Experiment Basics Designs Psych 231 Research Methods in
Experiment Basics: Designs Psych 231: Research Methods in Psychology
n Some specific experimental designs. n n Some bad (but not uncommon) designs (and potential fixes) Some good designs • • 1 Factor, two levels 1 Factor, multi-levels Factorial (more than 1 factor) Between & within factors Experimental designs
n Two or more factors n Some vocabulary • Factors - independent variables • Levels - the levels of your independent variables • 2 x 4 design means two independent variables, one with 2 levels and one with 4 levels • “Conditions” or “groups” is calculated by multiplying the levels, so a 2 x 4 design has 8 different conditions A 1 B 2 B 3 B 4 A 2 Factorial experiments
n Two or more factors n Dependent Variable Dependent n Main effects - the effects of your independent variables ignoring (collapsed across) the other independent variables Interaction effects - how your independent variables affect each other • Example: 2 x 2 design, factors A and B B 1 • Main effect of A: A 1 vs. A 2 B 2 A 1 A Factorial experiments
n Two or more factors n Dependent Variable n Main effects - the effects of your independent variables ignoring (collapsed across) the other independent variables Interaction effects - how your independent variables affect each other • Example: 2 x 2 design, factors A and B B 1 • Main effect of A: A 1 vs. A 2 B 2 • Main effect of B: B 1 vs. B 2 A 2 A 1 A A Factorial experiments
n Two or more factors n Dependent Variable n Main effects - the effects of your independent variables ignoring (collapsed across) the other independent variables Interaction effects - how your independent variables affect each other • Example: 2 x 2 design, factors A and B B 1 • Main effect of A: A 1 vs. A 2 B 2 • Main effect of B: B 1 vs. B 2 • Interaction: • At A 1, B 1 is bigger than B 2 • At A 2, B 1 and B 2 don’t differ A 2 A 1 A Everyday interaction = “it depends on …” Factorial experiments
n Rate how much you would want to see a movie (1 no interest, 5 high interest): n n Hail, Caesar! – new Cohen Brothers movie in 2016 Ask men and women – looking for an effect of gender Not much of a difference: no effect of gender Interaction effects
n Maybe the gender effect depends on whether you know who is in the movie. So you add another factor: n Suppose that George Clooney or Scarlett Johansson might star. You rate the preference if he were to star and if he were not to star. Effect of gender depends on whether George or Scarlett stars in the movie or not This is an interaction Interaction effects A video lecture from The. Psych. Files. com podcast
n The complexity & number of outcomes increases: • A = main effect of factor A • B = main effect of factor B • AB = interaction of A and B • With 2 factors there are 8 basic possible patterns of results: 1) No effects at all 2) A only 3) B only 4) AB only 5) A & B 6) A & AB 7) B & AB 8) A & B & AB Results of a 2 x 2 factorial design
A 1 B 2 A 2 Condition mean A 1 B 1 A 2 B 1 Condition mean A 1 B 2 A 2 B 2 A 1 mean Interaction of AB What’s the effect of A at B 1? What’s the effect of A at B 2? B 1 mean B 2 mean A 2 mean Main effect of A Marginal means 2 x 2 factorial design Main effect of B
A 1 A 2 Main Effect of B B 1 30 60 45 B 2 30 60 B 45 Dependent Variable A Main Effect of A Main effect of B Interaction of A x B B 1 B 2 A 1 A ✓ X X • At A 1: B 1 = B 2 • At A 2: B 1 = B 2 The effect of A doesn’t depend on level of B Examples of outcomes
B 1 A 2 Main Effect of B 60 60 60 B B 2 30 30 45 45 30 Dependent Variable A Main Effect of A Main effect of A X Main effect of B ✓ Interaction of A x B X B 1 B 2 A 1 A • At A 1: B 1 - B 2 = 30 • At A 2: B 1 - B 2 = 30 The effect of A doesn’t depend on level of B Examples of outcomes
B 1 A 2 Main Effect of B 60 30 45 30 60 45 45 B B 2 45 Dependent Variable A Main Effect of A Main effect of A X Main effect of B X Interaction of A x B ✓ B 1 B 2 A 1 A • At A 1: B 1 - B 2 = +30 • At A 2: B 1 - B 2 = -30 The effect of A does depend on level of B Examples of outcomes
B 1 A 2 Main Effect of B 30 60 45 30 30 30 45 B B 2 30 Dependent Variable A Main Effect of A Main effect of B Interaction of A x B B 1 B 2 A 1 A ✓ ✓ ✓ • At A 1: B 1 - B 2 = 0 • At A 2: B 1 - B 2 = 30 The effect of A does depend on level of B Examples of outcomes
Let’s add another variable: test difficulty. test performance easy medium hard low mod anxiety high Test difficulty anxiety hard medium easy low mod high 35 80 35 65 80 80 80 60 main effect of anxiety Interaction ? Yes: effect of anxiety depends on level of test difficulty Anxiety and Test Performance main effect of difficulty 50 70 80
n Consider the results of our class experiment n Main effect of cell phone ✓ ✓ Main effect of site type n n X An Interaction between cell phone and site type 0. 73 1. 19 Factorial designs Report for each main effect. Dr. and. Kahn's the interaction Resource: reporting stats. Means page (& SDs) from the table ANOVA, alpha level 0. 05 E. g. , “F(1, 126) = 26. 8, p <. 05”
n Advantages n Interaction effects – Always consider the interaction effects before trying to interpret the main effects – Adding factors decreases the variability – Because you’re controlling more of the variables that influence the dependent variable – This increases the statistical Power of the statistical tests – Increases generalizability of the results – Because you have a situation closer to the real world (where all sorts of variables are interacting) Factorial Designs
n Disadvantages n n n Experiments become very large, and unwieldy The statistical analyses get much more complex Interpretation of the results can get hard • In particular for higher-order interactions • Higher-order interactions (when you have more than two interactions, e. g. , ABC). Factorial Designs
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