University of Groningen
Flexible regression-based norming of psychological tests
Voncken, Lieke
DOI:
10.33612/diss.124765653
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Publication date: 2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Voncken, L. (2020). Flexible regression-based norming of psychological tests. University of Groningen. https://doi.org/10.33612/diss.124765653
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Flexible regression-based norming
of psychological tests
© Flexible regression-based norming of psychological tests. Lieke Voncken, University of Groningen
ISBN: 978-94-034-2465-1 (print version) ISBN: 978-94-034-2466-8 (electronic version) Cover design: Desirée van Dooren
Printed by: Ipskamp Printing, Enschede
The research presented in this thesis was funded by the Dutch Research Council (NWO) within research programme ‘Graduate Programme 2013’ with project number
022.005.003.
All rights reserved. No part of this publication may be reproduced or transmitted in any form by any means, without permission of the author.
Flexible regression-based norming
of psychological tests
Proefschrift
ter verkrijging van de graad van doctor aan de
Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. C. Wijmenga
en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op
donderdag 14 mei 2020 om 16.15 uur
door
Lieke Voncken
geboren op 10 februari 1992
te Geldrop
Promotores
Prof. dr. M.E. Timmerman Prof. dr. C.J. Albers
Beoordelingscommissie
Prof. dr. L.A. van der Ark Prof. dr. T.A.B. Snijders Prof. dr. P.H.C. Eilers
Table of contents
1 Introduction 7
2 Model selection in continuous test norming with GAMLSS 21
2.1 Introduction . . . 22
2.2 Method . . . 30
2.3 Results . . . .37
2.4 Discussion . . . 54
3 Bias-variance trade-off in continuous test norming 59 3.1 Introduction . . . 60
3.2 Simulation study . . . 66
3.3 Results . . . .72
3.4 Discussion . . . 81
4 Improving confidence intervals for normed test scores: Include uncertainty due to sampling variability 85 4.1 Introduction . . . 86
4.2 Method . . . 93
4.3 Results . . . .98
4.4 Discussion . . . 112
5 Bayesian Gaussian distributional regression models for more efficient norm esti-mation 115 5.1 Introduction . . . 116
5.2 Bayesian Gaussian distributional regression . . . 117
5.3 Simulation study . . . .119
5.4 Applications of Bayesian Gaussian norm estimation to the IDS-2 norm. data 127 5.5 Discussion . . . 394
6 Discussion 135
Appendix A - Additional material for Chapter 4 155
Appendix B - Additional material for Chapter 5 156
Samenvatting (Dutch summary) 158
Curriculum Vitae 166
List of publications 168
