Factorial Design

FACTORIAL DESIGN OVERVIEW

  • Focus on factorial designs in research involving multiple independent variables.

BASICS OF FACTORIAL DESIGNS

  • Notation includes designs like 2 x 2, 3 x 4, and 2 x 2 x 2.

  • Factorial matrix illustrates combinations of factors (e.g., A1B1, A2B2).

TYPES OF RESULTS

  • Main Effect: Overall impact of an independent variable.

  • Interaction Effect: Effect of one variable depends on another's level.

  • Two-factor designs yield main effects and interaction effects.

ANALYSIS OF MAIN EFFECTS AND INTERACTIONS

  • Effects are assessed using ANOVA.

  • Main effects can occur independently or interactively with others.

  • Interaction effects generally prioritized over main effects.

EXAMPLES AND INTERACTIONS

  • Example: Imagery's effect on memory, showing factor interactions.

  • Crossover interactions signify lack of main effects, only reveal interaction effect.

BOTTOM LINE ON INTERACTIONS

  • Parallel lines in data suggest no interaction; non-parallel indicates possible interaction,
    but requires statistical evaluation.

VARIETIES OF FACTORIAL DESIGNS

  • Mixed Designs: Combining between-subjects and within-subjects designs.

  • P x E Designs: Incorporate subject variables with manipulated variables.

INTERPRETATION OF ANOVA RESULTS

  • Discuss main effects, then interaction effects with appropriate tests.

  • Consider ceiling and floor effects in data interpretation regarding performance max/min limits.

SUBJECT SAMPLE SIZE

  • Recommended minimum: 20 participants per cell to ensure adequate power based on expected effect size.