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Institutional and Student Characteristics that Predict Graduation and Retention Rates

Institutional and Student Characteristics that Predict Graduation and Retention Rates. Braden J. Hosch, Ph.D. Director of Institutional Research & Assessment November 4, 2008 North East Association for Institutional Research Annual Meeting Providence, RI.

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Institutional and Student Characteristics that Predict Graduation and Retention Rates

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  1. Institutional and Student Characteristics that Predict Graduation and Retention Rates Braden J. Hosch, Ph.D. Director of Institutional Research & Assessment November 4, 2008 North East Association for Institutional Research Annual Meeting Providence, RI This presentation and paper are online at http://www.ccsu.edu/oira

  2. Overview • Impetus for Study – Institutional Findings • Methodology • Correlations and Major Factors in Isolation • Results from Regression Analyses • Implications

  3. Caveats • Graduation/retention rates of full-time, first-time students have serious limitations as metrics • Institutions participating in data sharing consortium have a special interest in progress rates • Institutional metrics include only students who enroll at these institutions

  4. Institutional Profile: Central Connecticut State University • Public – part of Connecticut State Univ. System • Carnegie 2005 Master’s-Larger Programs • New Britain, CT (Hartford MSA) • Fall 2008 Enrollment: • 12,233 headcount (9,906 undergraduate, 23% residential); 9,429 full-time equivalent enrollment • 52% female; 17% minority • Full-time, first-time students: 1,303 (57% residential) • Full-time, new transfer students: 779 • Six-year graduation rates: • 46% full-time, first-time students entering F ‘02 • 57% transfer students (full-time upon entry F ‘02)

  5. CCSU Six-Year Graduation Rates and One-Year Retention Rates

  6. CCSU Six-Year Graduation Rates Disaggregated (Entry F’99-F’01)

  7. Graduation Rates of FT, FT Students by Number of Course Grades of D, F, or W Full-Time, First-Time Students Entering CCSU in Fall 2001

  8. Methodology • Data requested from Consortium for the Study of Retention Data Exchange (Appendix 3) for Full-Time, First-Time Cohort Entering Fall 2001 • Institutions missing data about HS performance excluded • Supplemented with Data from IPEDS Peer Analysis System

  9. Institutions in Study Population

  10. Correlations with Six-Year Graduation Rates and Other Progress Rates

  11. Relationship Between Six-Year Graduation Rates and One-Year Retention Rates

  12. Factors that Correlate with Six-Year Graduation Rates

  13. Relationship Between SAT Scores and Success Rates *Includes converted ACT scores

  14. Relationship Between Six-Year Graduation Rates and SAT Scores Math + Verbal SAT Score; includes converted ACT scores

  15. Relationship Between HS Rank and Success Rates

  16. Relationship Between Six-Year Graduation Rate and HS Rank

  17. Relationship Between 1st Semester GPA and Success Rates

  18. Relationship Between Six-Year Graduation Rate and First Semester GPA

  19. Relationship Between Campus Housing and Success Rates

  20. Relationship Between Six-Year Graduation Rate and Housing

  21. Relationship Between Federal Grant Aid and Success Rates

  22. Relationship Between Six-Year Graduation Rate and Federal Grant Aid

  23. Relationship Between Expend On Instruction + Academic Support per FTE on Success Rates

  24. One-Year Retention Rate Regression Model Using SAT Scores Addition of following factors can increase model power by 4.1% (R2=0.681): percent graduating in the top quartile of HS class; percent of cohort receiving student loans, and the percent of the cohort receiving federal grants; Percent of Cohort with 1st Term GPA Under 2.0.

  25. Six-Year Graduation Rate Regression Model Using SAT Scores Addition of following factors can increase model power by 4.5% (R2=0.811): Percent of all undergraduates who attend part-time, baccalaureate institution (dummy var.), percent graduating in the top quartile of HS class; percent of cohort receiving student loans, and the percent of the cohort receiving federal grants.

  26. One-Year Retention Rate Regression Model NOT Using SAT Scores Addition of following factors can increase model power by 6.7% (R2=0.662): percent of the cohort receiving federal grants; expenditures on instruction and academic support per FTE; percent of cohort with a 1st term GPA under 2.0, public (dummy var.); percent of undergraduates who attend part-time, and percent of the cohort receiving student loans.

  27. Six-Year Graduation Rate Regression Model NOT Using SAT Scores Addition of following factors can increase model power by 4.5% (R2=0.811): Percent of all undergraduates who attend part-time, baccalaureate institution (dummy var.), percent graduating in the top quartile of HS class; percent of cohort receiving student loans, and the percent of the cohort receiving federal grants.

  28. Six-Year Graduation Rate Regression Model Using Academic Inputs ONLY

  29. Implications and Conclusions (1) • Results confirm and extend previous research: • Most predictive factors: • Admission inputs (SAT, followed by HS rank) • Proportion living in campus housing • First semester performance • Race, gender, and SES appear not to add significant predictive power AFTER controlling for above factors

  30. Implications and Conclusions (2) • Policy implications: • Evaluate institutional graduation rates in the context of an expected graduation rate • Communicate realistic expectations to stakeholders

  31. Implications and Conclusions (3) • Recognize the impact of academic inputs BEFORE and DURING college experience • Selectivity is a significant factor that intersects degree production as well as access; consider implications of resource allocation in context of degree yield rates • Set incentives to promote performance during college, e.g. loan forgiveness vs. merit-based scholarships

  32. Implications and Conclusions (4) • Gaming the system - Institutions may continue to realize incentives to inflate grades

  33. Implications and Conclusions (5) • Arms race in selectivity will be exposed by demographic change in next decade; downward pressure on graduation rates is likely Projections of Graduates of Public High Schools, by Racial and Ethnic Group in North East White, Non-Hispanic Hispanic Black, Non-Hispanic SOURCE:Knocking at the College Door (2008, Western Interstate Commission for Higher Education) Reproduced in The Chronicle of Higher Education 54.29. Asian/Pacific Islander

  34. Institutional and Student Characteristics that Predict Graduation and Retention Rates Braden J. Hosch, Ph.D. Director of Institutional Research & Assessment November 4, 2008 North East Association for Institutional Research Annual Meeting Providence, RI This presentation and paper are online at http://www.ccsu.edu/oira

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