The Asia-Pacific Education Researcher

, Volume 24, Issue 2, pp 319–327 | Cite as

Investigating the Pedagogical Approaches Related to Changes in Attitudes Toward Statistics in a Quantitative Methods Course for Psychology Undergraduate Students

  • Albert K. Liau
  • John E. Kiat
  • Youyan Nie
Regular Article


The purpose of this study was to examine the extent to which the pedagogical approaches used in the course were related to improvements in students’ attitudes toward statistics in a Quantitative Methods course for psychology undergraduate students in a Malaysian University. The study examined whether increasing availability of the instructor and tutor, carrying out hands-on activities, using collaborative learning, and utilizing scaffolding activities would be related to the improvement of students’ attitudes toward statistics. It also hypothesized that the various pedagogical approaches used would be related to higher levels of achievement in statistics. One hundred and three psychology students from a 14-week undergraduate course on statistics volunteered to participate in this study. The study found partial support that the approaches utilized were related to positive attitude changes toward statistics as well as achievement. Limitations are discussed and future research is suggested.


Attitudes toward statistics Statistics anxiety Student-centered learning Active learning Pedagogy for undergraduate statistics 



The cooperation of the students who participated in the study is gratefully appreciated. The authors acknowledge the support for printing costs provided by HELP University, Malaysia. Versions of this paper were presented at the International Conference in Applied Psychology, Malaysia in May 2009, and the 14th International Conference on Thinking, Malaysia in June 2009. Funding to attend the conferences was provided by HELP University, Malaysia. The first and second authors were employees of HELP University during the period of the study.


  1. Brundiers, K., Wiek, A., & Redman, C. L. (2010). Real-world learning opportunities in sustainability: From classroom into the real world. International Journal of Sustainability in Higher Education, 11(4), 308–324.CrossRefGoogle Scholar
  2. Cherney, I. D., & Cooney, R. R. (2005). Predicting student performance in a statistics course using the mathematics and statistics perception scale (MPSP). Transactions of the Nebraska Academy of Sciences, 30, 1–8.Google Scholar
  3. Delmas, G., Joan, G., Ooms, A., & Chance, B. (2007). Assessing student’s conceptual understanding after a first course in statistics. Statistics Education Research Journal, 6(2), 28–58.Google Scholar
  4. Dolinsky, B. (2001). An active learning approach to teaching statistics. Teaching of Psychology, 28, 55–56.Google Scholar
  5. Gal, I., Ginsburg, L., & Schau, C. (1997). Monitoring attitudes and beliefs in statistics education. In I. Gal & J. B. Garfield (Eds.), The assessment challenge in statistics education (pp. 37–51). Netherlands: IOS.Google Scholar
  6. Garfield, J., & Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75, 372–396.CrossRefGoogle Scholar
  7. Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  8. Griffith, J. D., Adams, L. T., Gu, L. L., Hart, C. L., & Nichols-Whitehead, P. (2012). Students’ attitudes toward statistics across the disciplines: A mixed methods approach. Statistics Education Research Journal, 1192, 45–46.Google Scholar
  9. Guttman, L. (1954). Some necessary conditions for common factor analysis. Psychometrika, 19, 149–161.CrossRefGoogle Scholar
  10. Harlow, L. L., Burkholder, G. J., & Morrow, J. A. (2002). Evaluating attitudes, skill, and performance in a learning-enhanced quantitative methods course: A structural modeling approach. Structural Equation Modeling, 9, 413–430.CrossRefGoogle Scholar
  11. Hood, M., Creed, P. A., & Neumann, D. L. (2012). Using the expectancy value model of motivation to understand the relationship between student attitudes and achievement in statistics. Statistics Education Research Journal, 11(2), 72–85.Google Scholar
  12. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185.Google Scholar
  13. Hung, D., Ng, P., Koh, T., & Lim, S. (2009). The social practice of learning: A craft for the 21st century. Asia Pacific Education Review, 10(2), 205–214.CrossRefGoogle Scholar
  14. Kao, M. T., & Lehman, J. D. (1997). Scaffolding in a computer-based constructivist environment for teaching statistics to college learners. ERIC Clearinghouse.Google Scholar
  15. Keeley, J., Zayac, R., & Correia, C. (2008). Curvilinear relationships between statistics anxiety and performance among undergraduate students: Evidence for optimal anxiety. Statistics Education Research Journal, 7, 4–15.Google Scholar
  16. Liau, A. K., & Kiat, J. E. (2009). Investigating the factors related to changes in statistical anxiety in an undergraduate statistics course. In Paper presented at the International Conference on Applied Psychology: The Asian Perspective at University Malaya, Malaysia.Google Scholar
  17. Magel, R. C. (1998). Using cooperative learning in a large introductory statistics class. Journal of Statistics Education, 6(3). Retrieved August 28, 2009 from
  18. McCombs, B. L. (1996). Alternative perspectives for motivation. In L. Baker, P. Afflerback, & D. Reinking (Eds.), Developing engaged readers in school and home communities. Mahwah, NJ: Erlbaum.Google Scholar
  19. National Research Council. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.Google Scholar
  20. National Research Council. (2005). How students learn: History, mathematics, and science in the classroom. Washington, DC: National Academy Press.Google Scholar
  21. Nolan, M. M., Beran, T., & Hecker, K. G. (2012). Surveys assessing students’ attitudes toward statistics: A systematic review of validity and reliability. Statistics Education Research Journal, 11(2), 103–123.Google Scholar
  22. Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments—a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195–209.CrossRefGoogle Scholar
  23. Osman, G., Duffy, T., Chang, J.-Y., & Lee, J. (2011). Learning through collaboration: student perspectives. Asia Pacific Education Review, 12(4), 547–558.CrossRefGoogle Scholar
  24. Pan, P., Pan, G.-M., Lee, C.-Y., & Chang, S. (2010). University students’ perceptions of a holistic care course through cooperative learning: implications for instructors and researchers. Asia Pacific Education Review, 11(2), 199–209.CrossRefGoogle Scholar
  25. Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative instructional methods on reducing statistics anxiety for graduate students in the social sciences. Journal of Instructional Psychology, 31(2), 149–159.Google Scholar
  26. Pan, W., & Tang, M. (2005). Students’ perceptions on factors of statistics anxiety and instructional strategies. Journal of Instructional Psychology, 32(3), 205–214.Google Scholar
  27. Perkins, D. V., & Saris, R. N. (2001). A “jigsaw classroom” technique for undergraduate statistics courses. Teaching of psychology, 28(2), 111–113.CrossRefGoogle Scholar
  28. Pfannkuch, M. (2005). Probability and statistical inference: How can teachers enable learners to make the connection? In G. A. Jones (Ed.), Exploring probability in school: Challenges for teaching and learning (pp. 267–294). New York: Springer.CrossRefGoogle Scholar
  29. Schau, C. (2003, August). Students’ attitudes: The “other” important outcome in statistics education. In Paper presented at the 2003 Joint statistical meetings—section on statistical education, San Francisco, CA.Google Scholar
  30. Schau, C. (2005). Scoring the SATS36©. Retrieved June 22, 2009, from
  31. Schau, C., Millar, M., & Petocz, P. (2012). Research on attitudes toward statistics. Statistics Education Research Journal, 11(2), 2–5.Google Scholar
  32. Schau, C. G., Stevens, J., Dauphinee, T. L., & Del Vecchio, A. (1995). The development and validation of the survey of attitudes toward statistics. Educational and Psychological Measurement, 55, 868–875.CrossRefGoogle Scholar
  33. Shaughnessy, J. M. (2007). Research on statistics learning and reasoning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957–1009). Charlotte: Information Age Publishing.Google Scholar
  34. Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327.CrossRefGoogle Scholar
  35. Zaneski, S. H., & Valenzi, E. R. (1997). Student anxiety and attitudes in business statistics. Journal of Education for Business, 73(1), 10–16.CrossRefGoogle Scholar

Copyright information

© De La Salle University 2014

Authors and Affiliations

  1. 1.Psychological Studies Academic Group, National Institute of EducationNanyang Technological UniversitySingaporeSingapore
  2. 2.University of Nebraska-LincolnLincolnUSA

Personalised recommendations