60 likes | 146 Views
Chapter 10 delves into increasing the number of levels of an independent variable, comparing simple and factorial designs, identifying main effects and interactions, and understanding mixed factorial designs. Learn how to analyze and interpret results using various design structures.
E N D
Complex Experimental Designs Chapter 10
Increasing the # of levels of an IV • Simple design = 2 levels • Doesn’t provide a lot of information • Level 1: reward • Level 2: no reward • Use a ratio scale • Linear • Monotonic • Curvilinear • Increases in one variable are accompanied by decreases and increases in the other variable • Inverted-U
Increasing the # of IVs: factorial designs • Many IVs are closer to real world conditions • Factorial designs – more than 1 IV • 2 x 2 factorial • 2 independent variables w/ 2 levels each • 2 x 2 = 4 experimental conditions • Main effects • Effect the variable has by itself • Averages across the levels of the other IV • Interactions • The effects of one IV is different at different levels of the other IV
Outcomes of a 2 x 2 factorial design • There may or may not be a significant main effect for variable A • There may or may not be a significant main effect for variable B • There may or may not be an interaction between both variables • When there is an interaction, next look at simple main effects (differences at each level of the IV) • This allows analysis of results as though there were separate experiments at each level of the other IV
Mixed factorial design • Combining independent groups AND repeated measures • Uses combined assignment of participants • Investigates the combined effects of situational factors and subject variables • Common manipulation is looking at gender differences • Below is a 2x3 mixed factorial • 6 total conditions
Increasing the # of IVs in a factorial design • Conducting a 2x2x2 design • Can be seen as two 2x2 designs, • i.e. one for males and one for females • Yields main effects for 3 IVs • Also allows us to look at interactions