Open AccessPAIRWISE COMPARISON PROCEDURES FOR ONE‐WAY ANALYSIS OF VARIANCE DESIGNS
Author(s) -
Zwick Rebecca
Publication year - 1991
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2333-8504.1991.tb01397.x
Subject(s) - pairwise comparison , normality , variance (accounting) , statistics , homogeneity (statistics) , population variance , type i and type ii errors , mathematics , one way analysis of variance , multiple comparisons problem , analysis of variance , selection (genetic algorithm) , econometrics , variable (mathematics) , computer science , machine learning , mathematical analysis , accounting , estimator , business
Research in the behavioral and health sciences frequently involves the application of one‐factor analysis of variance models. The goal may be to compare several independent groups of subjects on a quantitative dependent variable or to compare measurements made on a single group of subjects on different occasions or under different conditions. In analyzing data of this kind, it is usually of interest to determine which pairs of population means are likely to differ. In this paper, the selection of pairwise multiple comparison procedures for one‐way analysis of variance designs is considered, following a discussion of Type I error and power issues as they apply to the testing of multiple hypotheses. Procedures are included which are appropriate when normality or variance homogeneity assumptions are violated. The focus is on procedures that are easy to understand and apply. Single‐step procedures are emphasized because of their simplicity and because they allow for the construction of confidence intervals.