Week 1 revision questions Flashcards
Define a factorial design
An experimental design that has at least 2 factors with 2 or more levels each.
What are the two main advantages of factorial designs?
Factorials can analyze the interactions between data AND doesn’t use as many participants.
What are the research questions that can be asked by a two way factorial design?
Does factor A effect the DV.
Does factor B effect the DV.
Does the effect of factor A on scores on the DV depend on the levels of factor B.
What does an interaction signify?
Interactions signify that the scores of Factor A and B are conditional upon the levels of the other factor.
What is a Marginal Mean?
The mean of a variable alone.
What is a Cell Mean
The mean of one data cell with averages one variable over the other.
What effects are Marginal Means used to test in 2-way designs?
Main effects
What effects are Cell Means used to test in a 2-way design?
Simple effects
What is the difference between Main effects and Simple effects?
Main effects examine whether individual IV’s have an impact on the DV. Simple effects examine the impact of one IV at the level of another.
What is the difference between ordinal and Disordinal interactions?
Ordinal interactions is when the data stays parallel and the signs stay the same, Disordinal is when data crosses and the signs flip.
In a graph of a 2-way factorial ANOVA how would you identify a main effect?
You would see whether there is a significant difference between the levels of a IV.
In a graph of a 2-way factorial ANOVA how would you identify a simple effect?
You would see whether cell’s of data across the two variables differ significantly.
In a graph of a 2-way factorial ANOVA how would you identify a interaction?
If the data crosses.
Explain what it means to say that interactions and main effects in a factorial design are
Independent.
In data, there can be any number of main effects, but that doesn’t mean that there has to be an interaction/point where data crosses.
Explain what it means to say that a significant interaction qualifies a significant main effect.
This means that a main effect needs to be analysed in light of the significant interaction, as the data varies significantly in reaction to another variable a main effect won’t fully explain an IV’s relationship to the DV.
