1. Ayati M, Asadi YM, Azadegan A. Confirmatory factor analysis of the inentification scale of gifted students. . jem.atu.ac.ir. 2017; 27(7): 161-181. [Persian] [Link] 2. Fischer C, Müller K. Gifted education and talent support in Germany. Center for Educational Policy Studies Journal. 2014;4(3):31-54. [Link] [ DOI:10.26529/cepsj.194] 3. Cotton SM, Kiely PM, Crewther DP, Thomson B, Laycock R, Crewther SG. A normative and reliability study for the Raven's Coloured Progressive Matrices for primary school aged children from Victoria, Australia. Personality and individual differences. 2005;39(3):647-659. [Link] [ DOI:10.1016/j.paid.2005.02.015] 4. Raven J. The Raven's progressive matrices: change and stability over culture and time. Cognitive psychology. 2000;41(1):1-48. [Link] [ DOI:10.1006/cogp.1999.0735] 5. Rindermann H. Relevance of education and intelligence at the national level for the economic welfare of people. Intelligence. 2008;36(2):127-142. [Link] [ DOI:10.1016/j.intell.2007.02.002] 6. Raven JC, Court JH. Raven's progressive matrices and vocabulary scales: Oxford pyschologists Press Oxford; 1998. [Link] 7. Kline RB. Principles and practice of structural equation modeling: Guilford publications, Canadian Studies in Population 45; 2015, PP: 188-195. [Link] [ DOI:10.25336/csp29418] 8. Tzuriel D. Dynamic assessment of young children: Educational and intervention perspectives. Educational Psychology Review. 2000;12(4):385-435. [Link] [ DOI:10.1023/A:1009032414088] 9. Kambiz K. The New version of Tehran- Stanford Binet Intelligence Scales Practical Manual (TSB-5). second, editor. Tehran: Madares Karamad; 2012. 3 p. [Persian]. [Link] 10. Bildiren A. Reliability and Validity Study for the Coloured Progressive Matrices Test between the Ages of 3-9 for Determining Gifted Children in the Pre-School Period. Journal of education and training studies. 2017;5(11):13-20. [Link] [ DOI:10.11114/jets.v5i11.2599] 11. Na SD, Burns TG. Wechsler intelligence scale for children-V: Test review. Applied Neuropsychology: Child. 2016;5(2):156-60. [Link] [ DOI:10.1080/21622965.2015.1015337] 12. Castellano JA, Diaz EI. Reaching New Horizons: Gifted and Talented Education for Culturally and Linguistically Diverse Students: ERIC; 2002. [Link] 13. Lohman DF, Korb KA, Lakin JM. Identifying academically gifted English-language learners using nonverbal tests: A comparison of the Raven, NNAT, and CogAT. Gifted Child Quarterly. 2008;52(4):275-296. [Link] [ DOI:10.1177/0016986208321808] 14. Öner N. Türkiye'de Kullanılan Psikolojik Testler, İstanbul, Boğaziçi Üniversitesi Yayınları. 1997; 3. Basım: 10.39. [Link] 15. Lynn R, Hampson S. The rise of national intelligence: Evidence from Britain, Japan and the USA. Personality and individual differences. 1986;7(1):23-32. [Link] [ DOI:10.1016/0191-8869(86)90104-2] 16. Lynn R, Vanhanen T. Intelligence: A unifying construct for the social sciences: Ulster Institute for Social Research; 2012. [Link] 17. Rushton JP, Skuy M, Bons TA. Construct validity of Raven's advanced progressive matrices for African and non‐African engineering students in South Africa. International journal of selection and assessment. 2004;12(3):220-9. [Link] [ DOI:10.1111/j.0965-075X.2004.00276.x] 18. Bakhiet SFA, Lynn R. A standardization of the colored progressive matrices in Tripoli, Libya. Mankind Quarterly. 2015;56(1):79. [Link] [ DOI:10.46469/mq.2015.56.1.6] 19. Meisenberg G, Lynn R. Intelligence: A measure of human capital in nations. The Journal of Social, Political, and Economic Studies. 2011;36(4):421. [Link] 20. Schweizer K, Goldhammer F, Rauch W, Moosbrugger H. On the validity of Raven's matrices test: Does spatial ability contribute to performance? Personality and Individual Differences. 2007;43(8):1998-2010. [Link] [ DOI:10.1016/j.paid.2007.06.008] 21. August JO. A Normative Study of the Raven's Coloured Progressive Matrices for South African Children with Cognitive Barriers to Learning: Nelson Mandela Metropolitan University; 2017. [Link] 22. Rajabi G. Normalizing the Raven coloure progressive matrices test on students of city Ahvaz. Contemporary Psychology. 2008;3(1):23-32. [Persian] [Link] 23. Roid GH, Pomplun M. The stanford-binet intelligence scales: The Guilford Press; 2012; 2012. [Link] 24. Meyers LS, Gamst GC, Guarino A. Performing data analysis using IBM SPSS: John Wiley & Sons; 2013. [Link] 25. Flora DB, Curran PJ. An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological methods. 2004;9(4):466. [Link] [ DOI:10.1037/1082-989X.9.4.466] 26. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research. 1981;18(1):39-50. [Link] [ DOI:10.1177/002224378101800104] 27. Hair Jr JF, Sarstedt M, Hopkins L, Kuppelwieser VG. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review. 2014. [Link] [ DOI:10.1108/EBR-10-2013-0128] 28. Kline R. Data preparation and psychometrics review. Principles and practice of structural equation modeling. Guilford New York, NY; 2016, 4th ed. pp. 64-96. [Link]
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