13. Quasi-ANCOVA: When Treatments Affect Covariates

  1. Bradley E. Huitema

Published Online: 14 OCT 2011

DOI: 10.1002/9781118067475.ch13

The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, Second Edition

The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, Second Edition

How to Cite

Huitema, B. E. (2011) Quasi-ANCOVA: When Treatments Affect Covariates, in The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, Second Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118067475.ch13

Author Information

  1. Department of Psychology, Western Michigan University, Kalamazoo, Michigan, USA

Publication History

  1. Published Online: 14 OCT 2011
  2. Published Print: 14 OCT 2011

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780471748960

Online ISBN: 9781118067475

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Keywords:

  • computation;
  • covariates;
  • multiple quasi-analysis of covariance (ANCOVA)

Summary

If the covariate is affected by the treatments, an undesirable consequence is that ANCOVA will remove a portion of the treatment effect from the dependent variable. This chapter presents an approach that has major advantages over both conventional ANOVA and ANCOVA when the treatments have affected the covariate; this is referred as a quasi-ANCOVA analysis. It involves a modification of the conventional ANCOVA model that completely removes the influence of covariate mean differences from the treatment-effect estimates on the dependent variable. A comparison of the conventional ANCOVA and quasi-ANCOVA models is shown in the chapter. This analysis can be performed using conventional ANCOVA routines by substituting residuals of ANOVA performed on the covariates for the original covariates.

Controlled Vocabulary Terms

analysis of covariance