21. Analysis of Multiple-Baseline Designs

  1. Bradley E. Huitema

Published Online: 14 OCT 2011

DOI: 10.1002/9781118067475.ch21

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) Analysis of Multiple-Baseline Designs, 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.ch21

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

SEARCH

Keywords:

  • autocorrelated errors;
  • control series design;
  • cross-correlation;
  • independent errors;
  • intervention;
  • multiple baseline designs

Summary

Multiple baseline designs are widely recognized in many areas of research as easily implemented, highly sensitive, and internally valid. Many areas of research in which randomized-group designs and reversal single-case designs are disqualified by practical or ethical considerations are easily investigated using at least one of the variants of the multiple-baseline design. This chapter presents a brief overview of three major variants of multiple-baseline design. They may be labeled as: (1) multiple-baseline across subjects, (2) multiple-baseline across settings, and (3) multiple-baseline across dependent variables (or behaviors). An alternative to both the reversal design and the multiple baseline design are described. A test that contrasts the evidence for change in the experimental series against the evidence for change in the control series is presented. This design and analysis approach may provide protection against the internal validity threats of history and maturation to which most conventional AB designs are vulnerable.

Controlled Vocabulary Terms

cross-correlation