Transcript Research Methods - psychlotron.org.uk

Research Methods
Experiments
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IV & DV
Hypotheses (directional & non-directional)
Controls
Operationalisation
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Experimental Design
In an experiment we manipulate an IV
There are usually two values of the IV e.g.
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Noise or no noise
Rhymes or synonyms
These determine the conditions of the
experiment
The conditions can be arranged in several
different ways
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Experimental Design
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Independent measures
Repeated measures
Matched participants
Independent Measures
Recruit a group of
participants
Divide them
into two
This group does the
experimental task with the
IV set for condition 2
Measure the DV for
each group
Compare the results for the two groups
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This group does the
experimental task with the
IV set for condition 1
Repeated Measures
Recruit a group of
participants
The group does the
experimental task with the
IV set for condition 1
Condition 2
The group repeats the
experimental task with the
IV set for condition 2
Compare the results for the two conditions
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Condition 1
Matched Participants
Recruit a group of
participants
Recruit another
group that
matches them
one for one
Treat the experiment
as independent
measures
Condition 1
Condition 2
Compare the results for the matched pairs
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Find out what
sorts of people
you have in the
group
Participant Variables
Variation between PPs can affect DV
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Could mask an effect (false negative)
Could imply an effect where none exists (false
positive)
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Control by random assignment to groups
Use repeated measures or matched PPs
instead
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This is a problem with independent
measures
Fatigue, Boredom & Practice
Carrying out a task repeatedly leads to
changes in performance
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Deterioration as PPs become tired or bored
Improvement due to practice
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Leave a long gap between conditions
Counterbalanced design
Use independent measures or matched
participants
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This is a problem with repeated measures
Counterbalancing
Important control when using repeated
measures
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Reduces ‘carry over’ effects
Half PPs do condition A then B
Other half do condition B then A
Fully counterbalanced: ABBA
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Attrition
When PPs drop out of a study (attrition)
data are lost
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Fewer data = less powerful study
This can be a problem with matched PPs (and
repeated measures if there is a gap between
conditions)
Loss of a PP in these designs means
losing their data from both conditions
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