1. Research Methods Experiments IV & DV
Hypotheses (directional & non-directional)
Controls
Operationalisation
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2. Experimental Design In an experiment we manipulate an IV
There are usually two values of the IV e.g.
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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3. Experimental Design Independent measures Repeated measures
Matched participants
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4. Independent Measures psychlotron.org.uk
Recruit a group of participants
Divide them into two
This group does the experimental task with the IV set for condition 1
This group does the experimental task with the IV set for condition 2
Measure the DV for each group
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Compare the results for the two groups

5. Repeated Measures psychlotron.org.uk
Recruit a group of participants
Condition Condition 2
The group does the experimental task with the IV set for condition 1
The group repeats the experimental task with the IV set for condition 2
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Compare the results for the two conditions

6. Matched Participants psychlotron.org.uk
Recruit a group of participants
Find out what sorts of people you have in the group
Recruit another group that matches them one for one
Treat the experiment as independent measures
Condition 1 Condition 2
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Compare the results for the matched pairs

7. Participant Variables
Variation between PPs can affect DV
Could mask an effect (false negative)
Could imply an effect where none exists (false positive)
This is a problem with independent measures
Control by random assignment to groups
Use repeated measures or matched PPs instead
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8. Fatigue, Boredom & Practice
Carrying out a task repeatedly leads to changes in performance
Deterioration as PPs become tired or bored
Improvement due to practice
This is a problem with repeated measures
Leave a long gap between conditions
Counterbalanced design
Use independent measures or matched participants
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9. Counterbalancing Important control when using repeated measures
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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10. Attrition When PPs drop out of a study (attrition) data are lost
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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