Experiments and Randomisation

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  • 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. 
    • 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
    • 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
    • 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
    • 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
    • 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

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