Willem J. Heiser

Alexithymia and somatisation: A quantitative review of the literature

OBJECTIVE To present a quantitative review of the empirical literature on somatisation and alexithymia. METHODS Medline and PsycLIT searches for relevant studies were conducted. Meta-analytical techniques were applied to quantify the strength of the associations that were found. RESULTS A small to moderate relationship was found between general alexithymia and somatic symptom reporting. The alexithymia dimension measuring difficulty in identifying feelings showed the strongest association with symptom reports. The alexithymia dimension measuring externally oriented thinking was virtually unrelated to somatic symptom reports. Compared to healthy control populations, subjects suffering from a somatoform condition were significantly more alexithymic, with effect sizes ranging from moderate to large. The studies comparing somatoform to medical or psychiatric conditions yielded inconclusive results. CONCLUSIONS By means of quantitative procedures, an association between general alexithymia and somatic symptom reporting was established. Due to the use of questionnaires that can only check for symptoms, not whether these symptoms are medically explained or not, it is however not possible to draw conclusions on somatisation properly defined. The inconsistent results found when comparing somatoform conditions to medical and psychiatric controls may be attributed to confounding variables. In future studies, these variables should be statistically controlled to establish a more consistent pattern of associations between somatoform conditions and alexithymia. It is, however, equally feasible that this inconsistency reflects the nonspecific character of the association between alexithymia and somatisation. The presence of only one prospective study does not allow to draw conclusions on alexithymia as a predisposing factor for somatisation.

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.

13 Theory of multidimensional scaling

Publisher Summary This chapter discusses the theory of Multidimensional Scaling (MDS). MDS is quite old. The closely-related methods have been used by surveyors and geographers since Gauss, Kruskal has discovered a crude MDS method used in systematic zoology by Boyden around 1930, and algorithms for the mapping of chromosomes from crossing-over frequencies can already be found in an interesting paper of Fisher. The systematic development of MDS, however, has almost completely taken place in psychometrics. The first important contribution to the theory of MDS, not to the methods, is probably Stumpfs (1880). MDS in the broad sense includes various forms of cluster analysis and of linear multivariate analysis. MDS, in the narrow sense, represents dissimilarity data in a low-dimensional space.

The Identification of Parkinson's Disease Subtypes Using Cluster Analysis: A Systematic Review

The clinical variability between patients with Parkinsons 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.© 2010 Movement Disorder Society

Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima

Cluster differences scaling is a method for partitioning a set of objects into classes and simultaneously finding a low-dimensional spatial representation ofK cluster points, to model a given square table of dissimilarities amongn stimuli or objects. The least squares loss function of cluster differences scaling, originally defined only on the residuals of pairs of objects that are allocated to different clusters, is extended with a loss component for pairs that are allocated to the same cluster. It is shown that this extension makes the method equivalent to multidimensional scaling with cluster constraints on the coordinates. A decomposition of the sum of squared dissimilarities into contributions from several sources of variation is described, including the appropriate degrees of freedom for each source. After developing a convergent algorithm for fitting the cluster differences model, it is argued that the individual objects and the cluster locations can be jointly displayed in a configuration obtained as a by-product of the optimization. Finally, the paper introduces a fuzzy version of the loss function, which can be used in a successive approximation strategy for avoiding local minima. A simulation study demonstrates that this strategy significantly outperforms two other well-known initialization strategies, and that it has a success rate of 92 out of 100 in attaining the global minimum.

Two Purposes for Matrix Factorization: A Historical Appraisal

Matrix factorization in numerical linear algebra (NLA) typically serves the purpose of restating some given problem in such a way that it can be solved more readily; for example, one major application is in the solution of a linear system of equations. In contrast, within applied statistics/psychometrics (AS/P), a much more common use for matrix factorization is in presenting, possibly spatially, the structure that may be inherent in a given data matrix obtained on a collection of objects observed over a set of variables. The actual components of a factorization are now of prime importance and not just as a mechanism for solving another problem. We review some connections between NLA and AS/P and their respective concerns with matrix factorization and the subsequent rank reduction of a matrix. We note in particular that several results available for many decades in AS/P were more recently (re)discovered in the NLA literature. Two other distinctions between NLA and AS/P are also discussed briefly: how a generalized singular value decomposition might be defined, and the differing uses for the (newer) methods of optimization based on cyclic or iterative projections.

The Tunneling Method for Global Optimization in Multidimensional Scaling.

This paper focuses on the problem of local minima of the STRESS function. It turns out that unidimensional scaling is particularly prone to local minima, whereas full dimensional scaling with Euclidean distances has a local minimum that is global. For intermediate dimensionality with Euclidean distances it depends on the dissimilarities how severe the local minimum problem is. For city-block distances in any dimensionality many different local minima are found. A simulation experiment is presented that indicates under what conditions local minima can be expected. We introduce the tunneling method for global minimization, and adjust it for multidimensional scaling with general Minkowski distances. The tunneling method alternates a local search step, in which a local minimum is sought, with a tunneling step in which a different configuration is sought with the same STRESS as the previous local minimum. In this manner successively better local minima are obtained, and experimentation so far shows that the last one is often a global minimum.

The majorization approach to multidimensional scaling for Minkowski distances

The majorization method for multidimensional scaling with Kruskals STRESS has been limited to Euclidean distances only. Here we extend the majorization algorithm to deal with Minkowski distances with 1≤p≤2 and suggest an algorithm that is partially based on majorization forp outside this range. We give some convergence proofs and extend the zero distance theorem of De Leeuw (1984) to Minkowski distances withp>1.

Clusteringn objects intok groups under optimal scaling of variables

We propose a method to reduce many categorical variables to one variable withk categories, or stated otherwise, to classifyn objects intok groups. Objects are measured on a set of nominal, ordinal or numerical variables or any mix of these, and they are represented asn points inp-dimensional Euclidean space. Starting from homogeneity analysis, also called multiple correspondence analysis, the essential feature of our approach is that these object points are restricted to lie at only one ofk locations. It follows that thesek locations must be equal to the centroids of all objects belonging to the same group, which corresponds to a sum of squared distances clustering criterion. The problem is not only to estimate the group allocation, but also to obtain an optimal transformation of the data matrix. An alternating least squares algorithm and an example are given.

The role of permutation tests in exploratory multivariate data analysis

Abstract In sensory and consumer science the type of research and the type of data are such that using formal statistical testing is not always the best approach. A disadvantage of such ‘strict’ statistical methods is that assumptions should be fulfilled to be able to perform statistical testing. In practice the assumptions are seldom checked. The exploratory use of methods of multivariate data analysis may be a good alternative. The sometimes heard criticism that these methods suffer from a lack of experimental design, could be countered by realising that there are certain, implicit, design-choices associated with the use of these methods. Furthermore the paper presents the use of permutation tests as a means to test the results from an exploratory MVA method (PCA). It is concluded that permutation tests can almost always be performed for determining significant deviations from an alternative random explanation of the effects in the data.