Revisiting Retention: A Four Phase Retention Research Initiative 2012 SLOAN Conference October 10 th, 2012 Gary J. Burkholder, PhD Senior Research Scholar Center for Research Quality Jim Lenio, MS Interim Executive Director Office of Institutional Research and Assessment Nicole Holland Senior Research Analyst Office of Institutional Research and Assessment

Walden University Mission Statement “Walden University provides a diverse community of career professionals with the opportunity to transform themselves as scholar-practitioners so that they can effect positive social change.” 2 General Information Accredited by HLC Private, For-Profit Institution Primarily Online Undergrad & Grad Programs in: o Education o Social & Behavioral Sciences o Health Sciences & Nursing o Management & Technology Student Demographics Fall 2011 Enrollment = 50, % Undergraduate 82.5% Graduate 74.8% Female 46.8% Minority Average Age = 39 years

Literature Review College student retention continues to be a national priority. Tinto (1993) developed models primarily describing students in traditional university settings. Lovitts (2001) examined in detail factors associated with doctoral student attrition; again, her analysis focused on qualitative data from students in traditional doctoral programs. Drouin (2008) found mixed results regarding the role of sense of community for online students. More research is needed to understand what predicts success in online programs, particularly those that attract non-traditional students. 3

Four Phase Retention Initiative 4 Phase 1: Create Retention Profiles Descriptively present how retention differs on a series of measures. Model using logistic regression to predict retention at six months and one year. Phase 2: Models using Survival Analysis Use survival analysis to show conditional probabilities of retention over multiple terms. Phase 3: Analyze Retention Mechanisms Specific, in-depth analyses isolating factors to identify the underlying causal mechanisms and why they have or don’t have an impact. Phase 4: Study Retention Interventions Look retrospectively to examine success of retention interventions.

Possible Retention Predictors 5 Cognitive Institutional Economic Social Demographic Prior Online Experience Understanding Program Req. Expectations re: Instruction Expectations re: Performance Expectations re: Time Time Spent Studying Parent’s Education First Term GPA Skills Assessment Satisfaction with instructors Satisfaction with Course Design Satisfaction with Orientation Satisfaction with Acad. Advising Overall Satisfaction Likelihood to Recommend Number of Referrals Balancing Studies with Work Balancing Studies with Family Employment Status Financial Aid Feelings of Closeness Number of Children Lives with Spouse / Partner Commitment to Walden Geographic Location Age Ethnicity Gender Amount of Transfer Credit

Method & Sample 6 Method Identify significant predictors of retention. Create database of retention & predictor data integrating student satisfaction and Banner data Impute missing data using MVA in SPSS.  This procedure is an accepted procedure to handle item and unit non- response and results in less bias in parameter estimates. Run six month and one year retention models using logistic regression. Sample Criteria Students who started during the Summer or Fall 2009 term AND completed the 2009 Student Satisfaction Survey. Degree Level Model N Size (survey resp. / imputed data) Bachelors885 / 3,191 Master’s2,803 / 8,330 Prof. Doctorate405 / 1,174 PhD873 / 2,009

Graduate Retention Models 7 Predictors ( Exp. b provided if p<.05) Six MonthsOne Year MastersProf Doct.PhDMastersProf Doct.PhD Academic Advising Satisfaction Instructor Satisfaction Work/Family Balance Feelings of Closeness Course Satisfaction Time Spent Studying (> 5 hours) Employment Status (Employed) Mother's Education.968 Financial Aid (Yes) Understanding Requirements.923 First Term GPA Gender (Female) Ethnicity (Minority) Model Fit (Pseudo R ^ 2 Nagelkerke)

Undergraduate Retention Models 8 Predictors ( Exp. B provided if p<.05) Six MonthsOne Year Instructor Satisfaction Work/Family Balance Course Satisfaction Commitment to Walden Commitment to Any University Time Spent Studying (> 5 hours).814 Employment Status (Employed) Financial Aid (Yes).481 Understanding Requirements Skills Assessment Score.992 Number of Transfer Credits (zero) 1 to 60 credits More than 60 credits First Term GPA Age Gender (Female) Ethnicity (Minority).590 Measures of Model Fit *Pseudo R ^ 2 Nagelkerke Six Month Model =.364 One Year Model =.454

Key Ideas First term GPA  strongest predictor of retention at six months and one year. For Undergraduate students, long term retention is associated with: (when accounting for first term GPA)  Work-Family Balance, Commitment to University Education, more transfer credits, being female, and being minority. For Graduate students, long term retention is associated with: (when accounting for first term GPA)  Instructor satisfaction, course satisfaction, work-life balance, being minority ethnicity. 9

Key Ideas Cont’d Demonstrates the importance of modeling retention by sub-populations.  We are also looking at retention models by program. Work-life balance appears to be unique with online students.  This has not been seen much in the literature and requires further examination. 10

Next Steps Begin Phase Two: Survival Analysis Models. Pilot retention initiatives (focus on first year).  Vantage Writing Assessment  Face to Face Orientation  Faculty Intro Videos Design research to focus on significant retention predictors. 11

Questions…Comments…Contact Info Thank You! Gary Burkholder  Jim Lenio  Nicole Holland  12