Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks, Snapshot

Julie Bruch, Jonathan Gellar, Lindsay Cattell, John Hotchkiss and Phil Killewald

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: Pittsburgh Public Schools (PPS), the Propel Schools charter network, and the Allegheny County Department of Human Services (DHS) want to better identify students at risk for academic problems in the near term.

Keywords: attendance; data analysis; dropout prevention; dropout research; grades (scholastic); prediction; predictive measurement; predictive validity; predictor variables; standardized tests; statistical analysis; suspension (search for similar items in EconPapers)
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