Experiments and Randomisation
- Created by: kaytlhone101
- Created on: 03-05-18 19:52
View mindmap
- Experiments & Randomisation
- Designing
an experiment
- Manipulation: When the experimenter
systematically changes the levels of the
- The goal of the experiment is to investigate whether the manipulation of the IV causes an effect in the DV
- independent variable (IV) across the conditions (groups/ levels) of the experiment.
- Dependent variable (DV): The variable that is measured.
- Manipulation: When the experimenter
systematically changes the levels of the
- IVs
and DVs
- IV = What is manipulated; the CAUSE we are investigating
- DV = What is measured; where the EFFECT we are investigating would occur
- True
versus Quasi experiments
- True experiments
- Researchers can actively manipulate the IV and so assign participants to which condition they participate in (between-subjects) and/or assign the order that participants see the conditions (within-subjects)
- Quasi experiments
- researchers are unable to actively manipulate the IV, either because they are investigating naturally existing variables or because it would be unethical to do so
- True experiments
- Between-
or Within-subjects
- Between-subjects
- Independent groups
- Unrelated
- Unpaired
- Within-subjects
- Dependent groups
- Repeated measures
- Related
- Paired
- influences your choice of test and the conclusions you draw
- Between-subjects
- Confound/
nuisance variables
- Confounds = Other variables that may
influence the DV – these need to be controlled in the design
- 1.Variables that are related to the DV and are known to influence it
- 2.Disadvantages of the design
- Confounds/
nuisance variables are threats to internal validity
- Internal validity
- we have greater confidence that the IV was responsible for any changes/differences observed in the DV
- Internal validity
- Confounds = Other variables that may
influence the DV – these need to be controlled in the design
- Randomisation
- Participants
in between-subjects experiments will have a random
and equal chance of participating in any
condition
- By hand: e.g., drawing allocation out of a hat
- By computer: computer programme can assign participants
- Use a random number generator (e.g., random.org) to produce an allocation sequence
- Participants
in between-subjects experiments will have a random
and equal chance of participating in any
condition
- Counterbalancing
- Alternating the order that participants experience the condition
- Experimental
control
- Internal validity
- we have greater confidence that the IV was responsible for any changes/differences observed in the DV
- ?control helps our experiment to have high internal validity
- Internal validity
- Designing
an experiment
Comments
No comments have yet been made