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PRETEEN AGE: THE ANALYSIS OF THE MULTILEVEL PSYCHO-DIAGNOSTIC DATA BASED ON NEURAL NETWORK MODELS

Elena Slavutskaya, Leonid Slavutskii

Abstract


The use of the artificial neural network (ANN) models for vertical system analysis of psycho-diagnostic data is suggested. It is shown that the ANN training, as the problem of nonlinear multi-parameter optimization, allows to create effective algorithms for the psycho-diagnostic data processing when the results of psychological testing for the different level’s characteristics have different numerical scales. On the example of processing the author's data of psycho-diagnostics (preadolescent schoolchildren), it is shown that neural network models can be used to estimate latent (hidden) connections between psychological characteristics. The proposed algorithms are based on a statistical assessment of the quality of such models, do not require a large sample of respondents. The quantitative statistical criteria for evaluating the quality of the models are estimated. The approach is sufficiently clear for practical use by psychologists who do not have a special mathematical preparation.

Keywords


preadolescent schoolchildren; psychodiagnostic data; vertical system analysis; artificial neural networks

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References


Baxt, W.G. (1994). Complexity, chaos and human physiology: the justification for non-linear neural computational analysis. Cancer Lett, 77(2-3), 85-93.

Cattell, R.B. (1990). Advanced in Cattelian Personality Theory. Handbook of Personality. Theory and Research. New York: The Guilford Press.

Collins, W.A. (1984). Development during middle childhood: The years from six to twelve. Washington, DC: Natl. Acad. Press.

Erikson, E.H. (1950). Childhood and Society. New York: Norton.

Glass, J. V. & Stanly, Y.C. (1970). Statistical methods in education and psychology. New Jersey.

Grossberg, S. (1974). Classical and instrumental learning by neural networks. Progress in theoretical biology, Vol. 3. (pp. 51-141). New York: Academic Press.

Harter, S. (1990). Self and identity development. At the threshold: The developing adolescent. Cambridge, MA: Harvard Univ. Press.

Haykin, S. (1999). Neural networks: A comprehensive Foundation. New York: Prentice Hall.

Hebb, D. (1961). Organization of behavior. New York: Science Edition.

Kohlberg L. (1984). The psychology of moral development: The nature and validity of moral stages. B. Y.: Holt, Rinehart and Winston.

Piaget J. (1972). Intellectual evolution from adolescence to adulthood. Human Development, 15, 1-12.

Reznichenko, N.S., Shilov, S.N. & Abdulkin, V.V. (2013). Neuron Network Approach to the Solution of the Medical-Psychological Problems and in Diagnosis Process of Persons with Disabilities (Literature Review). Journal of Siberian Federal University. Humanities and Social Sciences, 9 (6), 1256-1264.

Slavutskaya, E.V. & Slavutskiy L. A. (2012) Using artificial neural networks for analysis of gender differences in younger teenagers. Psikhologicheskie Issledovaniya, 5(23), 4. http://psystudy.ru (in Russian, abstr. in English).

Slavutskaya, E.V. & Slavutskiy L. A. (2014) Neural network analysis of the relationship between verbal and nonverbal intelligence in younger adolescents Psychological Journal , 35(5) 28-36.

Usher M. & Zakay D. (1993). A neural network model for attribute-based decision processes Cognitive Science, 17, 349-396.




DOI: https://doi.org/10.17770/sie2018vol1.3348

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