Scorecard and Salary Surfer: An Update and Next Steps

1. Scorecard and Salary Surfer: An Update and Next Steps Alice van Ommeren, Dean of Research Technology, Research & Information Systems

2. Student Success Task Force • Create Student Success “Scorecard” • High order outcomes • Include momentum points • Focus on past performance • Expand populations measures • Build upon existing ARCC framework • Use existing MIS data, no new data collection • Improve transparency

3. Developing the Scorecard • Scorecard Technical Advisory Committee • Met in 2012 and will meet in Fall 2013 • Created new and expanded data definitions • Refined focus on final outcomes and significant momentum points • Simplified levels of reporting and identified proper reports for different audiences

4. The Reporting Triangle

5. Scorecard Metrics • Momentum/Milestone Points • Three Term Persistence Rate • 30Unit Completion Rate • Completion/SPAR Rate • Remedial Completion Rate • CTE Completion Rate • CDCP (non-credit ) Completion Rate

6. Scorecard Metrics • Who is Degree/Transfer Seeking? • We use “behavioral intent to get degree/transfer” • Who are Remedial students? • Attempted any remedial, non degree-applicable math, English or ESL course • Who is CTE seeking? • Completing 2 courses in the same CTE discipline • Why use first-time student or first course taken?

8. Who is Counted and Who is Not? • For any given first-time freshman class: • 65% of total headcount accounted for • 90% of total FTE accounted for • Of the 35% headcount not counted: • Single course takers (75% of the 35%) • Longer-term PE enrollees • Students that take courses, but never take math or English • Long-term stop outs

9. Scorecard: What’s Next? • Scorecard metrics on the DataMart • district level, multiple crosstabs, special populations • Advisory group reconvenes in October • Stateof the System Report in November • Further exploring the Scorecard metrics • Skills upgrade metric, non-credit, etc. • Scorecard for other reporting requirements • Guidelines to measure the achievement gap

10. Measuring the achievement gap • Developing guidelines for researchers • Several methodologies, including “proportionality” • Comparing subgroups between two conditions • Students in Cohort vs. Students in Outcome • Result is a ratio or index • 1.0 is equally present in both groups • < 1.0 is less prevalent in outcome • > 1.0 is more prevalent in outcome

13. Wage Tracker Framework • Salary Surfer • Public, by discipline, 5 cohorts of graduates • System Wage Tracker • Same as salary surfer, datamart, % matches • College Wage Tracker • College level, by discipline, 8 cohorts, 3 years after • Wages by Program • Data-on-Demand, college password, by program

14. Methodology • Two different methodologies • System wage tracker and college wage tracker • Completers or graduates are joined with EDDUI wage data using SSNs • Both wage methodologies break out by award discipline and award type • 6-digit Taxonomy of Program (TOP) code • Certificates and Associate Degrees

16. Data Caveats • EDDUI Data contains wages of occupations covered by CA Unemployment Insurance • Excludes military, federal government, self-employed, out of state and unemployed • Not able to determine part-time or full-time wages, hours worked not in the EDDUI data. • Only students with SSNs (11% missing) • Wages are adjusted for inflation to current dollars using the California CPI-U

17. Analysis Caveats • Region/locale significantly impacts wages, so caution in comparing earnings across the state • Wages are not necessarily from employment associated with a particular award discipline • Wage outcomes should not be a sole measure of institutional effectiveness/program quality • Other factors besides earnings motivate students to earn an award in a specific discipline

18. High ROI Programs • Health fields (by far) • Paramedic, RN/LVN, Rad/Cardio, Resp./PT/PA, Dental, PsychTech, Health Info Tech • Police & Fire Academies, Protective Svcs, AJ • Wastewater/Environmental Control • Electronics/Electric Tech • Plumbing/Fabrication • Computer Networking, CIS

19. Lower ROI Programs • Cosmetology* • Fashion/Interior Design • Early Child Dev’t • Fine/Liberal/Graphic Arts/Music • Culinary Arts • Things with “Assistant” • “Transfer” Degrees (w/out Transfer)

20. Salary Surfer: What’s Next? • Additional research on wage data • Explore the use of Industry (NAICS) Codes • North American Industry Classification System • Wages by demographics and regions • Highlight how colleges are using the tools

21. Alice van Ommeren, Dean of Researchavanommeren@cccco.edu

22. System vs. College Methodology System College

23. Salary Surfer (System Wage Tracker) • Wages by Discipline (TOP codes) • http://salarysurfer.cccco.edu/SalarySurfer.aspx • Measured median wages at: • 2 years before award date (previous employment) • 2 years after award date (roughly, starting salary) • 5 years after award date (roughly, journey salary) • Minimum n=10 wage matches • Includes listing of colleges with programs

24. Wages by College by Discipline • Wages by Discipline by College • http://datamart.cccco.edu/Outcomes/Default.aspx • Median wages for 8 years of graduates at: • 3 years after award date • Timeline for measuring 8 years of cohorts at -2 to +5 was too long (15 years)

25. College Profile • Demographics • Gender, Ethnicity/Race and Age Groups • Full-time Equivalent Students (FTES) • Number of Credit Sections • Number of Non-Credit Sections • Median Credit Section size • Percent of Full-Time Faculty • Student-Counselor Ratio (TBD)