California Community Colleges Data Resources Patrick Perry Vice

California Community Colleges Data Resources Patrick Perry, Vice Chancellor of Technology, Research, and Information Systems California Community Colleges Chancellor’s Office

Who is this guy? Why should we listen to you? u Brad Pitt-like looks. u Vin Diesel physique. u And, I have an ENORMOUS… l …. . database. u. I collect data and measure stuff for a living. u I have all the data. u Information Management & Institutional Research: l IM…therefore IR.

My Credo u. I realize that I will not succeed in answering all of your questions. Indeed, I will not answer any of them completely. The answers I provide will only serve to raise a whole new set of questions that lead to more problems, some of which you weren’t aware of in the first place. When my work is complete, you will be as confused as ever, but hopefully, you will be confused on a higher level and about more important things.

Today’s Learning Outcomes: u Learn how, why, and where data are collected u Learn how you can access this data u See some “golden nuggets” of data mining efforts u Understand accountability reporting for CCC’s u Know what new data tools are in the works

Technology, Research & Information Systems Data u Accountability Data/Reporting u Transfer Data u Data Mart u At the core of this is the MIS Data Collection system

MIS Data u Source: submissions from all 109 campuses/72 districts u End of term u Very detailed, unitary student and enrollment data u 1992 -present u Data Element Dictionary online

Database Relationships DSPS VTEA EOPS Matric. Student Demographics (SB) PBS Fin. Aid Assess. Emp. Assign. Emp. Demo. Enrollments (SX) Pgm. Awds. Calendar Sections Courses Assignments Sessions

Data Uses § § § § New and Continuing Students Non-credit Matriculation EOPS / DSPS Funding EOPS/ DSPS Program Justification VTEA (Vocational and Technical Education Act) ü VTEA Core Indicator Reports ü VTEA Allocations BOGW Administrative Funding Federal Integrated Postsecondary Education Data System (IPEDS) Reporting CCC Data Mart

Data Clients l l l l l Legislative Analyst Office Department of Finance California Postsecondary Education Commission Public Policy Institutes/Think Tanks UC/CSU Legislature – Committees and individual members Community College Organizations Newspapers Labor Unions Individuals

How Can I access the Data? u Data Mart – online u Reports – online u Ad-hoc report – call or email MIS u Ad-hoc request for unitary dataset Must be approved by system office l Scrubbed of identifying fields l Usage agreement l

Ad-Hoc requests u CO can cut reports or datasets, provided: Student-identifiable information is not given l Request must have stated purpose and focus l Playing “what-if” is very time consuming l

Data Mart (TRIS) l Demographics, FTES (not apportionment), awards, finaid, matric, assessment, student svcs progs, program retention/success, staffing reports l Demo

Golden Nuggets: Student Demography

Headcount & FTES Year 1995 -1996 -1997 -1998 -1999 -2000 -2001 -2002 -2003 -2004 -2005 -2006 -2007 Headcount 2, 118, 747 2, 241, 557 2, 306, 923 2, 437, 610 2, 546, 643 2, 648, 581 2, 812, 023 2, 829, 860 2, 545, 443 2, 515, 550 2, 550, 247 2, 621, 388 FTES 827, 135 923, 395 960, 069 996, 188 1, 036, 691 1, 053, 237 1, 136, 210 1, 159, 744 1, 114, 661 1, 095, 089 1, 121, 779 1, 133, 924 FTES per Head 0. 390 0. 412 0. 416 0. 409 0. 407 0. 398 0. 404 0. 410 0. 438 0. 435 0. 440 0. 433

What’s Going on in CCC? Fee Impacts Budget Volatility California’s Changing Demography

CCC Trends • • CCC now coming out of early 2000’s budget cuts and fee increases… …headcounts are starting to creep back up… …fees are stable (this week, at least)… …and its all just in time for a demography crash.

• CCC Pipeline • Coming in the door: • Early 2000’s: • • • Fee increases from $11 -$18 -$26, now $20 Budget cuts Pipeline issues now coming to fruition

• The Big Pipeline Factor: The State Budget • California has a volatile tax revenue collection history • • Very progressive taxation State budgets negotiated late • • College schedules set early College CBO’s need stability; State provides little

• The Budget • Downturns in revenue= • State: • • • Raising of fees Enrollment prioritization Local: • Expectation of cuts or no growth= • • Immediately become fiscally conservative; OR burn up your reserves THEN become fiscally conservative

• Local Budget Reaction • • Fall schedule set ~6 mo. beforehand Budget frequently passed late, Fall term already begun • • • If budget=good, then little chance to add sections to capture If budget=bad, then little chance to cut sections In both cases, only Spring/Summer left to balance

• Early 2000’s • • • Gray Davis came out with 10% budget reduction proposal in January 02 CCC’s began creating Fall 02 schedules shortly thereafter • High anxiety and conservatism • Sections slashed Final budget late in 02 • Cuts not nearly as drastic, but colleges already acted

Sections Offered Enrollments Average Section Size Fall 2001 166, 735 4, 564, 156 27. 37 Spring 2002 172, 811 4, 674, 836 27. 05 Fall 2002 170, 373 4, 867, 043 28. 57 Spring 2003 164, 597 4, 676, 951 28. 41 Fall 2003 160, 573 4, 684, 539 29. 17 Spring 2004 165, 261 4, 580, 776 27. 71 Fall 2004 165, 221 4, 618, 651 27. 95 Spring 2005 171, 295 4, 542, 878 26. 52 Fall 2005 171, 248 4, 630, 698 27. 04 Spring 2006 175, 445 4, 519, 494 25. 76 Term

• Who Left? • High headcount loss, not so much in FTES • • Enrollment priority to those already in system • • We lost a lot of single course takers Outsiders/first-timers-forget about getting your course Fee Impact burden on older students

Population Projections Year 15 -24 yo 2000 4, 850, 103 2010 5, 969, 955 2020 5, 953, 842 2030 6, 448, 117

HS Grad Projections Year HS Grads 2006 363, 662 2008 374, 877 2010 371, 848 2012 366, 720 2014 354, 046 2016 348, 000

Why The Drop? *The Children of Generation X • • • Gen X influence defined the 80’s-early 90’s culture (new wave music, big hair and shoulder pads) Overeducated and underemployed, highly cynical and skeptical Burdened by the societal debt of boomers Extremely entrepreneurial (tech & internet)

Gen X Parents • • • More hands-on than Baby Boomer parents Value higher education as more important to success than Boomer parents Gen X is a much smaller cohort than Boomers; so are their offspring

Enrollment Status Year First-Time Returning Continuing 1995 -1996 742, 149 436, 718 760, 329 1996 -1997 794, 652 455, 888 786, 364 1997 -1998 785, 323 454, 551 805, 397 1998 -1999 833, 902 481, 001 822, 105 1999 -2000 837, 361 458, 927, 359 2000 -2001 897, 931 462, 917 935, 607 2001 -2002 961, 722 498, 303 989, 068 2002 -2003 960, 954 489, 641 1, 068, 115 2003 -2004 824, 267 443, 340 1, 030, 396 2004 -2005 822, 830 472, 609 988, 516 2005 -2006 818, 207 501, 857 895, 893 2006 -2007 812, 348 530, 994 926, 795

Demography: Age Year 1995 -1996 -1997 -1998 -1999 -2000 -2001 -2002 -2003 -2004 -2005 -2006 -2007 0 -24 45% 44% 45% 46% 47% 48% 49% 50% 51% 25+ 55% 56% 55% 54% 53% 52% 51% 50% 49%

Demography: Ethnicity/Race Year Asian Afr. Am Hisp/Lat Other-Non. Wht White Unk/DTS 1995 -1996 12. 3% 7. 8% 22. 5% 6. 5% 45. 8% 5. 1% 1996 -1997 12. 2% 7. 8% 22. 9% 6. 5% 44. 7% 5. 9% 1997 -1998 12. 1% 7. 7% 23. 3% 6. 6% 43. 9% 6. 3% 1998 -1999 12. 2% 7. 6% 23. 9% 6. 6% 42. 5% 7. 1% 1999 -2000 12. 1% 7. 5% 24. 5% 6. 5% 41. 6% 7. 8% 2000 -2001 12. 1% 7. 3% 25. 2% 6. 5% 40. 3% 8. 6% 2001 -2002 12. 3% 7. 3% 26. 3% 6. 6% 40. 1% 7. 4% 2002 -2003 12. 3% 7. 5% 26. 5% 6. 6% 39. 2% 7. 9% 2003 -2004 12. 5% 7. 5% 27. 2% 6. 9% 37. 9% 8. 0% 2004 -2005 12. 2% 7. 6% 27. 9% 7. 0% 37. 1% 8. 2% 2005 -2006 12. 2% 7. 6% 28. 5% 7. 0% 36. 1% 8. 6% 2006 -2007 12. 3% 7. 5% 28. 8% 7. 0% 35. 4% 9. 1%

Demography: Gender • 55% Female, 45% Male • Ratio hasn’t changed +/- 1% in 15 years

Annual Units Attempted Year 1995 -1996 -1997 -1998 -1999 -2000 -2001 -2002 -2003 -2004 -2005 -2006 -2007 0 -11. 9 (PT-Low) 12 -23. 9 (PT-Hi) 68. 4% 18. 8% 69. 5% 18. 3% 69. 6% 18. 1% 70. 6% 17. 5% 71. 1% 17. 2% 71. 7% 16. 9% 71. 1% 17. 0% 69. 6% 17. 8% 66. 7% 19. 5% 66. 3% 19. 6% 66. 8% 19. 0% 67. 3% 18. 9% 24+ (FT-Year) 12. 7% 12. 2% 12. 3% 12. 0% 11. 7% 11. 5% 11. 9% 12. 5% 13. 8% 14. 2% 14. 1% 13. 8%

Demography of Success • “It is not so important who starts the game but who finishes it. ” –John Wooden

Demography of Success • Does the group of students starting out or already in look like the students leaving with various outcomes? • Demography in=demography out • = parity.

Demography of Parity (Example) Demog (06 -07) Input (Students) Output (Outcome) Afr. Am 9% 9% Asian 11% Hisp/Latino 35% White 29% F 55% 64% M 45% 36%

Demography of Process Demog. (06 -07) Afr. Am Asian FTF Students 9% 11% Total Students 8% 12% Hsp/Latino White F M 18 -24 25 -39 40+ 35% 29% 49% 56% 20% 17% 29% 35% 55% 44% 27% 22% BOG Waiver 13% 12% 39% 23% 51% 49% 75% 9% 5% Basic Skills 9% 15% 43% 20% 64% 36% 57% 28% 12%

Demography of Persistence Demog (06 -07) Afr. Am Asian Hisp/Latino White F M 18 -24 25 -39 40+ FTF Students 9% 11% 35% 29% 49% 56% 20% 17% All Students 8% 12% 29% 35% 55% 44% 27% 22% Fall-Spr Persist 8% 12% 33% 34% 51% 49% 75% 9% 5%

Demography of AA/AS/Cert Demog (06 -07) Afr. Am Asian Hisp/Latino White F M 18 -24 25 -39 40+ FTF Students 9% 11% 35% 29% 49% 56% 20% 17% All Students AA/AS/Cert 8% 7% 12% 29% 24% 35% 43% 55% 64% 44% 36% 44% 52% 27% 32% 22% 16%

Demography of Transfer Demog FTF All XFER(06 -07) Stdents CSU XFERUC XFERISP XFEROOS Afr. Am 9% 8% 5% 3% 11% 13% Asian 11% 12% 26% 8% 7% Hisp/ Latino 35% 29% 23% 16% 23% 13% White 29% 35% 37% 40% 44% 55%

Which Leads Us To…

Transfer Data u Located l at CPEC website: “Transfer Pathways” u Also in Accountability Report (ARCC), Research website u Demo

• Importance of Transfer in BA/BS Production • High dependence on CCC transfers in BA/BS production at CSU/UC • • • CSU: 55%. . . and declining UC: 28%. . . and steady 45% of all BA/BS awarded from public institutions were from CCC transferees

• Ten Years Ago… • Ten Years Ago: • We served 2. 44 million students • • Today: • We serve 2. 62 million students • • 36% were underrepresented (Afr. Am, Hisp/Latino, Filipino, Native Amer, Pac Isl) 42% are underrepresented (+6%) Headcount has grown only 7% • Not much…and one might expect similar outcome parity…

• However. . . Transfer • Ten Years Ago: • • • Today: • • • CSU Transfers: 44, 943…UC: 10, 177 CSU Underrepresented: 28%. . . UC: 20% (+6%) CSU Transfers: 54, 379, UC: 13, 874 CSU Underrepresented: 34%. . . UC: 26% (+6%) 24% increase in transfer volume (during a time when headcount went up only 7%) and achievement gap remained stable

• But…Times are a. Changing… u Measuring Transfer

• Transfer Measurement 101 • Method #1: Volumes • • “How many students transferred in year X from CCC’s to other institutions? ” Method #2: Rates • “Of all the students who started in Year X, what % of them eventually transferred in X number of years? ”

• Transfer Volumes • Very common metrics: • Annual volume of transfers from CCC to CSU/UC • CSU: ~50, 000 annually • UC: ~13, 000 annually • In-State Private (ISP) and Out of State (OOS): ~13, 000 -15, 000 annually each

• Transfer Volumes • • • Annual volume of Transfers • CSU=somewhat volatile • UC=somewhat stable Constrained by Enrollment Management at CSU/UC • 60/40, Fall/Spring admits, application deadlines • CSU/UC growth, FTES funding • CCC supply/pipeline • Functional barriers Unconstrained in the open Educational marketplace • Few barriers, ability to absorb and respond

Tracking Transfers • Annual Volume of Transfers • CSU/UC: they provide these figures based on their criteria • • We didn’t want to redefine this In-State Private/Out of State: National Student Clearinghouse data match Added another 30% to annual volumes ISP/OOS transfer not “traditional”

CCC Transfer Volumes Sector 01 -02 02 -03 03 -04 04 -05 05 -06 06 -07 CSU 50, 473 50, 746 48, 321 53, 695 52, 642 54, 391 UC 12, 291 12, 780 12, 580 13, 211 13, 462 13, 874 ISP 17, 070 15, 541 18, 100 18, 365 17, 840 18, 752 OOS 10, 762 10, 540 11, 150 11, 709 11, 726 11, 825 Total 90, 596 89, 607 90, 151 96, 980 95, 670 98, 842

Transfers: In State (not CSU/UC) UNIVERSITY OF PHOENIX 9, 216 NATIONAL UNIVERSITY 1, 250 DEVRY INSTITUTE OF TECHNOLOGY 975 CHAPMAN UNIVERSITY 849 UNIVERSITY OF SOUTHERN CALIFORNIA 587 ACADEMY OF ART UNIVERSITY 496 AZUSA PACIFIC UNIVERSITY 463 FRESNO PACIFIC UNIVERSITY 378 CALIFORNIA BAPTIST UNIVERSITY 375 UNIVERSITY OF SAN FRANCISCO 314

The Rise of The Phoenix 96 -97 2, 166 97 -98 2, 829 98 -99 3, 374 99 -00 4, 194 00 -01 5, 055 01 -02 5, 586 02 -03 6, 515 03 -04 8, 222 04 -05 8, 585 05 -06 8, 134 06 -07 9, 216

Who Transfers to Phoenix? Ethnicity UC Asian 29. 3% 14. 2% 4. 6% 2. 4% 5. 2% 16. 8% Hispanic/Latino 13. 6% 23. 8% 28. 6% White 39. 1% 43. 6% 37. 5% African American CSU Phoenix

• Who Transfers To Phoenix? • Start Age in CCC CSU U of Phx Other ISP UC Under 17 13. 4% 5. 3% 16. 4% 31. 2% 17 to 19 62. 6% 45. 2% 48. 6% 53. 3% 20 to 24 11. 0% 20. 7% 13. 4% 8. 6% 25 to 29 4. 3% 11. 3% 7. 2% 2. 6% 30 to 34 3. 2% 7. 7% 5. 6% 1. 7% 35 to 39 2. 4% 5. 3% 4. 0% 1. 0% 40 to 49 2. 4% 3. 8% 3. 9% 1. 0% Over 49 0. 7% 0. 9% 0. 6%

• Transfers Out of State UNIVERSITY OF NEVADA-LAS VEGAS 326 ARIZONA STATE UNIVERSITY 296 EMBRY RIDDLE UNIVERSITY* 262 UNIVERSITY OF NEVADA-RENO 215 UNIVERSITY OF MARYLAND* 200 BRIGHAM YOUNG UNIVERSITY 197 PORTLAND STATE UNIVERSITY 185 WESTERN GOVERNORS UNIVERSITY* 173 COLUMBIA COLLEGE* 171 UTAH VALLEY STATE COLLEGE 169

• Transfer: Sector of Choice % to UC% to CSU % to Instate % to Out Private of State White 17. 9% 60. 7% 11. 0% 10. 4% Afr. Am 11. 5% 51. 2% 18. 1% 19. 2% Hisp/Lat 15. 1% 67. 7% 12. 1% 5. 1% Asian 37. 0% 49. 9% 9. 2% 3. 9%

• Measuring Transfer: Rates • • “Transfer Rate” is frequently mistaken for transfer volume Rates are ratios---percentages • • “We transferred 352 people this year” is not a transfer rate “We transferred 38% of students with transfer behavior within 6 years of their entrance” is a transfer rate

• CCC Transfer Rate Methodology • • All first-timers, full year cohort Behavioral intent to transfer: • • Did they ever attempt transfer level math OR English; and Completed any 12 units Tracked 6 years forward (10 is better) Data match with CSU, UC, Nat’l Student Clearinghouse

• Transfer Rates • By Ethnicity: • • • Asian=56% White=44% Black/Afr. Am=36% Hispanic=31% Transfer Rates for older students are lower

• Assessing The Transfer “Pipeline” Effects • The loss in the early 2000’s will now be seen for this much smaller group moving through • Smaller group, but greater % of degreeseekers, younger students helps mitigate

• Adding to the Woes… • Current year budget shortfall CCC’s likely grew too much in 07 -08 (overcap) Property tax shortfall • Scenes of 2002 in the midst • •

• Back to The Pipeline… • Coming Out The Other End: • Transfer Pool Proxies

• Transfer Pool Proxies • Transfer Directed • • Transfer Prepared • • Completed Transfer Math and English Completed 60 UC/CSU transferable units Transfer Ready • • Completed Math, English, and 60 units These are starting to go down

• Transfer Pool Proxies 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Directed Prepared 76, 872 61, 752 77, 599 66, 316 77, 700 62, 122 75, 996 63, 022 77, 907 64, 803 81, 796 69, 375 85, 351 75, 201 83, 576 77, 818 85, 066 82, 239 81, 863 82, 462 Ready 44, 433 47, 976 45, 981 46, 798 48, 621 51, 842 55, 555 56, 298 57, 519 52, 873

• What Happens to them? Transfer Ready Transfer Directed Transfer Prepared (math+Eng) (60 units) (math+Eng + 60 units) Transferred or Earned Award 63. 5% 77. 0% 84. 5% Still Enrolled 30. 9% 17. 3% 10. 6% 5. 7% 4. 8% The Following Year: No transfer, award, or still enrolled

Accountability Reporting u ARCC Report: annual u “Dashboard” accountability report— not “pay for performance” u Online: 800+ page. pdf u demo

ARCC u The l Model: Measures 4 areas with 13 metrics: u Student Progress & Achievement. Degree/Certificate/Transfer u Student Progress & Achievement. Vocational/Occupational/Workforce Dev. u Pre-collegiate improvement/basic skills/ESL u Participation l “Process” is not measured

Student Prog. & Achievement: Degree/Cert/Xfer u College: Student Progress & Achievement Rate(s) (SPAR) l “ 30 units” Rate for SPAR cohort l 1 st year to 2 nd year persistence rate l u System: Annual volume of transfers l Transfer Rate for 6 -year cohort of FTF’s l Annual % of BA/BS grads at CSU/UC who attended a CCC l

Student Prog. & Achievement: Voc/Occ/Wkforce Dev u College: l Successful Course Completion rate: vocational courses u System: Annual volume of degrees/certificates by program l Increase in total personal income as a result of receiving degree/certificate l

Precollegiate Improvement/Basic Skills/ESL u College: Successful Course Completion rate: basic skills courses l ESL Improvement Rate l Basic Skills Improvement Rate l u System: l Annual volume of basic skills improvements

Participation u College: l None yet…but coming. u System: l Statewide Participation Rate (by demographic)

Major Advancements of ARCC u Creating participation rates. u Creating a viable grad/transfer rate. u Finding transfers to private/out of state institutions. u Doing a wage study. u Geo-mapping district boundaries. u Creating peer groups. u All unitary datasets available.

Participation Rates • (per 100 k 1844 year-olds) State CA AZ NM WA IL OR NV FL NC TX MN CO NY MA PA Partic. Rate Tuition/Fees 9, 567 $ 806 8, 697 1, 394 7, 366 1, 528 7, 309 2, 481 6, 778 1, 934 6, 142 2, 807 5, 531 1, 590 5, 379 1, 778 5, 074 1, 269 5, 033 1, 438 4, 745 3, 815 4, 339 2, 203 3, 069 3, 276 2, 978 3, 424 2, 066 3, 298

Participation (and Fees)

Participation Rates: Age 04 -05 05 -06 06 -07 <18 14 16 16 18 -19 353 352 354 20 -24 253 249 25 -29 122 125 30 -34 76 75 77 35 -39 60 60 60 40 -49 49 48 48 50 -64 34 34 35

Participation Rates: Eth 04 -05 05 -06 06 -07 Asian 91 90 90 Afr. Am 74 75 74 Hisp/Lat 54 54 55 Nat. Am 77 72 69 Pac. Isl 125 127 130 White 56 56 57

Defining Grad/Transfer Rate u Student Progress & Achievement Rate (SPAR Rate) u CCC’s have multiple missions, students have multiple purposes for attending u For grad/xfer rates, we only want to count students here who want are degree-seeking l Cohort denominator is key!

SPAR Rate u Defining l the cohort: Scrub “first-time” by checking against past records (CCC, UC, CSU, NSC)

SPAR Rate u Define “degree-seeking” behaviorally for CC populations l Not by self-stated intent; this is a poor indicator u Behavior: did student ever attempt transfer/deg-applicable level math OR English (at any point in academic history) l Students don’t take this for “fun”

Defining Degree-Seeking Behaviorally u Separates out remedial students not yet at collegiate aptitude l Measure remedial progression to this threshold elsewhere u Creates common measurement “bar” of student aptitude between colleges l Same students measured=viable comparison

SPAR Rate-Unit Threshold u CCC provides a lot of CSU/UC remediation Lots of students take transfer math/Eng and leave/take in summer l Should not count these as success or “our” student l u Set minimum unit completed threshold (12) for cohort entrance l Any 12 units in 6 years anywhere in system

SPAR Denominator: u First-Time (scrubbed) u Degree-seeking (at any point in 6 years, attempt transfer/degree applicable math or English) u 12 units (in 6 years) u This represents about 40% of students in our system

SPAR Numerator u Outcomes l l l Earned an AA/AS/certificate; OR Transfer: to a 4 -yr institution; OR Become “transfer-prepared”; OR u l l Completed 60 xferable units Became “transfer-directed”: u l the State wants: Completed both xfer level math AND English No double-counting, but any outcome counts SPAR Rate=51%

Wage Study u What was the economic value of the degrees (AA/AS/certificate) we were conferring? u Required l data match with EDD Had to pass a bill changing EDD code to allow match

Wage Study u Take year all degree recipients in a given Subtract out those still enrolled in a CCC l Subtract out those who transferred to a 4 -yr institution l u Match degree wage data 5 years before/after

Wage Study u Separate out two groups: Those with wages of basically zero before degree l Those with >$0 pre wage l u The result: The Smoking Gun of Success

Mapping Districts u CC Districts in CA are legally defined, have own elections, pass own bonds u We did not have a district mapping for all 72 districts l So we couldn’t do district participation rates

Mapping Project u Get a cheap copy of ESRI Suite u Collect all legal district boundary documents u Find cheap labor—no budget for this

Peer Grouping u “Peers” l l l historically have been locally defined: My neighbor college Other colleges with similar demography Other colleges with similar size

Peer Grouping u Taking peering to another level: l Peer on exogenous factors that predict the accountability metric’s outcome (outside campus control) l Thus leaving the “endogenous” activity as the remaining variance (within campus control)

Peer Grouping: Example u Peering the SPAR Rate: 109 rates as outcomes l Find data for all 109 that might predict outcomes/explain variance l Perform regression and other magical SPSS things l

Finding Data u What might affect a grad/transfer rate on an institutional level? Student academic preparedness levels l Socioeconomic status of students l First-gen status of students l Distance to nearest transfer institution l Student age/avg unit load l

Finding Data u We had to create proxy indices for much of these (142 tried) GIS system: geocode student zipcode/ZCTA l Census: lots of data to be crossed by zip/ZCTA l Create college “service areas” based on weighted zip/ZCTA values l u Different than district legal boundaries

Finding Data u The l Killer Predictor “Bachelor Plus Index”, or what % of service area population of college has a bachelor’s degree or higher u “Bachelor Plus Index” a proxy for: First gen l Academic preparedness l Socioeconomic status l Distance to nearest transfer institution l

Peering SPAR Rate u Exogenous Rate: factors that predict SPAR Bachelor Plus Index l % older students l % students in basic skills l u R 2 l =. 67 What’s left is implied institutional variance

Peering u Campuses with similar exogenous profiles are clustered together to form peer groups

Other Data u Program Approval Database u Fiscal Data

What’s in The Works: u New Perkins Reports and Reporting Portal l Reports. cccco. edu u Program l Evaluators Data Tool You upload the student ID’s, select reports to get in return—tell me everything about this set of students

Thank You u Feel l Free To Ask: Patrick Perry: u pperry@cccco. edu