title: Improving the analysis of routine outcome measurement data: what a Bayesian approach can do for you.
abstract: Since recent decades, clinicians offering interventions against mental problems must systematically collect data on how clients change over time. Since these data typically contain measurement error, statistical tests have been developed which should disentangle true changes from random error. These statistical tests can be subdivided into two types: classical tests and Bayesian tests. Over the past, there has been much confusion among analysts regarding the questions that are answered by each of these tests. In this paper we discuss each type of test in detail and explain which questions are, and which are not, answered by each of the types of tests. We then apply a test of each type on an empirical data set and compare the results. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: University of Groningen, Faculty of Behavioral and Social Sciences, Department of Psychometrics and Statistics, the Netherlands.
authors.forename: Rivka M
authors.initials: RM
authors.lastname: de Vries
authors.affiliation: University of Groningen, Faculty of Behavioral and Social Sciences, Department of Psychometrics and Statistics, the Netherlands. r.r.meijer@rug.nl.
authors.forename: Rob R
authors.initials: RR
authors.lastname: Meijer
authors.affiliation: Dimence, Almelo, The Netherlands.
authors.forename: Vincent
authors.initials: V
authors.lastname: van Bruggen
authors.affiliation: Cardiff, UK.
authors.forename: Richard D
authors.initials: RD
authors.lastname: Morey
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D001523:Mental Disorders / Q000628:therapy*; D017063:Outcome Assessment, Health Care / Q000379:methods*
keywords: Bayesian approach; classical approach; data analysis; evidence; hypothesis testing; routine outcome measurement