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Experimental Designs

Experimental Designs. Classifying Experimental Designs. Observations in a study can be divided into two components: Signal : The key variable—the construct you’re trying to measure Noise : All random factors in the situation that make it harder to see the signal. Observation. Signal.

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Experimental Designs

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  1. Experimental Designs

  2. Classifying Experimental Designs • Observations in a study can be divided into two components: • Signal: The key variable—the construct you’re trying to measure • Noise: All random factors in the situation that make it harder to see the signal

  3. Observation Signal Noise Classifying Experimental Designs

  4. Signal Noise Classifying Experimental Designs • You want the signal to be high relative to the noise

  5. Classifying Experimental Designs • Classify experimental designs into to categories • Signal Enhancers • Factorial designs • Noise Reducers • Correlated groups designs • Matched pairs, repeated measures, naturally-occurring pairs

  6. Single IV Designs • The basic two-group design Independent Variable: Customer Hearing Experimental group Control group Customers were deaf Customers were hearing

  7. Single IV Designs • Independent groups • Randomly assigned to groups • Correlated groups • Matched pairs • Repeated measures • Natural pairs

  8. Single IV Designs • Advantages of independent-groups designs • Simplicity • In some contexts, it is impossible to use correlated groups • Advantages of correlated-groups designs • Control—we have greater certainty of equality • Statistical benefits

  9. Signal between-groups variability statistic = error variability Noise Statistical benefits • Two sources of variability in your data: • The IV, or between-groups variability • Error variability, or within-groups variability

  10. Single IV Designs • The multiple-group design Independent Variable: Type of Noise Experimental group 2 Experimental group 3 Experimental group 1 White noise Music No noise

  11. between-groups variability statistic = within-groups variability Single IV Designs • The multiple-group design • Analysis: Oneway ANOVA

  12. Post-hoc comparisons Single IV Designs • The multiple-group design: Analysis Independent Variable: Type of Noise Experimental group 2 Experimental group 3 Experimental group 1 White noise Music No noise

  13. Single IV Designs • The multiple-group design: Analysis Independent Variable: Type of Noise Experimental group 2 Experimental group 3 Experimental group 1 White noise Music No noise Post-hoc comparisons

  14. Single IV Designs • The multiple-group design: Analysis Independent Variable: Type of Noise Experimental group 2 Experimental group 3 Experimental group 1 White noise Music No noise Post-hoc comparisons

  15. Factorial Designs • Multiple IV designs • Signal enhancers

  16. Factor A (First IV) Level A2 Level A1 A1B1 A2B1 Level B1 Factor B (Second IV) A1B2 A2B2 Level B2 Levels: Subdivisions of factors Factors: Major independent variables

  17. Main effect of time; no effectof setting Time 4 hr/wk 1 hr/wk 5 7 6 Inside Setting 5 7 6 Outside 5 7 Analysis, example 1

  18. Main effect of time Analysis, example 1

  19. Main effect of setting Analysis, example 1

  20. Main effect of setting,no effectof time Time 4 hr/wk 1 hr/wk 5 5 5 Inside Setting 7 7 7 Outside 6 6 Analysis, example 2

  21. Main effect of time Analysis, example 2

  22. Main effect of setting Analysis, example 2

  23. Main effect of bothtime and setting Time 4 hr/wk 1 hr/wk 5 7 6 Inside Setting 7 9 8 Outside 6 8 Analysis, example 3

  24. Main effect of time Analysis, example 3

  25. Main effect of setting Analysis, example 3

  26. Main effect of bothtime and setting, and aninteraction Time 4 hr/wk 1 hr/wk 5 5 5 Inside Setting 5 7 6 Outside 5 6 Analysis, example 4

  27. Basicinteraction Analysis, example 4

  28. Main effect of bothtime and setting, and aninteraction Time 4 hr/wk 1 hr/wk 7 5 6 Inside Setting 5 7 6 Outside 6 6 Analysis, example 5

  29. Crossover interaction Analysis, example 5

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