Chapter 8 Flashcards
Factor means
an independent variable
Factorial means
More than one independent variable
Factorial Design
An experiment that contains more than one IV.
Factorial Notation
A factorial is described with a numbering system that simultaneously identifies the # of IVs and the # of levels of each IV.
The # of digits =
The number of IVs.
The value of digits =
The # of levels of that IV.
In order to determine the number of conditions or cells,
we multiply the numbers in the notation.
In factorial designs, levels and conditions
cannot be used interchangeably, they now refer to different things.
A cell matrix helps
us figure out what the study is about.
The inferential statistic to be used in a single-factor two-level design is
A t-test.
The inferential statistic to be used in a single-factor multi-level design is
The one-way analysis of variance (ANOVA), is followed by a post-hoc test if the ANOVA is significant.
The ANOVA is performed and within it,
The inferential statistics are performed on the main effects and the interaction effects.
Main Effect
Refers to the overall effect of one particular IV.
The number of main effects =
The number of IVs.
To determine the main effect,
we combine the data over all levels of the other factor.
We find the mean in the cell matrix by
Adding the values of the two levels and dividing them by the number of IVs.
Interaction Effects
Occurs when the effect of one IV depends on the level of another IV.
Interaction effects take precedence over
Main effects
The advantage of factorials over single-factor designs is that
It is possible to detect interactions that provide more accurate results.
In factorial designs, which is better, a line graph, a bar graph, or a histogram?
A line graph
An easier, more efficient way of determining whether an interaction effect is present is by
Looking at the data that is plotted in a line graph. If the lines are not parallel, there is an interaction.
The interaction is considered to be more important than the main effect because it
Provides a more detailed account of how the variables are related.
The independent variable will always be on the x-axis or the y-axis?
The y-axis.
If we have a study with 2 IVs and want to determine if the main and interaction effects are statistically significant, we will perform
An ANOVA test.
An ANOVA is an
umbrella term for inferential statistics that you perform on each possible effect.
Each inferential test is called an
F-test
If a study has more than one dependent variable, we will have to draw
a separate graph for each dependent variable.
Ceiling Effect
When the mean of 2 or more conditions are near the maximum it gives the impression that no difference exists between them.
The floor effect is the exact opposite of a
Ceiling effect
Mixed Factorial Design
Atleast one IV is a between-subject and another IV is a within-subject.
Factorial designs can be
completely between-subject or completely within-subject.
Between-subject
All IVs are between-s factors and each condition has a different set of Ss.
Within-subject
All IVs are within-s factors and the same set of Ss is tested across all conditions.
P x E
P = person (subject)
E = environment (manipulated)
In a Mixed Factorial,
at least one IV is a between-subject factor and one IV is a within-subject factor. Now we have two control problems. Creating equivalent groups for the between-subject and controlling for order effect for the within-subject.
P x E Factorial Design
These between-subject designs have a manipulated IV and a subject IV.
P represents
subject variable
E represents
manipulated variable
The P x E Factorial Design involves a
-Between-subject factor
-Within-subject factor
-Manipulated variable
-Subject variable
If the ANOVA indicates that the interaction effect is statistically significant, that gives us the green light to perform a
Simple effects analysis to determine the more detailed nature of that interaction and whether these comparisons are statistically significant.
Simple Effects Analysis
Breaking down the interaction to further understand it. It examines the IV at each level of the other IV.
We don’t compare conditions in the matrix that are
Diagonal to one another.
