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Davison, Mark L.; Kim, Se-Kang; Ding, Shuai – 2001
A model for test scores called the profile analysis via multidimensional scaling (PAMS) model is described. The model reparameterizes the linear latent variable model in such a way that the latent variables can be interpreted in terms of profile patterns, rather than factors. The model can serve as the basis for exploratory multidimensional…
Descriptors: Mathematical Models, Multidimensional Scaling, Profiles, Scores
Peer reviewedDegerman, Richard – Perceptual and Motor Skills, 1981
The notion of multidimensional structure is discussed within the framework of an additive component model of multidimensional scaling, where a configuration is considered to be composed of disjoint subspaces, each one of which reflects variation due to a specific stimulus component. Empirical examples are given. (Author/BW)
Descriptors: Mathematical Models, Multidimensional Scaling, Multivariate Analysis
Peer reviewedBatagelj, Vladimir – Psychometrika, 1981
Milligan presented the conditions that are required for a hierarchical clustering strategy to be monotonic, based on a formula by Lance and Williams. The statement of the conditions is improved and shown to provide necessary and sufficient conditions. (Author/GK)
Descriptors: Cluster Analysis, Mathematical Models, Multidimensional Scaling
Peer reviewedCarroll, J. Douglas – Psychometrika, 1976
Hierarchical and non-hierarchical tree structures as models of similarity data are proposed and procedures for fitting both types of trees to data are discussed. Trees are viewed as intermediate between multidimensional scaling and simple clustering. Multiple tree structures and hybrid models are discussed and examples are presented. (Author/JKS)
Descriptors: Cluster Analysis, Geometric Concepts, Multidimensional Scaling
Peer reviewedPeay, Edmund R. – Psychometrika, 1988
An integrated method for rotating and rescaling a set of configurations to optimal agreement in subspaces of varying dimensionalities is developed. The approach relates existing orthogonal rotation techniques as special cases within a general framework, based on a partition of variation that provides measures of agreement. (Author/TJH)
Descriptors: Equations (Mathematics), Multidimensional Scaling, Research Methodology
Peer reviewedCaillez, Francis; Kuntz, Pascale – Psychometrika, 1996
The geometric properties and Euclidean nature of dissimilarity coefficients defined on finite sets are discussed. Several particular transformations are presented that preserve Euclideanarity. The study of a one-parameter family adds to current knowledge of the metric and Euclidean structure of coefficients based on binary data. (SLD)
Descriptors: Equations (Mathematics), Geometry, Matrices, Multidimensional Scaling
Peer reviewedMeara, Kevin; Robin, Frederic; Sireci, Stephen G. – Multivariate Behavioral Research, 2000
Investigated the usefulness of multidimensional scaling (MDS) for assessing the dimensionality of dichotomous test data. Focused on two MDS proximity measures, one based on the PC statistic (T. Chen and M. Davidson, 1996) and other, on interitem Euclidean distances. Simulation results show that both MDS procedures correctly identify…
Descriptors: Correlation, Multidimensional Scaling, Simulation, Test Items
Lombaerts, Koen; Engels, Nadine; Athanasou, James – Perspectives in Education, 2007
The purpose of this study was to develop and gather initial psychometric information on the Self-Regulated Learning Inventory for Teachers (SRLIT). The SRLIT is a self-report scale with 23 items measuring primary school teachers' realisations of self-regulated learning (SRL) practices. Information regarding the instrument's factor structure,…
Descriptors: Learning Strategies, Factor Structure, Factor Analysis, Learning Processes
Peer reviewedShikiar, Richard – Educational and Psychological Measurement, 1974
Descriptors: Comparative Analysis, Individual Differences, Models, Multidimensional Scaling
Peer reviewedBloxom, Bruce – Psychometrika, 1978
A gradient method is used to obtain least squares estimates of parameters in constrained multidimensional scaling in N spaces. Features and constraints of the method and two applications of the procedure are presented. (Author/JKS)
Descriptors: Individual Differences, Multidimensional Scaling, Psychometrics, Statistical Analysis
Peer reviewedYoung, Forrest W.; And Others – Psychometrika, 1978
For the ALSCAL multidimensional scaling computer program, it is reported that (1) a new coordinate estimation routine is superior to the original; (2) an oversight in the interval measurement level case has been found and corrected; and (3) a new initial configuration routine is also superior to the original. (Author/JKS)
Descriptors: Computer Programs, Multidimensional Scaling, Psychometrics, Rating Scales
Peer reviewedJones, Russell A.; And Others – Multivariate Behavioral Research, 1978
Values were elicited spontaneously from a sample of undergraduates and adults attending college, and were compared to Rokeach's terminal and instrumental values. Multidimensional scaling revealed a simpler structure among spontaneously mentioned values than Rokeach's values. (JKS)
Descriptors: College Students, Higher Education, Multidimensional Scaling, Values
Peer reviewedLevine, David M. – Multivariate Behavioral Research, 1977
Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)
Descriptors: Cluster Analysis, Information Processing, Multidimensional Scaling, Numbers
Peer reviewedRamsay, J. O. – Psychometrika, 1977
A variety of distributional assumptions for dissimilarity judgments in multidimensional scaling are considered, with the lognormal distribution being favored for most situations. Procedures for maximum likelihood estimation in this setting are described and examples are presented. (Author/JKS)
Descriptors: Hypothesis Testing, Maximum Likelihood Statistics, Multidimensional Scaling
Peer reviewedTakane, Yoshio; And Others – Psychometrika, 1977
A new procedure for nonmetric multidimensional scaling is proposed and evaluated in this extensive article. The procedure generalizes to a wide variety of situations and types of data and is robust with respect to measurement error. The statistical development of the procedure and examples of its use are presented. (JKS)
Descriptors: Measurement, Multidimensional Scaling, Research Methodology, Statistical Data

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