Open AccessImproving the analysis of routine outcome measurement data: what a Bayesian approach can do for you
Author(s) -
Vries Rivka M.,
Meijer Rob R.,
Bruggen Vincent,
Morey Richard D.
Publication year - 2016
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1496
Subject(s) - type i and type ii errors , bayesian probability , statistical hypothesis testing , test (biology) , psychological intervention , confusion , set (abstract data type) , bayesian statistics , computer science , econometrics , data set , psychology , statistics , bayesian inference , artificial intelligence , mathematics , psychiatry , paleontology , psychoanalysis , biology , programming language
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