2014 enrollment report - hsu strategic plan · 2019. 12. 14. · personal financial advisors...

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


44%

22%

34%

26%

15%

59%

33%

13%

54%

Pell-Elig Not Pell-Elig Unknown


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%


% 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


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


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**


N 919 919

xspring_14_gpa

Pearson

Correlation.241** 1



squ_basic_aca

d_ behaviorxsq_spring_14_gpa

xbasic_acad_behavio

rxPearson Correlation 1 .390**


N 482 482

xspring_14_gpa Pearson Correlation .390** 1



Correlations

squ_basic_

acad_behaviorxsq_fall_13_gpa

xbasic_acad_behaviorx Pearson

Correlation1 .380**


N 482 482

xfall_13_gpa Pearson

Correlation.380** 1



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**


N

919 919

sq_fall_13_gpa Pearson Correlation .158** 1



Correlations

squ_advanced_

acad_behaviorx sq_fall_13_gpa

squ_advanced_acad

_ behaviorx

Pearson Correlation 1 .256**


N

482 482

sq_fall_13_gpa Pearson Correlation .256** 1



squ_advanced_acad

_behavior sq_spring_14_gpa

squ_advanced_acad_

behavior

Pearson Correlation1 .151**


N 919 919

sq_spring_14_gpa Pearson Correlation.151** 1



squ_advanced_

acad_behaviorx sq_spring_14_gpa

squ_advanced_aca

d_behaviorx

Pearson Correlation 1 .208**


N 482 482

sq_spring_14_gpa Pearson Correlation .208** 1



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.

2014 enrollment report - hsu strategic plan · 2019. 12. 14. · personal financial advisors...

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