Publication

Improving the analysis of routine outcome measurement data: what a Bayesian approach can do for you

de Vries, R. M., Meijer, R. R., van Bruggen, V. & Morey, R. D., Sep-2016, In : International Journal of Methods in Psychiatric Research. 25, 3, p. 155-167 13 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

de Vries, R. M., Meijer, R. R., van Bruggen, V., & Morey, R. D. (2016). Improving the analysis of routine outcome measurement data: what a Bayesian approach can do for you. International Journal of Methods in Psychiatric Research, 25(3), 155-167. https://doi.org/10.1002/mpr.1496

Author

de Vries, Rivka M. ; Meijer, Rob R. ; van Bruggen, Vincent ; Morey, Richard D. / Improving the analysis of routine outcome measurement data : what a Bayesian approach can do for you. In: International Journal of Methods in Psychiatric Research. 2016 ; Vol. 25, No. 3. pp. 155-167.

Harvard

de Vries, RM, Meijer, RR, van Bruggen, V & Morey, RD 2016, 'Improving the analysis of routine outcome measurement data: what a Bayesian approach can do for you', International Journal of Methods in Psychiatric Research, vol. 25, no. 3, pp. 155-167. https://doi.org/10.1002/mpr.1496

Standard

Improving the analysis of routine outcome measurement data : what a Bayesian approach can do for you. / de Vries, Rivka M.; Meijer, Rob R.; van Bruggen, Vincent; Morey, Richard D.

In: International Journal of Methods in Psychiatric Research, Vol. 25, No. 3, 09.2016, p. 155-167.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

de Vries RM, Meijer RR, van Bruggen V, Morey RD. Improving the analysis of routine outcome measurement data: what a Bayesian approach can do for you. International Journal of Methods in Psychiatric Research. 2016 Sep;25(3):155-167. https://doi.org/10.1002/mpr.1496


BibTeX

@article{3e91bf42c99d4717861afa8bb4f7da62,
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 {\circledC} 2015 John Wiley & Sons, Ltd.",
author = "{de Vries}, {Rivka M.} and Meijer, {Rob R.} and {van Bruggen}, Vincent and Morey, {Richard D.}",
note = "Copyright {\circledC} 2015 John Wiley & Sons, Ltd.",
year = "2016",
month = "9",
doi = "10.1002/mpr.1496",
language = "English",
volume = "25",
pages = "155--167",
journal = "International Journal of Methods in Psychiatric Research",
issn = "1049-8931",
publisher = "Wiley",
number = "3",

}

RIS

TY - JOUR

T1 - Improving the analysis of routine outcome measurement data

T2 - what a Bayesian approach can do for you

AU - de Vries, Rivka M.

AU - Meijer, Rob R.

AU - van Bruggen, Vincent

AU - Morey, Richard D.

N1 - Copyright © 2015 John Wiley & Sons, Ltd.

PY - 2016/9

Y1 - 2016/9

N2 - 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.

AB - 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.

U2 - 10.1002/mpr.1496

DO - 10.1002/mpr.1496

M3 - Article

C2 - 26449152

VL - 25

SP - 155

EP - 167

JO - International Journal of Methods in Psychiatric Research

JF - International Journal of Methods in Psychiatric Research

SN - 1049-8931

IS - 3

ER -

ID: 25185057