Describing the elephant: Structure and function in multivariate data

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain.     

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of dissimilarities in a maximum likelihood framework. In each iteration, the probability that a dissimilarity belongs to each of the blocks conforming to a partition of the original dissimilarity matrix, and the rest of parameters, are estimated in a simulated annealing based algorithm. A model selection strategy is used to test the number of latent classes and the dimensionality of the problem. Both simulated and classical dissimilarity data are analyzed to illustrate the model.     

Indirect scaling methods for testing quantitative emotion theories

Two studies investigated the utility of indirect scaling methods, based on graded pair comparisons, for the testing of quantitative emotion theories. In Study 1, we measured the intensity of relief and disappointment caused by lottery outcomes, and in Study 2, the intensity of disgust evoked by pictures, using both direct intensity ratings and graded pair comparisons. The stimuli were systematically constructed to reflect variables expected to influence the intensity of the emotions according to theoretical models of relief/disappointment and disgust, respectively. Two probabilistic scaling methods were used to estimate scale values from the pair comparison judgements: Additive functional measurement (AFM) and maximum likelihood difference scaling (MLDS). The emotion models were fitted to the direct and indirect intensity measurements using nonlinear regression (Study 1) and analysis of variance (Study 2). Both studies found substantially improved fits of the emotion models for the indirectly determined emotion intensities, with their advantage being evident particularly at the level of individual participants. The results suggest that indirect scaling methods yield more precise measurements of emotion intensity than rating scales and thereby provide stronger tests of emotion theories in general and quantitative emotion theories in particular.     

What determines auditory similarity? The effect of stimulus group and methodology

Two experiments on the internal representation of auditory stimuli compared the pairwise and grouping methodologies as means of deriving similarity judgements. A total of 45 undergraduate students participated in each experiment, judging the similarity of short auditory stimuli, using one of the methodologies. The experiments support and extend Bonebright's (1996) findings, using a further 60 stimuli. Results from both methodologies highlight the importance of category information and acoustic features, such as root mean square (RMS) power and pitch, in similarity judgements. Results showed that the grouping task is a viable alternative to the pairwise task with N > 20 sounds whilst highlighting subtle differences, such as cluster tightness, between the different task results. The grouping task is more likely to yield category information as underlying similarity judgements.     

Cognitive organization of disease concepts in Singapore

Abstract

This research investigates illness cognition and its relationship to the use of different types of medicine in three ethnic groups in Singapore. Four hundred and twenty-nine Chinese, Malay, and Indian Singaporeans rated 24 diseases as to their similarity. Multidimensional scaling (MDS) indicated three dimensions. Regression of these dimensions against 17 disease attributes suggested that these dimensions represented spiritual/psychological causation, disease severity, and viral causation. When the dimensions were related to the use of, preference for and perceived effectiveness of different types of medicine it was found that Indian medicine tended to be used, preferred and perceived to be most effective for diseases low in severity whereas Chinese medicine was viewed most favourably for diseases perceived to be low in spiritual/psychological causation as well as those not seen as virally caused. Malay medicine tended to used, preferred and perceived as most effective for conditions believed to be non-serious and not caused by a virus. Western medicine was most likely to be used, preferred, and seen as most effective for diseases viewed as serious as well as those believed to be virally caused.     

Using a multidimensional scaling approach to investigate the underlying basis of ease of learning judgments

Jönsson, F. U. & Lindström, B. R. (2009) Using a multidimensional scaling approach to investigate the underlying basis of ease of learning judgments. Scandinavian Journal of Psychology, 51, 103–108. Before studying a material it is of strategic importance to first assess its difficulty, so called Ease of Learning (EOL) judgments. A multidimensional scaling (MDS) procedure was used to investigate the underlying basis of EOL judgments for 24 nouns, which to the authors’ knowledge has not been done before. In addition, Judgments of Learning (JOL) followed by a free recall test was performed. The MDS analysis indicated that EOL judgments for the nouns are based on multiple cues (dimensions), namely word length, frequency, and concreteness. Moreover, the concreteness values of the nouns, as judged by an independent group, were correlated with both the JOLs and the concreteness dimension from the MDS analysis. This indicates that EOLs and JOLs for single words are based, to some extent, on the same cues.