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Related Experiment Videos

Pairwise multiple comparisons: a model comparison approach versus stepwise procedures.

Robert A Cribbie1, H J Keselman

  • 1Department of Psychology, York University, Toronto, Canada. cribbie@yorku.ca

The British Journal of Mathematical and Statistical Psychology
|June 14, 2003
PubMed
Summary
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A new model selection approach for pairwise multiple comparisons testing effectively identifies true mean differences. Its protected version outperformed stepwise procedures and remained robust against non-normality and variance heterogeneity.

Area of Science:

  • Behavioral Sciences
  • Statistical Methods

Background:

  • Pairwise multiple comparison procedures aim to balance Type I error control, power, and usability.
  • Existing methods suffer from intransitive decisions and a piecemeal approach to statistical testing.

Purpose of the Study:

  • To compare Dayton's model selection approach for pairwise multiple comparisons with traditional stepwise procedures.
  • To evaluate the performance in identifying the true pattern of mean differences.

Main Methods:

  • A protected version of the model selection approach was compared against three stepwise multiple comparison procedures.
  • Performance was assessed by the proportion of times each procedure identified the true model across various one-way layouts.

Main Results:

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  • The protected model selection approach identified the true model significantly more often than stepwise procedures.
  • This approach demonstrated robustness against variance heterogeneity and non-normality in most tested scenarios.

Conclusions:

  • Dayton's model selection approach offers a more holistic and effective method for pairwise multiple comparisons.
  • The protected version provides superior accuracy and reliability, particularly in non-ideal data conditions.