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Experimental Designs Requirements: Types Manipulation of Conditions or Treatments Control for confounding variables Types Between Subjects Within Subjects Larger N Small n

Between Groups IV Random assignment to treatment groups Distribute evenly across levels of IV (e.g. treatment groups) individual differences among participants Minimize impact if these difference in DV

Within Groups IV All participants receive all levels of IV No individual differences across participants as potential confounds, therefore Randomnization is not needed (or possible) Bias: Order effects: carry-over, fatigue Counterbalancing (randomly assigned

Within Groups Design More statistical power than Between Groups: With same sample size, more observation per condition N=40 Treat 1 Treat 2 Between Groups 20 20 Within Groups: 40 40 Less variability across groups, therefore les sampling error (same individuals) and the higher the chance that p.alpha Source of bias: crossover effects- order and fatigue

question 22 IVs DVs Knowledge Score in test Treatment: Tech vs. lecture –True IV, BW- random assignment Gender : M F Quasi-Exp BG DVs Knowledge Score in test Design 2x2 factorial, between groups- quota

Analyses ANOVA P values Main Effect 1 p<.05 Gender Main Effect2 p>.05 Lesson Type Interaction Effect p<.05 Interaction

Main Effects Girls scored better on test than boys (regardless of type of instruction) Boys score = 75 Girls score = 86 p= <.05 There is no difference in test scores between the Tech and Lecture lesson groups (regardless of gender) Tech Avg 82.5 Lecture Avg 78.1 p>.05 ANOVA – for main effects

Interaction effect Boys Girls boys in Tech G > boys in Lecture group Girls girls in Tech G = girls in Lecture group Tech Lecture Boys 80 70 p<.025 Girls 85 87 p>.025 ANOVA for interaction effect --- followed by Test of simple effects – two T-tests; one per gender