AGEP Evaluation Capacity Building Meeting: Building a Data Collection Infrastructure at the Graduate School Level Panelist: Maia Bergman University of Michigan MAA Evaluation Coordinator

The Panel Charge: Focus on the building of a data collection infrastructure related to graduate students and their progression to the PhD. Offer how your system or institution developed its data collection infrastructure at the graduate level Share the lessons you learned, and Give advice to other AGEP Alliances

History: Interest in Doctoral Completion and Retention Research, literature, and data driven doctoral education concerns now span 60+ years –1945: Hollis, Toward Improving Ph.D. Programs [1930s data] –1960: Berelson, Graduate Education in the United States [1950s data] –… –1992: Bowen and Rudenstine, In Pursuit of the Ph.D. [1980s data] –Mid-1990s: Mellon Foundation: Graduate Education Project, and the UC-Berkeley Ph.D. 10-years later study –Later-1990s into 2000s: AGEP (antecedent: MGE) [ s data] –2004: Pfizer/Ford and CGS partnership [ s data] –Forthcoming NRC assessment of research doctoral programs (and antecedents)

Research Interests and Overarching Concerns Research has been driven by interests in: –understanding the nature of the doctoral education process –improving the enterprise (institution level, national level, and world scope) Concerns : –replenishing the academic workforce and meeting national needs –efficiencies in the production process –diversifying the communities –assessing the impact on individuals’ lives during the process as well as after graduation, etc.

Who we are often drives what we do Leadership –Has responsibilities –Involves investments –Requires sharing and collaboration University of Michigan –Invests in its own doctoral education analysis and reporting capacity (since the 1970s; housed in the Rackham Graduate School) – manages its own data repositories and invests in analytic staff –Invests in the university-wide information infrastructure developments, e.g., data warehouses, ad-hoc query tools, and business intelligence opportunities –Participates in many of the large scale research projects/collaborations fostering multi- institution comparative analyses regarding doctoral education

What we do drives our infrastructure (1) Developed an extensive Graduate School data mart –Derive extracts from University sources (data warehouses and production systems) recruiting and admissions enrollments and registration (courses, grades, etc.) financials (student support, costs, etc.) degrees, etc. –Extract the smallest denominator of data item available term specific information for enrollment application level information, etc. –Create derivative measures summary measures (counts, means and medians) time calculations (years to Candidacy, Ph.D., etc.) other (percentage calculations, constant dollar adjustments by base year)

What we do drives our infrastructure (2) Sustain an environment to store and manage resources –Network (secured environment, backups) –Space (allow for growth potential: 10-year+ series, 100,000s of records) –Access for multiple staff Use variety of tools to manage and display data, analyses, & reports –MS-Access, Excel –Stata, SPSS, SAS –Developing dashboard technology applications, e.g., Xcelcius

Our infrastructure enables our work Analyses, reporting, and research project participation –Provide aggregate data –Provide individual record data –Fit data to varying disciplinary taxonomies –Meet the need, e.g., census versus samples for analyses and surveys

Assure our interests are incorporated in the university-wide environment Invest in the university-wide information infrastructure development –data warehouse design, production system needs –query tools: ad-hoc, production systems –business intelligence needs and strategies

Infrastructure - Staff Resources and Capacity Database design experience Data handling capacity Knowledge of the content and definition of university resource data Statistical methodology experience Effective communication with leadership