Descriptor
Source
| Psychometrika | 7 |
Author
| DeSarbo, Wayne S. | 2 |
| Hutchinson, J. Wesley | 2 |
| Bissett, Randall | 1 |
| Browne, Michael W. | 1 |
| Cudeck, Robert | 1 |
| Mungale, Amitabh | 1 |
| Schneider, Bruce | 1 |
| Spence, Ian | 1 |
| Young, Martin R. | 1 |
Publication Type
| Journal Articles | 6 |
| Reports - Evaluative | 6 |
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Peer reviewedHutchinson, J. Wesley – Psychometrika, 1989
A Monte Carlo simulation and applications to eight sets of proximity data are presented to support the practical utility of a network scaling algorithm (NETSCAL)--NETwork SCALing. The algorithm determines which vertices within a network are directly connected by an arc and estimates the length of each arc. (TJH)
Descriptors: Algorithms, Diagrams, Monte Carlo Methods, Network Analysis
Peer reviewedHutchinson, J. Wesley; Mungale, Amitabh – Psychometrika, 1997
A nonmetric algorithm, pairwise partitioning, is developed to identify feature-based similarity structures. Presents theorems about the validity of the features identified by the algorithm, and reports results of Monte Carlo simulations that estimate the probabilities of identifying valid features for different feature structures and amounts of…
Descriptors: Algorithms, Error of Measurement, Estimation (Mathematics), Identification
Peer reviewedDeSarbo, Wayne S.; And Others – Psychometrika, 1989
A method is presented that simultaneously estimates cluster membership and corresponding regression functions for a sample of observations or subjects. This methodology is presented with the simulated annealing-based algorithm. A set of Monte Carlo analyses is included to demonstrate the performance of the algorithm. (SLD)
Descriptors: Algorithms, Cluster Analysis, Estimation (Mathematics), Least Squares Statistics
Peer reviewedYoung, Martin R.; DeSarbo, Wayne S. – Psychometrika, 1995
A new parametric maximum likelihood procedure is proposed for estimating ultrametric trees for the analysis of conditional rank order proximity data. Technical aspects of the model and the estimation algorithm are discussed, and Monte Carlo results illustrate its application. A consumer psychology application is also examined. (SLD)
Descriptors: Algorithms, Consumer Economics, Estimation (Mathematics), Maximum Likelihood Statistics
Peer reviewedBissett, Randall; Schneider, Bruce – Psychometrika, 1991
The algorithm developed by B. A. Schneider (1980) for analysis of paired comparisons of psychological intervals is replaced by one proposed by R. M. Johnson. Monte Carlo simulations of pairwise dissimilarities and pairwise conjoint effects show that Johnson's algorithm can provide good metric recovery. (SLD)
Descriptors: Algorithms, Comparative Analysis, Computer Simulation, Equations (Mathematics)
Peer reviewedSpence, 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
Peer reviewedCudeck, Robert; Browne, Michael W. – Psychometrika, 1992
A method is proposed for constructing a population covariance matrix as the sum of a particular model plus a nonstochastic residual matrix, with the stipulation that the model holds with a prespecified lack of fit. The procedure is considered promising for Monte Carlo studies. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Estimation (Mathematics), Factor Analysis
