NotesFAQContact Us
Collection
Advanced
Search Tips
Showing 1 to 15 of 32 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Franz Classe; Christoph Kern – Educational and Psychological Measurement, 2024
We develop a "latent variable forest" (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on "confirmatory factor analysis" (CFA) models with ordinal and/or numerical response variables. Through parametric model…
Descriptors: Algorithms, Item Response Theory, Artificial Intelligence, Factor Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
D. Steger; S. Weiss; O. Wilhelm – Creativity Research Journal, 2023
Creativity can be measured with a variety of methods including self-reports, others reports, and ability tests. While typical self-reports are best understood as weak proxies of creativity, biographical reports that assess previous creative activities seem more promising. Drawbacks of such measures -- including skewed item distributions, a lack of…
Descriptors: Creativity, Creativity Tests, Test Construction, Algorithms
Meyer, J. Patrick; Hu, Ann; Li, Sylvia – NWEA, 2023
The Content Proximity Project was designed to improve the content validity of the MAP® Growth™ assessments while retaining the ability for the test to adapt off-grade and meet students wherever they are in their learning. Two main features of the project were the development of an enhanced item selection algorithm, and a spring pilot study…
Descriptors: Achievement Tests, Mathematics Achievement, Content Validity, Mathematics Tests
Peer reviewed Peer reviewed
Ponocny, Ivo – Psychometrika, 2000
Introduces a new algorithm for obtaining exact person fit indexes for the Rasch model. The algorithm realizes most tests for a general family of alternative hypotheses, including tests concerning differential item functioning. The method is also used as a goodness-of-fit test in some circumstances. Simulated examples and an empirical investigation…
Descriptors: Algorithms, Goodness of Fit, Item Bias, Simulation
Peer reviewed Peer reviewed
Nevels, Klaas – Psychometrika, 1989
In FACTALS, an alternating least squares algorithm is used to fit the common factor analysis model to multivariate data. A. Mooijaart (1984) demonstrated that the algorithm is based on an erroneous assumption. This paper gives a proper solution for the loss function used in FACTALS. (Author/TJH)
Descriptors: Algorithms, Factor Analysis, Goodness of Fit, Least Squares Statistics
Peer reviewed Peer reviewed
ten Berge, Jos M. F.; Kiers, Henk A. L. – Psychometrika, 1989
The DEDICOM (decomposition into directional components) model provides a framework for analyzing square but asymmetric matrices of directional relationships among "n" objects or persons in terms of a small number of components. One version of DEDICOM ignores the diagonal entries of the matrices. A straightforward computational solution…
Descriptors: Algorithms, Factor Analysis, Goodness of Fit, Least Squares Statistics
Peer reviewed Peer reviewed
DeSarbo, Wayne S.; And Others – Psychometrika, 1990
A nonspatial operationalization of the Krumhansl distance-density model of similarity is presented. The conceptual model and empirical evidence are reviewed. A nonspatial, tree-fitting methodology is described, which is sufficiently flexible to fit several competing hypotheses of similarity formation. Extensions to spatial models, three-way…
Descriptors: Algorithms, Cluster Analysis, Goodness of Fit, Mathematical Models
Peer reviewed Peer reviewed
Lingoes, James C.; Schonemann, Peter H. – Psychometrika, 1974
Descriptors: Algorithms, Goodness of Fit, Matrices, Orthogonal Rotation
Peer reviewed Peer reviewed
Lingoes, James C. – Journal of Educational and Psychological Measurement, 1974
Descriptors: Algorithms, Computer Programs, Factor Analysis, Goodness of Fit
Peer reviewed Peer reviewed
Bock, R. Darrell; Aitkin, Murray – Psychometrika, 1981
The practicality of using the EM algorithm for maximum likelihood estimation of item parameters in the marginal distribution is presented. The EM procedure is shown to apply to general item-response models. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Goodness of Fit, Item Analysis
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
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
Peer reviewed Peer reviewed
Hakstian, A. Ralph; And Others – Multivariate Behavioral Research, 1982
Issues related to the decision of the number of factors to retain in factor analyses are identified. Three widely used decision rules--the Kaiser-Guttman (eigenvalue greater than one), scree, and likelihood ratio tests--are investigated using simulated data. Recommendations for use are made. (Author/JKS)
Descriptors: Algorithms, Data Analysis, Factor Analysis, Factor Structure
Peer reviewed Peer reviewed
Zwick, William R. – Multivariate Behavioral Research, 1982
The performance of four rules for determining the number of components (factors) to retain (Kaiser's eigenvalue greater than one, Cattell's scree, Bartlett's test, and Velicer's Map) was investigated across four systematically varied factors (sample size, number of variables, number of components, and component saturation). (Author/JKS)
Descriptors: Algorithms, Data Analysis, Factor Analysis, Factor Structure
Peer reviewed Peer reviewed
Polson, Peter G.; Huizinga, David – Psychometrika, 1974
Descriptors: Algorithms, Computer Programs, Goodness of Fit, Learning Processes
Previous Page | Next Page »
Pages: 1  |  2  |  3