Main Effects in Factorial Design

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  • 0:06 Factorial Design
  • 1:16 Main Effects
  • 3:25 Main Effects vs. Interactions
  • 4:42 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.

When you have more than one independent variable, sometimes you want to look at how they work independent of each other. In this lesson, we'll examine main effects in factorial design and how they differ from interactions.

Factorial Design

Joanne is a psychologist who studies the television habits of children. She wants to study whether gender plays a role in preferences for live action (or real) television shows over animated television shows. Specifically, she believes that little girls will prefer live action shows and that little boys will prefer animated shows. But there's something else, too. Joanne wonders if these preferences will change with age. For example, will children who are age 3 prefer animated shows, while 5-year-olds prefer live action shows?

Joanne's study has two independent variables, gender and age, and one dependent variable, which type of television program is preferred. When a study has two independent variables, it is a factorial design study. Factorial design gets its name from the fact that independent variables are often called 'factors.'

Within factorial designs, there are two ways to look at the results. Let's look closer at one of the ways, called main effects, and compare it with the other way to look at results.

Main Effects

Ok, so Joanne gets her results back, and she has two different questions that she wants to answer:

1. Do girls prefer live action and boys prefer animated shows?

2. Do older children prefer live action to animated shows?

The main effects of a factor are simply whether that factor has an effect on the dependent variable on its own. For example, Joanne wants to know if gender affects preference and whether age affects preference. She can look at the main effects for each of these factors separately, and it will tell her what each of them does.

One way that researchers look at the main effects of their factors is by drawing a table. Each column of the table represents one level (or option) for the first factor, and each row of the table represents one level of the second factor.

For example, if Joanne drew her table, she might have three columns: 3-year-olds, 4-year-olds, and 5-year-olds. She'd also have two rows: boys and girls.

Now all that Joanne has to do is fill in the mean, or average, for each of the boxes. For example, let's say that a 5 means that a person prefers live action shows and a 1 means they prefer animated shows. She'd average the numbers for all the 3-year-old boys and put that number in the box for 3-year-old boys. She'd do the same for 3-year-old girls, and 4-year-old boys, and so on until all the cells of her table are filled up.

Ok, so Joanne's filled up the cells in her table. But what does that have to do with main effects? Well, now she needs the mean for each row and each column. If the average score for the row labeled 'boys' is 1.3 and the average score for the row labeled 'girls' is 4.7, she can see that girls prefer live action shows and boys prefer animated shows. There is a main effect for gender on television preference.

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