Variables
Psychology 2020
Introduction to
Psychological Methods
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• Some examples are task difficulty (high or low,
amount of reinforcement (1,2, or 3 candies),
or number of responses made in an hour.
Unit 2
Defining and Measuring the
Subject Matter
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Research Variables
Situation variables
Response variables
Individual differences in participants variables
Mediating variables
Situation variables are often manipulated
by the experimenter.
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Individual difference variables
related to the participants.
• These are often minimized or controlled
such that the experimental results are
more clearly seen.
• Examples: Age, gender, diagnosis,
intelligence, grade level, income level,
living arrangement, sexual orientation,
etc.
Response variables are often measured as
the results of the experiment (what the
participants are doing differently after
receiving the experimental manipulation)
• These variables become the “dependent”
variables of the experiment.
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Other Variables
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Other Variables
• They become the “independent” variables in
the experiment or they are held constant
(controlled)
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There are four general categories of
variables.
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Variables in research are those things
which can change (have more than one
value).
Operational Definitions
Mediating variables are inferred
psychological processes that are
often used to explain research
outcomes.
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Measured variables must be defined clearly
When we define a variable in terms of the specific
method used to produce, manipulate and/or
measure that variable we have defined it
“operationally”.
Examples:
• Intelligent is operationally defined as a score above 120
on an IQ test.
• Courage is operationally defined as doing things that
could produce physical pain.
• A tantrum is operationally defined as screaming loudly
for more than 30 seconds when a toy is removed from
play.
• These variables are invented by the
experimenter and are related to the
theory that produced the experiment.
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Relationships Between
Variables
Operational Definitions
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Linear relationships
• Positive linear relationship.
When we define a variable in terms
of the specific method used to
produce, manipulate and/or measure
that variable we have defined it
“operationally”.
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Both variables increase or decrease at the
same rate in the same direction.
Represented as a straight line going up on a
graph
• Negative linear relationship
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As one variable increases the other variable
decreases at the same rate.
Represented as a straight line going down
on a graph
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Relationships Between
Variables
Linear Relationship Graphs
Positive
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Negative
Curvilinear relationship
• As one variable increases the other variable
both increases and decreases over time
(nonmonotonic).
• Represented as a curved line on a graph.
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No Relationship between variable
changes
• Represented by a flat, horizontal line on a
graph.
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Experimental vs.
Nonexperimental Methods
Curvilinear Relationship Graph
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Nonexperimental methods simply measure
the variables of interest in an attempt to
discover a relationship between them (if
there is a correlation between them).
Experimental methods systematically
change some variables and measure the
effects of these changes on other
variables to assess the relationship
between them.
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Nonexperimental Methods
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Nonexperimental Methods
These methods are good for discovering if
two or more variables covary.
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• Covariation or correlation is useful for
prediction (if you know the value of one
variable you can predict the value of correlated
variable.
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Causality MUST NOT be inferred from
correlational methods
• The problem of determining the
direction of cause and effect.
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Which change came first?
• The third-variable problem
Examples: High SAT scores are positively
correlated with success in college, seat
belt use is negatively correlated with
severe injury in auto accidents.
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Did some other variable produce the
changes in the two measured variables?
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Experimental Methods &
Causality
Experimental Methods
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Experimental methods introduce
techniques of control that allow inferences
of causality.
• Extraneous variables are held constant and/or
their effects are randomized across
experimental conditions.
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This solves the third variable problem.
• One variable is systematically changed (the
independent variable) while changes in the
other variable (dependent variable) are
measured.
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This solves the “cause/effect direction” problem.
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To infer causality we must consider
the following
• Temporal order (cause comes before
effect)
• There must be covariation between
variables (as one variable changes there
must be a corresponding change in the
other variable)
• Other explanations for the change must
be eliminated
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