Willem J. Heiser

Bio: Willem J. Heiser is an academic researcher from Leiden University. The author has contributed to research in topics: Multidimensional scaling & Item response theory. The author has an hindex of 36, co-authored 113 publications receiving 3684 citations.

Principal Components Analysis With Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data

Abstract: This chapter focuses on the analysis of ordinal and nominal multivariate data, using a special variety of principal components analysis that includes nonlinear optimal scaling transformation of the variables. Since the early 1930s, classical statistical methods have been adapted in various ways to suit the particular characteristics of social and behavioral science research. Research in these areas often results in data that are nonnumerical, with measurements recorded on scales having an uncertain unit of measurement. Data would typically consist of qualitative or categorical variables that describe the persons in a limited number of categories. The zero point of these scales is uncertain, the relationships among the different categories is often unknown, and although frequently it can be assumed that the categories are ordered, their mutual distances might still be unknown. The uncertainty in the unit of measurement is not just a matter of measurement error because its variability may have a systematic component. For example, in the data set that will be used throughout this chapter as an illustration, concerning feelings of national identity and involving 25,000 respondents in 23 different countries all over the world (International Social Survey Programme [ISSP], 1995), there are variables indicating how close the respondents feel toward their neighborhood, town, and country, measured on a 5-point scale with labels ranging from not close at all to very close. This response format is typical for a lot of behavioral research and definitely is not numerical (even though the categories are ordered and can be coded numerically).

The identification of Parkinson's disease subtypes using cluster analysis: a systematic review.

Abstract: The clinical variability between patients with Parkinson's disease (PD) may point at the existence of subtypes of the disease. Identification of subtypes is important, since a focus on homogeneous groups may enhance the chance of success of research on mechanisms of disease and may also lead to tailored treatment strategies. Cluster analysis (CA) is an objective method to classify patients into subtypes. We systematically reviewed the methodology and results of CA studies in PD to gain a better understanding of the robustness of identified subtypes. We found seven studies that fulfilled the inclusion criteria. Studies were limited by incomplete reporting and methodological limitations. Differences between studies rendered comparisons of the results difficult. However, it appeared that studies which applied a comparable design identified similar subtypes. The cluster profiles "old age-at-onset and rapid disease progression" and "young age-at-onset and slow disease progression" emerged from the majority of studies. Other cluster profiles were less consistent across studies. Future studies with a rigorous study design that is standardized with respect to the included variables, data processing, and CA technique may advance the knowledge on subtypes in PD.

Understanding the differential impact of outcome monitoring: Therapist variables that moderate feedback effects in a randomized clinical trial

Abstract: Providing outcome monitoring feedback to therapists seems to be a promising approach to improve outcomes in clinical practice. This study aims to examine the effect of feedback and investigate whether it is moderated by therapist characteristics. Patients (n=413) were randomly assigned to either a feedback or a no-feedback control condition. There was no significant effect of feedback in the full sample, but feedback was effective for not-on-track cases for therapists who used the feedback. Internal feedback propensity, self-efficacy, and commitment to use the feedback moderated the effects of feedback. The results demonstrate that feedback is not effective under all circumstances and therapist factors are important when implementing feedback in clinical practice.

The Outcome Questionnaire (OQ‐45) in a Dutch population: A cross‐cultural validation

Abstract: The cross-cultural validity of the Outcome Questionnaire (OQ) in the Dutch population has been examined by comparing the psychometric properties and equivalence in factor structure and normative scores of the Dutch OQ with the original American version. Data were collected from a university (n = 268), in a community (n = 810) and from three mental health care organizations (n = 1920). Results show that the psychometric properties of the Dutch OQ were adequate and similar to the original instrument. Some differences in equivalence were found though. In factor analysis, two additional factors were found: one consisting of social role items and another that reflected anxiety and somatic symptoms. Furthermore, normative scores were different for the Dutch and American samples, and this resulted in different cut-off scores for estimating a clinically significant change in the Dutch population. Copyright © 2007 John Wiley & Sons, Ltd.

Parkinson’s disease: clinical features and diagnosis

Abstract: Objective: Parkinson’s disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders. Methods: A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included “Parkinson’s disease”, “diagnosis” and “signs and symptoms”. Results: Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD. Conclusions: A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.

Handbook on Constructing Composite Indicators: Methodology and User Guide

Abstract: This Handbook aims to provide a guide for constructing and using composite indicators for policy makers, academics, the media and other interested parties. While there are several types of composite indicators, this Handbook is concerned with those which compare and rank country performance in areas such as industrial competitiveness, sustainable development, globalisation and innovation. The Handbook aims to contribute to a better understanding of the complexity of composite indicators and to an improvement of the techniques currently used to build them. In particular, it contains a set of technical guidelines that can help constructors of composite indicators to improve the quality of their outputs. It has been prepared jointly by the OECD (the Statistics Directorate and the Directorate for Science, Technology and Industry) and the Applied Statistics and Econometrics Unit of the Joint Research Centre of the European Commission in Ispra, Italy. Primary authors from the JRC are Michela Nardo, Michaela Saisana, Andrea Saltelli and Stefano Tarantola. Primary authors from the OECD are Anders Hoffmann and Enrico Giovannini. Editorial assistance was provided by Candice Stevens, Gunseli Baygan and Karsten Olsen. The research is partly funded by the European Commission, Research Directorate, under the project KEI (Knowledge Economy Indicators), Contract FP6 No. 502529. In the OECD context, the work has benefitted from a grant from the Danish government. The views expressed are those of the authors and should not be regarded as stating an official position of either the European Commission or the OECD.

Multidimensional scaling: Multidimensional scaling

Abstract: The concept of similarity, or a sense of 'sameness' among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis. WIREs Cogn Sci 2013, 4:93-103. doi: 10.1002/wcs.1203 This article is categorized under: Psychology > Perception and Psychophysics.