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

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

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: The study team collected and linked five academic years of student-level administrative data from Pittsburgh Public Schools (PPS), Propel Schools, and the Allegheny County Department of Human Services (DHS).

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)
Pages: 40
New Economics Papers: this item is included in nep-ure
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