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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 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 reviewedDenison, Daniel R. – Multivariate Behavioral Research, 1982
Structural equation modeling is applied in conjunction with constrained monotone distance analysis. These alternative methods are used in an evaluation of a social-psychological model derived from Likert's theory of organizational behavior. (Author/JKS)
Descriptors: Data Analysis, Hypothesis Testing, Mathematical Models, Multidimensional Scaling
Peer reviewedAnd Others; Carroll, J. Douglas – Psychometrika, 1980
A data analysis model called CANDELINC performs a broad range of multidimensional data analyses. The model allows for the incorporation of general linear constraints. Several examples are presented. (JKS)
Descriptors: Factor Analysis, Least Squares Statistics, Mathematical Models, Multidimensional Scaling
Peer reviewedKenny, David A.; Zautra, Alex – Journal of Consulting and Clinical Psychology, 1995
Describes a new approach for analyzing an individual's responses made at multiple times. Proposes that three sources of variance determine a person's current standing on a variable: trait (term does not change), state (term changes), and error (random term). Shows how structural equation modeling can be used and presents an extended example. (RJM)
Descriptors: Clinical Psychology, Factor Analysis, Factor Structure, Item Analysis
Mellenbergh, Gideon J.; Vijn, Pieter – 1980
Data are summarized in Scheuneman's Score x Group x Response frequency table in order to investigate item bias. The data can arise from two different sampling models: (1) multinomial sampling in which a fixed sample size is used and the responses are cross-classified according to score, group, and response; and (2) product-multinomial sampling in…
Descriptors: Black Students, Cognitive Measurement, Foreign Countries, Latent Trait Theory


