Notes on Factorial Designs

Factorial Designs

  • Definition: Factorial designs involve experiments with two or more independent variables (IVs). They allow researchers to observe interactions and main effects of the independent variables.

Research Goals and Claims

  • Describe behaviour: Claim frequency of behaviours (e.g., prevalence of depression).
  • Predict behaviour: Establish associations (e.g., relation between depression and social isolation).
  • Explain behaviour: Make causal claims (e.g., social isolation leading to depression).

Simple Experiments

  • Independent Variable (IV): Manipulated to examine effects on a dependent variable (DV).
    • Example 1: Treatment vs. Control on Materialism.
    • Example 2: Exercise vs. No exercise on depression levels.

Adding Levels to an IV

  • Increasing IV levels provides detailed insights:
    • Can identify boundary conditions and curvilinearity.
    • Example: Hours of exercise and its relationship to depression (0, 1-2, 3-5, 6+ hours).

Reasons to Add Levels to an IV

  1. Identify Boundary Conditions: Understand how different conditions affect the outcome.
  2. Curvilinearity: Analyze nonlinear relationships.
    • Example: Anxiety levels affecting test performance.
  3. Test Multiple Treatments: Compare various methods or interventions.

Costs of Adding Levels to an IV

  • Power and Error Rates: Increase in Type II errors due to the larger sample size requirements.
  • Resource Needs: More complex designs require more resources.

Factorial Designs Overview

  • Factorial designs cross two or more IVs:
    • Example: Effects of cell phone use and age on driving performance.
    • Participant Variables: Selected/measured characteristics (e.g., age, gender).

Testing Theories with Factorial Designs

  • Can test boundary conditions and generalizability of effects:
    • Does IV impact different groups the same way? (e.g., alcohol’s effect on aggression varies with body weight).
    • Testing whether effects generalize across different cultures.

Interpreting Effects in Factorial Designs

  1. Main Effect: Overall effect of one IV on the DV, averaging over other IVs.
  2. Interaction Effect: Effect of one IV depends on the level of another IV.
Examples:
  • Study of well-being with cats vs. dogs illustrates marginal means and main effects.

Complex Factorial Designs

  • Can include more IVs beyond simple 2x2 layouts (e.g., 2x2x2, 2x4x2).
  • Complexity increases power and sample size requirements.

Practice Questions

  • Apply knowledge of designs and effects to hypothetical studies.
    • Example: Determine main effects and interactions in a study comparing re-reading vs. flashcards across subjects (History and English).