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A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive(More)
The psychometric paradigm has been the most influential model in the field of risk analysis. The "cognitive maps" of hazards produced by the paradigm seem to explain how laypeople perceive the various risks they face. Because most of the studies used aggregated data, analyzed using principal component analysis, it is not known whether the model neglects(More)
We would like to thank Anton Béguin and Norman Verhelst for their valuable remarks that have helped to improve this paper. Abstract For many least-squares decomposition models efficient algorithms are well known. A more difficult problem arises in decomposition models where each residual is weighted by a nonnegative value. A special case is principal(More)
MultiLevel Simultaneous Component Analysis (MLSCA) is a data-analytical technique for multivariate two-level data. MLSCA sheds light on the associations between the variables at both levels by specifying separate submodels for each level. Each submodel consists of a component model. Although MLSCA has already been successfully applied in diverse areas(More)
Significance testing is widely used and often criticized. The Task Force on Statistical Inference of the American Psychological Association (TFSI, APA; Wilkinson & TFSI, 1999) addressed the use of significance testing and made recommendations that were incorporated in the fifth edition of the APA Publication Manual (APA, 2001). They emphasized the(More)
In this study we have focused on the problem of mapping the dynamics of co-wordmatrices from subsequent time periods. Methods for mapping dynamics are important for following trends in research. We have explored the possibilities of a three way multidimensional scaling method, INDSCAL. We are especially interested to find relations between adverse drug(More)
Several three-mode principal component models can be considered for the modelling of three-way, three-mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We(More)
MOTIVATION Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations(More)
This study sought to determine the nature of the relationship between cognition and striatal dopaminergic functioning in 28 patients with advanced Parkinson's disease (PD) using fluorodopa Positron emission tomography (FDOPA-PET) and neuropsychological test scores. Mental flexibility was related to putamen activity while mental organization (executive(More)