Non-Cognitive Variables and Academic Success

What Factors Influence Mathematics Achievement?
  • Ernest Afari
  • Myint Swe Khine
Part of the Contemporary Approaches to Research in learning Innovations book series (CARL)

Abstract

The past several decades of research in education has suggested that students’ attitudes, interests, beliefs, and values are important to educators and such affective dispositions are often predictor of students’ subsequent behaviour which leads to academic success (Popham, 2005).

Keywords

Partial Little Square Structural Equation Modeling Academic Achievement Academic Success Standardize Root Mean Square Residual 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Adelson, J., & McCoach. D. (2011). Development and psychometric properties of the maths and me survey: Measuring third through sixth graders’ attitudes towards mathematics. Measurement and Evaluation in Counseling and Development, 44, 225–247.CrossRefGoogle Scholar
  2. Aiken, L. R. (1970). Attitudes towards mathematics. Review of Educational Research, 40, 551–596.CrossRefGoogle Scholar
  3. Aiken, L. R., & Dreger, R. M. (1961). The effect of attitudes on performance in mathematics. Journal of Educational Psychology, 52(1), 19–24.CrossRefGoogle Scholar
  4. Arbuckle, J. L. (2007). AmosTM 16 user’s guide. Chicago, IL: SPSS.Google Scholar
  5. Bandura, A. (1994). Self-efficacy. In V. S. Ramachandran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71–81). New York, NY: Academic Press.Google Scholar
  6. Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to causal modeling: Personal computer adoption and uses as an illustration. Technology Studies, 2, 285−309.Google Scholar
  7. Bartlett, M. S. (1954). A note on the multiplying factors for various chi square approximations. Journal of the Royal Statistical Society, 16(Series B), 296–298.Google Scholar
  8. Bouchey, H. A., & Harter, S. (2005). Reflected appraisals, academic self-perceptions, and Math/Science performance during early adolescence. Journal of Educational Psychology, 97(4), 673–686.CrossRefGoogle Scholar
  9. Bragg, L. A. (2012). Testing the effectiveness of games as a pedagogical tool for mathematical learning. International Journal of Science and Mathematics Education, 10(6), 1445–1467.CrossRefGoogle Scholar
  10. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.Google Scholar
  11. Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications, and programming. New York, NY: Routledge, Taylor & Francis group.Google Scholar
  12. Carmines, E. G., & Mclver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In G. W. Bohrnstedt & E. F. Borgatta (Eds.), Social measurement: Current issues (pp. 65–115). Beverly Hills, CA: Sage.E. AFARI & M. S. Khine440Google Scholar
  13. Catell, R. B. (1966). The screen test for the number of factors. Multivariate Behavioral Research, 1, 245–276.CrossRefGoogle Scholar
  14. Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi.Google Scholar
  15. Fornell, C., & Larker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.CrossRefGoogle Scholar
  16. Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice-Hall.Google Scholar
  17. Hannula, M. S. (2002). Understanding of number concept and self-efficacy beliefs in mathematics. In P. di Martino (Ed.), Proceedings of the 2002 MAVI-XI European Workshop on Mathematical Beliefs (pp. 45–52). Pisa, Italy: University of Pisa, Italy.Google Scholar
  18. Harrington, D. (2009). Confirmatory factor analysis. New York, NY: Oxford University Press.Google Scholar
  19. Harter, S. (1981). A model of intrinsic mastery motivation in children: Individual differences and developmental change. In A. Collins (Ed.), Minnesota symposia on child psychology (pp. 215–255). Hillsdale, NJ: Erlbaum.Google Scholar
  20. Hemmings, B., Grootenboer, P., & Kay, R. (2011). Predicting mathematics achievement: The influence of prior achievement and attitudes. International Journal of Science and Mathematics Education, 9(3), 691–705.CrossRefGoogle Scholar
  21. Hu, L., & Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  22. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20, 195–204.CrossRefGoogle Scholar
  23. Joseph, G. G. (1987). Foundations of eurocentrism in mathematics. Race and Class, 28(3), 13–28.CrossRefGoogle Scholar
  24. Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.CrossRefGoogle Scholar
  25. Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.Google Scholar
  26. Ma, X., & Kishor, N. (1997). Attitude toward self, social factors, and achievement in mathematics: A meta-analytic review. Educational Psychology Review, 9(2), 89–120.CrossRefGoogle Scholar
  27. McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64–82.CrossRefGoogle Scholar
  28. McGorry, S. Y. (2000). Measurement in cross-cultural environment: Survey translation issues. Qualitative Market Research, 3, 74–81.CrossRefGoogle Scholar
  29. Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  30. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.Google Scholar
  31. Pallant, J. (2013). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (5th ed.). New York, NY: McGraw-Hill.Google Scholar
  32. Popham, W. (2005). Students’ attitudes count. Educational Leadership, 2, 84–85.Google Scholar
  33. Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York, NY: Taylor and Francis.Google Scholar
  34. Shen, C. (2002). Revisiting the relationship between students’ achievement and their self-perceptions: A cross-national analysis based on TIMSS 1999 data. Assessment in Education: Principles, Policy and Practice, 9(2), 161–184.CrossRefGoogle Scholar
  35. Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement: Effects of motivation, interest, and academic engagement. The Journal of Educational Research, 95(6), 323–332.CrossRefGoogle Scholar
  36. Skaalvik, E. M., & Skaalvik, S. (2006). Self-concept and self-efficacy in mathematics: Relation with mathematics motivation and achievement. In Proceedings of the 7th International Conference on Learning Sciences (pp. 709–715). Bloomington, IN: International Society of Learning Sciences.Google Scholar
  37. Stringer, R. W., & Heath, N. (2008). Academic self-perception and its relationship to academic performance. Canadian Journal of Education, 31(2), 327–345.Google Scholar
  38. Struik, D. J. (1987). A concise history of mathematics (4th ed.). New York, NY: Dover Publications.Google Scholar
  39. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson.Google Scholar
  40. Tapia, M., & Marsh, G. (2004). An instrument to measure mathematics attitudes. Academic Exchange Quarterly, 8, 1–8.Google Scholar
  41. Teo, T., & Ursavas, O. F., & Bahcekapili, E. (2012). An assessment of pre-service teachers’ technology acceptance in Turkey: A structural equation modeling approach. The Asia-Pacific Education Researcher, 21, 191–202.Google Scholar
  42. Watkins, M. W. (2000). Monte Carlo PCA for parallel analysis [Computer Software]. State College, PA: Ed & Psych Associates.Google Scholar
  43. Whitin, P. E. (2007). The mathematics survey: A tool for assessing attitudes and dispositions. Teaching Children Mathematics, 13, 426–433.Google Scholar

Copyright information

© Sense Publishers 2016

Authors and Affiliations

  • Ernest Afari
    • 1
  • Myint Swe Khine
    • 2
  1. 1.Petroleum InstituteUnited Arab Emirates
  2. 2.Emirates College for Advanced EducationUnited Arab Emirates

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