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Measuring (and Managing) the Costs of Student Attrition

Measuring (and Managing) the Costs of Student Attrition. State Policy Workshop: State Higher Education Executive Officers Chicago, August 8, 2012. Nate Johnson Postsecondary Analytics 423 East Virginia Street Tallahassee, Florida 32301. www.postsecondaryanalytics.com

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Measuring (and Managing) the Costs of Student Attrition

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  1. Measuring (and Managing) the Costs of Student Attrition State Policy Workshop: State Higher Education Executive Officers Chicago, August 8, 2012 Nate Johnson Postsecondary Analytics 423 East Virginia Street Tallahassee, Florida 32301 www.postsecondaryanalytics.com @NateJohnsonFL

  2. About the Cost of Attrition Project • Initiative of the Delta Project on Postsecondary Education Costs, Productivity, & Accountability • Work undertaken by Jane Wellman, Donna Desrochers, Colleen Lenihan, Patricia Steele, Nate Johnson • Funded through a grant from the Bill & Melinda Gates Foundation • Research and consultations took place 2010-2011 • Delta Project now housed at the American Institutes for Research

  3. Questions We Sought to Answer • What is attrition? • How should it be defined for public policy? • How should we talk about it? • How much does higher education spend on students who do not finish? • What are the most helpful ways to disaggregate attrition costs? • How can better understanding of attrition and costs improve policy and practice?

  4. Defining Attrition • Alternative focus to graduation, but not mirror image • Intentionally conservative definition of “attrition”

  5. Six-Year Same-Institution Attrition Rates = 61%                                        Only 39% of new postsecondary students complete within six years at the same institution where they started •  •           •           •           •           •          •          

  6. More Conservative Definition of Attrition = 35% Students who, within six years…                                        Completed at first institution attended: 38.8%           Completed at another institution: 10.6%                                            Still enrolled: 15% Did not complete anywhere, no longer enrolled: 35.5%

  7. Defining Costs • Beginning Postsecondary Students 2004/09 restricted use data • Linked each student to institution-level IPEDS data • Calculated expenditures per FT student (Delta Project “Education and Related Expenditures”) • Calculated cumulative cost for each student

  8. Costs for Students With and Without Degrees

  9. Proportions of Outcomes and Costs Differ Outcomes and Cumulative Education and Related Costs for BPS 2004/09 Students

  10. Magnitude of Attrition Cost Varies by Sector

  11. Attrition Magnitude and Costs

  12. Later/Early Leavers Cite Different Reasons

  13. Policy and Practice Implications for States • More reason to prioritize “near-completers” • Suggests different types of interventions and investments: • Broad-based efforts for early intervention, where volume is so high • More focused efforts on later-stage at-risk students, where cost per student is high • Potential to further disaggregate costs and causes, model different types of high- and low-cost strategies to reduce attrition • Other ideas? How could this type of analysis help?

  14. Financial Aid Investments and Attrition • Positive but small returns to aid investments • Where is biggest potential impact on completion rates? • Middle terms/years focus? • Risk profiles more accurate: ability to predict diagnose and intervene • Potentially greater ROI • More analysis & experimentation needed

  15. Some Studies Show Larger Impacts of Aid Programs After First Semester (Example: MDRC Performance-Based Scholarships)

  16. Key Recommendation: Look at Current Attrition • Do not wait six years to calculate a graduation rate • Look every year at year-to-year attrition and retention patterns • Who is dropping out between first- and second-year? • Second and third? Third and fourth? • What do you know about these students? • Academic status, loan burden, institutions, regional employment trends, UI records…

  17. Projecting Attrition (and Retention and Graduation) • Cohort studies not always practical or useful • Length of time needed poses problems • Data availability & quality • Measuring phenomena long ago • Alternative: estimate attrition using enrollment projection tools • Markov chain method uses most recent year-to-year retention/dropout rates • Only two-three years of student data needed

  18. Questions or comments? • Materials will be released soon on the Delta Project website: www.deltacostproject.org • Feedback and questions welcome: nate.johnson@postsecondaryanalytics.com

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