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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
Aiken, L. R. (1970). Attitudes towards mathematics. Review of Educational Research, 40, 551–596.
Aiken, L. R., & Dreger, R. M. (1961). The effect of attitudes on performance in mathematics. Journal of Educational Psychology, 52(1), 19–24.
Arbuckle, J. L. (2007). AmosTM 16 user’s guide. Chicago, IL: SPSS.
Bandura, A. (1994). Self-efficacy. In V. S. Ramachandran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71–81). New York, NY: Academic Press.
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.
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.
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.
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.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications, and programming. New York, NY: Routledge, Taylor & Francis group.
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. Khine440
Catell, R. B. (1966). The screen test for the number of factors. Multivariate Behavioral Research, 1, 245–276.
Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi.
Fornell, C., & Larker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice-Hall.
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.
Harrington, D. (2009). Confirmatory factor analysis. New York, NY: Oxford University Press.
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.
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.
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.
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.
Joseph, G. G. (1987). Foundations of eurocentrism in mathematics. Race and Class, 28(3), 13–28.
Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.
Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
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.
McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64–82.
McGorry, S. Y. (2000). Measurement in cross-cultural environment: Survey translation issues. Qualitative Market Research, 3, 74–81.
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.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
Pallant, J. (2013). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (5th ed.). New York, NY: McGraw-Hill.
Popham, W. (2005). Students’ attitudes count. Educational Leadership, 2, 84–85.
Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York, NY: Taylor and Francis.
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.
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.
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.
Stringer, R. W., & Heath, N. (2008). Academic self-perception and its relationship to academic performance. Canadian Journal of Education, 31(2), 327–345.
Struik, D. J. (1987). A concise history of mathematics (4th ed.). New York, NY: Dover Publications.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson.
Tapia, M., & Marsh, G. (2004). An instrument to measure mathematics attitudes. Academic Exchange Quarterly, 8, 1–8.
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.
Watkins, M. W. (2000). Monte Carlo PCA for parallel analysis [Computer Software]. State College, PA: Ed & Psych Associates.
Whitin, P. E. (2007). The mathematics survey: A tool for assessing attitudes and dispositions. Teaching Children Mathematics, 13, 426–433.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Sense Publishers
About this chapter
Cite this chapter
Afari, E., Khine, M.S. (2016). Non-Cognitive Variables and Academic Success. In: Khine, M.S., Areepattamannil, S. (eds) Non-cognitive Skills and Factors in Educational Attainment. Contemporary Approaches to Research in learning Innovations. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6300-591-3_20
Download citation
DOI: https://doi.org/10.1007/978-94-6300-591-3_20
Publisher Name: SensePublishers, Rotterdam
Online ISBN: 978-94-6300-591-3
eBook Packages: EducationEducation (R0)