one
variable changes, the other
variable changes in the same
direction.
•The more you study, the higher
your test scores are.
•Negative Correlation: as
one variable changes, the
other variable changes in
the opposite direction.
•The more time you spend
on FB, the lower your test
scores are.
•No correlation:
There is no
relationship
between the
variables.
•No relationship
between shoe
size and
intelligence!
Copyright © Allyn & Bacon 2007
Surveys
• A METHOD OF RESEARCH THAT
INVOLVES ASKING SUBJECTS
(PEOPLE) QUESTIONS ABOUT THEIR
FEELINGS, OPINIONS, OR
BEHAVIOR PATTERNS.
• THESE CAN BE “YES/NO”
QUESTIONS
• THESE CAN BE FILL IN THE BLANK
• THESE CAN BE “SCALE”
QUESTIONS
• ELICITS “QUICK” INFORMATION,
BUT NOT A RELIABLE MEANS OF
RESEARCH.
Copyright © Allyn & Bacon 2007
Naturalistic Observation
•A RESEARCH
METHOD THAT
TAKES PLACE IN
THE SUBJECT’S
NATURAL
ENVIRONMENT.
•THE SUBJECT IS
UNAWARE THEY
ARE BEING
OBSERVED.
Copyright © Allyn & Bacon 2007
Longitudinal Study
•A RESEARCH METHOD
THAT STUDIES OR
FOLLOWS AND
OBSERVES THE SAME
GROUP OF PEOPLE OVER
A LONG PERIOD OF TIME.
•GENERALLY, THIS IS
LONGER THAN 10 YEARS.
Copyright © Allyn & Bacon 2007
Cross-Sectional Studies
•A METHOD THAT LOOKS AT
DIFFERENT AGE GROUPS AT
THE SAME TIME IN ORDER TO
UNDERSTAND CHANGES THAT
OCCUR DURING THE LIFE
SPAN.
•AN EXAMPLE WOULD BE
QUESTIONING DIFFERENT AGE
GROUPS AND HOW THEY FEEL
ABOUT TOPICS LIKE WAR,
POLITICS, RELIGION.
Copyright © Allyn & Bacon 2007
Cohort –Sequential Studies
•THIS IS LIKE A
COMBINATION OF THE
CROSS-SECTIONAL AND
THE LONGITUDINAL
METHODS.
•TAKES A CROSS-SECTION
OF THE POPULATION, AND
THEN FOLLOWS THAT
COHORT (GROUP) FOR A
PERIOD OF TIME.
•YIELDS BETTER DATA
THAN THE CROSSSECTIONAL METHOD.
Copyright © Allyn & Bacon 2007
Ex-Post Facto Design
•PRE-EXISITING FACTORS.
•SUBJECTS ARE CHOSEN
BASED ON A PRE-EXISTING
CONDITION.
•USEFUL TO HELP
DISCOVER TREATMENT
METHODS FOR ILLNESS,
OR MENTAL ILLNESS
Copyright © Allyn & Bacon 2007
Sources of Bias
Sources of bias include:
Personal bias
Expectancy bias
Bias could affect the way an experimenter
designs a study, collects data, or
interprets results
Researchers must also attempt to control
confounding variables
Copyright © Allyn & Bacon 2007
Double Blind vs. Single Blind Studies
Single Blind
•The subjects do not know
which group they belong to
(either experimental or
control group).
•The researchers know who
is in which group.
•Can lead to experimenter
bias.
Double Blind
•The subjects do not know
which group they belong to
(either experimental or
control group).
•The researchers also do
not know who is in which
group.
•Very beneficial in studies
where new drugs are being
tested.
Copyright © Allyn & Bacon 2007
Ethics in Research
•Deception
•Debriefing
•Animal research
•See Table 2.3 on page
37 in your book
Copyright © Allyn & Bacon 2007
Questions Science Cannot Answer
The scientific method is not appropriate for
answering questions that cannot be put
to an objective, empirical test
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Ethics
Morality
Religious beliefs
Preferences
Copyright © Allyn & Bacon 2007
How Do We Make Sense of
the Data?
Researchers use statistics for
two major purposes:
(1) descriptively to characterize
measurements made on groups
or individuals and
(2) inferentially to judge whether
these measurements are the
result of chance
Copyright © Allyn & Bacon 2007
Organizing the Data
First results must be arranged in a
summary chart known as a frequency
distribution
We can convert the data into a bar graph
called a histogram
Copyright © Allyn & Bacon 2007
Examples of
organizing the
data:
Frequency distribution
chart – shows how
frequently each score
occurred.
Histogram or bar chart – gives a
visual representation of how the
scores look. This helps us to “see”
whether or not the scores are evenly
distributed.
A histogram can
also show whether
or not the scores
are more clustered
around the middle
of the distribution
or if there are
outliers (extreme
scores).
Copyright © Allyn & Bacon 2007
Descriptive Statistics
• In order to understand the data that was
gathered, statistics help to bring the data into
sharper focus.
• When using statistics, researchers are looking
for the central point around which the numbers
seem to cluster. This is called “measures of
central tendency.”
• This will then help the researchers to make
inferences about the data to determine if the
results are reliable or simply due to chance
(e.g. inferential statistics).
Copyright © Allyn & Bacon 2007
Describing the Data With Descriptive
Statistics
Descriptive statistics: Numbers that describe the
main characteristics of the data.
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The mean
The median
The mode
The range
The standard deviation
The normal distribution
Copyright © Allyn & Bacon 2007
The Mean
• The measure of central tendency most often used to
describe a set of data.
• Add all the scores and divide by the number of scores.
• It is the average.
• While it is a pretty good indicator of the center of the
distribution, its one flaw is that it can be skewed by
extreme scores.
• So, if the distribution of the scores is relatively
symmetrical (bell shaped), there is no problem; however, if
more scores fall toward either end of the distribution, then
the mean gets pulled in that direction and distorts the
overall inference of the data.
Copyright © Allyn & Bacon 2007
Examples of Skewed Distributions
More low scores than high scores – but there are a few
extremely high scores (mean is higher than the median)
More high scores than low – but there are a few extremely low
scores (mean is lower than the median)
On the last test, the class mean was 68. But, because it was not a symmetrical
distribution, that sounds like the class overall did poorly. When calculating the
median the scores look much better: the median score was 72. Due to low
extreme scores, the mean is a not a very good indicator of how the class did.
Copyright © Allyn & Bacon 2007
The Median
• The “middle” score.
• Think of the “median divider” in the center of the road
– it divides the upper half of the scores from the lower
half.
• This is a better measure of central tendency because
it is not affected by extreme scores.
• The scores are listed in order, and it is the number in
the middle. (e.g., 50, 55, 60, 65, 70)
• If you have an uneven set of numbers, take the two
middle numbers, add them, and divide by 2.
(e.g., 50, 55, 60, 65, 70, 72..add 60+65/2=62.5).
Copyright © Allyn & Bacon 2007
The Mode
• A measure of central tendency that is
used to identify the score that occurs the
mode, ooops, the most!
• 55, 55, 55, 63, 68, 70, 70, 82, 95
• It is often the least useful measure of
central tendency, especially if the sample
is small.
Copyright © Allyn & Bacon 2007
The Range
• The simplest measure of central
tendency that represents the difference
between the highest and the lowest
values.
• You use the range all the time in school
when you see what differentiates an A
from a B (90-100 and 80-89).
Copyright © Allyn & Bacon 2007
The Standard Deviation
• Psychologists prefer to take all scores into
consideration, not just the highest and the lowest, so
they use the standard deviation instead.
• The SD is a measure of central tendency that shows
an average difference between each score and the
mean.
• So, we are looking at the changes in the scores
across the spectrum of the scores.
• The larger the SD, the more spread out the scores
are; the smaller the SD, the more the scores bunch
together at the mean.
Copyright © Allyn & Bacon 2007
The Normal Distribution
• Together, the SD and the mean tell us much
about a distribution of scores. They indicate
where the center of the distribution is and how
closely the scores cluster around the center.
• In a normal distribution, or a bell curve, the
scores are all equally distributed around the
mean.
Copyright © Allyn & Bacon 2007
Normal
Distribution
• 68% of scores fall within 1
Mean
SD above and below the
mean
•If you have 100 scores, 50
are above, and 50 are below
68% of values
•Know how to compute
percentile
95% of values
•Know how to compute Z
score
% of scores
Standard
Deviations from
the Mean
Percentiles
Z Scores
2%
-4
-3
0
-4
-3
99% of values
14%
-2
2nd
-2
34%
-1
16th
-1
34%
0
50th
0
14% 2%
+1
84th
+1
+2
98th
+2
+3
+4
100th
+3
+4
Copyright © Allyn & Bacon 2007
Making Inferences with Inferential Statistics
• Inferential statistics are used to assess whether the results of a study
are reliable or whether they might be simply the result of chance.
• Researchers use inferential statistics to determine whether or not the
findings can be applied to the larger population from which the sample
was selected.
• Researchers compare the results of the experimental group to the
control group and determine (infer) whether the differences between
the groups are a result of the Independent Variable or could be the
result of chance.
• To have confidence in the results, the researchers have to take into
account the magnitude of the differences in scores, and go back to
make sure the sample was large enough and that the sample was
representative of the population at large. While a sample can never
truly represent the entire population, researchers do look at sampling
error, or how chance plays a factor in the results.
Copyright © Allyn & Bacon 2007
Making Inferences with Inferential Statistics
• Researchers then compute a “p” value for the scores,
which states how probable the results are due to the IV or
chance.
• What you need to know is that, in psychology, the cutoff
for statistical significance, or that the results are probably
due to the IV, is a value of p<.05. This means that the
probability of the results of the experiment being due to
chance are less than 5%, or 5 in 100.
• A “p” value can never equal zero because we can never be
100% sure that results did not happen due to chance.
• Therefore, researchers often try to replicate their results to
gather more evidence that their initial findings were not
due to chance.
Copyright © Allyn & Bacon 2007
End of Chapter 2
Copyright © Allyn & Bacon 2007