Jump to ContentJump to Main Navigation
Analysis of Multiple Dependent Variables$
Users without a subscription are not able to see the full content.

Patrick Dattalo

Print publication date: 2013

Print ISBN-13: 9780199773596

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199773596.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 12 April 2021

Multivariate Analysis of Covariance

Multivariate Analysis of Covariance

(p.63) 3 Multivariate Analysis of Covariance
Analysis of Multiple Dependent Variables

Patrick Dattalo

Oxford University Press

Analysis of covariance (ANCOVA) assesses group differences on a dependent variable (DV) after the effects of one or more covariates are statistically removed. By utilizing the relationship between the covariate(s) and the DV, ANCOVA can increase the power of an analysis. MANCOVA is an extension of ANCOVA to relationships where a linear combination of DVs is adjusted for differences on one or more covariates. The adjusted linear combination of DVs is the combination that would be obtained if all participants had the same scores on the covariates. That is, MANCOVA is similar to MANOVA, but allows a researcher to control for the effects of supplementary continuous IVs, termed covariates. In an experimental design, covariates are usually the variables not controlled by the experimenter, but still affect the DVs. Consequently, although not as effective as random assignment, including covariates may reduce both systematic and within-group error by equalizing groups being compared on important characteristics. This chapter discusses the assumptions of MANCOVA, sample size requirements, and strengths and limitations of MANCOVA. An annotated example is also provided.

Keywords:   MANCOVA, ANCOVA, dependent variables, covariates, statistical analysis

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .