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Hongxi Li; Shuwei Li; Liuquan Sun; Xinyuan Song – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social,…
Descriptors: Statistical Analysis, Statistical Studies, Structural Equation Models, Intervals
Peer reviewedLeeuw, Jan De – Psychometrika, 1982
A formula for the determinant of a partitioned matrix, possibly with singular submatrices, is derived and applied to some psychometric and numerical problems. (Author)
Descriptors: Algorithms, Matrices, Statistical Studies
Peer reviewedFerligoj, Anuska; Batagelj, Vladimir – Psychometrika, 1982
Using constraints with cluster analysis limits the possible number of clusters. This paper deals with clustering problems where grouping is constrained by a symmetric and reflexive relation. Two approaches, along with illustrations, are presented. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Mathematical Models
Peer reviewedDesarbo, Wayne S. – Psychometrika, 1982
A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These models generalize the Shepard-Arabie Additive clusters model. Two applications are given and extensions to three-way models, nonmetric analyses, and other model specifications are provided. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Mathematical Models
Peer reviewedBentler, P. M.; Tanaka, Jeffrey S. – Psychometrika, 1983
Rubin and Thayer recently presented equations to implement maximum likelihood estimation in factor analysis via the EM algorithm. It is argued here that the advantages of using the EM algorithm remain to be demonstrated. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Maximum Likelihood Statistics, Research Problems
Peer reviewedRubin, Donald B.; Thayer, Dorothy T. – Psychometrika, 1983
The authors respond to a criticism of their earlier article concerning the use of the EM algorithm in maximum likelihood factor analysis. Also included are the comments made by the reviewers of this article. (JKS)
Descriptors: Algorithms, Estimation (Mathematics), Factor Analysis, Maximum Likelihood Statistics
Peer reviewedFrei, H. P.; Stieger, D. – Information Processing & Management, 1995
Highlights semantic links and shows how the semantic content of hypertext links can be used for information retrieval. Discussion includes indexing and retrieval algorithms that exploit link content and node content; retrieval strategies exploiting semantic links, including conventional retrieval and constrained spreading activation techniques;…
Descriptors: Algorithms, Experiments, Graphs, Hypermedia
Peer reviewedMorris, John D. – Educational and Psychological Measurement, 1986
An empirical method (PRESS) for examining and contrasting the cross-validated prediction accuracies of some popular algorithms for weighting predictor variables was advanced and examined. The sample specific PRESS method was suggested as most appropriate when selecting among several alternative weighting algorithms to achieve maximum…
Descriptors: Algorithms, Least Squares Statistics, Predictive Measurement, Predictive Validity
Peer reviewedTsutakawa, Robert K.; Lin, Hsin Ying – Psychometrika, 1986
Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. (Author/LMO)
Descriptors: Algorithms, Bayesian Statistics, Estimation (Mathematics), Latent Trait Theory
Peer reviewedde Leeuw, Jan; Verhelst, Norman – Journal of Educational Statistics, 1986
Maximum likelihood procedures are presented for a general model to unify the various models and techniques that have been proposed for item analysis. Unconditional maximum likelihood estimation, proposed by Wright and Haberman, and conditional maximum likelihood estimation, proposed by Rasch and Andersen, are shown as important special cases. (JAZ)
Descriptors: Algorithms, Estimation (Mathematics), Item Analysis, Latent Trait Theory
Peer reviewedMislevy, Robert J. – Psychometrika, 1986
This article describes a Bayesian framework for estimation in item response models, with two-stage distributions on both item and examinee populations. Strategies for point and interval estimation are discussed, and a general procedure based on the EM algorithm is presented. (Author/LMO)
Descriptors: Algorithms, Bayesian Statistics, Estimation (Mathematics), Latent Trait Theory
Peer reviewedFischer, Gerhard H.; Ponocny, Ivo – Psychometrika, 1994
An extension to the partial credit model, the linear partial credit model, is considered under the assumption of a certain linear decomposition of the item x category parameters into basic parameters. A conditional maximum likelihood algorithm for estimating basic parameters is presented and illustrated with simulation and an empirical study. (SLD)
Descriptors: Algorithms, Change, Estimation (Mathematics), Item Response Theory
Peer reviewedTen Berge, Jos M. F.; And Others – Psychometrika, 1994
The suggestion that the IDIOSCAL model be fitted by the TUCKALS2 algorithm for three-way components analysis is examined. The claim that resulting coordinate matrices will be identical is supported when the data matrices are semidefinite. Counterexamples for indefinite matrices are also constructed. (SLD)
Descriptors: Algorithms, Correlation, Equations (Mathematics), Goodness of Fit
Peer reviewedCappelleri, Joseph C.; And Others – Evaluation Review, 1994
A statistical power algorithm based on the Fisher Z method is developed for cutoff-based random clinical trials and the single cutoff-point (regression-discontinuity) design that has no randomization. This article quantifies power and sample size estimates for various levels of power and cutoff-based assignment. (Author/SLD)
Descriptors: Algorithms, Cutting Scores, Estimation (Mathematics), Power (Statistics)
Peer reviewedAl-Kharashi, Ibrahim A.; Evens, Martha W. – Journal of the American Society for Information Science, 1994
Describes experiments in Arabic information retrieval using 29 queries against a base of 355 bibliographic records. Using roots and stems as index terms worked better than words, and the root performed as well as or better than the stem at low recall levels and definitely better at high recall levels. (Contains 30 references.) (KRN)
Descriptors: Algorithms, Arabic, Bibliographic Databases, Comparative Analysis

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