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Ordinal Approaches to Decomposing Between-group Test Score Disparities

The estimation of test score “gaps” and gap trends plays an important role in monitoring educational inequality. Researchers decompose gaps and gap changes into within- and between-school portions to generate evidence on the role schools play in shaping these inequalities. However, existing decomposition methods assume an equal-interval test scale and are a poor fit to coarsened data such as proficiency categories. This leaves many potential data sources ill-suited for decomposition applications. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic, and an extension of ordered probit models. Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. More broadly, our methods enable analysts to (1) decompose the difference between two groups on any ordinal outcome into portions within- and between some third categorical variable, and (2) estimate scale-invariant between-group differences that adjust for a categorical covariate.        

Education level
Document Object Identifier (DOI)
10.26300/3n9n-1c85

EdWorkingPaper suggested citation:

Quinn, David M., and Andrew D. Ho. (). Ordinal Approaches to Decomposing Between-group Test Score Disparities. (EdWorkingPaper: -257). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/3n9n-1c85

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