1. AAU Graduate Education Taskforce: Progress, Plans, Issues Presented to: AAUDE Annual Meeting May 2005

2. AAU/AAUDE groups and projects focused on data, spring 2005 You can’t tell the players (and groups) without a scorecard!

3. The Institutional Data Committee (IDC) Meetings April, October Presidents John Wiley (chair), Wisconsin John Casteen, Virginia Scott Cowen, Tulane Larry Faulkner, Texas Richard Levin, Yale Richard Herman, Illinois Provosts John Anderson, Case Western Paul Courant, Michigan Debbie Freund, Syracuse Marty Wyngaarden Kraus, Brandeis Dave Shulenburger, Kansas. Institutional Researchers/AAUDE Rana Glasgal, Stanford Bill Hayward, Northwestern Dennis Hengstler, UC-Berkeley

4. Graduate Education Data Task Force Meetings March, late August Graduate Deans Dick Attiyeh (chair), UCSD Karen Klomparens, Mich State Lawrence Martin, Stony Brook Lewis Siegel, Duke Bob Thach, Washington U Graduate School Reps T. Jim Matthews, NYU Judi Sui, UC Berkeley Harvey Waterman, Rutgers Researchers on Graduate Ed Maresi Nerad, UWashington Rachelle Brooks, Maryland (also AAUDE) Institutional Researchers/AAUDE Julie Carpenter-Hubin, Ohio St Bernard Lentz, Penn Jed Marsh, Princeton Lou McClelland, Colorado Lydia Snover, MIT Kendrick Tatum, Duke

5. Grad Ed Taskforce Subcommittees Reports due August 1 Program, institutional data Lawrence Martin, chair, Stony Brook Karen Klomparens, Mich St Lewis Siegel, Duke Harvey Waterman, Rutgers Bob Thach, Washington U Julie Carpenter-Hubin, Ohio State, AAUDE Lou McClelland, Colorado, AAUDE Student experience, student reported surveys Rachelle Brooks, chair, Maryland (AAUDE) Judi Sui, Berkeley Lydia Snover, MIT, AAUDE Kendrick Tatum, Duke, AAUDE Outcomes, placement, alumni Maresi Nerad, chair, UW Jim Matthews, NYU Barney Lentz, Penn, AAUDE Jed Marsh, Princeton, AAUDE

6. Goal: ID core data elements to be shared by AAU member institutions Used by members to guide institutional and program policy decisions and practices Not for prospective students or the public Not for rankings Data may be used to inform national discussions of graduate education and outcomes

7. Emphasis: Doctoral education Master’s programs or awards associated with doctoral programs included for completeness. Professional degrees, MBAs, and stand- alone master’s programs not an explicit focus

8. Program level data Unit of analysis/comparison: Programs or departments, often by degree level Issue: Need the division into programs be Mutually exclusive? Exhaustive? Issue: Should campus totals come from Summing over all programs Separate report

9. Priority topics = subcommittees Student experience, from student surveys Student placement, outcomes, alumni Not just immediately after the degree, but later Institutional or program data Admissions Retention, graduation rates and time-to-degree Financial support Enrollment/ demographics

10. Emphasis: Data to answer questions, use in management Examples: Placement Where graduates go Correlation between admission credentials and first professional placement Program Time to complete a doctoral degree Fraction of students graduating with a PhD Changes in the demographic profile of students applying for doctoral programs Financial Support Cost to support a PhD student Student surveys How students assess their doctoral experience

11. Operating principles endorsed by the Grad Data Taskforce Sufficient safeguards must be in place to ensure the privacy of individuals Self-reported student data should be use only when institutional data are not available Whenever possible data should be collected by degree level at the academic program level. The reporting of summarized data (e.g., percentages) should avoided as much as possible Even unit record data under consideration Data should be submitted electronically in a disaggregated format that facilitates inclusion in the data warehouse, in data files not in Excels. Each data element should be clearly defined and documented in a data dictionary that compares and contrasts similar data elements commonly available to the public.

12. Known needs and issues Definitions, especially for graduation rates and time to degree Discipline crosswalks and rollups among the NRC, IPEDS, NSF-SED, CGS, and other data collection systems. Grad deans realize there’s no right answer, want just to do something and not get bogged down on this. Programs vs. departments vs. disciplines Labeling vs. grouping; level of detail Rules about data release, including rules that appropriately limit the reporting of small cell sizes Ways of ensuring that data collected will allow meaningful aggregation

13. Known needs and issues (continued) Relationship to NRC, and NRC plans and timing both for scheduled review and further updates Fit to, extension of, exploitation of ongoing large-scale required or high-coverage data collections SED – Confidentiality agreements, local capture with supplemental questions, utility for things also in records. Already in warehouse for MIT, Florida, Colorado. IPEDS completions and enrollment CGS/GRE survey of grad enrollment CGS PhD completion project AAUDE exchanges such as grad stipends NSF Survey of Graduate Students and Postdocs

14. Timing, deliverables Subcommittee reports due August 1 Prioritized list of at least five key questions of interest to policy makers Set of data elements required to address the questions Circulate to entire Taskforce Meet late August or early September to discuss the sub-committee reports and prepare a draft report Report to provosts, AGS, and IDC before presidents’ meeting in October

15. AAUDE Reps: Accomplishments The “white paper” ftp://aaude.mit.edu/IDC/Grad/graded_paper20050320.doc Official name Graduate Student and Graduate Education Data Needs The glossary ftp://aaude.mit.edu/IDC/Grad/graded_glossary.doc Concordance of items on various student surveys Grad stipend exchange item Grad CDS This presentation!

16. White paper: Useful for you Julie, 2003, augmented by Julie, Jed, Lydia, Lou, 2005, used at 3/05 task force meeting Topics: numbers and demographics; credentials; financial support; student experience including graduation rates and time to degree; career track; policies. Sources discussed re coverage, availability, issues, recommendations

17. White paper: Sources covered CGS/GRE Survey of Graduate Enrollment Survey of Earned Doctorates (SED, to the DRF Doctoral Research File) Self-reports by students of records- type info IPEDS Fall Enrollments NSF Survey of Graduate Students and Postdoctorates in Science and Engineering Thomson Peterson’s Annual Survey of Graduate and Professional Institutions IPEDS Completions US News & World Report America’s Best Graduate Schools GRE Summary Statistics Reports Rutgers Graduate Education Survey (collection from selected PhD programs at 6-8 AAU’s, with both student survey and records information) Graduate CDS AAUDE Graduate Student Stipends Survey Rutgers and Duke/MIT student surveys CGS PhD Completion project NAGPS National Doctoral Program student survey Placement surveys by academic associations and other researchers, including the “Ph.D.’s-Ten Years Later Study” NSF Survey of Doctoral Recipients re science and engineering doctoral graduates. Institutional or program collections of placement data Responsive PhD, Re-envisioning the PhD, and Carnegie Initiatives on the Doctorate

18. AAUDE Reps: Endeavors Paper on cohort definition, time to degree, grad rates, etc. Build on work by Colorado, Maryland, and Wisconsin, especially for the CGS completion project Analysis of SED re time to degree for the schools with data in the warehouse. Making Penn’s mechanisms available to others, for collecting postdoc and first professional placement Possibilities for release of SED data from all AAU’s to the warehouse, and/or local or coordinated web administration with data to the warehouse Work on crosswalks, rollups, level of detail, etc. for PhD programs in particular.

19. AAU Graduate Education Taskforce: Progress, Plans, Issues Presented to: AAUDE Annual Meeting May 2005