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9: Factorial Designs

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    The research designs we have considered so far have been simple—focusing on a question about one variable or about a relationship between two variables. But in many ways, the complex design of this experiment undertaken by Schnall and her colleagues is more typical of research in psychology. Fortunately, we have already covered the basic elements of such designs in previous chapters. In this chapter, we look closely at how and why researchers use factorial designs, which are experiments that include more than one independent variable.

    • 9.1: Factorial Designs
      Schnall and her colleagues investigated whether feeling physically disgusted causes people to make harsher moral judgments. They manipulated 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 bodily sensations. They called this “private body consciousness.”
    • 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.

    9: Factorial Designs is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton.