The significance of the present study includes methodological advancement and practical updates. The introduction of quantile regression as an advanced statistical procedure will allow education researchers to make significant progress in the analysis of educational data. The reexamination of individual gender differences in mathematics self-efficacy will generate more credible implications for educational policy and practice.

The use of quantile regression in the educational field is relatively new, although quantile regression has become a common statistical tool in many other fields (Chen & Chalhoub-Deville, 2014). The present study attempts to advance the quantitative landscape of educational sciences by adding to its tool chest quantile regression as an effective statistical technique.

Based on the results, the quantile approach provides a more complete picture of the influence of gender on math  selfefficacy. In particular, quantile regression identifies potentially disparate effects of gender across the conditional distribution of math  self-efficacy. These differences can be obscured in traditional linear regression analysis.