26. Multiple Dependent Variables

  1. Bradley E. Huitema

Published Online: 14 OCT 2011

DOI: 10.1002/9781118067475.ch26

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) Multiple Dependent Variables, 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.ch26

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:

  • Bonferroni method;
  • correlation;
  • covariance;
  • Minitab;
  • multiple dependent variables;
  • multiple regression analysis;
  • multivariate analysis of covariance (MANCOVA);
  • uncorrected univariate ANCOVA

Summary

This chapter develops various procedures for analyzing studies with multiple dependent variables. They can be distinguished with respect to (i) how they control for multiplicity, (ii) whether the test statistic acknowledges correlation among dependent variables, and (iii) whether planning is required in the testing procedure. The most common practice in many areas is to do nothing in the analysis to formally acknowledge multiplicity of response measures. One simply computes a univariate ANCOVA on each dependent variable. A procedure known as multivariate analysis of covariance (MANCOVA) is an alternative to the Bonferroni <i>F</i> procedure. The computation of MANCOVA for the two-group situation can be carried out by using a multiple regression analysis computer program. The example analyses using a Minitab are illustrated in the chapter using a single data set. The strength of the Bonferroni approach is in detecting a large effect that shows up on only one of the measures.

Controlled Vocabulary Terms

analysis of covariance; correlation; covariance; Holm-Bonferroni method; Minitab; multivariate analysis of covariance