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Application of the Wavelet-Based Confirmatory Factor Analysis to Studying Dynamics of Psychological Characteristics 1422
Graduate student, Chair of Applied Informatics (in Psychology), Department of Informational
Technologies, Moscow State University of Psychology and Education, Moscow, Russia
Doctor of Engineering, Dean of the Computer Science Faculty , Moscow State University of Psychology and Education , Moscow, Russia
PhD in Engineering, Head of the Laboratory of Psychology and Applied Software, Moscow State University of Psychology & Education, Moscow, Russia
Graduate student, Chair of Applied Informatics (in Psychology), Department of Informational Technologies, Moscow State University of Psychology and Education, Moscow, Russia
A new technology for revealing and studying factors responsible for the dynamics of psychological characteristics is under consideration. It combines capabilities of wavelet transforms and trained factor structures. According to the proposed approach, the samples of coefficients resulted from discrete wavelet transform of initial parameter time series under study and responsible for different observation periods are considered as values of observed variables in the subsequent confirmatory factor analysis to reveal time history of factor influences and estimates of factor interaction. Identification of free factor model parameters (usually factor variances and covariances) is carried out by a new direct (noniterative) procedure based on the maximum likelihood method, which is an alternative to traditional local iterative solution of optimization problems. A statistical method to check significance of factor model components is discussed. Presented are advantages of the given approach over the traditional simplex method as well as a set of
approaches to development of factor models represented by path diagrams including their comparison.
Keywords: empirical mathematical model, factor analysis, wavelet analysis, simplex model, path diagram
Column: Mathematical Methods
- Kuravskii L. S., Kornienko P. A. Primenenie neironnyh setei dlya
identifikatsii lokusov kolichestvennyh priznakov v psihogenetike (Neural nets
application to quantittive attributes loci identification in Psychogenetics) /
Neirokomp'yutery: razrabotka i primenenie. (Neurocomputers: development and
application) № 4. 2007. S. 15–31.
- Louli D., Makswell A. Faktornyi analiz kak statisticheskii metod.
(Factor analysis as method in statistics) M.: Mir, 1967.
- Korolyuk V. S., Portenko N. I., Skorohod A. V., Turbin A. F.
Spravochnik po teorii veroyatnostei i matematicheskoi statistike. (Probability
theory and math statistics: reference guide) M.: Nauka, 1985.
- Bishop Y. M. M., Fienberg S. E., Holland P. W. Discrete
multivariate analysis: Theory and practice. (Cambridge, MA, M I T Press,
- Bollen K. A. Structural equations with latent variables (New York,
John Wiley, 1989.)
- Branum-Martin L., Carlson C. D., Carlo M., Fletcher J. M., Francis D.
J., Mehta P. D., Ortiz A. Bilingual phonological awareness: Multilevel
construct validation among Spanish-speaking kindergarteners in transitional
bilingual education classrooms. Journal of Educational Psychology. V. 98. № 1.
P. 170–181, 2006.
- Goldstein H. Multilevel statistical models. (3rd ed., London,
- Jöreskog K. G. Estimation and testing of simplex models. British
Journal of Mathematical and Statistical Psychology. V. 23. P. 121–145,
- Kuravsky L. S., Kornienko P. A. On the approach to identifying
quantitative trait loci in behavior genetics. In: Proc. 2nd World Congress on
Engineering Asset Management and 4th International Conference on Condition
Monitoring, Harrogate, United Kingdom. P. 1133–1146, June 2007.
- LabVIEW tutorial for Windows (National Instruments Corp., 1994–2007.)
- Loehlin J. C. Latent variable models: An introduction to factor,
path, and structural analysis. (Hillsdale, NJ, Erlbaum, 1987.)
- Neale M. C., Cardon L. R. Methodology for genetic studies of twins
and families (Dordrecht, the Netherlands, Kluwer Academic Publishers,