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2017, Number 5

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Salud Mental 2017; 40 (5)

Socioeconomic environment effect on inferential reasoning of Latin American students

Flores-Mendoza C, Saraiva RB, Vilela CGC, Guimarães LWM, Carvalho PPAP, Valladão PGAM, de Oliveira BV, Assunção RL, Ardila R, Rosas R, Gallegos M, Reategui N

Language: Portugués
References: 32
Page: 183-190
PDF size: 210.43 Kb.


ABSTRACT

Introduction. Inferential reasoning (IR) is a major component of intelligence, which comprises many different cognitive processes such as perception, memory, and logic. Many studies have proposed that socioeconomic status (SES) has a negligible association with IR, but more recent findings suggest that they may have a higher association when evaluating group instead of individual SES. Objective. The aim of this study is to test the effects of both individual (students) and group (schools) socioeconomic status on IR, comparing different countries of Latin America. Method. The sample was composed of 2 358 students aged 14 and 15 years from 52 different schools (44% public) of five Latin American countries (Argentina, Brazil, Chile, Colombia, and Peru). Participants took part in an inferential reasoning test and answered a socioeconomic questionnaire. Results. SES student showed a small positive correlation with IR (r = .10, p ‹ .001), while SES school had a more pronounced effect on IR (F [2, 1944] = 74.68, p ‹ .001, ηp2 = .07), with higher IR at schools with higher SES. A significant difference of IR between countries (F [4, 1976] = 20.68, p ‹ .001, ηp2 = .04), was also found with Peru showing the highest mean. Peru was the country with the higher percentage of private schools in the present study. A multilevel model was fitted using individual and group SES as predictors. Discussion and conclusion. Our findings showed that group SES have a higher predictive value of IR when compared to individual SES. This result suggests that individuals with low SES can benefit from studying on higher SES schools. Future research and the importance of public policies are discussed.


REFERENCES

  1. Arcidiácono, M., Cruces, G., Gasparini, L., Jaume, D., Serio, M., & Vázquez, E. (2014). La segregación escolar público-privada en América Latina. Retrieved from: http://repositorio.cepal.org/bitstream/handle/11362/36757/S2014249_es- .pdf?sequence=1&isAllowed=y

  2. Ashby-Mitchell, K., Jagger, C., Fouweather, T., & Anstey, K. J. (2015). Life Expectancy with and without Cognitive Impairment in Seven Latin American and Caribbean Countries. PloS one, 10(3), e0121867. doi:10.1371/journal. pone.0121867

  3. Beddington, J., Cooper, C. L., Field, J., Goswami, U., Huppert, F. A., Jenkins, R., … Thomas, S. M. (2008). The mental wealth of nations. Nature, 455(7216), 1057- 1060. doi: 10.1038/4551057a

  4. Beier, M. E., & Ackerman, P. L. (2001). Current-events knowledge in adults: An investigation of age, intelligence, and nonability determinants. Psychology and aging, 16(4), 615-628. doi: 10.1037/0882-7974.16.4.615

  5. Ciarrochi, J., Heaven, P. C., & Skinner, T. (2012). Cognitive ability and health-related behaviors during adolescence: A prospective study across five years. Intelligence, 40(4), 317-324. doi: 10.1016/j.intell.2012.03.003

  6. Coleman, J. S. (1966). Equality of educational opportunity (Vol. 2). Washington, DC: US Department of Health, Education, and Welfare, Office of Education. Colom, R., & Flores-Mendoza, C. E. (2007). Intelligence Predicts Scholastic Achievement Irrespective of SES Factors: Evidence from Brazil. Intelligence, 35(3), 243-251. doi: 10.1016/j.intell.2006.07.008

  7. Der, G., Batty, G. D., & Deary, I. J. (2009). The association between IQ in adolescence and a range of health outcomes at 40 in the 1979 US National Longitudinal Study of Youth. Intelligence, 37(6), 573-580. doi: 10.1016/j.intell.2008.12.002

  8. Douglas, B., Martin, M., Ben, B., Steve, W. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48.

  9. Duarte, J., Bos, M. S., & Moreno, M. (2010). Inequity in School Achievement in Latin America: Multilevel Analysis of SERCE results according to the socioeconomic status of students. Inter-American Development Bank. Retrieved from: https://publications. iadb.org/bitstream/handle/11319/2558/Inequity%20in%20School%20 Achievement%20in%20Latin%20America.pdf?sequence=1&isAllowed=y

  10. Flores-Mendoza, C., Mansur-Alves, M., Ardila, R., Rosas, R. D., Guerrero-Leiva, M. K., Maqueo, Lucio-Gómez, M. E., ... Burga, A. B. (2015). Fluid intelligence and school performance and its relationship with social variables in Latin American samples. Intelligence, 49, 66-83. doi: 10.1016/j.intell.2014.12.005

  11. Flynn, J. R., & Rossi-Casé, L. (2012). IQ gains in Argentina between 1964 and 1998. Intelligence, 40(2), 145-150. doi: 10.1016/j.intell.2012.01.006

  12. Gottfredson, L. S. (2002). Where and Why g Matters: Not a Mystery. Human Performance, 15(1-2), 25-46. doi: 10.1080/08959285.2002.9668082

  13. Gottfredson, L. S., & Deary, I. J. (2004). Intelligence Predicts Health and Longevity, but Why? Current Directions in Psychological Science, 13(1), 1-4. doi: 10.1111/j.0963-7214.2004.01301001.x

  14. Gottfredson, L. S. (2006). Consequências sociais das diferenças de grupo na capacidade cognitiva. In C. Flores-Mendoza & R. Colom (Eds.), Introdução à Psicologia das Diferenças Individuais (433-456). Porto Alegre: Artmed.

  15. Guàrdia-Olmos, J., Peró-Cebollero, M., Rivera, D., & Arango-Lasprilla, J. C. (2015). Methodology for the development of normative data for ten Spanish-language neuropsychological tests in eleven Latin American countries. NeuroRehabilitation, 37(4), 493-499. doi: 10.3233/NRE-151277

  16. Hox, J. J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel analysis: Techniques and applications. New York: Routledge.

  17. Jargowsky, P. A., & El Komi, M. (2011). Before or After the Bell? School Context and Neighborhood Effects on Student Achievement. In: S. Wachter, E. L. Birch, & H. Newberger (Eds.), Neighborhood and Life Chances: How Place Matters in Modern America (pp. 50-72). Philadelphia: University of Pennsylvania Press.

  18. Jensen, A. R. (1998). The g Factor: The Science of Mental Ability. Westport: Praeger.

  19. Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic Performance, Career Potential, creativity, and Job Performance: Can One Construct Predict Them All? Journal of Personality and Social Psychology, 86(1), 148-161. doi: 10.1037/0022-3514.86.1.148

  20. Liu, H., van Damme, J., Gielen, S., & van Den Noortgate, W. (2014). School processes mediate school compositional effects: model specification and estimation. British Educational Research Journal, 41(3), 423-447. doi: 10.1002/berj.3147

  21. Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex differences. Trends in cognitive sciences, 18(1), 37-45. doi: 10.1016/j.tics.2013.10.011

  22. Ministerio de Educación de Perú (2016). Magnitudes. ESCALE: Unidad de Estadística Educativa. Retrieved from: http://escale.minedu.gob.pe/magnitudes

  23. Neisser, U., Boodoo, G., Bouchard T. J., Jr., Boykin, A. W., Brody, N., Ceci, S. J., ... Urbina, S. (1996). Intelligence: Knowns and unknowns. American psychologist, 51(2), 77-101. doi: 10.1037/0003-066X.51.2.77

  24. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). California: Sage Publications Inc.

  25. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. [computer software] Retrieved from http://www.R-project.org. 2016.

  26. Sisto, F. F. (2006). Teste de raciocínio inferencial. São Paulo: Vetor Editora Psicopedagógica.

  27. Soares, J. F., & Andrade, R. J. (2006). Socioeconomic status, quality and equity in the schools of Belo Horizonte. Ensaio: Avaliação e Políticas Públicas em Educação, 14(50), 107-125. doi: 10.1590/S0104-40362006000100008

  28. Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence, 35(5), 401-426. doi: 10.1016/j.intell.2006.09.004

  29. Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250-270. doi: 10.1037/0033-2909.117.2.250

  30. UK Government for Science. (2016). Future of skills and lifelong learning [Web log post]. Retrieved from: https://www.gov.uk/government/collections/future-ofskills- and-lifelong-learning.

  31. United Nations International Children’s Emergency Fund. (2016). Monitoring the Situation of Children and Women. Retrieved from: http://data.unicef.org/topic/ education/secondary-education/#

  32. Walker, N. P., McConville, P. M., Hunter, D., Deary, I. J., & Whalley, L. J. (2002). Childhood mental ability and lifetime psychiatric contact: A 66-year follow-up study of the 1932 Scottish Mental Ability Survey. Intelligence, 30(3), 233-245. doi: 10.1016/S0160-2896(01)00098-8




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