ANCOVA
Analysis of Covariance is a statistical method that compares the means of two or more groups while controlling for the effects of other continuous variables (covariates).
What is ANCOVA?
ANCOVA blends ANOVA and linear regression. It evaluates whether population means of a dependent variable differ across categorical independent variables, after statistically removing the variance explained by one or more continuous covariates. This adjustment effectively equates the groups on the covariate, providing a clearer picture of the main group differences.
Why ANCOVA Matters
It reduces error variance and controls for confounding variables that were not experimentally controlled, thereby increasing the statistical power to detect treatment effects and improving the validity of causal inferences.
Example
A researcher testing three different diet plans compares final weight loss across the groups using ANCOVA, controlling for the participants' baseline starting weights.
Common Mistakes
- Using a covariate that is affected by the treatment itself, which removes part of the treatment effect.
- Violating the assumption of homogeneity of regression slopes (assuming the relationship between the covariate and dependent variable is the same across all groups).