Descriptor
| Graphs | 4 |
| Mathematical Models | 4 |
| Multidimensional Scaling | 4 |
| Equations (Mathematics) | 3 |
| Cluster Grouping | 2 |
| Algorithms | 1 |
| Botany | 1 |
| Cluster Analysis | 1 |
| Comparative Analysis | 1 |
| Factor Analysis | 1 |
| Groups | 1 |
| More ▼ | |
Source
| Psychometrika | 3 |
Publication Type
| Journal Articles | 3 |
| Reports - Evaluative | 2 |
| Information Analyses | 1 |
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewedMeulman, Jacqueline J. – Psychometrika, 1992
The distance approach to nonlinear multivariate analysis proposed by J. J. Meulman (1986) is reviewed. Several generalizations are discussed by combining features from the conventional multivariate analysis approach, which seeks weighted sums of variables, with the alternative approach, which seeks to fit distances. (SLD)
Descriptors: Equations (Mathematics), Factor Analysis, Graphs, Mathematical Models
Peer reviewedArabie, Phipps – Psychometrika, 1991
The current state of multidimensional scaling using the city-block metric is reviewed, with attention to (1) substantive and theoretical issues; (2) recent algorithmic developments and their implications for analysis; (3) isometries with other metrics; (4) links to graph-theoretic models; and (5) prospects for future development. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Graphs, Literature Reviews
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
PDF pending restorationRudnitsky, Alan N. – 1977
Three approaches to the graphic representation of similarity and dissimilarity matrices are compared and contrasted. Specifically, Kruskal's multidimensional scaling, Johnson's hierarchical clustering, and Waern's graphing techniques are employed to depict, in two dimensions, data representing the structure of a set of botanical concepts. Each of…
Descriptors: Botany, Cluster Analysis, Cluster Grouping, Comparative Analysis


