Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

PREVIEW
Does experiencing violence in video games have an effect
on the players’ behavior? One study suggests that the
answer is yes and no. Bartholow and Anderson (2002)
randomly assigned male and female undergraduate students
to play a violent video game or a nonviolent game.
After the game, each participant was asked to take part
in a competitive reaction time game with another student
who was actually part of the research team (a confederate).
Both students were instructed to respond as quickly
as possible to a stimulus tone and, on each trial, the loser
was punished with a blast of white noise delivered through
headphones. Part of the instructions allowed the participant
to set the level of intensity for the punishment noise
and the level selected was used as a measure of aggressive
behavior for that participant, with higher levels indicating
more aggressive behavior. The results of the study showed
that the level of violence in the video game had essentially
no effect on the behavior of the female participants but the
males were significantly more aggressive after playing the
violent game compared to the nonviolent game.
The Bartholow and Anderson study is an example of
research that involves two independent variables in the
same study. The independent variables are:
1. Level of violence in the video game (high or low)
2. Gender (male or female)
The results of the study indicate that the effect of
one variable (violence) depends on another variable
(gender).
You should realize that it is quite common to have
two variables that interact in this way. For example, a
drug may have profound effects on some patients and
have no effect whatsoever on others. Some children
survive abusive environments and live normal, productive
lives, while others show serious difficulties.
To observe how one variable interacts with another, it
is necessary to study both variables simultaneously in
one study. However, the analysis of variance (ANOVA)
procedures introduced in Chapters 12 and 13 are limited
to evaluating mean differences produced by one
independent variable and are not appropriate for
mean differences involving two (or more) independent
variables.
Fortunately, ANOVA is a very flexible hypothesis
testing procedure and can be modified again to
evaluate the mean differences produced in a research
study with two (or more) independent variables. In
this chapter we introduce the two-factor ANOVA,
which tests the significance of each independent variable
acting alone as well as the interaction between
variables.
14.1 An Overview of the Two-Factor, Independent-Measures,
ANOVA: Main Effects and Interactions
LEARNING OBJECTIVES
1. Describe the structure of a factorial research design, especially a two-factor
independent-measures design, using the terms factor and level.
2. Define a main effect and an interaction and identify the patterns of data that produce
main effects and interactions.
3. Identify the three F-ratios for a two-factor ANVOA and explain how they are related
to each other.
In most research situations, the goal is to examine the relationship between two variables.
Typically, the research study attempts to isolate the two variables to eliminate or reduce the
influence of any outside variables that may distort the relationship being studied. A typical
experiment, for example, focuses on one independent variable (which is expected to influence
behavior) and one dependent variable (which is a measure of the behavior). In real
life, however, variables rarely exist in isolation. That is, behavior usually is influenced by a
variety of different variables acting and interacting simultaneously. To examine these more
complex, real-life situations, researchers often design research studies that include more
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