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Kiselev, Sergey V.; Chernyavskaya, Yana S.; Bardasova, Eleonora V.; Galeeva, Gulnaz M.; Fazlieva, Elena P.; Krokhina, Julia A. – International Journal of Environmental and Science Education, 2016
The relevance of the study: The relevance of the research problem is conditioned by the intensification of innovative processes in modern economy and in the banking sector, in particular, as one of the most sensitive areas for innovation and innovative types of services and information and communication innovations today is one of the major…
Descriptors: Banking, Economic Factors, Factor Analysis, Correlation
Petrovskaya, Maria V.; Larionova, Anna A.; Zaitseva, Natalia A.; Bondarchuk, Natalya V.; Grigorieva, Elena M. – International Journal of Environmental and Science Education, 2016
The relevance of the problem stated in the article is that in conditions of nonstationary economy the modification of existing approaches and methods is necessary during the formation of the capital. These methods allow taking into account the heterogeneity of factors' change in time and the purpose of the development of a particular company,…
Descriptors: Risk, Correlation, Business, Factor Analysis
Chernyavskaya, Yana S.; Kiselev, Sergey V.; Rassolov, Ilya M.; Kurushin, Viktor V.; Chernikova, Lyudmila I.; Faizova, Guzel R. – International Journal of Environmental and Science Education, 2016
The relevance of research: The relevance of the problem studied is caused by the acceleration of transition of the Russian economy on an innovative way of development, which depends on the vector of innovative sphere of services and, to a large extent, information and communication services, as well as it is caused by the poor drafting of…
Descriptors: Foreign Countries, Correlation, Cost Effectiveness, Factor Analysis
Tran, Ulrich S.; Formann, Anton K. – Educational and Psychological Measurement, 2009
Parallel analysis has been shown to be suitable for dimensionality assessment in factor analysis of continuous variables. There have also been attempts to demonstrate that it may be used to uncover the factorial structure of binary variables conforming to the unidimensional normal ogive model. This article provides both theoretical and empirical…
Descriptors: Simulation, Factor Analysis, Correlation, Evaluation Methods
Drewes, Donald W. – Psychological Methods, 2009
A unifying theory of subject-centered scalability is offered that is grounded in structural true score modeling, is conceptually distinct from internal consistency and homogeneity as determined by item correlations, and is empirically confirmable. Scalability holds when item true scores are perfectly correlated but differ in their individual scale…
Descriptors: Rating Scales, Factor Analysis, True Scores, Mathematical Models
Osler, James Edward; Mansaray, Mahmud A. – Journal of Educational Technology, 2013
The online deployment of Technology Engineered online Student Ratings of Instruction (SRIs) by colleges and universities in the United States has dynamically changed the deployment of course evaluation. This research investigation is the fourth part of a post hoc study that analytically and psychometrically examines the design, reliability, and…
Descriptors: Course Evaluation, Educational Technology, Black Colleges, Higher Education
Peer reviewedWilliams, 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
Peer reviewedSchonemann, Peter H. – Psychometrika, 1971
A simplified proof of a lemma by Ledermann, which lies at the core of the factor indeterminacy issue, is presented. (Author)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Orthogonal Rotation
Peer reviewedVegelius, Jan – Educational and Psychological Measurement, 1982
The possibility of using a Q-analysis also for nominal data is discussed, using the J-index as a measure of similarity between persons. An example is given when ten persons sorted 16 playing cards into as many groups as they wished. A Q-analysis of these data gave a natural two-dimensional structure. (Author/BW)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Statistical Analysis
Peer reviewedBorg, 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
Horst, Paul – 1970
In the traditional Guttman-Harris type image analysis, a transformation is applied to the data matrix such that each column of the transformed data matrix is the best least squares estimate of the corresponding column of the data matrix from the remaining columns. The model is scale free. However, it assumes (1) that the correlation matrix is…
Descriptors: Correlation, Factor Analysis, Mathematical Models, Research Methodology
Peer reviewedDeSarbo, Wayne S. – Psychometrika, 1981
Canonical correlation and redundancy analysis are two approaches to analyzing the interrelationships between two sets of measurements made on the same variables. A component method is presented which uses aspects of both approaches. An empirical example is also presented. (Author/JKS)
Descriptors: Correlation, Data Analysis, Factor Analysis, Mathematical Models
Mittag, Kathleen Cage – 1993
Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…
Descriptors: Correlation, Factor Analysis, Heuristics, Mathematical Models
Dziuban, Charles D.; Harris, Chester W. – 1972
A reanalysis of Shaycroft's matrix of intercorrelations of 10 test variables plus 4 random variables is discussed. Three different procedures were used in the reanalysis: (1) Image Component Analysis, (2) Uniqueness Rescaling Factor Analysis, and (3) Alpha Factor Analysis. The results of these analyses are presented in tables. It is concluded from…
Descriptors: Correlation, Factor Analysis, Mathematical Models, Predictor Variables
Peer reviewedYoung, Forrest W.; And Others – Psychometrika, 1978
Principal components analysis is generalized to the case where any of the variables under consideration can be nominal, ordinal or interval. Hotelling's original formulation is seen to be a special case of this generalization. (JKS)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Matrices

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