week 8

Question 1

ANCOVA

Answer 1

Analysis of covariance is type of linear model that combines the best abilities of linear regression with the best of ANOVA
includes one or more continuous variables within the ANOVA
It allows you to test differences in group means and interactions, just like ANOVA, while controlling for effects of the covariates

Question 2

What is a covariate

Answer 2

Other variables might also influence the result

Question 3

controlling for covariation

Answer 3

Random allocation of participants to conditions to minimize the influence of covariates
Match participants in different conditions to minimise the influence of covariates
Statistically: Analysis of Covariance (ANCOVA)

Question 3

How do covariates influence ANOVA results?

Answer 3

f= variance between conditions/ variance within conditions

Variance in the DV the experiment can explain
Variance in the DV the experiment cannot explain (error variance)

Error variance includes uncontrolled sources of variability

With the covariate included, error variance is reduced as variance due to the covariate is removed

Question 4

Reasons to include covariates in ANOVA

Answer 4

Including covariates within the ANOVA serves at least two purposes:
Reducing within group ‘error’ variance (the bottom half of the ratio/unexplained variance)
Controlling for the influence of the covariates on the DV (in other words, eliminating confounding effects from the covariates)

Question 5

Not everything can be a covariate

Answer 5

1) covariates should be continuous
2) There should be a theoretical reason for including a covariate

Question 6

ANCOVA assumptions

Answer 6

1) linear relationship between the covariate and DV at each level of the IV
2) Homogeneity of regression slopes
3) Independence of the covariate and experiment effect