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

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

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: This report provides information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests.

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: 18
New Economics Papers: this item is included in nep-ure
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Citations: View citations in EconPapers (1)

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