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Chapter 5

PSYC 2040 Chapter Notes - Chapter 5: Lincoln Near-Earth Asteroid Research, Null Hypothesis, Repeated Measures Design

Course Code
PSYC 2040
Naseem Al- Aidroos

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Chapter #5 Completely Randomized Factorial Designs
When researchers want to investigate the eects of more than one
independent variable on a dependent variable.
CRF design is a form of analysis of variance that permits a
researcher to investigate the eects of two or more factors and
to assess any possible interactions between these factors
The primary characteristic of this type of design is that each cell
in the design is made up of a random sample of observations
Thus, in a two factor (AB) design, there are AXB groups of
observations and the researcher can assess the eects of the A
factor, the B factor and the combination of A and B factors
In a three factor design, there are axbxc random samples of
observations and the researcher can investigate the eects of A,
B, and C by themselves, the two way interactions of AB, Ac, and
BC and the three way interaction of ABC
In theory this can be extended to any # of factors
SPSS is limited to 5
This design is more complex than single factor analysis of
CRF is sometimes described as two or more single factor designs
in the same study
It permits the researcher to determine whether or not dierences
that can be attributed to one of the factors are consistent at all
levels of the other factor(s) –i.e., whether or not the two (or
more) factors interact with one another to produce eects that
could not be determined if the factors were investigated one at a
1 DV
2+ IV(s) and each needs to have 2 or more levels
Not repeated measures
Conducting a CRF is dierent from conducting two ONE WAY ANOVAs
There’s more than one independent variable
Interaction between two independent variables
Main Eects: Overall eect of that variable ignoring all other variables
Simple Main Eects: compare cell means and the interaction in this
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