PSY 1950 ANCOVA November 17, 2008

1. PSY 1950 ANCOVA November 17, 2008

2. Analysis of Covariance (ANCOVA)

3. Covariate • An independent variable that… • you do not manipulate • annoys you rather than interests you • covaries/correlates with dependent variable • may or may not relate to IVs of interest • Cohen (1968) • “A covariate is, after all, nothing but an independent variable which, because of the logic dictated by the substantive issues of the research, assumes priority among the set of independent variables for Y variance.” • e.g., pretest scores

4. ANCOVA • ANCOVA always does two things: • Reduces unexplained variance • Almost always a good thing • Only exception is when the reduction is so small that it is offset by the loss of df • Adjust the group DV means based upon group differences on covariate • Okay in experimental designs • Very questionable in non-experimental designs • Occasionally, an arguably okay thing • Oftentimes, a definitely bad thing

5. Reducing Error Variance Total variance red + blue + green Variance explained by covariate blue Variance explained by IV green Error variance red + blue Adjusted error variance red Adjusted total variance red + green DV IV Covariate

6. Analysis of Variance • Categorical IVs • e.g., Color (black, red, green), SAT (low, high) • Separable effects • Including “blocking” or nuisance factor reduces error term, increases F for effect of interest

7. ANCOVA • Categorical IV and quantitative covariate • e.g., Color (black, red, green), SAT score • Separable effects • Removing the effect of a covariate reduces error term, increasing F for effect of interest

9. Error Terms ANOVA ANCOVA

10. Controlling Confounds Total variance red + blue + green + yellow DV variance explained by covariate blue + yellow IV variance explained by covariate yellow+ purple DV variance explained by IV green + yellow Corrected DV variance explained by IV green Error variance red + blue Adjusted error variance red Adjusted total variance red + green DV IV Covariate

11. Adjusting Group Means Problem: Any group differences on covariate will bias group differences on DV Solution: Equate groups on covariate, and use regression to adjust DV accordingly

12. Group means on DV, adjusted for covariate Grand mean of covariate Group means of covariate Adjusting Group Means

13. Adjusting Group Means

14. Otherwise significant group differences can become insignificant

15. Otherwise significant group differences can stay significant

16. Otherwise significant group different can flip direction

17. Interpreting ANCOVA Results • If subjects are randomly assigned to groups, then… • Any preexisting group differences on covariates are due to chance • If covariate is measured after treatment, you’re in trouble • ANCOVA will reduce error term and remove any bias due to random variations in group assignment

18. Interpreting ANCOVA Results • If subjects are not randomly assigned to groups, then… • Any group differences on covariates may not be due to chance • Lord (1967): “…there is simply no logical or statistical procedure that can be counted on to make proper allowances for uncontrolled prexisting differences between groups [not due to random assignment]”

19. ANCOVA Assumptions • All the usual, plus homogeneity of regression slopes • In other words, the relationship between the DV and the covariate is the same across groups