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9: Factorial Designs

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    • 9.1: Prelude to Factorial Designs
      Factorial designs are introduced with a classic example: Schnall and her colleagues investigated whether feeling physically disgusted causes people to make harsher moral judgments. They manipulated multiple variables including participants’ feelings of disgust by testing them in either a clean room or a messy room that contained dirty dishes, an overflowing wastebasket, and a chewed-up pen. They also used a self-report questionnaire to measure the amount of attention that people pay to their own
    • 9.2: Setting Up a Factorial Experiment
      By far the most common approach to including multiple independent variables (which are often called factors) in an experiment is the factorial design. In a factorial design, each level of one independent variable is combined with each level of the others to produce all possible combinations. Each combination, then, becomes a condition in the experiment. Imagine, for example, an experiment on the effect of cell phone use (yes vs. no) and time of day (day vs. night) on driving ability.
    • 9.3: Interpreting the Results of a Factorial Experiment
      The results of factorial experiments with two independent variables can be graphed by representing one independent variable on the x-axis and representing the other by using different colored bars or lines.
    • 9.4: Factorial Designs (Summary)
      Key Takeaways and Exercises for the chapter on Factorial Designs.

    This page titled 9: Factorial Designs was last modified on Fri, 14 Nov 2025 22:34:38 GMT and is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Patricia Bellas via source content that was edited to the style and standards of the LibreTexts platform.