module 6

Question 1

when and why to use between subjects factorial ANOVA

Answer 1

• when: between subjects experiments have more than 1 independent variable
• why: modest gain in efficiency, and tests the joint effects of the IV

Question 2

one way vs two way between subjects ANOVA

Answer 2

• one way: test one effect (effect of the IV)
• two way: test three effects (main effect of IV1, main effect of IV2, and interaction effect of IV1+IV2

Question 3

interactions

Answer 3

• indicates that effect of one IV is not the same across levels of another IV (they effect differently)
• tests difference of differences

Question 4

what are the three null hypotheses used in 2 way anovas

Answer 4

1. H0: μa1=μa2
2. H0= μb1=μb2
3. H0= no interaction present (effect of each factor is independent)

Question 5

t or f: 2 way anovas with more than 2 levels each (ex 2x3) still only have 3 null hypotheses

Answer 5

true, they just have more means within the hypotheses

Question 6

f test formula for null hypotheses for a between subjects two way anova simple

Answer 6

f = variance between treatments/variance within treatments

Question 7

f test formula for null hypotheses for a between subjects two way anova with factors in numerator and denominator specified

Answer 7

f = treatment effect of (A/B/AxB) + individual difference + experimental error/individual difference+ experimental error

Question 8

what factors make up between variance in an f test for between subjects factorial anova

Answer 8

• factor a between variance (MSa)
• factor b between variance (MSb)
• factor a x factor b between treatment variance (MSaxb)

Question 9

f value near 1 indicates that that the given treatment effect is ____ and an f value way greater than 1 indicates that the given treatment effect is ____

Answer 9

non-existant/0, existant

Question 10

in a two way between subjects anova computation, what do the values G and N represent

Answer 10

G: grand total of all scores in experiment
N: total number of score in entire experiment

Question 11

in a two way between subjects anova computation, what do the values p, q, and n represent

Answer 11

p: # of levels in factor A
q: # of levels in factor B
n; # of scores in each treatment condition (each cell of the AxB matrix)

Question 12

AB total (two way between subjects anova)

Answer 12

sum totals of all scores in specific cell of AxB matrix (ex A1+B1, A1+B2, A2+B2, A2+B1)

Question 13

A and B totals (two way between subjects anova)

Answer 13

• total of all scores in a level of each factor (A1, A2, B1, B2)

Question 14

total sums of squares for two way between subjects anova

Answer 14

• SStotal= SSwithin+SSbetween
• or = ∑X^2 - G^2/N

Question 15

within treatment variability for two way between subjects anova

Answer 15

SS within = ∑ SS w/in each treatment cell

Question 16

general between treatment variability for two way between subjects anova

Answer 16

SS between = ∑ AB^2/n - G^2/N

Question 17

how does a two way anova shoe variability for A, B, and AxB

Answer 17

• A: SSa= ∑ A^2/qn - G^2/N
• B: SSb=∑ B^2/pn - G^2/N
• AxB: SSaxb= SSbetween - SSa-SSb

Question 18

t or f: each SS has the same df value in a two way between subjects anova

Answer 18

false, theyre all different

Question 19

df total= ___, and df within= ____

Answer 19

N-1, ∑ (n-1) or N-pq

Question 20

df for between subjects anova

Answer 20

• df between = pq-1
• dfA= p-1
• dfB= q-1
• dfAxB= df between-dfA-dfB

Question 21

means squared for two way between subjects anova

Answer 21

• general MS= SS/df
• MSa=SSa/dfA
• MSb=SSb/dfB
• MS within= SSwithin/dfwithin
• MSAxB=SSaxb/dfaxb

Question 22

f ratio formula for between subjects two way anova

Answer 22

f=MSa/Mswithin
or = MSb/MSwithin
or = MSaxb/MS within

Question 23

why are follow up tests required for 2 (and 3) way anovas

Answer 23

doesnt show nature of change in effect of one IV across the other

Question 24

follow up tests for between subjects ANOVA

Answer 24

• general: posteriori (post hoc) or priori (planned)
• main effect follow ups: none for main effects w/ 2 levels or planned contrasts if basis exists or post hoc if no expectation exists

Question 25

simple effect

Answer 25

- effect of one IV at a specific level of the other IV) - if the simple effect is significant, a difference exists somewhere

Question 26

abelson's types of claims

Answer 26

- ticks: clearly stated findings, detailed statements of distinct research results - buts: statements that qualify/constrain ticks - blobs: cluster of undifferentiated research results (shows difference but not where)

Question 27

examples of ticks and buts in 2x2 anova

Answer 27

- main effect of IV1 - main effect of IV2 - interaction of IV1xIV2

Question 28

simple effect analysis

Answer 28

- looks at the difference between MS across levels of one IV at a single level of the other IV (ex single column/row of AB design)

Question 29

setting alpha and beta in a two way between subjects factorial anova

Answer 29

- alpha: 0.05=norm for omnibus tests in factorial anova - beta: compute power for each test since there are multi, establish the min acceptable power and use it for all so its all strong

Question 30

standard effect size calculation for 2 way between subjects anova

Answer 30

- partial eta squared: ηp^2= SS effect/ SSeffect + SSwithin - cohen's f: f= √SSeffect/SSerror

Question 31

guide for cohen's f and partial eta squared

Answer 31

- cohen's f: .10=small, .25=med, .40=large - partial eta squared: .01=small, .06=med, .14=large