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On Linear Combinations Of Binary Item Scores
Peer reviewed Peer reviewed
Krishnan, T. – Psychometrika, 1973
Scores on different items of a test are often combined linearly (sometimes with equal weights) to form the test score, mainly for the sake of computational convenience. In this paper, the author studies the consequences of doing this, and also studies the problems involved in finding a suitable linear combination. (Author/RK)
Descriptors: Algorithms, Definitions, Discriminant Analysis, Psychometrics
An Individual Difference Model for the Multidimensional Analysis of Preference Data
Peer reviewed Peer reviewed
Schonemann, 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
Monotone Invariant Clustering Procedures
Peer reviewed Peer reviewed
Hubert, Lawrence – Psychometrika, 1973
The present paper discusses two rather different types of partitioning techniques that still have the same property of monotone invariance. (Author)
Descriptors: Algorithms, Cluster Analysis, Cluster Grouping, Goodness of Fit
Min and Max Hierarchial Clustering Using Asymmetric Similarity Measures
Peer reviewed Peer reviewed
Hubert, Lawrence – Psychometrika, 1973
The intent of this paper is to generalize the min and max clustering procedures in such a way that the assumption of a symmetric similarity measure is unnecessary. (Author)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Evaluation Methods
Some Extensions of Johnson's Hierarchical Clustering Algorithms
Peer reviewed Peer reviewed
Hubert, Lawrence – Psychometrika, 1972
Paper is an attempt to extend the hierarchical partitioning algorithms and to emphasize a general connection between these clustering procedures and the mathematical theory of lattices. (Author)
Descriptors: Algorithms, Cluster Grouping, Goodness of Fit, Mathematical Applications
Linear Programming Techniques For Multidimensional Analysis Of Preferences
Peer reviewed Peer reviewed
Srinivasan, V.; Shocker, Allan D. – Psychometrika, 1973
This paper offers a new methodology for analyzing individual differences in preference judgments with regard to a set of stimuli. (Author)
Descriptors: Algorithms, Goodness of Fit, Models, Multidimensional Scaling
Interactive Scaling With Individual Subjects
Peer reviewed Peer reviewed
Young, Forrest W.; Cliff, Norman – Psychometrika, 1972
A metric multidimensional scaling (MDS) procedure based on computer-subject interaction is developed, and an experiment designed to validate the procedure is presented. (Author)
Descriptors: Algorithms, Mathematical Models, Multidimensional Scaling, Predictive Measurement
Pairwise Nonmetric Multidimensional Scaling
Peer reviewed Peer reviewed
Johnson, Richard M. – Psychometrika, 1973
A method of nonmetric multidimensional scaling is described which minimizes pairwise departures from monotonicity. (Author)
Descriptors: Algorithms, Calculus, Computer Programs, Data Analysis
A Monte Carlo Evaluation Of Three Nonmetric Multidimensional Scaling Algorithms
Peer reviewed Peer reviewed
Spence, Ian – Psychometrika, 1972
Discusses the different strategies employed by three practical nonmetric multidimensional scaling algorithms using Monte Carlo techniques. (Author/RK)
Descriptors: Algorithms, Computer Programs, Error of Measurement, Evaluation Methods