Factorial Designs Tyson S. Barrett, PhD PSY 3500 Experiments - PowerPoint PPT Presentation

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Factorial Designs Tyson S. Barrett, PhD PSY 3500 Experiments Manipulating a factor (some attribute of a participant) to discover a causal relationship How do we know our manipulation of the independent variable worked? If we were studying

Factorial Designs Tyson S. Barrett, PhD PSY 3500
Experiments Manipulating a factor (some attribute of a participant) to discover a causal relationship How do we know our manipulation of the independent variable worked? If we were studying how fear affects individuals ratings of various things, how could we do a manipulation check here?
Multiple Dependent Variables Sometimes there’s one IV and many DVs Carryover Effects Can combine multiple There can be an affect of measuring dependent measures from the variables same construct into one (latent variable) Same Construct Several DVs can measure Cool statistical techniques can different aspects of the help with this same construct
Factorial Designs 2 or more independent variables are tested simultaneously on one or more DVs 2 x 2 Factorial Design 2 x 3 Factorial Design Treatment 1 and Treatment 1 and Morning Evening Treatment and Treatment and Low Dose High Dose Treatment 2 and Treatment 2 and Morning Evening Control and Control and Control and Control and Low Dose High Dose Morning Evening
Factorial Designs 2 or more independent variables are tested simultaneously on one or more DVs The independent variable can be quasi-experimental (i.e., not manipulated) We are interested in interactions and main effects When the effect of one IV The effect of one IV does not depends on the level of depend on the level of another IV another IV
Interactions When the effect of one IV depends on the level of another IV 0.0 X 2 0 1 0 The effect of X 1 depends on Interaction 1 1.5 whether the person has a 0 or a 1 in X 2 1.0 X 2 0 0.5 1 In this case, X 1 has no effect 0.0 when X 2 is zero but has a big effect when X 2 is one − 0.5 0 1 X 1 X 1
Main Effects The effect of one IV does not depend on the level of another IV These are the effect of the variable regardless of other variables • Essentially the opposite of the interaction effects There can be both an interaction effect and a main effect within the same factorial design
Which is Main Effect X1 Main Effect X2 2.0 2.0 which? 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 X 2 Which is: Outcome 0 1 0 1 0 Main Effect X1 and X2 Interaction 1 • Effect of X 1 ? 4 1.5 • Effect of X 2 ? 3 1.0 • Both have an effect? 2 0.5 • Interaction effect? 1 0.0 − 0.5 0 0 1 0 1 X 1

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