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Reliability and validity of the Statistical Anxiety Scale among students in Singapore and Australia

Published online by Cambridge University Press:  12 November 2014

Peter K. H. Chew  and
Denise B. Dillon
Peter K. H. Chew*
Affiliation:
James Cook University, Singapore, 600 Upper Thomson Road, Singapore574421
Denise B. Dillon
Affiliation:
James Cook University, Singapore, 600 Upper Thomson Road, Singapore574421
*
corresponding author: Peter K. H. Chew, James Cook University, Singapore574421 Email: [email protected]
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Abstract

Given the negative relationships between statistics anxiety and statistics achievement, it is important for researchers and instructors to have a reliable and valid measure to identify students with statistics anxiety. The purpose of the current study was to examine the psychometric properties of Vigil-Colet, Lorenzo-Seva, and Condon's (2008) Statistical Anxiety Scale (SAS) among students in Singapore and Australia. Unlike the commonly used Statistical Anxiety Rating Scale, the SAS provides a specific measure of statistics anxiety. Participants were 197 undergraduates (79.2% female) in the James Cook University Psychology programs at the Singaporean (70.1%) and Australian (29.9 %) campuses. Acceptable internal consistency reliabilities, ranging from 0.88 to 0.95 for the three factors of the SAS, were found in the current study. Confirmatory factor analysis suggested that a modified three-factor model best describe the data. Scores on the SAS shared positive correlations with another measure of statistics anxiety, and negative correlations with a measure of attitudes toward statistics. The results provided support for the use of the SAS among Singaporean and Australian psychology undergraduates.

Keywords

statistics anxietystatistical anxiety scalereliabilityvalidity
Type
Articles
Information
Journal of Tropical Psychology , Volume 4 , 2014 , e7
Copyright
Copyright © The Author(s) 2014 

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References

Baloğlu, M. (2004). Statistics anxiety and mathematics anxiety: Some interesting differences I. Educational Research Quarterly, 27 (3), 38–48.Google Scholar
Bell, J. A. (2001). Length of course and levels of statistics anxiety. Education, 121 (4), 713–716.Google Scholar
Byrne, B. M. (2010). Structural Equation Modeling with AMOS: Basic concepts, applications, and programming, second edition (2nd ed.). New York: Routledge.Google Scholar
Chew, P. K. H., & Dillon, D. B. (2014). Statistics anxiety update: Refining the construct and recommendations for a new research agenda. Perspectives on Psychological Science, 9 (2), 196–208. doi:10.1177/1745691613518077Google Scholar
Chiesi, F., & Primi, C. (2010). Cognitive and non-cognitive factors related to students’ statistics achievement. Statistics Education Research Journal, 9 (1), 6–26.CrossRefGoogle Scholar
Chiesi, F., Primi, C., & Carmona, J. (2011). Measuring statistics anxiety: Cross-country validity of the statistical anxiety scale (SAS). Journal of Psychoeducational Assessment, 29 (6), 559–569. doi:10.1177/0734282911404985Google Scholar
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, Chicago, IL.Google Scholar
Earp, M. S. (2007). Development and validation of the statistics anxiety measure (Unpublished doctoral dissertation). University of Denver, Colorado.Google Scholar
Galesic, M., & Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73 (2), 349–360. doi:10.1093/poq/nfp031Google Scholar
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis (7th ed.). United Kingdom: Prentice Hall.Google Scholar
Hanna, D., & Dempster, M. (2009). The effect of statistics anxiety on students’ predicted and actual test scores. The Irish Journal of Psychology, 30 (3–4), 201–209. doi:10.1080/03033910.2009.10446310Google Scholar
Hanna, D., Shevlin, M., & Dempster, M. (2008). The structure of the statistics anxiety rating scale: A confirmatory factor analysis using UK psychology students. Personality and Individual Differences, 45 (1), 68–74. doi:10.1016/j.paid.2008.02.021Google Scholar
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 (1), 4–15.Google Scholar
Kline, R. B. (2010). Principles and Practice of Structural Equation Modeling (3rd ed.). New York: The Guilford Press.Google Scholar
Liu, S., Onwuegbuzie, A. J., & Meng, L. (2011). Examination of the score reliability and validity of the statistics anxiety rating scale in a Chinese population: Comparisons of statistics anxiety between Chinese college students and their Western counterparts. Journal of Educational Enquiry, 11 (1), 29–42.Google Scholar
MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111 (3), 490.Google Scholar
Mji, A., & Onwuegbuzie, A. J. (2004). Evidence of score reliability and validity of the statistical anxiety rating scale among technikon students in South Africa. Measurement and Evaluation in Counseling and Development, 36 (4), 238–251.Google Scholar
Nasser, F. M. (2004). Structural model of the effects of cognitive and affective factors on the achievement of arabic-speaking pre-service teachers in introductory statistics. Journal of Statistics Education, 12 (1). Retrieved from http://www.amstat.org/publications/jse/v12n1/nasser.htmlGoogle Scholar
Nunnally, J. C. (1978). Psychometric Theory. New York: McGraw-Hill.Google Scholar
Onwuegbuzie, A. J. (2003). Modeling statistics achievement among graduate students. Educational and Psychological Measurement, 63 (6), 1020–1038. doi:10.1177/0013164402250989Google Scholar
Onwuegbuzie, A. J., & Seaman, M. A. (1995). The effect of time constraints and statistics test anxiety on test performance in a statistics course. Journal of Experimental Education, 63 (2), 115–124.Google Scholar
Papousek, I., Ruggeri, K., Macher, D., Paechter, M., Heene, M., Weiss, E. M., . . . Freudenthaler, H. H. (2012). Psychometric evaluation and experimental validation of the statistics anxiety rating scale. Journal of Personality Assessment, 94 (1), 82–91. doi:10.1080/00223891.2011.627959Google Scholar
Pretorius, T. B., & Norman, A. M. (1992). Psychometric data on the statistics anxiety scale for a sample of South African students. Educational and Psychological Measurement, 52 (4), 933–937. doi:10.1177/0013164492052004015CrossRefGoogle Scholar
Stoloff, M., McCarthy, M., Keller, L., Varfolomeeva, V., Lynch, J., Makara, K., . . . Smiley, W. (2009). The undergraduate psychology major: An examination of structure and sequence. Teaching of Psychology, 37 (1), 4–15.CrossRefGoogle Scholar
SurveyGizmo [Computer Software]. (2013). Boulder, CO: SurveyGizmo.Google Scholar
Thompson, B., & Vacha-Haase, T. (2000). Psychometrics is datametrics: The test is not reliable. Educational and Psychological Measurement, 60 (2), 174–195. doi:10.1177/0013164400602002Google Scholar
Tremblay, P. F., Gardner, R. C., & Heipel, G. (2000). A model of the relationships among measures of affect, aptitude, and performance in introductory statistics. Canadian Journal of Behavioural Science/Revue Canadienne Des Sciences Du Comportement, 32 (1), 40–48. doi:10.1037/h0087099Google Scholar
Vigil-Colet, A., Lorenzo-Seva, U., & Condon, L. (2008). Development and validation of the statistical anxiety scale. Psicothema, 20 (1), 174–180.Google Scholar
Watson, F. S., Kromrey, J. D., & Hess, M. R. (2003, February). Toward a conceptual model for statistics anxiety intervention. Paper presented at the annual meeting of the Eastern Educational Research Association, Hilton Head, SC.Google Scholar
Watson, F. S., Lang, T. R., & Kromrey, J. D. (2002, November). Breaking ground for EncStat: A statistics anxiety intervention program. Paper presented at the annual meeting of the Florida Educational Research Association, Gainesville, FL.Google Scholar
Wise, S. L. (1985). The development and validation of a scale measuring attitudes toward statistics. Educational and Psychological Measurement, 45 (2), 401–405. doi:10.1177/001316448504500226Google Scholar
Zanakis, S. H., & Valenzi, E. R. (1997). Student anxiety and attitudes in business statistics. Journal of Education for Business, 73 (1), 10–16. doi:10.1080/08832329709601608Google Scholar
Zeidner, M. (1991). Statistics and mathematics anxiety in social science students: Some interesting parallels. The British Journal of Educational Psychology, 61 (3), 319–328.Google Scholar