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Experimental Designs Multiple-Group Designs Multiple-IV Designs

Experimental Designs Multiple-Group Designs Multiple-IV Designs. Single IV Designs. The multiple-group design. Independent Variable. Condition 2. Condition 3. Condition 1. Single IV Designs. Bransford & Johnson (1972). Single IV Designs. Bransford & Johnson (1972). Context.

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Experimental Designs Multiple-Group Designs Multiple-IV Designs

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  1. Experimental DesignsMultiple-Group DesignsMultiple-IV Designs

  2. Single IV Designs • The multiple-group design Independent Variable Condition 2 Condition 3 Condition 1

  3. Single IV Designs • Bransford & Johnson (1972)

  4. Single IV Designs • Bransford & Johnson (1972) Context No context 1 rep No context 2 reps Context before Context after Partial context

  5. Single IV Designs • How to analyze a multiple-group design? Context No context 1 rep No context 2 reps Context before Context after Partial context

  6. between-groups variability statistic = within-groups variability Single IV Designs • How to analyze a multiple-group design? • Analysis: Oneway ANOVA

  7. Single IV Designs • Analysis: Oneway ANOVA • Null hypothesis? • Alternative hypothesis?

  8. Post-hoc comparisons Single IV Designs • The multiple-group design: Analysis Independent Variable Condition 2 Condition 3 Condition 1

  9. Single IV Designs • The multiple-group design: Analysis Independent Variable Condition 2 Condition 3 Condition 1 Post-hoc comparisons

  10. Single IV Designs • The multiple-group design: Analysis Independent Variable Condition 2 Condition 3 Condition 1 Post-hoc comparisons

  11. Single IV Designs • Bransford & Johnson (1972) Context No context 1 rep No context 2 reps Context before Context after Partial context 3.6 (.64) 3.8 (.79) 8.0 (.65) 3.6 (.75) 4.0 (.60)

  12. Single IV Designs

  13. Single IV Designs • Advantages of multiple-groups designs • Can discover nonlinear relationships • Test (and possibly rule out) potential alternative explanations

  14. Multiple IV designs • Factorial Designs • Signal enhancers • Advantages over single-IV designs: • Allow us to look at combinations of IVs at the same time • More time efficient than running multiple single-IV studies

  15. Factor A (First IV) Level A2 Level A1 A1B1 A2B1 Conditions: The combination of levels that participants experience Level B1 Factor B (Second IV) A1B2 A2B2 Level B2 Levels: Subdivisions of factors Factors: Major independent variables

  16. Factorial Designs • 2 x 2 design • Number of numbers tells us how many factors • Value of numbers tells us how many levels in each IV

  17. Presentation Rate 4 sec 2 sec Imagery Training Type Rote

  18. Presentation Rate 4 sec 2 sec Imagery Training Type Rote

  19. Factorial Designs • Main Effect: • Sole effect of one IV

  20. Presentation Rate 4 sec 2 sec 20 17 23 Imagery Training Type 15 12 18 Rote 14.5 20.5

  21. Main effect

  22. Factorial Designs • Main Effect: • Sole effect of one IV • Interaction: • Joint, simultaneous effect of both IVs at the same time • If you have a significant interaction, the effects of one IV depend upon the level of the other IV

  23. Grant et al. (1998) Study Environment Noisy Silent 12.8 14.3 12.7 Silent Testing Environment 13.5 12.7 14.3 Noisy 12.8 13.5

  24. Assigning Participants to Groups in Factorial Designs • Independent groups • Randomly assigned to groups • Correlated groups • Matched pairs • Repeated measures • Natural pairs • Mixed groups • At least one IV has participants randomly assigned to groups, at least one IV has participants in correlated groups

  25. Signal between-groups variability statistic = error variability Noise Statistics in Factorial Designs • Factorial ANOVA (in SPSS: General Linear Model, Univariate Analysis of Variance)

  26. IV A variability IV B variability interaction variability Factor A Main Effect = Factor B Main Effect = Interaction of A and B = error variability error variability error variability Statistics in Factorial Designs • Factorial ANOVA

  27. Statistics in Factorial Designs • Factorial ANOVA

  28. Statistics in Factorial Designs • Steps for interpretation (2 x 2) • Is there a significant interaction? • If yes, you must describe what’s going on in the experiment using both IVs

  29. Statistics in Factorial Designs • Steps for interpretation (2 x 2) • Is there a significant interaction? • If yes, you must describe what’s going on in the experiment using both IVs • If no, ask: • Are there significant main effects? • Interpret effect of factor A • Interpret effect of factor B

  30. Statistics in Factorial Designs • How about a 2 x 3 factorial design? • Is there a significant interaction? • If yes, you must describe what’s going on in the experiment using both IVs • If no, ask: • Are there significant main effects? • Significant effect of factor A? • Significant effect of factor B? • If significant, then what?

  31. Statistics in Factorial Designs • How many interactions in a 2 x 2 x 2 factorial design?

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