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Research_and_Methods_Final-1 - Chapter 12 Factorial Designs...

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Chapter 12 Factorial Designs Factors = IV At least 2 IVs in order to have factorial design o More practical and most common are 2 or 3 levels 2 X 2 – doubling of our basic 2 level, one IV design from Ch. 10, 11 How many numbers? 2 X 2 X 3 (3 IVs) Value of numbers – 2 levels X 2 levels X 3 levels 2 X 2 = 4 treatment conditions, 2 X 3 = 6 treatment conditions o Multiply get number of treatment conditions If you don’t use a factorial design and separate into smaller studies you will lose time efficiency and the interaction Make sure the IVs go together o Self esteem and eye color doesn’t go with test performance Consider control issues; repeated measures gives us greater confidence that there is equality in our groups when we don’t have 10 Ss per group Keep it simple stupid (KISS); don’t make more complicated than it needs to be o More Ss required, more experimental conditions, more chances things can go wrong Data interpretation becomes nearly impossible with 4, 5, 6 IVs; most people use 2 or 3 Adding levels into factorial design increases groups in multiplicative fashion o 2 X 2 X 2 = 8 conditions, 3 X 2 X 2 = 12 conditions Ex post facto – only way to study sex, personality, race, etc. Assigning Participants to Groups Random assignment o IVs involve random assignment ( between-subjects factorial designs or completely randomized designs ) Completely within-groups (or within-subjects) designs o Correlated assignment in order to assure the equality of participant groups Mixed assignment o Involve a combination of random and correlated assignment, with at least one IV using each type of assignment to groups o The use of repeated measures is probably more likely than other types of correlated assignment Main effects and Interactions Main effect o Looking at result of each IV separately on the DV. Interaction o Exists when one IV depends on particular level of another IV o Crossing lines or lines that converge typically suggest an interaction; parallel lines always equals no interaction Ex: Attractiveness and crime. Ordinary guy gets a mean of 5.1 months for robbery and 6.4 months for swindling. Attractive guy
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gets a mean of 2.2 months for robbery and 10.5 months for
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Research_and_Methods_Final-1 - Chapter 12 Factorial Designs...

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