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Chapter 10

PSYB01H3 Chapter Notes - Chapter 10: Pocket Cube, Experiment, Behavioural Sciences

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Anna Nagy

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Week 10 Reading Notes
Chapter 10: Complex Experimental Designs
Researchers often investigate probs that demand more complicated designs
An Independent Varaibel Can Have More Than Two Levels
In simplest experimental design, there are only 2 levels/groups/conditions
of the IV
A researcher might want to design an experiment with 3 or more levels for
several reasons
o i.e. a design with only 2 levels of the IV may not give enough info
about the exact form of the rela w the IV and DVs
When an experiment with a single independent variable has 2 levels, it can
be analyzed with s t-test
When an experiment has 3 or more levels, it can be analyzed with an analysis
of variance
Variables are sometimes related in a curvilinear fashion the direction of
the rela changes
This is an inverted-U rela bc with a wide range of levels of the IV, the rela has
an upside-down U shape:
An experimental design with only  levels of the IV can’t detect curvilinear
relas bw variables
o If a curvilinear rela is predicted, at least 3 levels must be used
Complex designs are imp bc researchers are frequently interested in
comparing more than 2 groups
An Experiment Can Have More Than One Independent Variable: Factorial Designs
Researchers often manipulate more than one IV in a single experiment
Typically 2 or 3 IVs are operating simultaneously
o This type of experimental design is a closer approximation of real-
world conditions where IVs coexist
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Researchers recognize that in any given situation a number of variables are
operating to affect behaviour
FACTORIAL DESIGN: a design with more than one IV (or factor); all levels of
each IV are combined with all leveld of the other IVs
Simplest factorial design is 2x2
o Has 2 IVs with 2 levels each
o Has 4 groups total
Interpretation of Factorial Designs
Factorial designs yield 2 distinct kinds of info
o MAIN EFFECT: Info about the effect of each IV taken by itself
In a design with 2 IVs, there are 2 main effects (one for each IV)
If there’s an interaction bw  IVs, the way that one IV affecs the
DV depends on the particular level of the other variable
These are new and valuable sources of info that can’t be
obtained in a single-experimental design wher e only one IV is
Main Effects
Main effect is the effect that each IV BY ITSELF has on the DV
The main effect of each IV is the overall rela bw the IV and the DV
The main effect pretends that the other IV didn’t exist in the experiment
Stat tests would enable us to determine whether a main effect is sig
The main effect for IV A is the overall rela bw that IV, by itself, and the DV
There’s a possibility that an interaction exists
o If there is, it indicates the main effects of the IVs must be qualified bc
an interaction bw IVs indicate that the effect of one IV is diff at diff
levels of the other IV
An interaction tells us that the effect of one IV depends on the
particular level of the other
When we say it depends we’re usually indicating that some sort of
interaction is operating it depend on some other variable
Sometimes it helps to see interactions when the means for all conditions are
presented in a graph
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o All 4 cells have been graphed above in both of the graphs
o One IV is placed on the horizontal (X) axis
o Bars are then drawn to represent each of the levels of the other IV
o Graphing the results in this way is useful for visualizing interactions in
a factorial design
Factorial Designs with Manipulated and Non-Manipulated Variables
IV X PV DESIGN: a common type of factorial design that includes both
experimental (manipulated) and non-experimental (measured or non-
manipulated) variables
o Allows researchers to investigate how diff types of people respond to
the same manipulated variable
PARTICPANT VARIABLES are often personal attributes like sex, age, ethnic
group, personality characteristics, or clinical diagnostic category
o Also called subject variables and attribute variables
Participant variables can’t be randomly assigned or controlled
participants bring those characteristics with them to the study, so the IV X PV
design isn’t a fully true experiment
The simplest IV X PV design includes one manipulated IV that has at least 2
levels and one participant variable with at least 2 levels
Factorial designs with both manipulated IVs and PVs offer a very appealing
method for investigating many interesting research questions
Such studies recognize that full understanding of behaviour requires
knowledge of both situational variables that are able to be manipulated and
the personal attributes of individs
Bc participant variables can’t be manipulated and randomly assigned, we
must be cautious not to make unwarranted causal claims when interpreting
these results
Interactions Illuminate Moderator Variables
MODERATOR VARIABLE: influences the rela bw 2 other variables
The main enables us to state the rela bw variables
Bc we have an interaction, we must then make a qualifying statement that
one variable influences or moderates this rela
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