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Peer reviewedMacCallum, Robert C. – Psychometrika, 1977
The role of conditionality in the INDSCAL and ALSCAL multidimensional scaling procedures is explained. The effects of conditionality on subject weights produced by these procedures is illustrated via a single set of simulated data. Results emphasize the need for caution in interpreting subject weights provided by these techniques. (Author/JKS)
Descriptors: Individual Differences, Mathematical Models, Multidimensional Scaling, Statistical Analysis
Peer reviewedDunn, Terrence R.; Harshman, Richard A. – Psychometrika, 1982
The kinds of individual differences in perceptions permitted by the weighted euclidean model for multidimensional scaling are more restrictive than those allowed by models developed by Tucker or Carroll. It is shown how problems which occur when using the more general models can be removed. (Author/JKS)
Descriptors: Data Analysis, Individual Differences, Mathematical Models, Multidimensional Scaling
Peer reviewedPennell, Roger – Educational and Psychological Measurement, 1972
Author argues that simplistic and/or heuristic approaches to the Tucker and Messick model (an individual differences model for multidimensional scaling, 1963) are often inadequate. (Author/CB)
Descriptors: Data Analysis, Evaluation, Individual Differences, Mathematical Models
Peer reviewedMacCallum, Robert C.; Cornelius, Edwin T., III – Psychometrika, 1977
A Monte Carlo study was carried out to investigate the ability of the ALSCAL multidimensional scaling program to recover true structure inherent in simulated proximity data. The results under varying conditions were mixed. Practical implications and suggestions for further research are discussed. (Author/JKS)
Descriptors: Computer Programs, Individual Differences, Mathematical Models, Monte Carlo Methods
Peer reviewedSchonemann, Peter H.; Wang, Ming Mei – Psychometrika, 1972
A model for the analysis of paired comparison data is presented which is metric, mathematically tractable, and has an exact algebraic solution. (Authors/MB)
Descriptors: Algorithms, Individual Differences, Mathematical Models, Multidimensional Scaling
Peer reviewedLingoes, James C.; Borg, Ingwer – Psychometrika, 1978
A family of models for the representation and assessment of individual differences for multivariate data called PINDIS (Procrustean Individual Differences Scaling) is presented. PINDIS sheds new light on the interpretability and applicability of a variety of multidimensional scaling models. (Author/JKS)
Descriptors: Computer Programs, Individual Differences, Mathematical Models, Multidimensional Scaling
Peer reviewedBockenholt, Ulf – Psychometrika, 1990
This paper proposes a generalization of Thurstonian probabilistic choice models for analyzing both multiple preference responses and their relationships. The approach is illustrated by modeling data from two multivariate preference experiments. Preliminary data analyses show that the extension can yield an adequate representation of multivariate…
Descriptors: Equations (Mathematics), Individual Differences, Mathematical Models, Multidimensional Scaling
Peer reviewedNishisato, Shizuhiko – Psychometrika, 1978
An alternative formulation for Guttman scaling is presented. The new formulation is described, and advantages over Guttman's formulation are detailed. The method is assumption-free and capable of multidimensional analysis. (Author/JKS)
Descriptors: Individual Differences, Mathematical Models, Measurement Techniques, Multidimensional Scaling
Peer reviewedLangeheine, Rolf – Psychometrika, 1982
The degree to which Procrustean Individual Differences Scaling can be extended to related topics such as target analysis is discussed and a Monte Carlo study investigating the fit of the model under various conditions is presented. (JKS)
Descriptors: Data Analysis, Goodness of Fit, Individual Differences, Mathematical Models
Peer reviewedCliff, Norman – Applied Psychological Measurement, 1977
An attempt was made to validate for sentence type items a mathematical model for inventory response. Data were gathered from subjects responding under candid and under faking sets. In the former case only limited support for the model was found, but in the latter it seemed highly relevant. (Author/RC)
Descriptors: Cognitive Processes, Individual Differences, Mathematical Models, Multidimensional Scaling
Peer reviewedMacCallum, Robert C. – Psychometrika, 1976
Concerned with consequences of employing the INDSCAL model when one of its assumptions are known to be violated. Under study is the notion that all individuals perceive the object space dimensions to be independent. (RC)
Descriptors: Factor Analysis, Goodness of Fit, Individual Differences, Mathematical Models
Peer reviewedMeulman, Jacqueline J.; Verboon, Peter – Psychometrika, 1993
Points of view analysis, as a way to deal with individual differences in multidimensional scaling, was largely supplanted by the weighted Euclidean model. It is argued that the approach deserves new attention, especially as a technique to analyze group differences. A streamlined and integrated process is proposed. (SLD)
Descriptors: Cluster Grouping, Equations (Mathematics), Graphs, Groups
Peer reviewedSchonemann, Peter H.; And Others – Multivariate Behavioral Research, 1975
Descriptors: Algorithms, Data Analysis, Dimensional Preference, Individual Differences
Nishisato, Shizuhiko – 1978
A bibliography with approximately 1,000 references to articles on multidimensional scaling (MDS) is preceded by a summary review tracing the major developments in the areas covered, and identifying significant references in each. With the exception of a few earlier documents, the period covered is 1966 to 1978. Topics mentioned in the summary…
Descriptors: Bibliographies, Computer Programs, History, Individual Differences
Peer reviewedDavison, Mark L. – Psychometrika, 1976
Proposes a quadratic programming, least squares solution to Carroll's weighted unfolding model with nonnegativity constraints imposed on weights. It can be used to test various hypotheses about the weighted unfolding model with or without constraints. (RC)
Descriptors: Algorithms, Correlation, Goodness of Fit, Hypothesis Testing
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