repeated measures one way ANOVA

Question 1

what contributes to variance? between IV levels

Answer 1

manipulation of the IV (treatment effects)
experimental error (random and potentially constant error)
RM designs: variance between IV levels due to individual differences is absent

Question 2

what contributes to variance? within IV level

Answer 2

Experimental error (random error)

RM designs: we remove variance due to individual differences from the variance within IV levels

Question 3

total variance is the sum of

Answer 3

model variance
residual variance
individual differences

Question 4

repeated measures ANOVA (RM) null hypothesis

Answer 4

there is no difference between the population means under the different levels of the IV

H0: u1 = u2 = u3

Question 5

variance when F value is close to 0

Answer 5

small variance between IV levels relative to within IV levels

Question 6

variance when F values is further from 0

Answer 6

large variance between IV levels relative to variance within IV levels

Question 7

repeated measures one way ANOVA assumptions

Answer 7

Normality:
* Unlikely to be a problem (we wont check)

Sphericity (homogeneity of covariance)

Equivalent sample size: sample size under each level of the IV should be roughly equal

Question 8

sphericity (homogeneity of covariance)

Answer 8

the variance in difference scores under each IV level pair should reasonably equivalent

Question 9

how to test for sphericity

Answer 9

mauchly’s test of sphericity

Question 10

how to correct for heterogeneity of covariance

Answer 10

greenhouse-geisser corrects for this

Question 11

mauchly’s null hypothesis

Answer 11

there is no differences between covariances under each IV level pair

if p<= .05 we reject the null hypothesis

Question 12

post hoc test for RM one way ANOVA

Answer 12

Bonferroni

Question 13

advantages of repeated measures designs

Answer 13

Recruitment: needs fewer participants to gain the same number of measurements

Error variance (within IV levels) is reduced
-removal of variance due to individual differences from error variances
-because this variance is eliminating from our model variance (each participant acts as his/her own control)

More power with the same number of participants
-easier to find significant difference (avoid type II error)
-because with the same variance due to IV manipulation, the resulting F/t value is larger

Question 14

disadvantages of repeated measures and solutions

Answer 14

order effects - counterbalaning

• Practice effects: extensive pre-study practise
• Fatigue effects: short experiments
• Sensitisation: intervals between exposure to IV levels
• Carry-over effects: include a control group