Psych Stats Exam #3

Question 1

What is ANOVA used for?

Answer 1

Comparing more than two means (i.e. 3+ levels of an IV)

Question 2

Pros and cons of within groups design

Answer 2

pro: reduced variability within participants
con: participants may figure out the experiment and change behavior

Question 3

Why don’t we just do a bunch of t-tests?

Answer 3

The false alarm rate (finding an effect that is not there, type I error) becomes extremely high
- have to multiply probability for each test (95% CI = 5% false alarm x 3 tests = 0.95 x 0.95 x 0.95 = 85% CI, 15% error)

Question 4

What does ANOVA stand for?

Answer 4

analysis of variance

Question 5

Looking for overall significant differences (One way ANOVA)

Answer 5

look for omnibus anova: see if there is a difference
- compare p to alpha to do this
- if there is a difference: run post hoc testing
- no difference: you are done, fail to reject the null

Question 6

Post hoc tests (one way ANOVA)

Answer 6

tells us which specific conditions are statistically different

Question 7

F statistic

Answer 7

ANOVA: same logic as NHST – testing against sampling dsitribution to see how unlikely / likely your results are
- use F distribution

Question 8

effect size for ANOVA

Answer 8

eta squared (η2)

Question 9

Eta squared cutoffs

Answer 9

0.01 = small effect
0.06 = medium effect
0.14 = large effect

Question 10

F distribution

Answer 10

one tailed - rejection region is only in one of the tails

Question 11

Measures of degrees of freedom in ANOVA

Answer 11

1) df between group
2) df within group

Question 12

F statistic formula

Answer 12

F = (between group variance) / (within group variance)

Question 13

Between group variance

Answer 13

• the variance from our IV (difference from manipulation)
  Grand mean: mean of all the data
• found by averaging each condition means
• between condition mean: measures how much the condition means differ from the grand mean
• “MS between” + “condition”
• large: more likely to reject the null hypothesis

Question 14

Within group variance

Answer 14

variability not due to our manipulation (individual differences, error)
- small: more likely to reject null (want less error)

Question 15

expected value under F statistic

Answer 15

1 (if it is 1, we fail to reject the null hypothesis)

Question 16

Writing an Omnibus Anova

Answer 16

Overall: there is/is not an effect of the IV on the DV (F stat, p, eta squared)

Question 17

Writing Post hoc tests

Answer 17

Condition A (M, SD) showed higher / lower levels of DV then condition B (M, SD) (t, p , d)
- For ALL comparisons

Question 18

Degrees of freedom between (one way between groups ANOVA)

Answer 18

• first number
• conditions in the study minus 1

Question 19

Degrees of freedom within (one way between groups ANOVA)

Answer 19

• second (usually larger) number
• participants in each condition - # of conditions

Question 20

Assumptions of ANOVA

Answer 20

1) DV is scale
2) IV is nominal / ordinal
3) homogeneity of variances
4) DV is normally distributed in population

Question 21

Homogeneity of Variances

Answer 21

the amount of variability within each condition should be similar across all conditions
- check this: if the p value is less than alpha, you have to run a correction

Question 22

calculating residual (MS within)

Answer 22

sum of squares (within) / degrees of freedom (within)

Question 23

calculating condition (MS between)

Answer 23

sum of squares (between) / degrees of freedom (between)

Question 24

If you have more between condition variance…

Answer 24

you are more likely to reject the null (as experiment had more influence on the difference you saw)

Question 25

Within condition study allows researchers to...

Answer 25

more likely to find an effect if one exists (more power) - less participant variance

Question 26

Within groups ANOVA degrees of freedom

Answer 26

1) Df between 2) Df within

Question 27

Df between (one-way repeated measures ANOVA)

Answer 27

of conditions-1 - same as between groups ANOVA

Question 28

Df within (one-way repeated measures ANOVA)

Answer 28

(conditions-1) * (participants -1) - Different from between groups ANOVA

Question 29

Factorial ANOVA

Answer 29

2+ independent variables each with 2+ levels - used to see an interaction

Question 30

Interaction definition

Answer 30

when the effect of one independent variable depends on the other independent variable

Question 31

Main effect

Answer 31

the effect of one IV by ignoring/averaging the other IV - calculated using marginal means

Question 32

Marginal Means

Answer 32

the mean for one level of the IV (down a column or across a row) - used to find main effects

Question 33

Simple Effect

Answer 33

the effect of one IV at a specific level of the other IV - found by looking across each row (or down each column) and seeing if there is a difference

Question 34

How to identify an interaction

Answer 34

Compare simple effects: see if they differ in effect size (magnitude) or direction

Question 35

Quantitative interaction

Answer 35

difference in effect size not direction

Question 36

Qualitative interaction

Answer 36

difference in direction not effect size

Question 37

Can you have no main effects and still a qualitative interaction

Answer 37

YES

Question 38

When to use a t-test

Answer 38

- 1 nominal IV (2 levels) - Scale DV - independent or paired samples

Question 39

When to use a One-way ANOVA

Answer 39

- 1 nominal IV (3+ levels) - Scale DV - Between groups or repeated measures

Question 40

Factorial ANOVA

Answer 40

- 2+ nominal IV (2+ levels) - Scale DV - Between groups, repeated measures, or mixed design

Question 41

What does a 2x2x3 design mean?

Answer 41

there are 3 independent variables, the first with 2 levels, the second with 2 levels and the third with three levels

Question 42

Write up example for interaction

Answer 42

“The effect on test performance of using a mnemonic device changes depending on whether or not the student was trained in mnemonic use: mnemonic use improves test performance with and without training but is more beneficial with training than without”

Question 43

3 Patterns of an interaction

Answer 43

1) An effect is larger at one level than the other, but they are in the same direction 2) An effect is present at one level but not the other (i.e. one simple effect is null) 3) An effect is reversed at one level compared to the other (i.e. the simple effects are in opposite directions)

Question 44

Number of simple and main effects in a 2x2 interaction

Answer 44

- 2 main effects - 4 simple effects

Question 45

Manual Bonferroni correction:

Answer 45

We run multiple sets of t-tests then we divide our original alpha by the number of post hoc tests we ran ex: If 2 post hoc tests and original alpha of 0.05 - 0.05/2 = 0.025 → this becomes a new alpha that we compare the p values of the t-test to this