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Yunxiao Chen; Xiaoou Li; Jingchen Liu; Gongjun Xu; Zhiliang Ying – Grantee Submission, 2017
Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class…
Descriptors: Item Analysis, Classification, Graphs, Test Items

Hafner, Robert – Psychometrika, 1981
The method proposed by Harman and Fukuda to treat the so-called Heywood case in the minres method in factor analysis (i.e., the case where the resulting communalities are greater than one) involves the frequent solution of eigenvalue problems. A simple method to treat this problem is presented. (Author/JKS)
Descriptors: Algorithms, Data Analysis, Factor Analysis
Garbarino, Jennifer J. – 1996
All parametric analysis focuses on the "synthetic" variables created by applying weights to "observed" variables, but these synthetic variables are called by different names across methods. This paper explains four ways of computing the synthetic scores in factor analysis: (1) regression scores; (2) M. S. Bartlett's algorithm…
Descriptors: Algorithms, Factor Analysis, Regression (Statistics), Scores

Trendafilov, Nickolay T. – Multivariate Behavioral Research, 1996
An iterative process is proposed for obtaining an orthogonal simple structure solution. At each iteration, a target matrix is constructed such that the relative contributions of the target majorize the original ones, factor by factor. The convergence of the procedure is proven, and the algorithm is illustrated. (SLD)
Descriptors: Algorithms, Factor Analysis, Factor Structure, Matrices

Kiers, Henk A. L.; ten Berge, Jos M. F. – Psychometrika, 1989
Two alternating least squares algorithms are presented for the simultaneous components analysis method of R. E. Millsap and W. Meredith (1988). These methods, one for small data sets and one for large data sets, can indicate whether or not a global optimum for the problem has been attained. (SLD)
Descriptors: Algorithms, Factor Analysis, Least Squares Statistics, Statistical Analysis

Kiers, Henk A. L. – Psychometrika, 1994
A class of oblique rotation procedures is proposed to rotate a pattern matrix so that it optimally resembles a matrix that has an exact simple pattern. It is demonstrated that the method can recover relatively complex simple structures where other simple structure rotation techniques fail. (SLD)
Descriptors: Algorithms, Factor Analysis, Factor Structure, Matrices

Boik, Robert J. – Psychometrika, 1996
Joint correspondence analysis is a technique for constructing reduced-dimensional representations of pairwise relationships among categorical variables. An alternating least-squares algorithm for conducting joint correspondence analysis is presented that requires fewer iterations than the algorithm previously proposed by M. J. Greenacre. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Factor Analysis, Least Squares Statistics

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

Wood, Phillip – Multivariate Behavioral Research, 1992
Two Statistical Analysis System (SAS) macros are presented that perform the modified principal components approach of L. R. Tucker (1966) to modeling generalized learning curves analysis up to a rotation of the components. Three SAS macros are described that rotate the factor patterns to have characteristics Tucker considered desirable. (SLD)
Descriptors: Algorithms, Change, Computer Software, Factor Analysis

Rozeboom, William W. – Multivariate Behavioral Research, 1992
Enriching factor rotation algorithms with the capacity to conduct repeated searches from random starting points can make the tendency to converge to optima that are merely local a way to catch rotations of the input factors that might otherwise elude discovery. Use of the HYBALL computer program is discussed. (SLD)
Descriptors: Algorithms, Comparative Analysis, Factor Analysis, Factor Structure

Tisak, John; Meredith, William – Psychometrika, 1989
A longitudinal factor analysis model that is entirely exploratory is proposed for use with multiple populations. Factorial collapse, period/practice effects, and an invariant and/or stationary factor pattern are allowed. The model is formulated stochastically and implemented via a stage-wise EM algorithm. (TJH)
Descriptors: Algorithms, Factor Analysis, Longitudinal Studies, Maximum Likelihood Statistics

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

ten Berge, Jos M. F.; Zegers, Frits E. – Multivariate Behavioral Research, 1990
Arguments by J. Levin (1988) challenging the convergence properties of the Harman and Jones (1966) method of Minres factor analysis are shown to be invalid. Claims about the invalidity of a rank-one version of the Harman and Jones method are also refuted. (TJH)
Descriptors: Algorithms, Comparative Analysis, Equations (Mathematics), Factor Analysis

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

Takane, Yoshio; And Others – Psychometrika, 1995
A model is proposed in which different sets of linear constraints are imposed on different dimensions in component analysis and classical multidimensional scaling frameworks. An algorithm is presented for fitting the model to the data by least squares. Examples demonstrate the method. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Factor Analysis, Least Squares Statistics
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