The Role of Opportunity to Learn and School Socioeconomic Composition in Reducing Racial and Gendered Disparities in Mathematics Achievement

Abstract

Mathematics literacy is crucial in many STEM fields, yet Black and Hispanic students are less likely to achieve high math proficiency. While previous literature investigated potential factors to mitigate racial or gendered disparities in mathematics literacy, few studies attended to the conditions under which the causal interpretation of the results obtained can be established. Guided by intersectionality theory and causal decomposition analysis, we examined the degree to which disparities in mathematics literacy (a) exist at the intersection of race and gender and (b) can be reduced by hypothetical interventions that equalize school socioeconomic status (SES) or opportunity to learn (OTL) across groups. We found large racial/ethnic differences in math literacy favoring Asians and whites and much smaller gender differences. We also found that equalizing school SES may reduce disparities for Black and Hispanic males and females; equalizing OTL may reduce disparities for Black and Hispanic males as well as Asian males and females; compared to white males. Our findings suggest that interventions that target specific race–gender groups are required to reduce disparities in math literacy.

Keywords:

• Intersectionality
• math literacy
• school socioeconomic status
• opportunity to learn
• causal decomposition analysis

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 BM: Black male, BF: Black female, HM: Hispanic male, and HF: Hispanic female.

The omitted variable is denoted as $U$. One sensitivity parameter is presented on the X-axis, indicating the association between $U$ and math literacy. Another sensitivity parameter is presented on the Y-axis, indicating the association between $U$ with the mediator (school SES or OTL). Both sensitivity parameters are represented as partial $R^2$ values. The solid line shows the combinations of the two sensitivity parameters that would result in zero disparity reduction. The dashed line shows the points where the 95% confidence intervals cover zero. The region below the dashed line is where the estimates are still significant despite omitted confounding. The region above the dashed line is when the significance of the estimate changes to non-significance due to omitted confounding. The dots in each figure represent the amount of confounding that will result in zero disparity reduction or change the significance of the estimate to non-significance, given the equal amount of confounding between two sensitivity parameters.

2 BM: Black male, BF: Black female, HM: Hispanic male, and HF: Hispanic female.

The omitted variable is denoted as $U$. One sensitivity parameter is presented on the X-axis, indicating the association between $U$ and math literacy. Another sensitivity parameter is presented on the Y-axis, indicating the association between $U$ with the mediator (school SES or OTL). Both sensitivity parameters are represented as partial $R^2$ values. The solid line shows the combinations of the two sensitivity parameters that would result in zero disparity reduction. The dashed line shows the points where the 95% confidence intervals cover zero. The region below the dashed line is where the estimates are still significant despite omitted confounding. The region above the dashed line is when the significance of the estimate changes to non-significance due to omitted confounding. The dots in each figure represent the amount of confounding that will result in zero disparity reduction or change the significance of the estimate to non-significance, given the equal amount of confounding between two sensitivity parameters.

Additional information

Funding

This work was supported by American Educational Research Association; Division of Behavioral and Cognitive Sciences.