Week 5 Lecture 5 - factorial ANOVAS (specifically 2-way independent ANOVA)

Question 1

What is the criteria for a Factorial ANOVA?

Answer 1

• more than 1 IV
• at least 2 levels in each

Question 2

What do factorial ANOVAS explore?

Answer 2

explore effects of each IV and interactions between IVs

Question 3

What are the 3 types of factorial ANOVA?

Answer 3

• all IVs are between-subjects (independent)
• all IVs are within subjects (repeated measures)
• mix of between-subjects and within subjects (mixed)

Question 4

What does a 422 ANOVA mean?

Answer 4

• 3 IVs
• 1st IV has 4 levels
• other 2 both have 2 levels

Question 5

What 2 things does a 2-way ANOVA tell us?

Answer 5

• main effects
• interaction

Question 6

How many main effects are there for a 2-way ANOVA?

Answer 6

2

Question 7

How many interactions are there for a 2-way ANOVA?

Answer 7

1

Question 8

Do factorial ANOVAs control for familywise error?

Answer 8

yes

Question 9

How many f stats are there in a factorial ANOVA?

Answer 9

• 3 (one for each effect = 2 main effects and 1 interaction)

Question 10

How do you determine whether you can reject the null hypothesis in a factorial ANOVA?

Answer 10

• consider whether you can reject each for each effect and interaction

Question 11

What makes up the variance between IV levels in a factorial ANOVA?

Answer 11

• IV 1
• IV 2
• interaction

Question 12

What makes up the variance within IV levels in a factorial ANOVA?

Answer 12

• error (inc. individual differences and experimental error)
• this stay the same for the whole ANOVA

Question 13

With more IVs;
- does it get harder or easier to find significant results?
- is there more or less chance of a type two error?

Answer 13

• harder to find significant results
• more chance of a type 2 error

Question 14

What is a significant interaction?

Answer 14

effects of manipulating 1 IV depends on the level of the other IV

Question 15

What are cell means?

Answer 15

means for each condition

Question 16

What are marginal mean?

Answer 16

average for an IV level e.g., male
ignore other IVs

Question 17

What is the overall mean?

Answer 17

average mean overall

Question 18

What are the assumptions for a 2 way individual ANOVA?

Answer 18

• normality –> within each condition
• homogeneity of variance –> check with Leven’s test but don’t have a correction for this
• equivalent sample size
• independence of observations

Question 19

Is there a non-parametric equivalent for a Factorial ANOVA?

Answer 19

no but it is normally quite robust as a test

Question 20

How do you finds to dofs for a main effect?

Answer 20

• m = IV looking at
• r = error row

Question 21

Do you report partial eta^2?

Answer 21

yes –> find on SPSS

Question 22

What is the difference between classical eta^2 and partial eta^2?

Answer 22

classical eta^2 = proportion of the total variance attributed to the factor
partial eta^2 = only takes into account the variance for 1 IV at a time rather than the total variance

Question 23

What post hoc test is used for a 2-way independent ANOVA?

Answer 23

• Tukey HSD

Question 24

When are post hoc tests relevant to report?

Answer 24

• when main effect of IV is significant and the IV has more than 2 levels

Question 25

Do you report Cohen’s d for post hoc tests in factorial ANOVAS?

Answer 25

no

Question 26

How do you report the interaction effects?

Answer 26

find IV1 * IV 2 row
report F stat as normal including partial eta^2

Question 27

When do we consider simple effects?

Answer 27

when there is a presence of an interaction

Question 28

What do simple effects use?

Answer 28

profile plots on SPSS

Question 29

What are simple effects?

Answer 29

the effect of an IV at a single level of another IV
- comparison of all means (conditions)

Question 30

How do you calculate simple effects?

Answer 30

conduct t-tests to determine if interactions between different conditions are significant

Question 31

Do you need to run simple effects t-tests with a correction for multiple comparisons?

Answer 31

yes use Bonferroni correction

Question 32

How do you calculate Bonferroni’s?

Answer 32

• divide required alpha level by number of comparisons
  e.g., a/c
  e.g., if there were 4 comparisons: 0.05/4 = 0.013

Question 33

What are 2 key things to remember when conducting simple effects t-tests?

Answer 33

• remember to check Levene’s for each
• all of the t-tests have to be follow up with Cohen’s d