Learning Environments Research

, Volume 18, Issue 3, pp 409–424 | Cite as

Development and use of an instrument to measure students’ perceptions of a business statistics learning environment in higher education

  • Thuyuyen H. Nguyen
  • Michael Newby
  • Panayiotis G. Skordi
Original Paper

Abstract

Statistics is a required subject of study in many academic disciplines, including business, education and psychology, that causes problems for many students. This has long been recognised and there have been a number of studies into students’ attitudes towards statistics, particularly statistical anxiety. However, none of these studies investigated the classroom learning environment in relation to students’ attitudes towards statistics. This paper describes the development and validation of two new instruments, the Business Statistics Computer Laboratory Environment Inventory and the Attitude towards Business Statistics instrument. The former measures appropriate aspects of the learning environment of a computer-based statistics class, whilst the latter measures students’ attitudes towards business statistics. The instruments were administered to undergraduate business students at a university in Southern California. When exploratory factor analysis was carried out, it was found that the items of both instruments loaded onto their a priori scales. All the scales exhibited both internal reliability and discriminant validity. Correlation analysis between the scales of the two instruments demonstrated a strong relationship between aspects of the learning environment and students’ attitudes towards business statistics.

Keywords

Attitude Business statistics Computer-based learning environments Instrument 

References

  1. Afari, E., Aldridge, J. M., Fraser, B. J., & Khine, M. S. (2013). Students’ perceptions of the learning environment and attitudes in game-based mathematics classrooms. Learning Environments Research, 16(1), 131–150.CrossRefGoogle Scholar
  2. Allen, D., & Fraser, B. J. (2007). Parent and student perceptions of classroom learning environment and its association with student outcomes. Learning Environments Research, 10(1), 67–82.CrossRefGoogle Scholar
  3. Anderson, G. J., & Walberg, H. J. (1968). Classroom climate and group learning. International Journal of Educational Sciences, 2, 178–180.Google Scholar
  4. Anderson, G. J., Walberg, H. J., & Welch, W. W. (1968). Curriculum effects on the social climate of learning: A new representation of discriminant functions. American Educational Research Journal, 6, 315–328.CrossRefGoogle Scholar
  5. Association to Advance Collegiate Schools of Business. (2012). Eligibility procedures and accreditation standards for business accreditation. http://www.aacsb.edu/accreditation/standards-busn-jan2012.pdf
  6. Bell, J. A. (1998). International students have statistics anxiety too! Education, 118, 634–636.Google Scholar
  7. British Academy. (2012). Society counts. http://www.britac.ac.uk/policy/Society_Counts.cfm
  8. Carlson, K. A., & Winquist, J. R. (2011). Evaluating an active learning approach to teaching introductory statistics: A classroom workbook approach. Journal of Statistics Education, 191, 1–22.Google Scholar
  9. Chandra, V., & Fisher, D. L. (2009). Students’ perceptions of a blended web-based learning environment. Learning Environments Research, 12(1), 31–44.CrossRefGoogle Scholar
  10. Chang, V., & Fisher, D. L. (2003). The validation and application of a new learning environment instrument for online learning in higher education. In M. S. Khine & D. L. Fisher (Eds.), Technology-rich learning environments: A future perspective (pp. 1–20). Singapore: World Scientific.CrossRefGoogle Scholar
  11. Cook, A. (2010). Improving the success rate in statistics (No. 415). Brisbane: University of Queensland, School of Economics.Google Scholar
  12. Cronbach, L. J. (1951). Coefficient alpha and the internal structure tests. Psychometrika, 16, 297–334.CrossRefGoogle Scholar
  13. Cruise, R. J., Cash, R. W., & Bolton, D. L. (1985, August). Development and validation of an instrument to measure statistical anxiety. Paper presented at the annual meeting of the statistical education section. Proceedings of the American Statistical Association, Chicago, IL.Google Scholar
  14. Dorman, J. P. (2008). Use of multitrait-multimethod modeling to validate actual and preferred forms of the What Is Happening In this Class? (WIHIC) questionnaire. Learning Environments Research, 11, 179–193.CrossRefGoogle Scholar
  15. Dorman, J. P., & Fraser, B. J. (2009). Psychosocial environment and affective outcomes in technology-rich classrooms: Testing a causal model. Social Psychology of Education, 12(1), 77–99.CrossRefGoogle Scholar
  16. Finney, S. J., & Schraw, G. (2003). Self-efficacy beliefs in college statistics courses. Contemporary Educational Psychology, 28(2), 161–186.CrossRefGoogle Scholar
  17. Francis, G., & Lipson, K. (2010, July). The importance of teaching statistics in a professional context. In Proceedings of the eighth international conference on teaching statistics, Ljubljana, Slovenia.Google Scholar
  18. Fraser, B. J. (1978). Development of a test of science-related attitudes. Science Education, 62(4), 509–515.CrossRefGoogle Scholar
  19. Fraser, B. J. (1998). Classroom environment instruments: Development, validity and applications. Learning Environments Research, 1(1), 7–34.CrossRefGoogle Scholar
  20. Fraser, B. J., Fisher, D. L., & McRobbie, C. J. (1996, April). Development, validation and use of personal and class forms of a new classroom environment instrument. Paper presented at the annual meeting of the American Educational Research Association, New York.Google Scholar
  21. Fraser, B. J., McRobbie, C. J., & Giddings, G. J. (1993). Development and cross-national validation of a laboratory classroom environment instrument for senior high school science. Science Education, 77(1), 1–24.CrossRefGoogle Scholar
  22. Fraser, B. J., & Treagust, D. F. (1986). Validity and use of an instrument for assessing classroom psychosocial environment in higher education. Higher Education, 15(1–2), 37–57.CrossRefGoogle Scholar
  23. Freedman, M. P. (1997). Relationship among laboratory instruction, attitude toward science, and achievement in science knowledge. Journal of Research in Science Teaching, 34(4), 343–357.CrossRefGoogle Scholar
  24. Gordon, S. (2004). Understanding students’ experiences of statistics in a service course. Statistics Education Research Journal, 3(1), 40–59.Google Scholar
  25. Green, J. J., Stone, C. C., Zegeye, A., & Charles, T. A. (2009). How much math do students need to succeed in business and economics statistics? An ordered probit analysis. Journal of Statistics Education, 17(3), 1–22.Google Scholar
  26. Hannula, M. S. (2002). Attitude towards mathematics: Emotions, expectations and values. Educational studies in Mathematics, 49(1), 25–46.CrossRefGoogle Scholar
  27. 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
  28. Hsu, M. K., Wang, S. W., & Chiu, K. K. (2009). Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Computers in Human Behaviour, 252, 412–420.CrossRefGoogle Scholar
  29. Jegede, O. J., Fraser, B. J., & Fisher, D. L. (1995). The development and validation of a distance and open learning environment scale. Educational Technology Research and Development, 43(1), 89–94.CrossRefGoogle Scholar
  30. Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200.CrossRefGoogle Scholar
  31. Kek, M., & Huijser, H. (2011). Exploring the combined relationships of student and teacher factors on learning approaches and self-directed learning readiness at a Malaysian University. Studies in Higher Education, 36(2), 185–208.CrossRefGoogle Scholar
  32. Khine, M. S., & Fisher, D. L. (Eds.). (2003). Technology-rich learning environments: A future perspective. Singapore: World Scientific.Google Scholar
  33. Kline, P. (1994). An easy guide to factor analysis. London: Routledge.Google Scholar
  34. Lalonde, R. N., & Gardner, R. C. (1993). Statistics as a second language? Model for predicting performance in psychology students. Canadian Journal of Behavioral Science, 25, 108–125.CrossRefGoogle Scholar
  35. Liu, S., Onwuegbuzie, A. J., & Meng, L. (2012). Examination of the score reliability and validity of the Statistics Anxiety Rating Scale. The Journal of Educational Enquiry, 11(1), 29–42.Google Scholar
  36. Ma, X., & Kishor, N. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for Research in Mathematics Education, 28(1), 26–47.CrossRefGoogle Scholar
  37. McGuire, W. J. (1969). The nature of attitudes and attitude change. In G. Lindzey & E. Aronson (Eds.), The handbook of social psychology (pp. 136–314). Reading, MA: Addision-Wesley.Google Scholar
  38. Meletiou-Mavrotheris, M., Lee, C., & Fouladi, R. T. (2007). Introductory statistics, college student attitudes and knowledge—A qualitative analysis of the impact of technology based instruction. International Journal of Mathematical Education, 19, 65–83.Google Scholar
  39. Newby, M. (2002). An empirical study comparing the learning environments of open and closed computer laboratories. Journal of Information Systems Education, 13(4), 303–314.Google Scholar
  40. Newby, M., & Fisher, D. L. (1997). An instrument for assessing the learning environment of a computer laboratory. Journal of Educational Computing Research, 16, 179–190.CrossRefGoogle Scholar
  41. Newby, M., & Fisher, D. L. (2000). A model of the relationship between university computer laboratory environment and student outcomes. Learning Environments Research, 3(1), 51–66.CrossRefGoogle Scholar
  42. Newby, M., & Nguyen, T. H. (2010). Using the same problem with different techniques in programming assignments: An empirical study of its effectiveness. Journal of Information Systems Education, 21(4), 375–382.Google Scholar
  43. Nguyen, T. H., & Hampson, P. (2012, March). Improving students’ learning outcomes in university business statistics modules: Increase contact hours or increase relevance? Paper presented at the 5th Annual Northeast Universities (3 Rivers Consortium) Regional Learning and Teaching Conference, Middlesbrough, Tees Valley, UK.Google Scholar
  44. Nguyen, T. H., & Ulbrich, F. (2013, August). The effectiveness of different grading strategies: An empirical study. In Proceedings of the 19th Americas conference on information systems, AMCIS 2013, 15–17 Chicago, IL.Google Scholar
  45. Okan, Z. (2008). Computing laboratory classes as language learning environments. Learning Environments Research, 11(1), 31–48.CrossRefGoogle Scholar
  46. Onwuegbuzie, A. J. (2000). Statistics anxiety and the role of self-perceptions. Journal of Educational Research, 93, 323–335.CrossRefGoogle Scholar
  47. Onwuegbuzie, A. J. (2004). Academic procrastination and statistics anxiety. Assessment & Evaluation in Higher Education, 29(1), 3–19.CrossRefGoogle Scholar
  48. Onwuegbuzie, A. J., Da Ros, D., & Ryan, J. M. (1997). The components of statistics anxiety: A phenomenological study. Focus on Learning Problems in Mathematics, 19(4), 11–35.Google Scholar
  49. Onwuegbuzie, A. J., & Seaman, M. (1995). The effect of time and anxiety on statistics achievement. Journal of Experimental Psychology, 63, 115–124.Google Scholar
  50. 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, 195–209.CrossRefGoogle Scholar
  51. Pan, W., & Tang, M. (2005). Students’ perceptions on factors of statistics anxiety and instructional strategies. Journal of Instructional Psychology, 32, 205–214.Google Scholar
  52. Petocz, P. (1998). Effective video-based resources for learning statistics. In Statistical educationExpanding the network, fifth international conference on teaching statistics (pp. 985–991).Google Scholar
  53. Petocz, P., & Reid, A. (2005). Something strange and useless: Service students’ conceptions of statistics, learning statistics and using statistics in their future profession. International Journal of Mathematical Education in Science and Technology, 36(7), 789–800.CrossRefGoogle Scholar
  54. Ramirez, C., Schau, C., & Emmioglu, E. (2012). The importance of attitudes in statistics education. Statistics Education Research Journal, 11(2), 57–71.Google Scholar
  55. Ramsey, J. B. (1999, August). Why do students find statistics so difficult? In Proceedings of the 52th Session of the International Statistical Institute, Helsinki, Finland.Google Scholar
  56. Schau, C. (2003). Students’ attitudes: The “other” important outcome in statistics education. In Proceedings of the joint statistical meetings (pp. 3673–3681).Google Scholar
  57. Tabachnick, B. G., & Fidell, L. S. (2010). Using multivariate statistics (6th ed.). Boston, MA: Pearson.Google Scholar
  58. Taylor, B. A., & Fraser, B. J. (2013). Relationships between learning environment and mathematics anxiety. Learning Environments Research, 16, 297–313.CrossRefGoogle Scholar
  59. Trickett, E. J., & Moos, R. H. (1973). Social environment of junior high and high school classrooms. Journal of Educational Psychology, 65(1), 93–102.CrossRefGoogle Scholar
  60. Walberg, H. J., & Anderson, G. J. (1968). Classroom climate and individual learning. Journal of Educational Psychology, 59, 414–419.CrossRefGoogle Scholar
  61. Wilensky, U. (1997). What is normal anyway? Therapy for epistemological anxiety. Educational Studies in Mathematics, 33(2), 171–202.CrossRefGoogle Scholar
  62. Williams, A. S. (2010). Statistics anxiety and instructor immediacy. Journal of Statistics Education, 18, 1–18.Google Scholar
  63. Williams, M., Payne, G., Hodgkinson, L., & Poade, D. (2008). Does British sociology count? Sociology students’ attitudes toward quantitative methods. Sociology, 42, 1003–1021.CrossRefGoogle Scholar
  64. Wolf, S. J., & Fraser, B. J. (2008). Learning environment, attitudes and achievement among middle-school science students using inquiry-based laboratory activities. Research in Science Education, 38(3), 321–341.CrossRefGoogle Scholar
  65. Wu, W., Chang, H. P., & Guo, C. J. (2009). The development of an instrument for a technology-integrated science learning environment. International Journal of Science and Mathematics Education, 7(1), 207–233.CrossRefGoogle Scholar
  66. Zanakis, S. H., & Valenzi, E. R. (1997). Student anxiety and attitudes in business statistics. Journal of Education for Business, 73, 10–16.CrossRefGoogle Scholar
  67. Zeidner, M. (1991). Statistics and mathematics anxiety in social science students: Some interesting parallels. British Journal of Educational Psychology, 61, 319–328.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Thuyuyen H. Nguyen
    • 1
  • Michael Newby
    • 1
  • Panayiotis G. Skordi
    • 1
  1. 1.Information Systems and Decision SciencesCalifornia State University FullertonFullertonUSA

Personalised recommendations