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MSU Retention Study Preliminary Findings. Acknowledgements. Chris Gilstrap – Graduate Student (Computer Science and B.S. in statistics). Chris Fastnow Phil Gaines & Erika Swanson University of Central Florida. Greg Young and Allen Yarnell for supporting study. Purpose of the Study.
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Acknowledgements • Chris Gilstrap – Graduate Student (Computer Science and B.S. in statistics). • Chris Fastnow • Phil Gaines & Erika Swanson • University of Central Florida. • Greg Young and Allen Yarnell for supporting study.
Purpose of the Study • Develop an understanding of who (incoming freshman) are more likely to “persist” and who are more likely to “withdraw” from MSU using incoming variables (H.S. GPA, H.S. Percentile, ACT Verbal, ACT Math, etc). • Develop a “retention profile” for departments and key centers on campus. • Develop a “profile” of persisters and withdrawers. • Develop an understanding of why persisters and withdrawers leave MSU (what variables may affect departure?). • Develop a sense of MSU’s culture or orientation towards retention (primarily faculty/staff based).
The Odyssey of Data Mining “Data mining means the discovery of knowledge from (a large amount of) data. Data mining should provide not only predictions but also knowledge such as rules that are comprehensible (that permit “next steps”). H. Tsukimoto.
Data Mine • Data mines should be triangulated with qualitative research. • Outcomes from data mining should “inform and track” the process not direct the process. • Data mining can inform an opportunity for improvement and change.
Preliminary Facets in our “Data Mine” • Decision Tree/Logistical Regression of persisters and withdrawers • Descriptive Analysis “college/department” retention profiles • Descriptive Analysis from data set compiled from survey sent to freshman class of 2007. • Inferential Analysis set compiled from survey sent to freshman class of 2007.
Predictive Modeling In essence we want to know: • What must we influence to “load the coin flip?” • Answer probably lies in some (but not exclusively) incoming variables. • If we understand what variables are likely to put a student “at risk” we can respond with a meaningful intervention by influencing incoming variables with “environmental variables.”
Logistical Regression/Decisions Trees • Used UCF as a model. • Attribute 2-4% increase in retention rate using this method. • Efficient - target those “most” at risk with intervening methods. • Student success center • Meet with an advisor at least once per semester usually twice. • “Knight” success program • Residential advising • Targeted communication, etc. • Believe “cross influencing of methods/intervention” is contributing to an improving retention rate 11% increase in 12 years.
Logistical Regression/Decision Trees • Model improves over time as understanding of influences of incoming variables improves (i.e. training the model). • UCF can now predict with 90% accuracy of who their persisters and withdrawers are
Input Variables Considered • Gender • High School GPA • Ethnicity • High School Rank • Age • High School Size • Citizenship • High School Percentile • Home County • ACT Math • Home State • ACT English • Home Region • SAT Verbal • Academic Status (Full/Part Time) • SAT Composite • College • ACT Equivalent • Department • Major • Concentration • Survey Questions 1-100 • University College (yes/no) • Lived in Residence Halls (yes/no) • ACT Composite • Residence Status • SAT Math • Admission Term
What we still need to do…. • Continue to run log reg/trees using other “unconsidered” variables in an attempt to bolster predictability and train the models. • Develop “campus-cultural” appropriate interventions. • Identify “beta” group to test retention interventions.
Retention Profile of Campus Departments/Offices “Run the retention” numbers by college or office affiliation (i.e. College of EHHD, Residence Life) or some other definable category (instate versus out-of-state). • Analysis was directed towards students who withdrew from MSU based upon affiliation with a department or office (or other categorical variable). • Data captured for “first-time freshman.” Changes (i.e. residency, change in major, etc) were not accounted for in the analysis.
Potential Intervention Groups • Out-of-State Students • Provisional Admits • Non Traditional Admits • College of Art, Business, University Studies • First Generation Students • “Off-campus” residential students • Part-time Students
What we still need to do… • Conduct a “progression analysis” of students as they move through the system (where did they “land”?). • Information provides for a good “beta” group to begin testing retention interventions. Example: • freshman living off campus must participate in a program to help them “bond” with the institution • Out-of-state residents could be paired with on campus mentors to host “groups by state” • Develop a “major stalking” program for University Studies students.
Bean Model • From Indiana University • One of the major “theorists” of student persistence/retention • Tinto , Pascarella, Bean • Bean uses “socialization” model to under-pin student persistence theory • Essentially he wants to know if “institutional selection” promotes student retention or socialization methods influence retention methods.
Bean Model • Variables: • Goals (is it important to receive your degree?) • Utility (how good is your education for earning a job?) • Alienation • Faculty/Staff Contact • Social Life • Finances • Opportunity to Transfer • Outside Friends • Institutional Fit • Institutional Commitment • Drop Out Syndrome • Grades
Survey MethodsEntering Class Fall 2007 • Population Group (N=2107) • Emailed/called persisters (n= 1446) • 338 responded (23.3%) • Called/Emailed withdrawers (n=661) • 104 responded (15.7%) • Postcard sent to withdrawers • Still collecting …
Significant Tests Between Groups“Persisters” and “Withdrawers” Differences at the .001 level 1.How difficult was it to leave MSU and transfer to another university or college as good as this one (from an academic stand point)? Persister = 3.12 Withdrawer =3.64 2. How useful do you think your education at MSU will be for securing future employment? Persister = 4.44 Withdrawer = 3.28
Significant Tests Between Groups“Persisters” and “Withdrawers” Differences at the .01 level All in all, how good an education do you think you can get at MSU? Persister = 4.2 Withdrawer =3.61
Significant Tests Between Groups“Persisters” and “Withdrawers” Differences at the .05 level How difficult was it to leave MSU (i.e. loss of friends, change of routine, the application process, etc.) and transfer to another college/university? Persister = 3.55 Withdrawer =3.05
Significant Tests Between Groups“Persisters” and “Withdrawers” Differences at the .10 level 1.Is your academic program at MSU exciting? Persisters = 3.73 Withdrawers =3.37 2. About how many times per semester have you met with a FACULTY member outside the classroom and spoken to them (for 10 minutes or more)? Persisters = 2.43 Withdrawers = 2.24 3. To what extent have you discussed leaving MSU with your friends? Persisters = 1.84 Withdrawers = 2.61
Next Steps • Continue to refine analysis of datasets • Conduct interviews and focus groups on campus • Triangulate findings • Develop invention strategies • Propose intervention strategies to SPOC.
