Using Large-Scale Datasets to Amplify Equitable Learning in Urban Mathematics
DOI:
https://doi.org/10.21423/jume-v13i2a381Keywords:
large-scale data, methodology, NCESAbstract
Our purpose in this article was to provide researchers using NCES datasets to analyze secondary students’ mathematics achievement in urban schools with methodological considerations and analytical suggestions. We are hopeful that researchers interested in students’ mathematics achievement in urban schools will consider accessing and using NCES studies to deepen our collective understanding beyond IES summary statistics featured in reports such as NAEP and TIMMS and tables from The Condition of Education. We believe that studies of large-scale data that are attentive to the methodological considerations of complex sampling, data clustering and causal inference, will contribute nuanced perspectives of promising policy and practice directions for improving disparities in urban secondary school students’ inequitable mathematics achievement outcomes.
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