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California Community Colleges Data Resources

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Title: California Community Colleges Data Resources


1
California Community Colleges Data Resources
  • Patrick Perry, Vice Chancellor of Technology,
    Research, and Information Systems
  • California Community Colleges Chancellors Office

2
Who is this guy? Why should we listen to you?
  • Brad Pitt-like looks.
  • Vin Diesel physique.
  • And, I have an ENORMOUS
  • ..database.
  • I collect data and measure stuff for a living.
  • I have all the data.
  • Information Management Institutional Research
  • IMtherefore IR.

3
My Credo
  • 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 werent 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.

4
Todays Learning Outcomes
  • Learn how, why, and where data are collected
  • Learn how you can access this data
  • See some golden nuggets of data mining efforts
  • Understand accountability reporting for CCCs
  • Know what new data tools are in the works

5
Technology, Research Information Systems Data
  • Accountability Data/Reporting
  • Transfer Data
  • Data Mart
  • At the core of this is the MIS Data Collection
    system

6
MIS Data
  • Source submissions from all 109 campuses/72
    districts
  • End of term
  • Very detailed, unitary student and enrollment
    data
  • 1992-present
  • Data Element Dictionary online

7
Database Relationships
Emp. Assign.
EOPS
Emp. Demo.
DSPS
Matric.
Student Demographics (SB)
VTEA
Calendar
Assignments
Enrollments (SX)
Sessions
Sections
PBS
Pgm. Awds.
Courses
Fin. Aid
Assess.
8
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

9
Data Clients
  • 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

10
How Can I access the Data?
  • Data Mart online
  • Reports online
  • Ad-hoc report call or email MIS
  • Ad-hoc request for unitary dataset
  • Must be approved by system office
  • Scrubbed of identifying fields
  • Usage agreement

11
Ad-Hoc requests
  • CO can cut reports or datasets, provided
  • Student-identifiable information is not given
  • Request must have stated purpose and focus
  • Playing what-if is very time consuming

12
Data Mart (TRIS)
  • Demographics, FTES (not apportionment), awards,
    finaid, matric, assessment, student svcs progs,
    program retention/success, staffing reports
  • Demo

13
Golden Nuggets Student Demography
14
Headcount FTES
Year Headcount FTES FTES per Head
1995-1996 2,118,747 827,135 0.390
1996-1997 2,241,557 923,395 0.412
1997-1998 2,306,923 960,069 0.416
1998-1999 2,437,610 996,188 0.409
1999-2000 2,546,643 1,036,691 0.407
2000-2001 2,648,581 1,053,237 0.398
2001-2002 2,812,023 1,136,210 0.404
2002-2003 2,829,860 1,159,744 0.410
2003-2004 2,545,443 1,114,661 0.438
2004-2005 2,515,550 1,095,089 0.435
2005-2006 2,550,247 1,121,779 0.440
2006-2007 2,621,388 1,133,924 0.433
15
Whats Going on in CCC?
  • Fee Impacts
  • Budget Volatility
  • Californias Changing Demography

16
CCC Trends
  • CCC now coming out of early 2000s 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.

17
CCC Pipeline
  • Coming in the door
  • Early 2000s
  • Fee increases from 11-18-26, now 20
  • Budget cuts
  • Pipeline issues now coming to fruition

18
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 CBOs need stability State provides
    little

19
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

20
Local Budget Reaction
  • Fall schedule set 6 mo. beforehand
  • Budget frequently passed late, Fall term already
    begun
  • If budgetgood, then little chance to add
    sections to capture
  • If budgetbad, then little chance to cut sections
  • In both cases, only Spring/Summer left to balance

21
Early 2000s
  • Gray Davis came out with 10 budget reduction
    proposal in January 02
  • CCCs 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

22
Term 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
23
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

24
Population Projections
Year 15-24 yo
2000 4,850,103
2010 5,969,955
2020 5,953,842
2030 6,448,117
25
HS Grad Projections
Year HS Grads
2006 363,662
2008 374,877
2010 371,848
2012 366,720
2014 354,046
2016 348,000
26
Why The Drop?
  • The Children of Generation X
  • Gen X influence defined the 80s-early 90s
    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)

27
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

28
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 837,361 458,927 927,359
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
29
Demography Age
Year 0-24 25
1995-1996 45 55
1996-1997 44 56
1997-1998 45 55
1998-1999 46 54
1999-2000 47 53
2000-2001 48 52
2001-2002 48 52
2002-2003 49 51
2003-2004 49 51
2004-2005 50 50
2005-2006 51 49
2006-2007 51 49
30
Demography Ethnicity/Race
Year Asian AfrAm Hisp/Lat Other-NonWht 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
31
Demography Gender
  • 55 Female, 45 Male
  • Ratio hasnt changed /- 1 in 15 years

32
Annual Units Attempted
Year 0-11.9 (PT-Low) 12-23.9 (PT-Hi) 24 (FT-Year)
1995-1996 68.4 18.8 12.7
1996-1997 69.5 18.3 12.2
1997-1998 69.6 18.1 12.3
1998-1999 70.6 17.5 12.0
1999-2000 71.1 17.2 11.7
2000-2001 71.7 16.9 11.5
2001-2002 71.1 17.0 11.9
2002-2003 69.6 17.8 12.5
2003-2004 66.7 19.5 13.8
2004-2005 66.3 19.6 14.2
2005-2006 66.8 19.0 14.1
2006-2007 67.3 18.9 13.8
33
Demography of Success
  • It is not so important who starts the game but
    who finishes it. John Wooden

34
Demography of Success
  • Does the group of students starting out or
    already in look like the students leaving with
    various outcomes?
  • Demography indemography out
  • parity.

35
Demography of Parity (Example)
Demog (06-07) Input (Students) Output (Outcome)
AfrAm 9 9
Asian 11 11
Hisp/Latino 35 35
White 29 29
     
F 55 64
M 45 36
36
Demography of Process
Demog. (06-07) FTF Students Total Students BOG Waiver Basic Skills
AfrAm 9 8 13 9
Asian 11 12 12 15
Hsp/Latino 35 29 39 43
White 29 35 23 20
         
F 49 55 51 64
M 49 44 49 36
         
18-24 56 44 75 57
25-39 20 27 9 28
40 17 22 5 12
37
Demography of Persistence
Demog (06-07) FTF Students All Students Fall-Spr Persist
AfrAm 9 8 8
Asian 11 12 12
Hisp/Latino 35 29 33
White 29 35 34
       
F 49 55 51
M 49 44 49
       
18-24 56 44 75
25-39 20 27 9
40 17 22 5
38
Demography of AA/AS/Cert
Demog (06-07) FTF Students All Students AA/AS/Cert
AfrAm 9 8 7
Asian 11 12 12
Hisp/Latino 35 29 24
White 29 35 43
       
F 49 55 64
M 49 44 36
       
18-24 56 44 52
25-39 20 27 32
40 17 22 16
39
Demography of Transfer
Demog (06-07) FTF Stdents All Stdents XFER-CSU XFER-UC XFER-ISP XFER-OOS
AfrAm 9 8 5 3 11 13
Asian 11 12 12 26 8 7
Hisp/ Latino 35 29 23 16 23 13
White 29 35 37 40 44 55
40
Which Leads Us To
41
Transfer Data
  • Located at CPEC website
  • Transfer Pathways
  • Also in Accountability Report (ARCC), Research
    website
  • Demo

42
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

43
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 muchand one might expect similar outcome
    parity

44
However...Transfer
  • Ten Years Ago
  • CSU Transfers 44,943UC 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

45
ButTimes are a-Changing
  • Measuring Transfer

46
Transfer Measurement 101
  • Method 1 Volumes
  • How many students transferred in year X from
    CCCs 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?

47
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

48
Transfer Volumes
  • Annual volume of Transfers
  • CSUsomewhat volatile
  • UCsomewhat 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

49
Tracking Transfers
  • Annual Volume of Transfers
  • CSU/UC they provide these figures based on their
    criteria
  • We didnt 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

50
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
51
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
52
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
53
Who Transfers to Phoenix?
Ethnicity UC CSU Phoenix
Asian 29.3 14.2 4.6
African American 2.4 5.2 16.8
Hispanic/Latino 13.6 23.8 28.6
White 39.1 43.6 37.5
54
Who Transfers To Phoenix?
  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 0.7 0.9 0.6
  • Start Age in CCC

55
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
56
Transfer Sector of Choice
  to UC to CSU to Instate Private to Out of State
White 17.9 60.7 11.0 10.4
AfrAm 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
57
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

58
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, Natl Student
    Clearinghouse

59
Transfer Rates
  • By Ethnicity
  • Asian56
  • White44
  • Black/AfrAm36
  • Hispanic31
  • Transfer Rates for older students are lower

60
Assessing The Transfer Pipeline Effects
  • The loss in the early 2000s will now be seen for
    this much smaller group moving through
  • Smaller group, but greater of degree-seekers,
    younger students helps mitigate

61
Adding to the Woes
  • Current year budget shortfall
  • CCCs likely grew too much in 07-08 (overcap)
  • Property tax shortfall
  • Scenes of 2002 in the midst

62
Back to The Pipeline
  • Coming Out The Other End
  • Transfer Pool Proxies

63
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

64
Transfer Pool Proxies
Directed Prepared Ready
1997 76,872 61,752 44,433
1998 77,599 66,316 47,976
1999 77,700 62,122 45,981
2000 75,996 63,022 46,798
2001 77,907 64,803 48,621
2002 81,796 69,375 51,842
2003 85,351 75,201 55,555
2004 83,576 77,818 56,298
2005 85,066 82,239 57,519
2006 81,863 82,462 52,873
65
What Happens to them?
The Following Year Transfer Directed (mathEng) Transfer Prepared (60 units) Transfer Ready (mathEng 60 units)
Transferred or Earned Award 63.5 77.0 84.5
Still Enrolled 30.9 17.3 10.6
No transfer, award, or still enrolled 5.6 5.7 4.8
66
Accountability Reporting
  • ARCC Report annual
  • Dashboard accountability reportnot pay for
    performance
  • Online 800 page .pdf
  • demo

67
ARCC
  • The Model
  • Measures 4 areas with 13 metrics
  • Student Progress Achievement-Degree/Certificate/
    Transfer
  • Student Progress Achievement-Vocational/Occupati
    onal/Workforce Dev.
  • Pre-collegiate improvement/basic skills/ESL
  • Participation
  • Process is not measured

68
Student Prog. Achievement Degree/Cert/Xfer
  • College
  • Student Progress Achievement Rate(s) (SPAR)
  • 30 units Rate for SPAR cohort
  • 1st year to 2nd year persistence rate
  • System
  • Annual volume of transfers
  • Transfer Rate for 6-year cohort of FTFs
  • Annual of BA/BS grads at CSU/UC who attended a
    CCC

69
Student Prog. Achievement Voc/Occ/Wkforce Dev
  • College
  • Successful Course Completion rate vocational
    courses
  • System
  • Annual volume of degrees/certificates by program
  • Increase in total personal income as a result of
    receiving degree/certificate

70
Precollegiate Improvement/Basic Skills/ESL
  • College
  • Successful Course Completion rate basic skills
    courses
  • ESL Improvement Rate
  • Basic Skills Improvement Rate
  • System
  • Annual volume of basic skills improvements

71
Participation
  • College
  • None yetbut coming.
  • System
  • Statewide Participation Rate (by demographic)

72
Major Advancements of ARCC
  • Creating participation rates.
  • Creating a viable grad/transfer rate.
  • Finding transfers to private/out of state
    institutions.
  • Doing a wage study.
  • Geo-mapping district boundaries.
  • Creating peer groups.
  • All unitary datasets available.

73
Participation Rates
State Partic. Rate Tuition/Fees
CA 9,567 806
AZ 8,697 1,394
NM 7,366 1,528
WA 7,309 2,481
IL 6,778 1,934
OR 6,142 2,807
NV 5,531 1,590
FL 5,379 1,778
NC 5,074 1,269
TX 5,033 1,438
MN 4,745 3,815
CO 4,339 2,203
NY 3,069 3,276
MA 2,978 3,424
PA 2,066 3,298
  • (per 100k 18-44 year-olds)

74
Participation (and Fees)
75
Participation Rates Age
Age 04-05 05-06 06-07
lt18 14 16 16
18-19 353 352 354
20-24 253 249 249
25-29 122 122 125
30-34 76 75 77
35-39 60 60 60
40-49 49 48 48
50-64 34 34 35
76
Participation Rates Eth
Eth 04-05 05-06 06-07
Asian 91 90 90
AfrAm 74 75 74
Hisp/Lat 54 54 55
NatAm 77 72 69
PacIsl 125 127 130
White 56 56 57
77
Defining Grad/Transfer Rate
  • Student Progress Achievement Rate (SPAR Rate)
  • CCCs have multiple missions, students have
    multiple purposes for attending
  • For grad/xfer rates, we only want to count
    students here who want are degree-seeking
  • Cohort denominator is key!

78
SPAR Rate
  • Defining the cohort
  • Scrub first-time by checking against past
    records (CCC, UC, CSU, NSC)

79
SPAR Rate
  • Define degree-seeking behaviorally for CC
    populations
  • Not by self-stated intent this is a poor
    indicator
  • Behavior did student ever attempt
    transfer/deg-applicable level math OR English (at
    any point in academic history)
  • Students dont take this for fun

80
Defining Degree-Seeking Behaviorally
  • Separates out remedial students not yet at
    collegiate aptitude
  • Measure remedial progression to this threshold
    elsewhere
  • Creates common measurement bar of student
    aptitude between colleges
  • Same students measuredviable comparison

81
SPAR Rate-Unit Threshold
  • CCC provides a lot of CSU/UC remediation
  • Lots of students take transfer math/Eng and
    leave/take in summer
  • Should not count these as success or our
    student
  • Set minimum unit completed threshold (12) for
    cohort entrance
  • Any 12 units in 6 years anywhere in system

82
SPAR Denominator
  • First-Time (scrubbed)
  • Degree-seeking (at any point in 6 years, attempt
    transfer/degree applicable math or English)
  • 12 units (in 6 years)
  • This represents about 40 of students in our
    system

83
SPAR Numerator
  • Outcomes the State wants
  • Earned an AA/AS/certificate OR
  • Transfer to a 4-yr institution OR
  • Become transfer-preparedOR
  • Completed 60 xferable units
  • Became transfer-directed
  • Completed both xfer level math AND English
  • No double-counting, but any outcome counts
  • SPAR Rate51

84
Wage Study
  • What was the economic value of the degrees
    (AA/AS/certificate) we were conferring?
  • Required data match with EDD
  • Had to pass a bill changing EDD code to allow
    match

85
Wage Study
  • Take all degree recipients in a given year
  • Subtract out those still enrolled in a CCC
  • Subtract out those who transferred to a 4-yr
    institution
  • Match wage data 5 years before/after degree

86
Wage Study
  • Separate out two groups
  • Those with wages of basically zero before degree
  • Those with gt0 pre wage
  • The result The Smoking Gun of Success

87
(No Transcript)
88
Mapping Districts
  • CC Districts in CA are legally defined, have own
    elections, pass own bonds
  • We did not have a district mapping for all 72
    districts
  • So we couldnt do district participation rates

89
Mapping Project
  • Get a cheap copy of ESRI Suite
  • Collect all legal district boundary documents
  • Find cheap laborno budget for this

90
(No Transcript)
91
Peer Grouping
  • Peers historically have been locally defined
  • My neighbor college
  • Other colleges with similar demography
  • Other colleges with similar size

92
Peer Grouping
  • Taking peering to another level
  • Peer on exogenous factors that predict the
    accountability metrics outcome (outside campus
    control)
  • Thus leaving the endogenous activity as the
    remaining variance (within campus control)

93
Peer Grouping Example
  • Peering the SPAR Rate
  • 109 rates as outcomes
  • Find data for all 109 that might predict
    outcomes/explain variance
  • Perform regression and other magical SPSS things

94
Finding Data
  • What might affect a grad/transfer rate on an
    institutional level?
  • Student academic preparedness levels
  • Socioeconomic status of students
  • First-gen status of students
  • Distance to nearest transfer institution
  • Student age/avg unit load

95
Finding Data
  • We had to create proxy indices for much of these
    (142 tried)
  • GIS system geocode student zipcode/ZCTA
  • Census lots of data to be crossed by zip/ZCTA
  • Create college service areas based on weighted
    zip/ZCTA values
  • Different than district legal boundaries

96
(No Transcript)
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Finding Data
  • The Killer Predictor
  • Bachelor Plus Index, or what of service area
    population of college has a bachelors degree or
    higher
  • Bachelor Plus Index a proxy for
  • First gen
  • Academic preparedness
  • Socioeconomic status
  • Distance to nearest transfer institution

98
Peering SPAR Rate
  • Exogenous factors that predict SPAR Rate
  • Bachelor Plus Index
  • older students
  • students in basic skills
  • R2 .67
  • Whats left is implied institutional variance

99
Peering
  • Campuses with similar exogenous profiles are
    clustered together to form peer groups

100
Other Data
  • Program Approval Database
  • Fiscal Data

101
Whats in The Works
  • New Perkins Reports and Reporting Portal
  • Reports.cccco.edu
  • Program Evaluators Data Tool
  • You upload the student IDs, select reports to
    get in returntell me everything about this set
    of students

102
Thank You
  • Feel Free To Ask
  • Patrick Perry
  • pperry_at_cccco.edu
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