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

Author

Listed:
  • Julie Bruch
  • Jonathan Gellar
  • Lindsay Cattell
  • John Hotchkiss
  • Phil Killewald

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).

Suggested Citation

  • Julie Bruch & Jonathan Gellar & Lindsay Cattell & John Hotchkiss & Phil Killewald, "undated". "Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks, Appendixes," Mathematica Policy Research Reports 1386d532a9684dda96c3589da, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:1386d532a9684dda96c3589da4052428
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    File URL: https://www.mathematica.org/-/media/publications/pdfs/education/2020/rel_using-data-from-schools-and-child-welfare-agencies_appendices.pdf
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    References listed on IDEAS

    as
    1. Matthew T. Johnson & Stephen Lipscomb & Brian Gill, 2015. "Sensitivity of Teacher Value-Added Estimates to Student and Peer Control Variables (Journal Article)," Mathematica Policy Research Reports 4a9776a57ae9477e80df47e7d, Mathematica Policy Research.
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