www.uhasselt.be
DSpace

Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26394

Title: Establishing normative data for multi-trial memory tests: the multivariate regression-based approach
Authors: Van der Elst, Wim
Molenberghs, Geert
van Tetering, Marleen
Jolles, Jelle
Issue Date: 2017
Publisher: TAYLOR & FRANCIS INC
Citation: CLINICAL NEUROPSYCHOLOGIST, 31(6-7), p. 1173-1187
Abstract: Objective: Multi-trial memory tests are widely used in research and clinical practice because they allow for assessing different aspects of memory and learning in a single comprehensive test procedure. However, the use of multi-trial memory tests also raises some key data analysis issues. Indeed, the different trial scores are typically all correlated, and this correlation has to be properly accounted for in the statistical analyses. In the present paper, the focus is on the setting where normative data have to be established for multi-trial memory tests. At present, normative data for such tests are typically based on a series of univariate analyses, i.e. a statistical model is fitted for each of the test scores separately. This approach is suboptimal because (1) the correlated nature of the data is not accounted for, (2) multiple testing issues may arise, and (3) the analysis is not parsimonious. Method and results: Here, a normative approach that is not hampered by these issues is proposed (the so-called multivariate regression-based approach). The methodology is exemplified in a sample of N = 221 Dutch-speaking children (aged between 5.82 and 15.49 years) who were administered Rey's Auditory Verbal Learning Test. An online Appendix that details how the analyses can be conducted in practice (using the R software) is also provided. Conclusion: The multivariate normative regression-based approach has some substantial methodological advantages over univariate regression-based methods. In addition, the method allows for testing substantive hypotheses that cannot be addressed in a univariate framework (e.g. trial by covariate interactions can be modeled).
Notes: [Van der Elst, Wim] Johnson & Johnson, Janssen Pharmaceut Co, Stat & Decis Sci Quantitat Sci, Beerse, Belgium. [Van der Elst, Wim; Molenberghs, Geert] Katholieke Univ Leuven, Ctr Stat CenStat, Diepenbeek, Belgium. [Van der Elst, Wim; Molenberghs, Geert] UHasselt, Diepenbeek, Belgium. [van Tetering, Marleen; Jolles, Jelle] Vrije Univ Amsterdam, Fac Behav & Movement Sci, Ctr Brain & Learning, Amsterdam, Netherlands.
URI: http://hdl.handle.net/1942/26394
DOI: 10.1080/13854046.2017.1294202
ISI #: 000409318000014
ISSN: 1385-4046
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version1.39 MBAdobe PDF
Peer-reviewed author version243.53 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.