Perceptions of quantitative methods in higher education: Mapping student profiles

Higher Education

Volumn 61, Issue 6, 2011, Pages 629-647

  Ramos, Madalena ^a   Carvalho, Helena ^a  

^a INSTITUTO UNIVERSITÁRIO DE LISBOA ISCTE IUL   (Portugal)

Author keywords

Categorical regression (CATREG); Cluster analysis; Mapping student profiles; Multiple correspondence analysis (MCA); Perceptions of quantitative methods

Indexed keywords

EID: 79957897568     PISSN: 00181560     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10734-010-9353-3     Document Type: Article

References (25)

1. Ainley, M. (2006). Connecting with learning: Motivation, affect and cognition in interest processes. Educational Psychology Review, 18, 391-405.
2. Borasi, R. (1990). The invisible hand operating in mathematics instruction: Students' conceptions and expectations. In T. J. Cooney & C. Hirsh (Eds.), Teaching and learning mathematics in the 1990s. Reston: NCTM.
3. Carvalho, H. (2008). Análise de Multivariada de Dados Qualitativos, Utilização da Análise de Correspondências Múltiplas com o SPSS. Lisboa: Sílabo.
4. de Geer, V. (1993a). Multivariate analysis of categorical data: Theory. London: Sage Publications.
5. de Geer, V. (1993b). Multivariate analysis of categorical data: Applications. London: Sage Publications.
6. Fennema, E. (2000). Gender and mathematics: What is known and what do I wish was known? In Fifth Annual forum of the national institute for science education. Detroit.
7. Fennema, E., & Sherman, J. (1977). Sex-related differences in mathematics achievement and spatial visualization and affective factors. American Educational Research Journal, 14(1), 51-71.
8. Fennema, E., & Sherman, J. (1978). Sex-related differences in mathematics achievement and related factors: a further study. Journal for Research in Mathematics Education, 9(3), 189-203.
9. Gifi, A. (1996). Nonlinear multivariate analysis. New York: Wiley.
10. Grootenboer, P., & Hemmings, B. (2007). Mathematics performance and the role played by affective and background factors. Mathematics Education Research Journal, 19(3), 3-20.
11. Heiser, W., & Meulman, J. (1994). Homogeneity analysis: Exploring the distribution of variables and their nonlinear relationships. In M. Greenacre & J. Blasius (Eds.), Correspondence analysis in the social sciences (pp. 179-209). London: Academic Press.
12. Kilpatrick, J., & Silver, E. (2000). Unfinished business: Challenges for mathematics educators in the next decades. Yearbook (pp. 223-235).
13. Kloosterman, P., Raymond, A., & Emenaker, C. (1996). Students' beliefs about mathematics: A three-year study. The Elementary School Journal, 97(1), 39-56.
14. Kloosterman, P., & Stage, F. (1992). Measuring beliefs about mathematical problem solving. School Science and Mathematics, 92(3), 109-115.
15. Lawless, K. A., & Kulikowich, J. M. (2006). Domain knowledge and individual interest: The effects of academic level and specialization in statistics and psychology. Contemporary Educational Psychology, 31(1), 30-43.
16. Meulman, J. (1992). The integration of multidimensional scaling and multivariate analysis with optimal transformations. Psychometrika, 57(4), 539-565.
17. Muis, K. (2004). Personal epistemology and mathematics: A critical review and synthesis of research. Review of Educational Research, 74(3), 317-377.
18. Onwuegbuzie, A., & Wilson, V. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments-A comprehensive review of the literature. Teaching in Higher Education, 8(2), 195-209.
19. Ramalho, G. (1994). As Nossas Crianças e a Matemática. Caracterização da Participação dos Alunos Portugueses no Second International Assessment of Educational Progress. Lisboa: DEPGEF.
20. Ramos, M., & Carvalho, H. (2009). Quantitative methods in higher education: A typology of representations. Educação e Pesquisa [online], 35(1), 15-32. Available from: < http://www. scielo. br/scielo. php?script=sci_arttext& pid=S1517-97022009000100002& lng=en& nrm=iso>. ISSN 1517-9702. doi: 10. 1590/S1517-97022009000100002.
21. Schoenfeld, A. (1988). When good teaching leads to bad results: The disasters of "well-taught" mathematics courses. Educational Psychologist, 23(2), 145-166.
22. Schoenfeld, A. (1989). Explorations of students' mathematical beliefs and behavior. Journal for Research in Mathematics Education, 20(4), 338-355.
23. SPSS Inc. (2009). SPSS base 15. 0 user's guide. Chicago, USA: Prentice Hall.
24. Stage, F. K., & Kloosterman, P. (1995). Gender, beliefs and achievement in remedial college-level mathematics. Journal of Higher Education, 66(3), 294-311.
25. Walker, W., & Plata, M. (2000). Race/gender/age differences in college mathematics students. Journal of Developmental Education, 23(3), 24-32.