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Psychology

PSYB01H3

Connie Boudens

Fall

Description

Chapter 10 Complex Experimental Designs
Increasing the number of levels of an independent variable
- A researcher might want to design an experiment with three or
more levels for several reasons:
o A design with only two levels of the independent variable
cannot provide very much information about the exact
form of the relationship between the independent and
dependent variable
o An experimental design with only two levels of the
independent variable cannot detect curvilinear
relationships (the direction of the relationship changes, like
an inverted-U) between variables
o Finally, researchers frequently are interested in comparing
more than two groups
Increasing the number of independent variables: Factorial designs
- This type of experimental design is a closer approximation of
real-world conditions, in which independent variables do not exist
by themselves; researchers recognize that in any situation a
number of variables are operating to affect behaviour
- Factorial designs are designs with more than one independent
variable (or fact); in a factorial design, all levels of each
independent variable are combined with all levels of the other
independent variables
o The simplest factorial design known as a 2 x 2 (two by
two) factorial design has two independent variables, each
having two levels
Interpretation of Factorial Designs
- Factorial designs yield two kinds of information:
o Information about the effect of each independent variable
taken by itself: the main effect of an independent variable
In a design with two independent variables, there are
two main effects one for each independent variable
o The second type of information is called an interaction; if
there is an interaction between two independent variables, the effect of one independent depends on the particular
level of the other variable
In other words, the effect that an independent
variable has on the dependent variable depends on
the level of the other independent variable
Main Effects
- A main effect is the effect each variable has by itself
- The main effect of each independent variable is the overall
relationship between the independent variable and the
dependent variable
Interactions
- An interaction between independent variables indicates that the
effects of one independent variable is different at different levels
of the other independent variable
o That is, an interaction tells us that the effect of one
independent variable depends on the particular level of the
other
- Interactions can be seen easily when the means for all conditions
are presented in a graph
o The dependent variable is always placed on the vertical
axis
o One independent variable is placed on the horizontal axis
o Bars are then drawn to represent each of the levels of the
other independent variable
- The concept of interaction is a relatively simple one that you
probably use all the time; when we say it depends, we are
actually indicating that some sort of interaction is operating it
depends on some other variable
Factorial Designs with Manipulated and Nonmanipulated Variables
- One common type of factorial design includes both experimental
(manipulated) and nonexperimental (measured or
nonmanipulated) variables - These designs sometimes called IV x PV designs (independent
variable by participant variable) allow researchers to
investigate how different types of individuals respond to the
same manipulated variable
- Participant variables are personal attributes such as gender, age,
ethnic group, and personality characteristics
o Participant variables are also called subject variables or
attribute variables
- The simples IV x PV design includes one manipulated
independent variable that has at least two levels and one
participant variable with at least two levels
o The two levels of the subject variable might be two
different age groups, two different gender groups, etc.
Interactions and Moderator Variables
- In many research studies, interactions are discussed in terms of
the operations of a moderator variable
- A moderator variable influences the relationship between two
other variables
Outcomes of a 2 x 2 Factorial Design
- A 2 x 2 factorial design has two independent variables, each with
two levels
- When analyzing results, there are several possibilities:
o There may or may not be a significant main effect for
independent variable A
o There may or may not be a significant main effect for
independent variable B
o There may or may not be a significant interaction between
the independent variables
- In a 2 x 2 factorial design, there are eight possible outcomes
Interactions and Simple Main Effects
- Analysis of variance is used to assess the statistical significance
of the main effects and the interaction in a factorial design

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