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

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

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

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

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

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

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 YearHeadcountFTES FTES per Head ,118,747827, ,241,557923, ,306,923960, ,437,610996, ,546,6431,036, ,648,5811,053, ,812,0231,136, ,829,8601,159, ,545,4431,114, ,515,5501,095, ,550,2471,121, ,621,3881,133,

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

Term Sections OfferedEnrollments Average Section Size Fall ,7354,564, Spring ,8114,674, Fall ,3734,867, Spring ,5974,676, Fall ,5734,684, Spring ,2614,580, Fall ,2214,618, Spring ,2954,542, Fall ,2484,630, Spring ,4454,519,

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

Population Projections Year15-24 yo ,850, ,969, ,953, ,448,117

HS Grad Projections Year HS Grads , , , , , ,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 YearFirst-TimeReturningContinuing ,149436,718760, ,652455,888786, ,323454,551805, ,902481,001822, ,361458,927927, ,931462,917935, ,722498,303989, ,954489,6411,068, ,267443,3401,030, ,830472,609988, ,207501,857895, ,348530,994926,795

Demography: Age Year %55% %56% %55% %54% %53% %52% %52% %51% %51% % %49% %49%

Demography: Ethnicity/Race YearAsianAfrAmHisp/Lat Other-NonWht WhiteUnk/DTS %7.8%22.5%6.5%45.8%5.1% %7.8%22.9%6.5%44.7%5.9% %7.7%23.3%6.6%43.9%6.3% %7.6%23.9%6.6%42.5%7.1% %7.5%24.5%6.5%41.6%7.8% %7.3%25.2%6.5%40.3%8.6% %7.3%26.3%6.6%40.1%7.4% %7.5%26.5%6.6%39.2%7.9% %7.5%27.2%6.9%37.9%8.0% %7.6%27.9%7.0%37.1%8.2% %7.6%28.5%7.0%36.1%8.6% %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 (PT-Low) (PT-Hi)24+ (FT-Year) %18.8%12.7% %18.3%12.2% %18.1%12.3% %17.5%12.0% %17.2%11.7% %16.9%11.5% %17.0%11.9% %17.8%12.5% %19.5%13.8% %19.6%14.2% %19.0%14.1% %18.9%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) AfrAm9% Asian11% Hisp/Latino35% White29% F55%64% M45%36%

Demography of Process Demog. (06-07) FTF Students Total Students BOG Waiver Basic Skills AfrAm9%8%13%9% Asian11%12% 15% Hsp/Latino35%29%39%43% White29%35%23%20% F49%55%51%64% M49%44%49%36% %44%75%57% %27%9%28% 40+17%22%5%12%

Demography of Persistence Demog (06-07) FTF StudentsAll Students Fall-Spr Persist AfrAm9%8% Asian11%12% Hisp/Latino35%29%33% White29%35%34% F49%55%51% M49%44%49% %44%75% %27%9% 40+17%22%5%

Demography of AA/AS/Cert Demog (06-07) FTF StudentsAll StudentsAA/AS/Cert AfrAm9%8%7% Asian11%12% Hisp/Latino35%29%24% White29%35%43% F49%55%64% M49%44%36% %44%52% %27%32% 40+17%22%16%

Demography of Transfer Demog (06-07) FTF Stdents All Stdents XFER- CSU XFER- UC XFER- ISP XFER- OOS AfrAm9%8%5%3%11%13% Asian11%12% 26%8%7% Hisp/ Latino35%29%23%16%23%13% White29%35%37%40%44%55%

Which Leads Us To…

Transfer Data u Located at CPEC website: l “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 36% were underrepresented (AfrAm, Hisp/Latino, Filipino, Native Amer, Pac Isl) Today: We serve 2.62 million students 42% are underrepresented (+6%) Headcount has grown only 7% Not much…and one might expect similar outcome parity…

However...Transfer Ten Years Ago: CSU Transfers: 44,943…UC: 10,177 CSU Underrepresented: 28%...UC: 20% (+6%) Today: 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 CSU50,47350,74648,32153,69552,64254,391 UC12,29112,78012,58013,21113,46213,874 ISP17,07015,54118,10018,36517,84018,752 OOS10,76210,54011,15011,70911,72611,825 Total90,59689,60790,15196,98095,67098,842

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 , , , , , , , , , , ,216

Who Transfers to Phoenix? EthnicityUCCSUPhoenix Asian29.3%14.2%4.6% African American2.4%5.2%16.8% Hispanic/Latino13.6%23.8%28.6% White39.1%43.6%37.5%

Who Transfers To Phoenix? CSU U of Phx Other ISPUC Under %5.3%16.4%31.2% 17 to %45.2%48.6%53.3% 20 to %20.7%13.4%8.6% 25 to %11.3%7.2%2.6% 30 to %7.7%5.6%1.7% 35 to %5.3%4.0%1.0% 40 to %3.8%3.9%1.0% Over % 0.9%0.6% Start Age in CCC

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 Private % to Out of State White17.9%60.7%11.0%10.4% AfrAm11.5%51.2%18.1%19.2% Hisp/Lat15.1%67.7%12.1%5.1% Asian37.0%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/AfrAm=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 degree- seekers, younger students helps mitigate

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

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

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

Transfer Pool Proxies DirectedPreparedReady ,87261,75244, ,59966,31647, ,70062,12245, ,99663,02246, ,90764,80348, ,79669,37551, ,35175,20155, ,57677,81856, ,06682,23957, ,86382,46252,873

What Happens to them? The Following Year: Transfer Directed (math+Eng) Transfer Prepared (60 units) Transfer Ready (math+Eng + 60 units) Transferred or Earned Award63.5%77.0%84.5% Still Enrolled30.9%17.3%10.6% No transfer, award, or still enrolled5.6%5.7%4.8%

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

ARCC u The Model: l 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: l Student Progress & Achievement Rate(s) (SPAR) l “30 units” Rate for SPAR cohort l 1 st year to 2 nd year persistence rate u System: l 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

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

Precollegiate Improvement/Basic Skills/ESL u College: l Successful Course Completion rate: basic skills courses l ESL Improvement Rate l Basic Skills Improvement Rate 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 StatePartic. RateTuition/Fees CA9,567 $ 806 AZ8,6971,394 NM7,3661,528 WA7,3092,481 IL6,7781,934 OR6,1422,807 NV5,5311,590 FL5,3791,778 NC5,0741,269 TX5,0331,438 MN4,7453,815 CO4,3392,203 NY3,0693,276 MA2,9783,424 PA2,0663,298 (per 100k year-olds)

Participation (and Fees)

Participation Rates: Age Age <

Participation Rates: Eth Eth Asian9190 AfrAm Hisp/Lat54 55 NatAm PacIsl White56 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 the cohort: l 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 l Lots of students take transfer math/Eng and leave/take in summer l Should not count these as success or “our” student 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 the State wants: l Earned an AA/AS/certificate; OR l Transfer: to a 4-yr institution; OR l Become “transfer-prepared”;OR u Completed 60 xferable units l Became “transfer-directed”: u Completed both xfer level math AND English l No double-counting, but any outcome counts l SPAR Rate=51%

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

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

Wage Study u Separate out two groups: l Those with wages of basically zero before degree l Those with >$0 pre wage 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” historically have been locally defined: l My neighbor college l Other colleges with similar demography l 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: l 109 rates as outcomes l Find data for all 109 that might predict outcomes/explain variance l Perform regression and other magical SPSS things

Finding Data u What might affect a grad/transfer rate on an institutional level? l 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

Finding Data u We had to create proxy indices for much of these (142 tried) l 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 u Different than district legal boundaries

Finding Data u The Killer Predictor l “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: l First gen l Academic preparedness l Socioeconomic status l Distance to nearest transfer institution

Peering SPAR Rate u Exogenous factors that predict SPAR Rate: l Bachelor Plus Index l % older students l % students in basic skills u R2 =.67 l 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 Evaluators Data Tool l You upload the student ID’s, select reports to get in return—tell me everything about this set of students

Thank You u Feel Free To Ask: l Patrick Perry: u