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?

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:

IM…therefore IR.

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 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:

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
CCC’s

Know what new data tools are in the works

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

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

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

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


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?

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

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

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

Golden Nuggets: Student
Demography

Headcount & FTES
Year
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
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

Term
Fall 2001
Spring 2002
Fall 2002
Spring 2003
Fall 2003
Spring 2004
Fall 2004
Spring 2005
Fall 2005
Spring 2006
Sections
Offered
166,735
172,811
170,373
164,597
160,573
165,261
165,221
171,295
171,248
175,445
Enrollments
4,564,156
4,674,836
4,867,043
4,676,951
4,684,539
4,580,776
4,618,651
4,542,878
4,630,698
4,519,494
Average
Section Size
27.37
27.05
28.57
28.41
29.17
27.71
27.95
26.52
27.04
25.76

• 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
Year 15-24 yo
2000
2010
4,850,103
5,969,955
2020
2030
5,953,842
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
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
First-Time
742,149
794,652
785,323
833,902
837,361
897,931
961,722
960,954
824,267
822,830
818,207
812,348
Returning
436,718
455,888
454,551
481,001
458,927
462,917
498,303
489,641
443,340
472,609
501,857
530,994
Continuing
760,329
786,364
805,397
822,105
927,359
935,607
989,068
1,068,115
1,030,396
988,516
895,893
926,795

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

Demography: Ethnicity/Race
Year Asian
1995-1996 12.3%
1996-1997 12.2%
1997-1998 12.1%
1998-1999 12.2%
1999-2000 12.1%
2000-2001 12.1%
2001-2002 12.3%
2002-2003 12.3%
2003-2004 12.5%
2004-2005 12.2%
2005-2006 12.2%
2006-2007 12.3%
AfrAm
7.8%
7.8%
7.7%
7.6%
7.5%
7.3%
7.3%
7.5%
7.5%
7.6%
7.6%
7.5%
Hisp/Lat Other-NonWht
22.5% 6.5%
22.9%
23.3%
6.5%
6.6%
23.9%
24.5%
25.2%
26.3%
6.6%
6.5%
6.5%
6.6%
26.5%
27.2%
27.9%
28.5%
28.8%
6.6%
6.9%
7.0%
7.0%
7.0%
White
45.8%
44.7%
43.9%
42.5%
41.6%
40.3%
40.1%
39.2%
37.9%
37.1%
36.1%
35.4%
Unk/DTS
5.1%
5.9%
6.3%
7.1%
7.8%
8.6%
7.4%
7.9%
8.0%
8.2%
8.6%
9.1%

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

Annual Units Attempted
Year
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
0-11.9 (PT-Low) 12-23.9 (PT-Hi) 24+ (FT-Year)
68.4%
69.5%
18.8%
18.3%
12.7%
12.2%
69.6%
70.6%
71.1%
71.7%
71.1%
18.1%
17.5%
17.2%
16.9%
17.0%
12.3%
12.0%
11.7%
11.5%
11.9%
69.6%
66.7%
66.3%
66.8%
67.3%
17.8%
19.5%
19.6%
19.0%
18.9%
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)
AfrAm
Asian
Hisp/Latino
White
Input (Students)
9%
11%
35%
29%
Output
(Outcome)
9%
11%
35%
29%
F
M
55%
45%
64%
36%

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

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

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

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%
Hisp/
Latino 35% 29%
White 29% 35%
12% 26%
23% 16%
8%
23%
7%
13%
37% 40% 44% 55%

Which Leads Us To…

Transfer Data

Located at CPEC website:

“Transfer Pathways”

Also in Accountability Report (ARCC),
Research website

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…

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
NATIONAL UNIVERSITY
DEVRY INSTITUTE OF TECHNOLOGY
CHAPMAN UNIVERSITY
UNIVERSITY OF SOUTHERN CALIFORNIA
ACADEMY OF ART UNIVERSITY
AZUSA PACIFIC UNIVERSITY
FRESNO PACIFIC UNIVERSITY
CALIFORNIA BAPTIST UNIVERSITY
UNIVERSITY OF SAN FRANCISCO
9,216
1,250
975
849
587
496
463
378
375
314

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

Who Transfers to Phoenix?
Ethnicity
Asian
African American
Hispanic/Latino
White
UC CSU Phoenix
29.3% 14.2% 4.6%
2.4% 5.2% 16.8%
13.6% 23.8% 28.6%
39.1% 43.6% 37.5%

• Who Transfers To Phoenix?
•
Start Age in CCC
CSU
Under 17 13.4%
17 to 19
U of
Phx
5.3%
62.6% 45.2%
20 to 24 11.0% 20.7%
25 to 29 4.3% 11.3%
30 to 34 3.2% 7.7%
35 to 39 2.4% 5.3%
40 to 49 2.4% 3.8%
Over 49 0.7% 0.7%
Other
ISP
16.4%
48.6%
13.4%
7.2%
5.6%
4.0%
3.9%
0.9%
UC
31.2%
53.3%
8.6%
2.6%
1.7%
1.0%
1.0%
0.6%

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

• Transfer: Sector of Choice
White
AfrAm
Hisp/Lat
Asian
% to UC
% to
CSU
% to
Instate
Private
% to Out of State
17.9% 60.7% 11.0% 10.4%
11.5% 51.2% 18.1% 19.2%
15.1% 67.7% 12.1% 5.1%
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/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 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
•
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

1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
• Transfer Pool Proxies
Directed Prepared
76,872 61,752
77,599
77,700
75,996
77,907
66,316
62,122
63,022
64,803
81,796
85,351
83,576
85,066
81,863
69,375
75,201
77,818
82,239
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?
The Following Year:
Transfer
Directed
(math+Eng)
Transfer
Prepared
(60 units)
Transfer
Ready
(math+Eng
+ 60 units)
Transferred or Earned
Award
Still Enrolled
63.5%
30.9%
77.0%
17.3%
84.5%
10.6%
No transfer, award, or still enrolled 5.6% 5.7% 4.8%

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

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

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

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

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

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

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.

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

Participation (and Fees)

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

Participation Rates: Eth
Eth
Asian
AfrAm
Hisp/Lat
NatAm
PacIsl
White
04-05
91
74
54
77
125
56
05-06
90
75
54
72
127
56
06-07
90
74
55
69
130
57

Defining Grad/Transfer Rate

Student Progress & Achievement Rate
(SPAR Rate)

CCC’s 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!

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

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 don’t take this for “fun”

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 measured=viable comparison

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

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

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

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

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 4yr institution

Match wage data 5 years before/after degree

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

Income Increase from Attaining CCC Degree or Certificate
60 000
50 000
40 000
30 000
CCC Income Data: Received any award during 2000-2001 and not enrolled in next 2 years and not transferred to 4-yr and on EDD wage file
20 000
10 000
Source: CA Dept of Finance Table D20
(Median H-hold), Table D6 (Per Capita);
EDD Base Wage File
0
CA Per Capita Income
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
22 635 23 203 24 161 25 312 26 490 28 374 29 828 32 464 32 877 32 807 33 389
CCC Median Income (no zeroes) 17 408 19 197 21 004 22 995 25 696 27 468 29 109 32 456 42 891 47 331 48 718
Years

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 couldn’t do district participation rates

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

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

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

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

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

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

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

Peering SPAR Rate

Exogenous factors that predict SPAR
Rate:


Bachelor Plus Index
% older students

% students in basic skills

R2 = .67

What’s left is implied institutional variance

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

Other Data

Program Approval Database

Fiscal Data

What’s in The Works:

New Perkins Reports and Reporting
Portal

Reports.cccco.edu

Program 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

Feel Free To Ask:

Patrick Perry:
 pperry@cccco.edu