Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 0 |
Since 2006 (last 20 years) | 4 |
Descriptor
Matrices | 60 |
Mathematical Models | 45 |
Factor Analysis | 24 |
Equations (Mathematics) | 19 |
Models | 15 |
Statistical Analysis | 14 |
Algorithms | 9 |
Correlation | 9 |
Data Analysis | 9 |
Goodness of Fit | 9 |
Multivariate Analysis | 9 |
More ▼ |
Source
Psychometrika | 60 |
Author
Kiers, Henk A. L. | 5 |
ten Berge, Jos M. F. | 5 |
Browne, Michael W. | 2 |
McDonald, Roderick P. | 2 |
Peay, Edmund R. | 2 |
Shapiro, Alexander | 2 |
Stegeman, Alwin | 2 |
Takane, Yoshio | 2 |
Williams, James S. | 2 |
Arabie, Phipps | 1 |
Arri, P. S. | 1 |
More ▼ |
Publication Type
Journal Articles | 43 |
Reports - Evaluative | 25 |
Reports - Research | 13 |
Reports - Descriptive | 3 |
Information Analyses | 1 |
Reports - General | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Krijnen, Wim P.; Dijkstra, Theo K.; Stegeman, Alwin – Psychometrika, 2008
The CANDECOMP/PARAFAC (CP) model decomposes a three-way array into a prespecified number of "R" factors and a residual array by minimizing the sum of squares of the latter. It is well known that an optimal solution for CP need not exist. We show that if an optimal CP solution does not exist, then any sequence of CP factors monotonically decreasing…
Descriptors: Factor Analysis, Models, Matrices
Chiu, Chia-Yi; Douglas, Jeffrey A.; Li, Xiaodong – Psychometrika, 2009
Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and…
Descriptors: Test Length, Identification, Multivariate Analysis, Item Response Theory
Revuelta, Javier – Psychometrika, 2008
This paper introduces the generalized logit-linear item response model (GLLIRM), which represents the item-solving process as a series of dichotomous operations or steps. The GLLIRM assumes that the probability function of the item response is a logistic function of a linear composite of basic parameters which describe the operations, and the…
Descriptors: Item Response Theory, Models, Matrices, Probability

Halff, Henry M. – Psychometrika, 1976
Two forms of stationarity prior to criterion in absorbing Markov chains are examined. Both forms require that the probability of a particular response on a particular trial before absorption be independent of trial number. Simple, necessary and sufficient conditions for both forms are developed and applied to several examples. (Author)
Descriptors: Learning Processes, Mathematical Models, Matrices
Sufficient Conditions for Uniqueness in Candecomp/Parafac and Indscal with Random Component Matrices
Stegeman, Alwin; Ten Berge, Jos M. F.; De Lathauwer, Lieven – Psychometrika, 2006
A key feature of the analysis of three-way arrays by Candecomp/Parafac is the essential uniqueness of the trilinear decomposition. We examine the uniqueness of the Candecomp/Parafac and Indscal decompositions. In the latter, the array to be decomposed has symmetric slices. We consider the case where two component matrices are randomly sampled…
Descriptors: Goodness of Fit, Matrices, Factor Analysis, Models

Williams, James S. – Psychometrika, 1981
A revised theorem is presented concerning uniqueness of minimum rank solutions in common factor analysis. (Author)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Matrices

Riccia, Giacomo Della; Shapiro, Alexander – Psychometrika, 1982
Some mathematical aspects of minimum trace factor analysis (MTFA) are discussed. The uniqueness of an optimal point of MTFA is proved, and necessary and sufficient conditions for any particular point to be optimal are given. The connection between MTFA and classical minimum rank factor analysis is discussed. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Mathematical Models, Matrices

Hubert, L. J.; Golledge, R. G. – Psychometrika, 1981
A recursive dynamic programing strategy for reorganizing the rows and columns of square proximity matrices is discussed. The strategy is used when the objective function measuring the adequacy of the reorganization has a fairly simple additive structure. (Author/JKS)
Descriptors: Computer Programs, Mathematical Models, Matrices, Statistical Analysis

ten Berge, Jos M. F.; Kiers, Henk A. L. – Psychometrika, 1989
Centering a matrix row-wise and rescaling it column-wise to a unit sum of squares requires an iterative procedure. It is shown that this procedure converges to a stable solution that need not be centered row-wise. The results bear directly on several types of preprocessing methods in Parafac/Candecomp. (Author/TJH)
Descriptors: Correlation, Equations (Mathematics), Mathematical Models, Matrices

Nishisato, Shizuhiko; Arri, P. S. – Psychometrika, 1975
A modified technique of separable programming was used to maximize the squared correlation ratio of weighted responses to partially ordered categories. The technique employs a polygonal approximation to each single-variable function by choosing mesh points around the initial approximation supplied by Nishisato's method. Numerical examples were…
Descriptors: Algorithms, Linear Programing, Mathematical Models, Matrices

Peay, Edmund R. – Psychometrika, 1975
Peay presented a class of grouping methods based on the concept of the r-clique for symmetric data relationships. The concepts of the r-clique can be generalized readily to directed (or asymmetric) relationships, and groupings based on this generalization may be found conveniently using an adoption of Peay's methodology. (Author/BJG)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Mathematical Models

Kruskal, Joseph B.; Shepard, Roger N. – Psychometrika, 1974
Descriptors: Comparative Analysis, Computer Programs, Factor Analysis, Matrices

Borg, Ingwer – Psychometrika, 1978
Procrustean analysis is a form of factor analysis where a target matrix of results is specified and then approximated. Procrustean analysis is extended here to the case where matrices have different row order. (Author/JKS)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Matrices

Shapiro, Alexander – Psychometrika, 1982
The extent to which one can reduce the rank of a symmetric matrix by only changing its diagonal entries is discussed. Extension of this work to minimum trace factor analysis is presented. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Mathematical Models, Matrices

ten Berge, Jos M. F. – Psychometrika, 1988
A summary and a unified treatment of fully general computational solutions for two criteria for transforming two or more matrices to maximal agreement are provided. The two criteria--Maxdiff and Maxbet--have applications in the rotation of factor loading or configuration matrices to maximal agreement and the canonical correlation problem. (SLD)
Descriptors: Correlation, Equations (Mathematics), Mathematical Models, Matrices