Statistical Methodology: IV. Analysis of Variance, Analysis of Co variance, and Multivariate Analysis of Variance
Article first published online: 28 JUN 2008
Academic Emergency Medicine
Volume 5, Issue 3, pages 258–265, March 1998
How to Cite
Gaddis, M. L. (1998), Statistical Methodology: IV. Analysis of Variance, Analysis of Co variance, and Multivariate Analysis of Variance. Academic Emergency Medicine, 5: 258–265. doi: 10.1111/j.1553-2712.1998.tb02624.x
- Issue published online: 28 JUN 2008
- Article first published online: 28 JUN 2008
- Received: March 7, 1996; revision received: February 28, 1997; second revision received: October 24. 1997; accepted: November 4. 1997.
- statistical methods;
- analysis of variance;
- analysis of covariance;
- multivariate analysis of variance
Medical research frequently involves the statistical comparison of >2 groups, often using data obtained through the application of complex experimental designs. Fortunately, inferential statistical methodologies exist to address these situations. Analysis of variance (ANOVA) in its many forms is used to simultaneously test the equality of all groups in a study. One-way (with 1 independent variable), 2-way (with 2 independent variables), and repeated-measures (patients serve as their own controls) ANOVAs are forms of this technique. Each form has been developed to analyze data from a specific experimental design. Analysis of covariance (ANCOVA) allows the researcher to control for confounding variables that may influence the response of the dependent variable. Finally, multivariate analysis of variance (MANOVA) evaluates the simultaneous responses of multiple dependent variables to s 1 independent variable. Whereas ANOVA is the correct alternative to statistically inappropriate multiple t-tests, MANOVA is the correct alternative to statistically inappropriate multiple uni-variate ANOVA calculations. Use of each of these statistical methods requires an appropriate experimental design and data meeting a number of assumptions. When used properly, each of these methods provides a powerful statistical analysis technique.