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Chapter 9

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Psychology

PSYC 201W

A.George Alder

Spring

Description

Chapter 9 – Factorial Designs
Introduction
• Factorial design – includes two or more independent variables and crosses (i.e.,
combines) every level of each independent variable with every level of all the other
independent variables
Basic Characteristics of Factorial Designs
• Most basic factorial design possible is combining two independent variables, which
have two levels each, creating an experimental design that has four conditions
Describing A Factorial Design
• Between-subjects factorial design – a factorial design in which each subject
engages in only one condition
• Within-subjects factorial design – a factorial design in which each subject engages
in every condition
• Mixed-factorial design – a factorial design that includes at least one between-
subjects variable and at least one within-subjects variable
Advantages of Factorial Designs
• Factorial designs are better able to capture multiple factors operating simultaneously
than designs that manipulate only one independent variable
• Main effect – occurs when an independent variable has an overall effect on a
dependent variable
• Example A main effect for humidity would mean that overall, as humidity
changes, this influences task performance
Examining Interactions Between Independent Variables
• Studying both variables in the same experiment allows us to determine whether the
way one independent variable influences behaviour differs, depending on the level of
a second independent variable
• Interaction (or interaction effect) – occurs when the way in which an independent
variable influences behaviour differs, depending on the level of another independent
variable
• Example the effect of one drug differs, depending on whether a second drug is
also being used
• Most important advantage of factorial designs:
o Ability to test whether unique combinations of two or more independent
variables affect our behaviour in ways that cannot be predicted simply by
knowing how each variable individually affects behaviour
Examining Moderator Variables
• Moderator variable – a variable that alters the strength or direction of the relation
between an independent and dependent variable
• The effect of the independent variable on behaviour depends on the level of the
moderator variable, and this is precisely what an interaction involves
Other Advantages
• More efficient to conduct, as compared to conducting a series of separate
experiments, each of which examines only one independent variable
o Example rather than setting up equipment and initiating procedures for
recruiting and scheduling participants for each individual experiment, all these administrative and procedural aspects of conducting an experiment
can be done just once when a factorial design is used
• Researchers frequently wish to examine whether situational factors have different
effects on different types of people
o Example would the effects of room temperature and humidity on task
performance be the same for women and men, or older versus younger
adults?
Limitations of Factorial Designs
• As the number of independent variables increases, and as the number of levels
within each independent variable increases, the total number of conditions in an
experiment can rapidly increase beyond manageable proportions
o Practical issues may make it difficult to conduct the experiment:
▪ Examples:
• Number of participants that need to be recruited
• Amount of time that each participant must devote to the
experiment
• The amount of time and effort that the researchers need to
expend
• As the number of independent variables and conditions increases, interpreting the
results often becomes more difficult and mentally taxing, because of all the possible
outcomes that can occur
Designing A Factorial Experiment
• Practical limitations often necessitate a choice between adding independent
variables to a factorial design or adding more levels to a smaller number of
independent variable
Examining Non-Linear Effects
• When a researcher seeks to determine whether an independent variable has a
nonlinear influence on behaviour, that variable must be designed to have three or
more levels
• Example errors increase at higher temperature levels more rapidly when humidity
is high than when it is low
Incorporating Subject Variables
• Subject variables – represent characteristics of the people or non-human animals
who are being studied
• Scientific and practical questions:

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