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Pairwise multiple comparisons: A model comparison approach versus stepwise procedures
Author(s) -
Cribbie Robert A.,
Keselman H. J.
Publication year - 2003
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711003321645412
Subject(s) - pairwise comparison , selection (genetic algorithm) , normality , model selection , multiple comparisons problem , set (abstract data type) , variance (accounting) , mathematics , statistics , type i and type ii errors , computer science , econometrics , machine learning , accounting , business , programming language
Researchers in the behavioural sciences have been presented with a host of pairwise multiple comparison procedures that attempt to obtain an optimal combination of Type I error control, power, and ease of application. However, these procedures share one important limitation: intransitive decisions. Moreover, they can be characterized as a piecemeal approach to the problem rather than a holistic approach. Dayton has recently proposed a new approach to pairwise multiple comparisons testing that eliminates intransitivity through a model selection procedure. The present study compared the model selection approach (and a protected version) with three powerful and easy‐to‐use stepwise multiple comparison procedures in terms of the proportion of times that the procedure identified the true pattern of differences among a set of means across several one‐way layouts. The protected version of the model selection approach selected the true model a significantly greater proportion of times than the stepwise procedures and, in most cases, was not affected by variance heterogeneity and non‐normality.