Factorial ANOVAs Flashcards
what do ANOVAs help us to reduce?
type 1 errors (based on the use of multiple comparisons -> we want to avoid family wise error)
what is a Factorial Independent ANOVA?
has multiple independent variables
* different participants in all conditions
*one dependent/outcome variable
* you still need to run one ANOVA per dependent variable
what is a factorial design?
when you have several independent variables
what is a two-way factorial anova?
when there are two independent variables
i.e.
IV1: democratic status (4 levels)
IV2: GDP (3 levels)
DV: happiness score
what is a three-way factorial anova?
when there are three independent variables
what does a factorial design allow you to do?
allows you to look at interaction effects between variables
* the effect of one IV may depend on the level of another IV
what are the assumptions of a factorial anova?
- independence (each participant contributes one data point)
- normality (K-S, Shapiro Wilk and observe your graphs)
- homogeneity of variance (Levene’s Test and observe your graphs Q-Q Plots)
what should you do when assumptions are violated?
non-parametric alternative
how to structure your data (for a two-way factorial ANOVA)
IV1 (Coded)
IV2 (Coded)
DV
How to inspect your data for a One-Way Factorial ANOVA?
- drag the type of graph you need (multiple line graphs)
- drag one IV to the x-axis -> choose the one with the most levels first
- drag the second IV to set colour
- drag the DV to the y-axis
- Element Properties -> Error Bars -> SE +/- 1
- Click OK to generate your graph
Running the Factorial ANOVA
Analyse > General Linear Model > Univariate
* use this option for any factorial independent ANOVA (two-way, three-way etc.)
* Drag IVs to fixed factors box
* Drag DV to to dependent variable box
* Click Plots
â—‹ Plot Window allows you to draw a graph
â—‹ Drag IV with most levels to Horizontal axis
â—‹ Drag IV with fewest levels to separate lines
â—‹ Click Add > Continue
* Click Post-Hoc Tests (options same as ones in One-Way ANOVA)
â—‹ Post Hoc Window - Drag IVs to post-hoc box (select appropriate test(s))
* Click Options
â—‹ Drag your IVs and the interaction across to the Display means box
* Display
â—‹ Descriptive statistics, estimates of effect size, homogeneity tests
How should you interpret the output?
- Descriptive statistics box/table
- Levene’s Test of Equality of Error Variances
- Tests of Between-Subjects Effects
- Write up your results
descriptive statistics box/table
- check sample sizes are what you expected
Levene’s Test of Equality of Error Variances
homogeneity test -> we want Levene’s to be non-significant so we can assume equal variance
Test of Between-Subjects Effect
- main ANOVA summary table
- highlights main effects of your IVs -> interaction effect between the IVs
- Partial Eta Squared
what is Partial Eta Squared?
Effect Size
Writing up your results:
- Descriptive for graphs
- Results of assumptions tests
- Test statistics (F)
- Degrees of Freedom (model, error)
- P Value
- Effect Size (n2)
(if there’s a significant main effect, the graph should technically show us)
how do we know if there’s a significant interaction?
if there isn’t then two variables do not influence one another -> main effect of IV1 is not influenced by the level of IV2
in what designs do interactions happen>
deigns where there are more than one IV
* the effect of IV1 changes depend on IV2
* can suggest ideas and future research of interactions -> help aid critical analyses
how do we know if there’s an interaction effect?
if they cross, there’s likely a interaction effect happening (or you can extrapolate the line to see if they’ll eventually cross over -> visualising the data)
what is not an interaction effect?
lines that are next to each other -> but not crossing over
when can we use planned contrasts?
if your ANOVA is significant, we can explore the effects more deeply through planned contrasts (i.e. planned comparisons) and compare every level specific
Planned Contrasts
- most systematic -> used for testing specific hypotheses
- make a smaller number of comparisons and can include multiple conditions in each comparison
- useful for examples if you compare your group against a control -> or if you expect an effect to get larger as the level of treatment increases
IN THIS CASE, you don’t have to be as conservative in your corrections to avoid specific family wise errors -> running fewer tests is often preferred but only works if you have a specific hypothesis
How do planned contrasts actually work?
- general idea that you’re further dividing the variance explained by the model (comparing the amount of variance that can be attributed by your IV with any variance left over)
- allow us to further divide these control groups to find out which comparisons is driving the effect we saw -> chunks of variance explained by your variables