What is ANCOVA?
•- ANCOVA stands for Analysis of Covariance.
• - It combines ANOVA and regression.
• - It adjusts for the effect of covariates to better
assess the impact of independent variables on
a dependent variable.
• - Commonly used in experimental and
observational studies.
3.
Purpose of ANCOVA
•- Removes the influence of confounding
variables.
• - Increases statistical power by reducing error
variance.
• - Helps in comparing group means while
controlling for covariates.
• - Provides a more precise analysis than ANOVA
alone.
4.
Assumptions of ANCOVA
•- Linearity: The covariate must have a linear
relationship with the dependent variable.
• - Homogeneity of Regression Slopes: The
effect of the covariate should be consistent
across groups.
• - Independence: Observations should be
independent.
• - Normality: The dependent variable should be
normally distributed.
• - Homogeneity of Variance: Groups should
5.
Steps in ConductingANCOVA
• 1. Check assumptions (linearity, homogeneity,
normality, independence).
• 2. Fit the model using ANOVA.
• 3. Incorporate the covariate into the model.
• 4. Assess interaction effects and adjust the
model if necessary.
• 5. Interpret results and determine the
adjusted group means.
6.
Applications of ANCOVA
•- Clinical trials (adjusting for baseline
measurements).
• - Educational research (controlling for prior
knowledge levels).
• - Market research (analyzing customer
preferences while adjusting for
demographics).
• - Psychological studies (controlling for pre-
existing conditions).
7.
Example of ANCOVA
•Scenario: Studying the effect of a training
program on employee performance while
controlling for prior experience.
• 1. Independent Variable: Training program
(Yes/No).
• 2. Dependent Variable: Employee
performance score.
• 3. Covariate: Years of prior experience.
• 4. ANCOVA helps in determining if training
impacts performance after accounting for
8.
Conclusion
• - ANCOVAis a powerful tool for analyzing
differences while adjusting for covariates.
• - Ensures more accurate comparisons in
research studies.
• - Must check and satisfy all assumptions for
valid results.
• - Widely applied in various scientific and
business fields.
