What is Factorial Design? - Definition & Example

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  • 0:05 Experimental Design
  • 1:38 Factorial Design
  • 3:28 Experimental Names
  • 5:27 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

The simplest studies involve one independent and one dependent variable. But what happens when a researcher wants to study more than one independent variable? In this lesson, we'll look closer at factorial design in research.

Experimental Design

Jessie is a psychologist. She's interested in studying whether or not girls will do better than boys on a history test. She also wonders if it will matter if the test is hard or easy. In other words, will girls do better than boys on both hard and easy tests?

The first step for Jessie or any researcher to take when trying to answer an experimental question is to engage in experimental design, or the process of choosing how to run an experiment to answer the research question of interest.

In order to figure out her experimental design, Jessie has to identify her variables, or measures that change. There are two main types of variables: independent variables, which are also sometimes called factors, are variables whose values do not depend on the value of another variable; and dependent variables are variables whose values depend on the independent variables.

Jessie has two independent variables, gender and difficulty of test, and one dependent variable, performance on the test. We know that gender and difficulty are independent variables because they are not caused by any of the other variables in the study. The subjects' gender does not change because they are given a harder test or because they did well on a test.

But performance on the test does change (or so Jessie assumes) based on the independent variables. If Jessie's hypothesis is correct, a subject will do better on the test if she is a girl.

Factorial Design

Many studies ask the question: 'How does this one independent variable affect this one dependent variable?' For example, perhaps Jessie just wants to know how gender affects how subjects do on a test. That's a basic experimental design.

But much of research is concerned with more than one independent variable. For example, Jessie wants to know if gender and difficulty of test affect performance on the test. Her question is, essentially: 'How do these two independent variables affect this one dependent variable?'

Remember that independent variables are sometimes called factors. When you have multiple independent variables in a single study, it is called factorial design.

A factorial design does not have to have just two independent variables; it can have as many as you want. What if Jessie decides that, besides gender and difficulty of test, the time of day when the test is taken also affects how subjects do? Now she has three independent variables to look at: gender, difficulty, and time of day.

One thing that's different in factorial designs from simpler designs is that we have to look at how each independent variable affects the dependent variable, but also how the combination of the independent variables affect the dependent variable.

Let's go back to Jessie's simplest experiment for a moment: she wants to know how gender affects test performance. In that study, all she has to look at is how that one independent variable (gender) affects her dependent variable (test performance).

But when Jessie adds in an independent variable like difficulty of the test, suddenly she has many different things to look at. She has to look at how gender affects test performance and how difficulty affects test performance. But she also has to look at how gender and difficulty together affect test performance.

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