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Chapter 10

PSYB01 Chapter 10

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University of Toronto Scarborough
Anna Nagy

PSYB01 - Chapter 10 Complex Experimental Designs Increasing The Number Of Levels Of An Independent Variable  Experiment may with 3- more levels for several reasons .  Design with only two levels of independent variable cannot provide much info. about exact form of relationship between independent and dependent variables .  Relationship is monotonic positive relationship, rather than strictly linear relationship .  Two levels only have a solid line .  Variables are sometimes related in curvilinear/ non-monotonic fashions .  The direction of relationship changes (e.g. Inverted-U) .  Experiment with only 2 levels of independent variable cannot detect curvilinear relationship .  At least need 3 levels in experimental design . Increasing The Number Of Independent Variables: Factorial Design  Two/Three independent variables are operating simultaneously .  Closer to real-world conditions, which independent variable do not exist by themselves .  Factorial design: Designs with more than one independent variable .  All levels of each independent variable are combined with all levels of the other independent variables .  Simplest factorial design: 2 X 2 factorial design, with 2 independent variables .  Format of factorial designs: # of levels of first xV # of levels of second IVx Number of levels of third IV Interpretation Of Factorial Designs  Factorial design yield 2 kinds of information: 1) Main effect : Information about effect of each independent variable taken by itself:  Two independent variables = 2 main effects . 2) Interaction: Interaction between 2 independent variables .  Effect of one independent variable depends on particular level of other variable .  Effect that independent variable has on dependent variable depends on level of other independent variable . Main Effects  Main effect: Effect each variable has by itself .  e.g. Type of question is overall effect of variable on dependent measure .  Main effect of each independent variable = Relationship between the independent & dependent variable. Interactions  Interaction betwee
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