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Analysis of Covariance

  • Harold R. Lindman
Part of the Springer Texts in Statistics book series (STS)

Abstract

In multivariate analysis of variance we test all of the dependent variables simultaneously. In analysis of covariance we use some dependent variables as “controls” when testing for others. If we cannot control for certain variables experimentally by making them the levels of a factor, we may be able to control for them statistically by analysis of covariance. In this chapter, we will first give a simple example. We will then describe the model for analysis of covariance, the problems of interpretation, and the assumptions that must be made. Next, we will discuss the advantages and disadvantages of analysis of covariance, as compared with other possible ways of solving the same problem. Finally, we will describe the general analysis of covariance, with examples. The reader who wants only a general understanding of analysis of covariance can skip the final section.

Keywords

Ordinary Analysis Equal Slope Numerator Degree Single Dependent Variable Covariance Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1992

Authors and Affiliations

  • Harold R. Lindman
    • 1
  1. 1.Department of PsychologyIndiana UniversityBloomingtonUSA

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