M
Marieke E. Timmerman
Researcher at University of Groningen
Publications - 152
Citations - 6071
Marieke E. Timmerman is an academic researcher from University of Groningen. The author has contributed to research in topics: Principal component analysis & Sample size determination. The author has an hindex of 33, co-authored 143 publications receiving 5067 citations. Previous affiliations of Marieke E. Timmerman include University Medical Center Groningen.
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Journal ArticleDOI
Dimensionality Assessment of Ordered Polytomous Items With Parallel Analysis
TL;DR: In this paper, the authors considered the most appropriate parallel analysis procedure to assess the number of common factors underlying ordered polytomously scored variables, and proposed minimum rank factor analysis (MRFA) as an extraction method, rather than the currently applied principal component analysis (PCA) and principal axes factoring.
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ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data
Age K. Smilde,Jeroen J. Jansen,Huub C. J. Hoefsloot,Robert-Jan A. N. Lamers,Jan van der Greef,Marieke E. Timmerman +5 more
TL;DR: ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets, is described, a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case.
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The Hull Method for Selecting the Number of Common Factors
TL;DR: The Hull method, which aims to find a model with an optimal balance between model fit and number of parameters, is examined in an extensive simulation study in which the simulated data are based on major and minor factors.
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Early predictors of outcome after mild traumatic brain injury (UPFRONT): an observational cohort study.
Joukje van der Naalt,Marieke E. Timmerman,Myrthe E. de Koning,Harm J. van der Horn,Myrthe E. Scheenen,Bram Jacobs,Gerard Hageman,Tansel Yilmaz,Gerwin Roks,Jacoba M. Spikman +9 more
TL;DR: A prognostic model for functional outcome was created by combining demographics, injury severity, and psychological factors to identify patients at risk for incomplete recovery at 6 months following mTBI.
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ASCA: Analysis of multivariate data obtained from an experimental design
Jeroen J. Jansen,Huub C. J. Hoefsloot,Jan van der Greef,Marieke E. Timmerman,Johan A. Westerhuis,Age K. Smilde +5 more
TL;DR: The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods.