2014 enrollment report - hsu strategic plan · 2019. 12. 14. · personal financial advisors...
TRANSCRIPT
2014 Enrollment ReportActuals, Projections & Future
ConsiderationsPresented to
Enrollment Management Working GroupOctober 27, 2014
Dr. Lisa CastellinoOffice of Institutional Research & Planning
Washington
North
Dakota
Oregon
Idaho South
Dakota
Minnesota
Nebraska
Iowa
Wisconsin
Nevada
Kansas
New
Mexico
Arizona
Texas
Oklahoma Arkansas
Alaska
Hawaii
Missouri
Illinois
Kentucky
Michigan
Indiana
Ohio
Tennessee
West
Virginia
Louisiana
Alabama
North
Carolina
Georgia
South
Carolina
Virginia
Pennsylvania
New York
Maine
New
Hampshire
Vermont
Rhode Island
Connecticut
New Jersey
Maryland
Delaware
Mass
Mississippi
>=5% higher
<5% higher
<5% lower
>=5% lower
Projected percentage change in the number of public high school graduates by state: School years 2009-10 through 2022-23
Summary:
*Decrease 10% between 09-10
and 22-23 in the Northeast
*Decrease 8% in the Midwest
*Increase 9% in the South
*Increase 5% in the West
Looking ahead, the West
will continue to see
growth in high school
graduates while the
Northeast will continue
to see declines.
Projected numbers for enrollment in PUBLIC 4-year postsecondary degree-granting institutions
8,345 8,443 8,551 8,668 8,790 8,896 9,020 9,120
3,682 3,705 3,739 3,776 3,818 3,857 3,903 3,944
4,662 4,737 4,812 4,893 4,971 5,039 5,117 5,176
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2015 2016 2017 2018 2019 2020 2021 2022
Enro
llment
(in t
housands)
Total M F
By 2022, Women are still expected to
outpace men in enrollments in both
number and percent change. 11%
Female, 7% Male.
Projected numbers for enrollment in ALL postsecondary degree-granting institutions: Ethnicity
3,391 3,487 3,581 3,666 3,743 3,817 3,886 3,940
3,119 3,220 3,321 3,423 3,526 3,624 3,697 3,7571,337 1,345 1,355 1,369 1,385 1,400 1,398 1,400
12,913 12,948 13,010 13,099 13,191 13,249 13,393 13,492
0
5,000
10,000
15,000
20,000
25,000
30,000
2015 2016 2017 2018 2019 2020 2021 2022
Enro
llment
(in t
housands)
Black Hispanic Asian/Pacific Islander Alaskan Native White
By 2022, the number of Hispanic enrolled is projected to increase by
20%, while Black, Non-Hispanic is projected to increase by about
16%
Projected numbers for bachelor’s degrees conferred by ALL postsecondary degree-granting institutions: Gender
1,861,000
1,882,000
1,895,000
1,913,000
1,934,000
1,958,000
1,986,000
2,012,000
1,082,000
1,100,000
1,113,000
1,127,000
1,143,000
1,160,000
1,180,000
1,198,000
779,000
782,000
783,000
786,000
792,000
798,000
807,000
815,000
0 500,000 1,000,000 1,500,000 2,000,000 2,500,000
2015
2016
2017
2018
2019
2020
2021
2022
Projected Bachelor’s Degrees
total women men
By 2022,
Women are
still expected
to outpace
men in degree
conferrals in
both number
and percent
change. 11%
Female, 5%
Male.
Fastest Growing Occupations Requiringa Four-Year Degree: Percent Change 2012-2022
46%
37%
33%
32%
29%
27%
27%
27%
26%
26%
26%
25%
23%
23%
23%
22%
21%
21%
21%
21%
0%10%20%30%40%50%
Interpreters and Translators
Information Security Analysts
Meeting, Convention, and Event…
Market Research Analysts and…
Geographers
Personal Financial Advisors
Operations Research Analysts
Biomedical Engineers
Cost Estimators
Actuaries
Petroleum Engineers
Computer Systems Analysts
Medical and Health Services Managers
Mental Health and Substance Abuse…
Software Developers, Applications
Logisticians
Athletic Trainers
Dietitians and Nutritionists
Social and Community Service…
Credit Counselors
United States
50%
43%
40%
40%
38%
37%
36%
35%
33%
33%
32%
32%
32%
30%
29%
27%
25%
24%
24%
24%
0%10%20%30%40%50%60%
Foresters
Biomedical Engineers
Information Security Analysts
Market Research Analysts and…
Interpreters and Translators
Operations Research Analysts
Cartographers and Photogrammetrists
Soil and Plant Scientists
Actuaries
Meeting, Convention, and Event Planners
Cost Estimators
Logisticians
Management Analysts
Environmental Scientists and…
Software Developers, Applications
Computer Systems Analysts
Environmental Engineers
Agents and Business Managers of…
Software Developers, Systems Software
Training and Development Specialists
California
Research Approach
Student
Success
Student
Success
Applications
• Application Trend
• HS GPA
• Location
• Exposure and Pell
• Program Interests
• Readiness x Majors
Highlights
Application Trend
4,850
9,205
11,911
747511 247
1,441
2,821
4,029
7,038
12,537
16,187
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
Fall 2002 Fall 2003 Fall 2004 Fall 2005 Fall 2006 Fall 2007 Fall 2008 Fall 2009 Fall 2010 Fall 2011 Fall 2012 Fall 2013 Fall 2014
Count of Applications by Student Type: Fall 2002 through Fall 2014
First-time Undergrads Lower Division Transfer Upper Division Transfer Grand Total
Since 2010, Overall applications have increased by
30%; first-time undergrads by 29%; and upper-
division transfers by 43%.
Percent change: [(current year- base year)/ base year] *100
Median High School GPA of First-Time UG Applicants: Fall 2000 through 2014
3.20
3.15
3.16
3.143.14
3.13 3.13
3.12
3.14
3.16
3.17
3.15
3.14
3.17
3.18
3.08
3.10
3.12
3.14
3.16
3.18
3.20
3.22
Fall 2000 Fall 2001 Fall 2002 Fall 2003 Fall 2004 Fall 2005 Fall 2006 Fall 2007 Fall 2008 Fall 2009 Fall 2010 Fall 2011 Fall 2012 Fall 2013 Fall 2014
2014 Applicants by Location
2.3%
6.5%
18.1%
3.1%
2.5%
5.8%
47.3%
8.1%
3.9%
1.9%
0.4%
4.9%
7.7%
8.5%
2.8%
7.3%
6.1%
31.6%
6.5%
7.3%
10.1%
7.3%
4.9%
10.5%
17.9%
3.3%
7.7%
6.0%
34.7%
8.1%
2.7%
3.9%
0.3%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Local
Northern CA
SF Bay
Sacramento
Coast
Central CA
Los Angeles
San Diego
WUE state
Other state
Foreign
First-time Undergrads Lower Division Transfer Upper Division Transfer
Nearly ½ of all first-time undergraduate
applicants represent the LA area while
almost 20% represent the San Francisco
Bay area.
2014 Applicants: College Exposure and Pell
64%
34%
2%
43%
50%
8%
57%
40%
2%
0%
10%
20%
30%
40%
50%
60%
70%
First-gen Not First-gen Unknown
First-time Undergrads Lower Division Transfer Upper Division Transfer
44%
22%
34%
26%
15%
59%
33%
13%
54%
Pell-Elig Not Pell-Elig Unknown
First-time Undergrads Lower Division Transfer Upper Division Transfer
In general, the majority of applicants are first-
generation with the largest percentage represented
by first-time undergraduates. Further, over 2/5ths of
first-time undergraduate applicants are Pell-eligible.
2014 Applicants: Programmatic Interests by Type
14%
24%
27%
35%
4%
32%
36%
28%
1%
33%
35%
31%
0%
5%
10%
15%
20%
25%
30%
35%
40%
All University Arts, Hum & Soc Sci CPS College Offerings Natural Resources & Sci
First-time Undergrads LD Transfer UD Transfer
Over 1/3rd of first-time
undergrads indicate an interest
in a CNR&S major while over
1/3rd of transfers indicate
interest in CPS and AH&SS
majors, respectively.
Readiness of Applicants vs Top 10 Intended Majors (Fall 2014)
30%
44%
5%
21%
College Level Ready English and Math English Only Math Only
Almost 1/3rd of applying first-
time undergraduates are
college-level ready; over 2/3rd
need some form of
remediation. Further, more
than 2/5th need both English
and Math remediation.
14%
8%
5% 5%
4% 4% 3%3% 3% 2%
0%
2%
4%
6%
8%
10%
12%
14%
16%
% o
f F
irst-
Tim
e U
nderg
raduate
s
Of the 11,911 applicants around 14% (or 1,667)
of first-time undergraduates applied without
declaring an intended major; 37% (or 4,407)
indicated interest in majors that include the need
to be college ready in both Math and English.
• Sub Groups
• New Freshmen
• New Transfer
• FT/Part Time
• FTES
• Change from 2013
• First-time
Undergraduates
• Demographics
• Region
• URM
• First-
Generation
• Pell
• Gender
• Ethnicity
Highlights
Enrollment
Student
Success
Enrollment Summary
Fall 2014 Fall 2013 % Change
Student Headcount 8,485 8,293 2.3%
Full-Time
Equivalent
Students
7,960 7,772 2.4%
First Time
Undergraduates1,386 1,368 1.3%
Transfer Students 971 971 0.0%
Continuing
Undergraduates5,506 5,299 3.9%
Masters Enrollment 387 412 -6.1%
Credential
Enrollment101 90 12.2%
Fall 2014 Average Unit Loads
Female MaleOverall
Average
Undergraduate
- Full Time14.6 14.3 14.4
Undergraduate
- Part Time8.2 8.3 8.2
Credential -
Full Time 22.6 20.7 22.0
Masters - Full
Time13.1 11.4 12.5
Masters - Part
Time5.3 5.5 5.4
Overall
Average 14.1 13.8 14.0
Headcount Enrollment Summary (Cont.)
Fall
12
Fall
13
Fall
14
%
Change
2012
2014
% of
Overall
Population
2014
Fall
12
Fall
13
Fall
14
%
Change
2012
2014
% of
Overall
Population
2014
Class Ethnicity
Frosh 1,814 1,890 1,928 6.28% 22.72% American Indian 110 91 85 -22.73% 1.00%
Sophomore 1,054 1,022 1,079 2.37% 12.72% African American 291 291 320 9.97% 3.77%
Junior 2,061 2,034 2,110 2.38% 24.87% Hispanic/Latino 1,800 2,119 2,441 35.61% 28.77%
Senior 2,668 2,807 2,847 6.71% 33.55% Asian American 248 266 293 18.15% 3.45%
Masters 371 412 386 4.04% 4.55% Pacific Islander 23 20 21 -8.70% 0.25%
Credential 108 90 97 -10.19% 1.14% Two or More Races 468 492 531 13.46% 6.26%
2nd Bachelor 24 18 20 -16.67% 0.24% White 4,272 4,211 4,069 -4.75% 47.96%
Other Postbac 16 20 18 12.50% 0.21% Unknown 817 716 605 -25.95% 7.13%
TOTAL 8,116 8,293 8,485 Nonresident Alien 87 87 120 37.93% 1.41%
TOTAL 8,116 8,293 8,485
Headcount Enrollment Summary (Cont.)
Fall
12
Fall
13
Fall
14
%
Change
2012
2014
% of Total
Population
2014
Fall
12
Fall
13Fall 14
%
Change
2012
2014
% of Total
Population
2014LocationResidency
AB540 26 39 52 100.00% 0.61% Local 1,481 1,340 1,189 -19.72% 14.01%
CA resident 7,348 7,601 7,830 6.56% 92.28% Northern CA 826 824 872 5.57% 10.28%
International (non-
res fees)61 58 73 19.67% 0.86% SF Bay 1,013 1,053 1,076 6.22% 12.68%
International
(resident fees)10 15 18 80.00% 0.21% Sacramento 232 276 297 28.02% 3.50%
Other state (non-
res fees)172 146 120 -30.23% 1.41% Coast 380 365 350 -7.89% 4.12%
Other state
(resident fees)21 -100.00% 0.00% Central CA 432 473 532 23.15% 6.27%
WUE 475 425 374 -21.26% 4.41% Los Angeles 2,121 2,399 2,631 24.05% 31.01%
WUE grad 3 9 18 500.00% 0.21% San Diego 564 598 644 14.18% 7.59%
TOTAL 8,116 8,293 8,485 WUE state 616 553 497 -19.32% 5.86%
Other state 381 342 311 -18.37% 3.67%
Foreign 70 70 86 22.86% 1.01%
New Undergraduates
Programmatic Interests of New Undergraduates x Type (Fall 2014)
14%
21%23%
43%
8%
19%
42%
32%
1%
31%32%
37%
0%
10%
20%
30%
40%
50%
All University Arts, Hum & Soc Sci CPS College Offerings Natural Resources & Sci
% E
nro
lled in
ea
ch
co
lleg
e
First-Time Undergraduate LD Transfer UD Transfer
N = 188 N = 289 N = 314 N = 595
2%
increase
from last
fall
2%
increase
from last
fall
3%
decrease
from last
fall
1%
increase
from last
fall
First-Time Undergraduates: College Readiness and Program Choice (Fall 2014)
54%
10%
18%
18%
College Ready English Only Math Only Need Both
At enrollment, over ½ of
incoming first-time
undergraduates are ‘college
level ready’ while only 18%
need both English and Math
remediation.
All University AH&SS CPS NR&S
College Ready 44% 51% 45% 64%
English Only 7% 8% 14% 9%
Math Only 21% 21% 21% 14%
Need Both 28% 20% 20% 13%
0%
10%
20%
30%
40%
50%
60%
70%
% o
f firs
t-tim
e u
nderg
raduate
s in e
ach c
olle
ge b
y pre
para
tion
2%
decrease
from last
year
1%
increase from
last year
Median High School GPA of First-Time UG Enrolled: Fall 2000 through 2014
3.193.20
3.18
3.15
3.13 3.13
3.11
3.06
3.16
3.14 3.14
3.103.11
3.183.17
2.95
3.00
3.05
3.10
3.15
3.20
3.25
Fall 2000 Fall 2001 Fall 2002 Fall 2003 Fall 2004 Fall 2005 Fall 2006 Fall 2007 Fall 2008 Fall 2009 Fall 2010 Fall 2011 Fall 2012 Fall 2013 Fall 2014
First-Time Undergraduate Demographics(Fall 2008, 2011, 2014)
Local Northern CA SF Bay Sacramento Coast Central CA Los Angeles San Diego WUE state Other state Foreign
Fall 08 18% 10% 12% 3% 3% 5% 26% 6% 14% 2% 1%
Fall 11 10% 11% 16% 3% 3% 6% 32% 10% 8% 2% 0%
Fall 14 6% 8% 15% 4% 2% 8% 40% 8% 6% 2% 0%
0%
10%
20%
30%
40%
50%
Fall 08 Fall 11 Fall 14
American Indian African American Hispanic/Latino Asian American Pacific IslanderTwo or More
RacesWhite Unknown
NonresidentAlien
Fall 08 2% 4% 13% 3% 1% 0% 48% 29% 1%
Fall 11 1% 6% 29% 4% 0% 7% 49% 4% 0%
Fall 14 1% 5% 40% 4% 0% 7% 38% 4% 1%
0%
10%
20%
30%
40%
50%
60%
• Retention to 2nd Year
• 6-Year Graduation
Rates
• Achievement Gap
• Gender
• URM
• First Generation
• Remediation
Highlights
Persistence
Student
Success
Slicing First-Year Retention
Fall 2009
Fall 2010
Fall 2011
Fall 2012
Fall 20130%
10%
20%
30%
40%
50%
60%
70%
80%
90%
F NonURM
FURM
M NonURM
MURM
Fall 2009 79% 73% 69% 77%
Fall 2010 75% 75% 75% 72%
Fall 2011 76% 75% 71% 66%
Fall 2012 83% 76% 79% 72%
Fall 2013 79% 74% 74% 70%
Fall 2009
Fall 2010
Fall 2011
Fall 2012
Fall 2013
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Overall Female URM First Gen Remediation
Fall 2009 74% 76% 74% 76% 71%
Fall 2010 74% 74% 74% 73% 67%
Fall 2011 73% 76% 71% 70% 68%
Fall 2012 78% 79% 74% 74% 67%
Fall 2013 74% 75% 73% 73% 69%
Program Choice
Fall 09
Fall 10
Fall 11
Fall 12
Fall 13
0%10%20%30%40%50%
60%
70%
80%
90%
AllUniversity Arts, Hum
& Soc Sci CPSCollege
Offerings
NaturalResources
& Sci
All University Arts, Hum & Soc Sci CPS College Offerings Natural Resources & Sci
Fall 09 60% 77% 74% 76%
Fall 10 68% 75% 70% 78%
Fall 11 70% 78% 71% 73%
Fall 12 65% 82% 78% 79%
Fall 13 69% 75% 76% 74%
Fall 09 Fall 10 Fall 11 Fall 12 Fall 13
Choice of program seems to have
a relationship to retention. Students
who do not identify a field of study
seem less likely to retain after the
first year than others.
6-Year Graduation Rate
2004
2005
2006
2007
0%
10%
20%
30%
40%
50%
60%
AllURM F
URM MNon URM F
Non URM M
All URM F URM M Non URM F Non URM M
2004 38% 25% 21% 48% 34%
2005 41% 40% 30% 49% 39%
2006 42% 32% 24% 50% 46%
2007 42% 40% 30% 50% 38%
Student success is inconsistent
across ethnic x gender
categories. Students who are
BOTH URM and Male tend to
graduate at a lower rates than
overall and Female counterparts.
Conversely, Females who are
also Non-URM have show higher
than mean success rates.
Predicting Future Six-Year Graduation Rates
41%40% 40% 39% 38%
45%42%
42% 41% 41% 42% 41%
0%
10%
20%
30%
40%
50%
60%
2008 2009 2010 2011 2012 2013 2014 2015
Pre
dic
ted S
ix-Y
ear
Gra
duation R
ate
Cohort Year
Random Forest Model Logistic Regression Model
2014 2021
Models based on 20 pre-
matriculation variables such as
HS GPA, Gender,
Ethnicity..etc.
2025
CSU goal:
53%
Risk Assessment• If we do nothing, current rates will continue.
• Students who come to campus initially without a planned major and without college ready skill sets in BOTH Math and English are especially at risk.
• Male-URM students are also at risk.
• We do not know enough about our incoming students’ perceptions, expectations, and goals to influence their migration through pipeline. What to do? When to do it?
• HSU Version of “Murky Middle”
• Retention and Finance
• Beyond
Demographics
• Modeling
• Assessing Program
Efficacy
Actionable Intelligence
Analytics
Student
Success
Moving the number: Retention
Cohort 1,386 Each percentage point
represents 14 additional
students from cohortAverage retention 74% 1,026
> 1% point 75% 1,040 14
> 2% points 76% 1,053 27
> 3% points 77% 1,067 41
> 4% points 78% 1,081 55
> 5% points 79% 1,095 69
> 6% points 80% 1,109 83
Financial Implication of Improving RetentionAnnual Revenue Generated (Based on Undergraduate Tuition Only)Average student credit hours taken: 14
Per Student Tuition Resident WUENon-
Resident
State Tuition Fee5,472 8,208 5,472
Non-Resident Tuition10,416
less waivers & other adjustments (192) (287) (816)
Net Tuition per Student5,280 7,921 15,072
# of Additional Students Retained
83 83 83
Total Tuition$
438,240 $
657,443 $
1,250,976
Annual Revenue Generated (based on Undergraduate Tuition + State Funding)Average student credit hours taken: 14
Per Student Tuition Resident WUENon-
Resident
State Tuition Fee5,472 8,208 5,472
Non-Resident Tuition10,416
14/15 State Funding for Enrollment Growth 5,270 less 1/3 Financial Aid Set Aside (14/15 CO) (1,320)less waivers & other adjustments (192) (287) (816)
Net Tuition per Student9,230 7,921 15,072
# of Additional Students Retained 83 83 83
Total Tuition$
766,090 $
657,443 $
1,250,976 Source: HSU Budget Office- Author Amber Blakeslee
Deconstructing the Class of 2013What happened?
By the numbers• When looking at the ‘traditional’ input measures typically used to predict
retention we see the following differences between 2013 compared to 2012:
2013 had:• 2% more URM• 6% more female• 7% more Pell• 3% more Low Income• 6% more First Generation• 4% more from LA Area, local area and SF Bay down by 1% each• 0.07 higher median High School GPA• 10 point lower Total SAT score• Median Loan Aid was $1,475 lower in 2013 and Median unmet need was $404
higher. With higher proportions of low income/Pell students this might be something to explore further.
Student
Attrition
01Institutional
Commitment
02 Time Management
03 Financial Means
04Basic Acad
Behaviors
05 Adv. Acad Behaviors
06 Academic self-
efficacy
07 Peer connections
08 Homesickness
09Academic
Integration
10Social Integration
11Satisfaction w
campus
12On campus living:
Social
13On campus living:
Environ
14On campus living
roommates
15Off campus living:
Environ
16Test Anxiety
The Role of Research
Descriptive Statistics Fall 2013 Transition Group (N = 926)
Sub-Group Percent
Female 66.8%
Hispanic 40.3%
Multi-racial 6.8%
African American 4.4%
Asian 4%
STEM 46.9%
First Generation: Mother 40%
First Generation: Father 46%
Procedures*
• Tested data for normality
• Where appropriate and necessary data were transformed
• Paired T-tests (Fall transition to Check up) or Repeated Measures ANOVA (Fall transition, Check up, Spring transition) to compare change in factor scores
• Bivariate correlations on factor scores and GPA (Fall 2013 and Spring 2014)
• Conducted Binary Logistic Regression relationship between factor scores, GPA and retention to Fall 2014
• Presentation is NOT exhaustive but illustrates what we can learn through inferential statistics about this class and what we may want to consider in future program planning. Analyses will be ongoing through rest of Fall term. (RFP Action Research and Data Readiness).
*analyses will not match previous reports from RISS
Example 1: Basic Academic BehaviorsQ044. Academic Behaviors - To what degree are you the kind of person who: Attends class
Q045. Academic Behaviors - To what degree are you the kind of person who: Takes good notes in class
Q046. Academic Behaviors - To what degree are you the kind of person who: Turns in required homework assignments
Q047. Academic Behaviors - To what degree are you the kind of person who: Spends sufficient study time to earn good grades
Q051. Advanced Study Skills - To what degree are you the kind of person who: Records your assignments and tests in a calendar
Test 1: Paired T-test Fall Transition to Fall Check-up
Paired Samples Statistics
Mean N Std. Deviation
Std. Error
Mean
Pair 1 xbasic_acad_b
ehavior 2.4170 481 .15761 .00719
xbasic_acad_b
ehaviorx 2.3386 481 .22150 .01010
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)Mean
Std.
Deviation
Std. Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair 1 xbasic_ac
ad_behav
ior -
xbasic_ac
ad_behav
iorx
.07843 .17588 .00802 .06267 .09419 9.780 480 .000
Does this change matter?
First-time undergraduate basic
academic behavior factor scores
significantly decreased by .08 points
from fall transition (M= 2.41, SD = 2.41)
to fall check up (M = 2.33, SD = .221),
t(480) = 9.780, p <.001, d = .4459
Example 1: Relationship Between Basic Academic Behaviors / Fall and Spring GPA
Correlations
squ_basic_acad_
behaviorsq_fall_13_gpa
xbasic_acad_beha
vior
Pearson
Correlation1 .244**
Sig. (2-tailed) .000
N 919 919
xfall_13_gpa Pearson
Correlation.244** 1
Sig. (2-tailed) .000
N 919 926**. Correlation is significant at the 0.01 level (2-tailed).
squ_basic_acad_
behaviorsq_spring_14_gpa
xbasic_acad_beha
vior
Pearson
Correlation1 .241**
Sig. (2-tailed) .000
N 919 919
xspring_14_gpa
Pearson
Correlation.241** 1
Sig. (2-tailed) .000
N 919 926**. Correlation is significant at the 0.01 level (2-tailed).
squ_basic_aca
d_ behaviorxsq_spring_14_gpa
xbasic_acad_behavio
rxPearson Correlation 1 .390**
Sig. (2-tailed) .000
N 482 482
xspring_14_gpa Pearson Correlation .390** 1
Sig. (2-tailed) .000
N 482 926**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
squ_basic_
acad_behaviorxsq_fall_13_gpa
xbasic_acad_behaviorx Pearson
Correlation1 .380**
Sig. (2-tailed) .000
N 482 482
xfall_13_gpa Pearson
Correlation.380** 1
Sig. (2-tailed) .000
N 482 926**. Correlation is significant at the 0.01 level (2-tailed).
Fall Transition Basic Academic Behavior Factor
Score & GPA
Fall Check-Up Basic Academic Behavior Factor Score &
GPA
Example 1 Logistic Regression: Using GPA to Predict Retention
Variables in the Equation
B S.E. Wald df Sig. Exp(B)Step 1
asq_fall_
13_gpa-.368 .366 1.012 1 .314 .692
sq_spring
_14_ gpa4.192 .500 70.273 1 .000 66.124
Constant -4.594 .530 75.179 1 .060 .010a. Variable(s) entered on step 1: sq_fall_13_gpa, sq_spring_14_gpa.
Model Summary
Step
-2 Log
likelihood
Cox & Snell R
Square
Nagelkerke R
Square1 785.824a .203 .309a. Estimation terminated at iteration number 5 because parameter estimates changed
by less than .001.
Omnibus Tests of Model Coefficients
Chi-
square df Sig.Step 1 Step 210.541 2 .000
Block 210.541 2 .000
Model 210.541 2 .000
Logistic regression was performed to ascertain
the effects of Fall 2013 GPA and Spring 2014
GPA on the likelihood of students retaining to
Fall 2014.
The model was statistically significant, X2(2) =
210.5, p<.001. The model explained 30.9%
(Nagelkerke R2) of the variance in enrollment
and correctly classified 84.3% of the cases. Of
the two predictor variables, only one was
statistically significant: Spring GPA.
Response: Partnership between AA and H&RL
• Identify courses with high D/F/W rates and provide additional services
• Tutoring in residence halls
Course Name NumberGeneral Biology BIOL 104
Calculus I MATH 109
Algebra and Elem. Functions MATH 115
Calculus II MATH 110
Introduction to Zoology ZOOL 110
Intro Radio/TV/FILM TFD 109B
General Botany BOT 105
U.S. History to 1877 HIST 110
Cultural Anthropology ANTH 104
General Chemistry CHEM 109
Calculus for Bio / Sci MATH 105
Elementary Statistics STAT 108
Physical Geography GEOG 106
Beginning Algebra MATH 042
Intro to Human Communic. COMM 105
Intro Statistics/Health Sci STAT 106
Elementary Algebra MATH 040
Intermediate Algebra MATH 044
Q048. Academic Behaviors - To what degree are you the kind of person who: Participates in class
Q049. Academic Behaviors - To what degree are you the kind of person who: Communicates with instructors outside of class
Q050. Academic Behaviors - To what degree are you the kind of person who: Works on large projects well in advance of the due date
Q052. Advanced Study Skills - To what degree are you the kind of person who: Studies in a place where you can avoid distractions
Q053. Advanced Study Skills - To what degree are you the kind of person who: Studies on a regular schedule
Q054. Advanced Study Skills - To what degree are you the kind of person who: Reads the assigned readings within a day before class
Example 2: Advanced Academic Behaviors
Test 1: Paired T-test Fall Transition to Fall Check up
Paired Samples Statistics
Mean N
Std.
Deviati
on Std. Error Mean
Pair 1 squ_advanced_acad
_behavior2.1893 481 .26412 .01204
squ_advanced_acad
_behaviorx2.2373 481 .24806 .01131
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)Mean Std. Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
Lower Upper
Pair 1 squ_advanced_acad_
behavior -
squ_advanced_acad_
behaviorx
-.04801 .23702 .01081-
.06924
-
.02677-4.442 480 .000
Does this
change matter?
Advanced academic behavior factor
scores significantly increased by .045
points from fall transition (M= 2.1893,
SD = .2641) to fall check up (M = 2.23,
SD = .248), t(480) = -4.442, p <.001, d
= -.2025
Example 2: Advanced Academic Behaviors (cont.) (bivariate correlations)
Correlations
squ_advanced_acad_be
havior sq_fall_13_gpa
squ_advanced_acad
_behavior
Pearson Correlation1 .158**
Sig. (2-tailed) .000
N
919 919
sq_fall_13_gpa Pearson Correlation .158** 1
Sig. (2-tailed) .000
N 919 926**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
squ_advanced_
acad_behaviorx sq_fall_13_gpa
squ_advanced_acad
_ behaviorx
Pearson Correlation 1 .256**
Sig. (2-tailed) .000
N
482 482
sq_fall_13_gpa Pearson Correlation .256** 1
Sig. (2-tailed) .000
N 482 926**. Correlation is significant at the 0.01 level (2-tailed).
squ_advanced_acad
_behavior sq_spring_14_gpa
squ_advanced_acad_
behavior
Pearson Correlation1 .151**
Sig. (2-tailed) .000
N 919 919
sq_spring_14_gpa Pearson Correlation.151** 1
Sig. (2-tailed) .000
N 919 926**. Correlation is significant at the 0.01 level (2-tailed).
squ_advanced_
acad_behaviorx sq_spring_14_gpa
squ_advanced_aca
d_behaviorx
Pearson Correlation 1 .208**
Sig. (2-tailed) .000
N 482 482
sq_spring_14_gpa Pearson Correlation .208** 1
Sig. (2-tailed) .000
N 482 926**. Correlation is significant at the 0.01 level (2-tailed).
Fall Transition Advanced Academic Behavior Factor
Score & GPA
Fall Check-Up Advanced Academic Behavior Factor
Score & GPA
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a
squ_advanced _
acad_behavior .773 .564 1.878 1 .171 2.167
squ_advanced _
acad_behaviorx -1.294 .660 3.845 1 .050 .274
sq_fall_ 13_gpa .661 .569 1.349 1 .245 1.937
sq_spring_14_ gpa 3.188 .673 22.446 1 .000 24.244
Constant -1.448 1.343 6.591 1 .059 .032a. Variable(s) entered on step 1: squ_advanced_acad_behavior, squ_advanced_acad_behaviorx,
sq_fall_13_gpa, sq_spring_14_gpa.
Omnibus Tests of Model Coefficients
Chi-
square df Sig.
Step 1 Step 85.247 4 .000
Block 85.247 4 .000
Model 85.247 4 .000
Model Summary
Step -2 Log likelihood
Cox & Snell R
Square
Nagelkerke R
Square1 395.585a .162 .257a. Estimation terminated at iteration number 5 because parameter estimates changed by
less than .001.
Example 2 Logistic Regression: Using GPA & Adv. Academic Behaviors to Predict Retention
Logistic regression was performed to
ascertain the effects Fall 2013 GPA, Spring
2014 GPA and Advanced Academic
Behavior Factor Scores on the likelihood of
students retaining to Fall 2014.
The model was statistically significant, X2(4)
= 85.247, p<.001. The model explained
25.7% (Nagelkerke R2) of the variance in
enrollment and correctly classified 84.5% of
the cases. Of the four predictor variables,
only two were statistically significant: Spring
GPA and advanced academic behavior factor
scores at check up.
• Literature suggests increased participation in advanced academic behaviors such as discussing assignments outside of class; positively influences URM retention, particularly Males.
• Significance found in results indicate a need to further refine the model to study covariance between the relationships between these behaviors and retention among different student populations.
Response: Further Review Needed
Considerations and Follow-up
• The statement “HSU is not like other campuses” has some merit. But we really don’t know what we are like.
• We need a comprehensive assessment of student characteristics beyond demographics to help design and deploy support programming.
• Factors related to persistence—how do we encourage, model, and support?
• A need for a more robust assessment mechanism for support programs that results in actionable intelligence-grounded in theory, research design, and inferential statistics.
• Looking ahead- MAP works? EAB SSC? Etc.