Analysis of Variance Designs
Prerequisites
Introduction to ANOVA
Learning Objectives
 Be able to identify the factors and levels of each factor from a description
of an experiment
 Determine whether a factor is a betweensubjects or a withinsubjects
factor
 Define factorial design
There are many types of experimental designs that
can be analyzed by ANOVA. This section discusses many of these
designs and defines many key terms used.
Factors and Levels
The section on variables defined an independent
variable as a variable manipulated by the experimenter. In
the case study Smiles
and Leniency, the effect of different types of smiles on
the leniency showed to a person was investigated. Four different
types of smiles (neutral, false, felt, miserable, on leniency)
were investigated. In this experiment, "Type of Smile" is
the independent variable. In describing an ANOVA design, the term factor is
used as a synonym of independent variable. Therefore, "Type
of Smile" is the factor in this experiment. Since four types
of smiles were compared, the factor "Type of Smile" has
four levels.
An ANOVA conducted on a
design in which there is only one factor is called a oneway
ANOVA.
If an experiment has two factors, then the ANOVA is called a twoway
ANOVA.
Between and WithinSubject Factors
When different subjects are used for the levels
of the factor, the factor is called a betweensubjects factor or
a betweensubjects variable.
The term "between subjects" reflects the fact that comparisons
are between different groups of subjects.
When the same subjects are
used for the levels of the factor, the factor is called a withinsubjects
factor or withinsubjects
variable. Withinsubjects variables are sometimes referred
to as repeatedmeasures variables since
there are repeated measurements of the same subjects.
MultiFactor Designs
It is common for designs to have more than one
factor. For example, consider a hypothetical study of the effects
of age and gender on reading speed in which males and females
from the age levels of 8 years, 10 years, and 12 years were tested.
There would be a total of six different groups as shown in Table
1.
This design has two factors: age and
gender. Age has three levels and gender has two levels. When all
combinations of the levels are included (as they are here) the
design is called a factorial design.
A concise way of describing this design is as a Gender (2) x Age
(3) factorial design where the numbers in parentheses indicate
the number of levels. Complex designs frequently have more than
two factors and may have combinations of between and withinsubject
factors.
