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Measuring the Effect of Freshman Mentoring on Retention

Measuring the Effect of Freshman Mentoring on Retention. Joe Jurczyk Stephanie Triplett Cleveland State University Presentation at 2004 EERA Annual Meeting February 12, 2004. Freshman Year Experience.

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Measuring the Effect of Freshman Mentoring on Retention

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  1. Measuring the Effect of Freshman Mentoring on Retention Joe Jurczyk Stephanie Triplett Cleveland State University Presentation at 2004 EERA Annual Meeting February 12, 2004

  2. Freshman Year Experience • In terms of keeping students enrolled at a school (retention), the freshman year represents the period in a student’s academic life when he or she is most likely to leave an institution (Levitz, Noel, & Richter, 1999).

  3. Freshman Year Experience Barriers to education are identified by Cross (1981) as coming in three forms: • institutional • situational • dispositional

  4. Freshman Year Experience Programs • pre-college programs • bridge programs • mentoring programs • development education programs • counseling • academic skills improvement • special services

  5. Mentoring Programs • Mentoring programs couple a student with a faculty or staff member who can provide the student with assistance in their academic endeavors

  6. Mentoring Program (study) Program in this study: • Voluntary • Monthly Events • Regular communication between mentor/mentee • Approximately 100-150 mentees annually

  7. Mentoring Program (study) Goals: • to increase institutional participation of incoming new students • to impact retention of these students

  8. Mentoring Program (study) • Institutional Statistics: • Freshman retention: 63% (overall) • Range: • 67%: White, Asian-Americans • 58%: Hispanic • 53%: Black • 50%: Native American

  9. Retention • Definition: In this study a student is considered retained for the Fall semester if he/she returns to the institution the following Fall semester. (i.e. second-year retention)

  10. Research Question • Is there a relationship between participation in a freshman mentoring program and second-year retention independent of race, gender, age and standardized test scores?

  11. Methodology Data Collection: • List of Mentees from Office of Student Life: • Student ID’s Demographics and Enrollment Information from Office of Institutional Research • Student ID’s • Test Score • Course Load • Gender • Age • Race • Semester Enrollment

  12. Methodology

  13. Methodology • Ex post facto study • Basic Statistics • Logistic Regression

  14. Results – Basic Statistics (all)

  15. Results – Basic Statistics (mentees)

  16. Logistic Regression • What is….. Logistic regression predicts a dichotomous (binary) variable from a combination of independent variables

  17. Logistic Regression • Probability / Logit model ln((P/(1-P)) = a +bX where ln is the natural logarithm function P = the probability of the outcome being equal to 1 P/(1-P) = the odds of the outcome being equal to 1 a + bX = the linear combination of variables being tested

  18. Graphic Representation • An Introduction to Logistic Regression

  19. Logistic Regression Models Model 1 Returnfull=a0U + a1Mentee + a2Test + a3Hours + E Model 2 Returnfull=a0U + a1Mentee + a2Test + a3Hours + a4Male + a5Female+ a6Age + E Model 3 Return full=a0U + a1Mentee + a2Test + a3Hours + a4Male + a5Female+ a6Age + a7White +a8Black+a9Hispanic + a10Asian + a11Native + a12Unknown + a13NonRes + Ewhere U is a constant (unit vector) and E represents the error component.

  20. Results – Logistic Regression (Return = dependent)Beta coefficients and model stats

  21. Conclusions • Mentoring Participation does have a positive relationship with retention independent of age, gender, hours, test score, race • Model improves with more variables but still not significant at the .05 level

  22. Limitations • One institution • No measurement of the degree of participation • Ex post facto

  23. Future Research • Use more detailed participation information • Look at other FYE programs • Look at year-by-year • Experimental design

  24. References – Freshman Year Experience • Levitz, R., Noel, L. & Richter, B. Strategic moves for retention success. (1999). New Directions for Higher Education, 108 (Winter), 31-49. San Francisco: Jossey-Bass. • Upcraft, M.L. & Gardner, J.N. (1989). The Freshman Year Experience. San Francisco: Jossey-Bass.

  25. References – Logistic Regression • Hosmer, D.W. & Lemeshow, S. (2000). Applied Logistic Regression (2nd Edition). New York: John Wiley & Sons. • Menard, S. (2001). Applied Logistic Regression Analysis. Newbury Park, CA: Sage Publications. • Whitehead,J. (n.d.) An Introduction to Logistic Regression. http://personal.ecu.edu/whiteheadj/data/logit/intro.htm

  26. Contact Information • Presenters Joe Jurczyk : jjurczyk@csuohio.edu jurczyk@apk.net Stephanie Triplett: s.triplett@csuohio.edu Cleveland State University: http://www.csuohio.edu Institutional Research: http://www.csuohio.edu/iraa Department of Student Life: http://www.csuohio.edu/student-life

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