15.9
If a researcher is curious about a topic, such as people’s food preferences, they may consider a factorial analysis design—an experimental approach used when examining the effects of more than one factor, at least two independent variables, on one dependent variable.
For example, they can decide to manipulate two independent variables: food category and food temperature. Each factor consists of two levels: the ice cream and soup are served hot or cold. This case is referred to as a 2x2 factorial design, with favorability ratings operating as the single dependent variable.
As a result, the researcher can test two types of hypotheses. One type predicts the main effects, which assess the influence of conditions across each factor separately. For instance, to examine the main effect for food category, the favorability ratings of ice cream would be compared against soup.
Similarly, observing participants’ preferences between these foods when served warm versus cold relates to the main effect for temperature.
The other type of hypothesis—which is a major advantage of the approach—involves the assessment of interaction effects. These are observed when the effect of a factor is dependent on the level of the other factors in the experimental model.
For example, the researcher would predict that people prefer warm soup and cold ice cream.
Consequently, this procedure offers a more efficient and cost-effective means to test several combinations of two or more conditions at the same time. However, the application of the design may prove more challenging as the number of levels and factors increases.
Accordingly, researchers must take certain precautions both in terms of methodology and statistical analyses when interpreting complex experimental designs.
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent varia…
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