Extracting Useful and Targeted State-Level Data from IPEDS

1. Extracting Useful and Targeted State-Level Data from IPEDS Experiences from the Land of 10,000 Lakes

2. Minnesota Measures • First of a planned annual series of reports related to accountability • Timeline • May 2005 (initial charge to work on project) • August 2005 (appropriations, NCHEMS contract, initial meetings) • November 2005 (discussion of goals and indicators) • January 2006 (statewide meetings) • March 2006 (review of goals and indicators) • June 2006 (NCHEMS final report) • September 2006 (meetings with systems)

3. Indicators – Goal 1Improve success of all students, particularly students from groups traditionally underrepresented in higher education. • College participation rates • IPEDS data • First to second year retention • 3-, 4-, and 6-year graduation rates • Degrees awarded as a proportion of total headcount enrollment • Degrees awarded in critical fields (STEM and healthcare), disaggregated by race/ethnicity • Proportion of young adults (25-34) in the state holding a postsecondary degree

4. Indicators – Goal 2Create a responsive system that produces graduates at all levels who meet the demands of the economy. • Credentials awarded at each level (IPEDS), per 1000 people 20 and older in the state’s population (ACS). • Proportion of credentials awarded at each level in STEM fields and number awarded (IPEDS) per 1000 people 20 and older (ACS). • Proportion of credentials awarded at each level in healthcare fields and number awarded (IPEDS) per 1000 people 20 and older (ACS).

5. Indicators – Goal 3Increase student learning and improve skill levels of students so they can compete effectively in the global marketplace. • Did not gather data in this area for the initial report. • Currently looking into a variety of assessment instruments. • Aware that IPEDS COOL will incorporate assessment results in the future.

6. Indicators – Goal 4Contribute to the development of a state economy that is competitive in the global market through research, workforce training and other appropriate means. • The share of national academic research being done in Minnesota • The ranking of the University of Minnesota on various studies of research activity (University of Florida report, London Times, Shanghai study, and Newsweek) • Total research expenditures in the state as a proportion of gross state profit

7. Indicators – Goal 5Provide access, affordability and choice for all students. • Proportion of residents aged 18-24 and 25-44 participating in postsecondary education (ACS) • Assigned family expectation (OHE data) • Using NPSAS data • Net tuition (after grants and scholarships) • Average borrowing and rate at which students borrowed

8. Some Findings • In general, Minnesota does not consistently rank among the top states. More often, we’re near the national average. • The degree attainment of our citizens is high, but that is due in part to in-migration of college-educated citizens from other states. • Native American, Black, and Hispanic students in Minnesota do not do well in college compared to their white and Asian counterparts.

9. Using the Dataset Cutting Tool (DCT) to get State-Level Data • Create a custom dataset • Advantages • Web-based interface • Very customizable, can get data from multiple files over multiple years • Can create a file that you can download • Disadvantages • Interface can be cumbersome • Time-out issues • Limited to 1,000 institutions in creating the file

10. Using the Dataset Cutting Tool (DCT) to get State-Level Data • Download entire data file • Advantages • Very straightforward • You get all of the data for all institutions • Can be imported into a program like SPSS, SAS or Access for report generation • Disadvantages • You get all of the data for all institutions, which includes a lot of imputation fields • Data dictionaries are cumbersome

11. What We Did • Download entire data files • Import into an Access table, to provide control over • Which fields were brought in • The data type of those fields • The names of the fields • Consider a sample • Building queries helps a great deal, as results can be copied/pasted into Excel for easy manipulation • Why not just take data directly to Excel? • Limits on Excel table size

12. Successes • Getting state-level data is reasonably easy • Crosstab queries • Reports • State and national averages • Beware of averages of averages • Actual averages reasonably easy

13. What We Will Do Differently (regarding our use of IPEDS data) • Degrees awarded as a proportion of total headcount enrollment will be rethought • Goal: incorporate part-time students into a degree completion measure • Needs more context • May talk more about and give more detail about transfers out • No more mixing of IPEDS and ACS data • May look more deeply into the use of DAS

14. Observations • Data dictionaries (e.g., imputation variables) • Variable names in general • Lack of retention data by race • Which is being addressed in part by new IPEDS data collection procedures • But only for SMART grant fields • Loss of transfer students and part-time students in computation of graduation rates

15. For More Information • On Minnesota Measures • The on-line version is available at: • http://www.ohe.state.mn.us/mPg.cfm?pageID=1733 • You can also download a .pdf version of the entire report from this page. • On the Office of Higher Education • http://www.ohe.state.mn.us • jim.bohy@state.mn.us