title: Inequality constrained analysis of variance: a Bayesian approach.
abstract: Researchers often have one or more theories or expectations with respect to the outcome of their empirical research. When researchers talk about the expected relations between variables if a certain theory is correct, their statements are often in terms of one or more parameters expected to be larger or smaller than one or more other parameters. Stated otherwise, their statements are often formulated using inequality constraints. In this article, a Bayesian approach to evaluate analysis of variance or analysis of covariance models with inequality constraints on the (adjusted) means is presented. This evaluation contains two issues: estimation of the parameters given the restrictions using the Gibbs sampler and model selection using Bayes factors in the case of competing theories. The article concludes with two illustrations: a one-way analysis of covariance and an analysis of a three-way table of ordered means.
authors.affiliation: Department of Methodology and Statistics, University of Utrecht, Utrecht, Netherlands. i.klugkist@fss.uu.nl
authors.forename: Irene
authors.initials: I
authors.lastname: Klugkist
authors.forename: Olav
authors.initials: O
authors.lastname: Laudy
authors.forename: Herbert
authors.initials: H
authors.lastname: Hoijtink
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D006801:Humans; D008960:Models, Psychological