How to Cite
Abstract
Background: This study developed the Brief Math Anxiety Scale (BMAS), a brief version of the Shortened Math Anxiety Rating Scale (sMARS), maintaining its original three-factor structure, by applying item response theory. Method: The sMARS was administered to 1,349 undergraduates, along with other questionnaires to measure their math ability, trait and test anxieties, and attitudes toward mathematics. Results: Results showed that the original scale could be reduced to nine items (three for each subscale). We provided evidence of good psychometric properties: strong internal consistency, adequate 7-week test-retest reliability, and good convergent/discriminant validity. Conclusions: In conclusion, the BMAS provides valid interpretations and reliable scores for assessing math anxiety in university students, and is especially useful in situations with time constraints where the longer form is impractical.
References
Alexander, L. & Martray, C. (1989). The development of an abbreviated version of the Mathematics Anxiety Rating Scale. Measurement and Evaluation in Counseling and Development, 22, 143-150. https://doi.org/10.1080/07481756.1989.12022923
Ashcraft, M. H., & Moore, A. M. (2009). Mathematics anxiety and the affective drop in performance. Journal of Psychoeducational Assessment, 27(3), 197–205. https://doi.org/10.1177/0734282908330580
Baker, F. B. (2001). The basics of item response theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, College Park, MD. Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Daucourt, M. C. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134–168. https://doi.org/10.1037/bul0000307
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29. https://doi.org/10.18637/jss.v048.i06
Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). Lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39(8), 1–30. https://doi.org/10.18637/jss.v039.i08
Devine, A., Fawcett, K., Szucs, D., & Dowker, A. (2012). Gender differences in mathematics anxiety and the relation to mathematics performance while controlling for test anxiety. Behavioral and Brain Functions, 8, Article 33. https://doi.org/10.1186/1744-9081-8-33
Edelen, M. O., & Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16(Suppl. 1), 5–18. https://doi.org/10.1007/s11136-007-9198-0
Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: a meta-analysis. Psychonomic Bulletin, 136(1), 103–127. https://doi.org/10.1037/a0018053
Faust, M. W., Ashcraft, M. H., & Fleck, D. E. (1996). Mathematics anxiety effects in simple and complex addition. Mathematical Cognition, 2(1), 25–62. https://doi.org/10.1080/135467996387534
French, J. W., Ekstrom, R.B., & Price, L. A. (1963). Manual for kit of reference tests for cognitive factors. Princeton, NJ: E. T. Service., Ed. Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R., & Hall, N. C. (2013). Do girls really experience more anxiety in mathematics? Psychological Science, 24(10), 2079–2087. https://doi.org/10.1177/0956797613486989
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21(1), 33–46. https://doi.org/10.2307/749455
Hodapp, V. (1991). Das Prüfungsängstlichkeitsinventar TAI-G: Eine erweiterte und modifizierte Version mit vier Komponenten [The Test Anxiety Inventory TAI-G: An expanded and modified version with four components]. Zeitschrift für Pädagogische Psychologie, 5, 121–130.
Hopko, D. R., Mahadevan, R., Bare, R. L., & Hunt, M. K. (2003). The Abbreviated Math Anxiety Scale (AMAS): Construction, validity, and reliability. Assessment, 10, 178–182. https://doi.org/10.1177/1073191103252351
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. http://dx.doi.org/10.1080/10705519909540118
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., Rosseel, Y., Miller, P., Quick, C., & Garnier-Villarreal, M. (2018). semTools: Useful tools for structural equation modeling. R package version 0.5-1. URL https://github.com/simsem/semTools/wiki
McKenna, J. S., & Nickols, S. Y. (1988). Planning for retirement security: What helps or hinders women in the middle years? Home Economics Research Journal, 17, 153–164. http://dx.doi.org/10.1177/1077727X8801700204
McMullan, M., Jones, R., & Lea, S. (2012). Math anxiety, self-efficacy, and ability in British undergraduate nursing students. Research in Nursing & Health, 35, 178–186. http://dx.doi.org/10.1002/nur.21460
Namkung, J. M., Peng, P., & Lin, X. (2019). The relation between mathematics anxiety and mathematics performance among school-aged students: a meta-analysis. Review of Educational Research, 89(3), 459–496. https://doi.org/10.3102/0034654319843494
Núñez-Peña, M. I., & Bono, R. (2019). Academic anxieties: which type contributes the most to low achievement in methodological courses? Educational Psychology, 39(6), 797–814. https://doi.org/10.1080/01443410.2019.1582756
Núñez-Peña, M. I., Suárez-Pellicioni, M., Guilera, G., & Mercadé-Carranza, C. (2013). A Spanish version of the short Mathematics Anxiety Rating Scale (sMARS). Learning and Individual Differences, 24, 204–210. https://doi.org/10.1016/j.lindif.2012.12.009
OECD. (2013). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. OECD Publishing. https://doi.org/10.1787/9789264190511-en
Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist, 53(3), 145–164. https://doi.org/10.1080/00461520.2018.1447384
Richardson, F. C., & Suinn, R. M. (1972). The mathematics anxiety rating scale: Psychometric data. Journal of Counceling Psychology, 19(6), 551–554. https://doi.org/10.1037/h0033456
Rolison, J. J., Morsanyi, K., & O’Connor, P. A. (2016). Can I count on getting better? Association between math anxiety and poorer understanding of medical risk reductions. Medical Decision Making, 36, 876–886. http://dx.doi.org/10.1177/0272989X15602000
Rolison, J. J., Morsanyi, K., & Peters, E. (2020). Understanding health risk comprehension: The role of math anxiety, subjective numeracy, and objective numeracy. Medical Decision Making, 40, 222–234. http://dx.doi.org/10.1177/0272989X20904725
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
Sesé, A., Palmer, A., & Pérez-Pareja, J. (2010). Construct validation for the German test anxiety inventory-Argentinean version (GTAI-A) in a Spanish population. Cognition, Brain, Behavior, 14(4), 413–429.
Shi, D., & Maydeu-Olivares, A. (2020). The effect of estimation methods on SEM fit indices. Educational and Psychological Measurement, 80(3), 421-445. https://doi.org/10.1177/0013164419885164
Spielberger, C. D., Gorsuch, R., & Lushene, R. (2008). Cuestionario de Ansiedad Estado-Rasgo, STAI. TEA Ediciones.
Spielberger, C. D., Gorsuch, R., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the State-Trait anxiety inventory. Consulting. Suárez-Pellicioni, M., Núñez-Peña, M. I., & Colomé, À. (2016). Math anxiety: A review of its cognitive consequences, psychophysiological correlates, and brain bases. Cognitive, Affective & Behavioural Neuroscience, 16(1), 3–22. https://doi.org/10.3758/s13415-015-0370-7
Tomasetto, C., Morsanyi, K., Guardabassi, V., & O’Connor, P. A. (2020). Math anxiety interferes with learning novel mathematics contents in early elementary school. Journal of Educational Psychology, 113(2), 315–329. https://doi.org/10.1037/edu0000602
Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51(1), 409–428. https://doi.org/10.3758/s13428-018-1055-2
Zhang, J., Zhao, N., & Kong, Q. P. (2019). The relationship between math anxiety and math performance: a meta-analytic investigation. Frontiers in Psychology, 10, Article 1613. https://doi.org/10.3389/fpsyg.2019.01613