Analysis on mathematics grit scale and its gender-based DIF
List of Authors
  • Ching-Lin Shih , Yuan-Horng Lin

Keyword
  • differential item functioning, factor analysis, mathematics affect, mathematics grit

Abstract
  • The purpose of this study aims to analyze the factors structure, item characteristics as well as gender-based differential item functioning (DIF) of mathematics grit scale (Math-Grit scale). Issue of grit is one branch of positive psychology and most literatures show that grit is domain-general latent. On the other hand, quite a few literatures confirm grit is also school-specific latent which means the grit construct exists in specific disciplines. Therefore, it is feasible to develop Math-Grit scale and explore its factors structure and item characteristics. Besides, little is known about the gender-based DIF phenomenon as to the Math-Grit scale. The researchers develop the Math-Grit scale which consists of two dimensions of eight items. They are consistency of interest and perseverance of effort respectively. The subject comes from 1142 elementary school pupils. Results show the Math-Grit scale has well-defined validity and its reliability is acceptable. The item mean difficulty of all eight items in the instrument are met the criterion of fitness. The infit MNSQ and t statistic are fitted well and these two dimensions are moderately high correlated. Both subscales also have satisfactorily reliability. Moreover, the mean latent trait differences on these two dimensions for pupils of different gender are not significant, which means there is not DIF so that male and female are comparable on both subscales. Based on the findings, some suggestions for future studies and mathematics affect are discussed.

Reference
  • 1. Adams, R. J., Wilson, M. R., & Wang, W.-C. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21, 1–23. 2. Al-Mutawah, M. A., & Fateel, M. J. (2018). Students' Achievement in Math and Science: How Grit and Attitudes Influence?. International Education Studies, 11(2), 97-105. 3. Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561–573. 4. Duckworth, A. L. (2016). Grit: The Power of Passion and Perseverance. New York, NY: Scribner. 5. Duckworth, A. L., & Gross, J. J. (2014). Self-control and grit: Related but separable determinants of success. Current Directions in Psychological Science, 23, 319-325. 6. Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (GRIT–S). Journal of Personality Assessment, 91, 166-174. 7. Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087-1101. 8. George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston: Allyn & Bacon 9. Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 10, 631-639. 10. Osterlind, S. J. & Everson, H. T. (2009). Differential Item Functioning. Thousand Oaks, CA: Sage Publishing. 11. Rimfeld, K., Kovas, Y., Dale, P. S., & Plomin, R. (2016). True grit and genetics: Predicting academic achievement from personality. Journal of Personality and Social Psychology, 111, 780-789. 12. Robertson-Kraft, C., & Duckworth, A. L. (2014). True grit: Trait-level perseverance and passion for long-term goals predicts effectiveness and retention among novice teachers. Teachers College Record (1970), 116(3). 13. Schmidt, F. T., Fleckenstein, J., Retelsdorf, J., Eskreis-Winkler, L., & Möller, J. (2019). Measuring grit: A German validation and a domain-specific approach to grit. European Journal of Psychological Assessment, 35, 436-447. 14. Schmitt, T. A., & Sass, D. A. (2011). Rotation criteria and hypothesis testing for exploratory factor analysis: Implications for factor pattern loadings and interfactor correlations. Educational and Psychological Measurement, 71, 95-113. 15. Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9, 4-11. 16. Wang, W. C. & Shih, C. L. (2010). MIMIC Methods for Assessing Differential Item Functioning in Polytomous Items. Applied Psychological Measurement, 34, 166-180. 17. Wang, W. C., Chen, P. H., & Cheng, Y. Y. (2004). Improving measurement precision of test batteries using multidimensional item response models. Psychological Methods, 9, 116-136. 18. Wang, W. C., Shih, C. L., & Sun, G. W. (2012). The DIF-free-then-DIF strategy for the assessment of differential item functioning. Educational and Psychological Measurement, 72, 687-708. 19. Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ: Erlbaum Associates. 20. Wright, B. D. & Stone, M. H. (1979). Best Test Design. Chicago, ILL: MESA Press. 21. Wu, M. L., Adams, R. J., &Wilson, M. R. (1998). ConQuest [Computer software and manual]. Camberwell, Victoria, Australia: Australian Council for Educational Research.