Structure of intelligence and profession
The paper presents the study of factorial structures of intelligence invariance in the samples of participants with professional background in liberal arts and social studies (N=184) and in exact sciences (N=95). We used 14-scale test of knowledge in the humanities and exact sciences, 4 tests of intellectual abilities (Raven's matrices, arithmetic test, anagram solving, and spatial test). The invariance of factorial structures was assessed with respect to 3-factor model which included the factor of knowledge in the humanities, the factor of knowledge in exact sciences and the factor of intellectual abilities all combined in a higher order factor of general intelligence (g). Configurative invariance, as well as partial metric and intercept invariance of this model were confirmed in both samples. We revealed that structures of intelligence in the samples were identical with respect to the set of factors (configurative invariance). Along with that g-loading of "knowledge in the humanities" factor in the sample of participants with social and humanities background was superior to that in the sample of participants with background in exact sciences (partial metric invari-ance).The participants with professional background in exact sciences obtained higher scores of the factor of knowledge in exact science, the intellectual abilities' factor and the g-factor compared to those of social and humanities professional group. Their advantage accounted for 0.58, 0.43 и 0.42 of standard deviation, respectively. The participants with social and humanities professional background outperformed the participants with exact science background on the "knowledge in the humanities" factor. The obtained results are discussed in the framework of structural-dynamic theory of intelligence by D. Ushakov. The revealed facts of partial factorial invariance of intelligence structures in the two samples may be explained by the specificity of need for intellectual functions in different professional domains.
Keywords
интеллект,
способности,
осведомленность,
структура интеллекта,
профессия,
культура,
востребованность,
структурно-динамическая теория,
intelligence,
abilities,
information,
structure of intelligence,
profession,
culture,
cultural relevance,
structural-dynamic theoryAuthors
Valueva Ekaterina А. | Institute of Psychology of Russian Academy of Sciences; Moscow State University of Psychology & Education | ekval@list.ru |
Belova Sofya S. | Institute of Psychology of Russian Academy of Sciences; Moscow State University of Psychology & Education | sbelova@gmail.com |
Всего: 2
References
Ушаков Д.В. Психология интеллекта и одаренности. М. : Ин-т психологии РАН, 2011. 464 с.
Ушаков Д.В. Интеллект: структурно-динамическая теория. М. : Ин-т психологии РАН, 2003. 264 с.
Валуева Е.А., Белова C.C., Морозова О.А. Культурная востребованность способно стей и психометрические свойства когнитивных тестов // Психология. Журнал Высшей школы экономики. 2017. Т. 14, № 3. P. 491-500.
Валуева Е. А., Ушаков Д. В. Культурная релевантность и свойства тестов интеллекта: проверка предсказаний структурно-динамической теории // Психология. Журнал Высшей школы экономики. 2013. Т. 10, № 3. С. 29-40.
Roivainen E. European and American WAIS-III norms: Cross-national differences in per formance subtest scores // Intelligence. 2010. Vol. 38, № 1. P. 187-192.
Liu J., Lynn R. Factor structure and sex differences on the Wechsler Preschool and Primary Scale of Intelligence in China, Japan and United States // Personality and Individual Differences. 2011. Vol. 50, № 8. P. 1222-1226. DOI: 10.1016/j.paid.2011.02.013.
Валуева E.A., Ушаков Д.В. Принцип востребованности» в когнитивной системе че ловека // Когнитивные исследования : сб. науч. тр. / ред. Б.М. Величковский, B.B. Рубцов, Д.В. Ушаков. М. : Изд-во ГБОУ ВПО МГППУ, 2014. Вып. 6. С. 34-48.
Bors D.A., Stokes T.L. Raven's Advanced Progressive Matrices: Norms for First-Year Uni versity Students and the Development of a Short Form // Educational and Psychological Measurement. 1998. Vol. 58, № 3. P. 382-398. DOI: 10.1177/0013164498058003002.
Спивак А.В. Математический праздник. М. : Квантум, 2004. Вып. 88. 288 с.
Yoon S.Y. Psychometric properties of the Revised Purdue Spatial Visualization Tests: Visualization of Rotations (The Revised PSVT:R) (Doctoral Dissertation). Retrieved from ProQuest Dissertations and Theses. 2011. (Order Number: 3480934).
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, 2017.
Revelle W. Psych : Procedures for Personality and Psychological Research, 2017, Northwestern University, Evanston, Illinois. URL:CRAN.R-project.org/package=psych.
Rosseel Y. lavaan: An R Package for Structural Equation Modeling // Journal of Statisti cal Software. 2012. Vol. 48, № 2. P. 1-36.
Byrne B.M. Structural equation modeling with Mplus: Basic concepts, applications, and programming. London : Routledge, 2011. 431 p.
Hu L., Bentler P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives // Structural Equation Modeling: A Multidisciplinary Journal. 1999. Vol. 6, № 1. P. 1-55. DOI: 10.1080/10705519909540118.
Cheung G.W., Rensvold R.B. Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance // Structural Equation Modeling: A Multidisciplinary Journal. 2002. Vol. 9, № 2. P. 233-255. DOI: 10.1207/s15328007sem0902_5.
Demetriou A. et al. The architecture, dynamics, and development of mental processing: Greek, Chinese, or Universal? // Intelligence. 2005. Vol. 33, № 2. P. 109-141. DOI: 10.1016/j.intell.2004.10.003.
Корнилов С.А. Кросс-культурная инвариантность аналитических, творческих и практических способностей российских, английских и американских учащихся : дис.. канд. психол. наук. М., 2012. 211 с.
Kvist A.V., Gustafsson J.E. The relation between fluid intelligence and the general factor as a function of cultural background: A test of Cattell's Investment theory // Intelligence. 2008. Vol. 36, № 5. P. 422-436. DOI: 10.1016/j.intell.2007.08.004.
Tan L.H. et al. The Neural System Underlying Chinese Logograph Reading // NeuroImage. 2001. Vol. 13, № 5. P. 836-846. DOI: 10.1006/nimg.2001.0749.