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(1)

Factors, Practices, and Policies Influencing

Students’ Upward Transfer to

Baccalaureate-Degree Programs and

Institutions

Barbara K. Townsend Dissertation of the Year Presentation National Institute for the Study of Transfer Students Conference

Atlanta, GA – February 5, 2014

Robin LaSota, PhD, University of Washington

Post Doctoral Research Associate, University of Illinois Urbana-Champaign (UIUC)

(2)

Presentation Overview

Research Questions

Quantitative and Qualitative

Strands

Design Choices Findings Implications

Analytical Limitations

Manuscripts to submit

(3)

A First Area of Inquiry

Q1.1 How do student behaviors, community

college characteristics, and state policies and conditions influence students’ upward

transfer probability?

Q 1.2 How do these factors, policies, and conditions influence upward transfer

probability, particularly for low-income and first-generation community college

(4)

Student, College, and State Factors Influencing 2/4 Transfer

(5)

A Second Area of Inquiry

Q2.1 What are promising practices in

colleges and states aimed at improving

students’ upward transfer, and how

may they constitute a system of

support for improved 2/4 transfer?

Q2.2 How do leaders engage in ongoing

innovation around these practices?

(6)

Explanatory Sequential Mixed Methods Design

Multi-level modeling analysis of BPS and supplemental data 2003-09 Case studies of 6 colleges in 3 states QUANT/QUAL Integration: 1) Using QUANT anal. To guide

QUAL sampling 2) Cross-reference claims from

QUANT and QUAL

3) Use QUAL to complement and extend QUAN.

4) Integrate both strands to guide future mixed methods

(7)

Sampling Design Choices

from BPS

N=5010 community college students; weighted

N=1,528,900

Students not co-enrolled in two or more colleges

BPS nationally representative longitudinal population

survey of all postsecondary entrants

BPS not representative of states, colleges, or CC entrants

State and community college factors investigated build

(8)

State articulation and transfer policies? Very little, if any influence

(Roksa, Kienzl, Goldhaber & Gross)

State cooperative agreements? Maybe. (Kienzl)

Community college practices? Depends. Perhaps not much. (MDRC)

Community college expenditures? Slight/student services

expenditures (Gross and Goldhaber). None (Stange).

Community college smaller size, higher faculty-to-student ratio?

Yes. (Bailey et al., Gross & Goldhaber)

Degree of college mission stratification (emphasis on

transfer-oriented programs vs. non-transfer oriented, e.g. health/vocational/technical). Influential. (Dougherty)

Proximity and selectivity of nearest public four-year institution?

Maybe. (Rouse)

Which State and Community College Factors

May Influence Transfer?

(9)

Rationale for Multi-Level Methodology

Results of Unconditional Model or Intra-Class Correlation –

State location – explains 2% of variance in 2/4 transfer probability Primary college attended – explains 6% of variance

Student characteristics – explains most of the variance Therefore, used multi-level logistic regression –

Randomly varying intercepts and slopes between colleges and states for Low-income, first generation

First generation, not low income

Planned to transfer at time of entry

(10)

Positive Predictors Associated with Upward Transfer

Probability

0.01 0.04 0.05 0.06 0.07 0.08 0.09 0.09 0.12 0.21 0.00 0.05 0.10 0.15 0.20 0.25 0 = 50/50 Probability of 2/4 Transfer Conditioned on Factors in the Model

Primarily Full-Time

Planned to Transfer at Entry

Aged 15-19 at Entry

Worked 1-19 Hrs/Wk on Average GPA in First Year (tenths)

Sports Participation Often or Sometimes STEM, Humanities, Education Major

Gross State Product (standardized) Academic Advising Often or Sometimes CC Transfer Out Rate

(11)

Negative Predictors Associated with Upward Transfer

Probability

-0.01 -0.02 -0.04 -0.04 -0.14 -0.15 -0.19 -0.25 -0.20 -0.15 -0.10 -0.05 0.00

0 = 50/50 Predicted Probability of 2/4 Transfer Conditioned on Factors in the Random Effects Model

Primarily Part Time

First Generation, Low Income First Generation, Not Low Income

CC Pct Health Voc Completions Took Any Remedial Education Unemployment in CC's County Health, Vocational, or Prof/Tech Major

(12)

College Characteristics: Association with 2/4 Transfer

Proportion of associates’ degree completions in health/vocational fields (neg., p<.10)

College transfer-out rate (2% increased odds of transfer) in regression without analysis of random effects by slope

County-level unemployment (neg., p<.10)

Not sig. = i.e. per-student expenditures for instruction or student services, distance to nearest public four-year institution, distance to nearest non or less-selective four-year institution, faculty-to-student ratio, community college enrollment size, percent of full-time faculty, percent of full-time students

(13)

State Policies’ Association with Students’ Upward

Transfer

Main Effects Model, Random

intercepts only, no varying slopes

+ 35% higher 2/4 transfer odds: State with one standard deviation higher Gross State Product Per Capita in 2003

None of the State Articulation and Transfer Policy Components explained variance in 2/4 transfer probability.

Policy Components - Transfer data reporting - State transfer incentives - State transfer guide

- Transferable general education curriculum

- Statewide cooperative agreements - Common course numbering

- Statewide articulation/transfer policy

(14)

Regression Results: Slopes for Sub-Populations that Vary by College and/or State

Low-Income, First Generation:

Higher gross state product

Common course numbering

College transfer-out rate

First Generation, Not Low Income:

Higher Gross State Product

Common Course Numbering

Planned to Transfer (vs. Not Transfer

Intending):

College transfer-out rate

Health/Vocational Major (vs. business/undeclared):

State articulation/transfer policies not sig.

Transfer-out rate not sig.

Random and Fixed Effects Model

showed that these factors moderate 2/4 transfer probability for these populations.

(15)

Findings:

Quantitative Inquiry Strand

Affirmed prior research about ambiguous or unknown effects of

state transfer and articulation policies

Offered new evidence about the role of state common course

numbering in increasing first-generation students’ transfer

Influential college-level factor – College mission focus; college’s

transfer-out rate

Full-time attendance and transfer intention are particularly

(16)

Some Implications:

Quantitative Strand

Promising areas for policy intervention, esp. in high

schools

Help students create specific plans for obtaining a

bachelor’s degree aligned in a specific field and outline a transfer pathway

Promote continuous full-time attendance and

advising with incentives and accountability

Widely promote available state resources and

(17)

Rationale for Case Study Design

Goal: To explore and identify possible state policy actions

and college policies or practices that enhance student 2/4 transfer probability

Structure analysis for meaningful contrasts relative to the

goal

States and Colleges with Higher Transfer vs. Average Transfer Rates (within their state)

Policy Innovative States in Articulation and Transfer Colleges Engaged in Data-Use and Innovation

States with significant CC sector and states & colleges with student populations of interest

(18)

State Case Selection: Florida, Georgia, and

Washington

Used OLS regression to find states performing above

average in transfer, controlling for state and student population characteristics

Considered prior research on policy innovative states in

transfer and articulation

Chose states with a considerable proportion of

postsecondary students enrolled in two-year colleges and with racial/income diversity

(19)

College Selection: Above-Average and Average

Performer

Used OLS regression to find colleges performing above average

in transfer, controlling for college and student population characteristics

Consulted State Higher Education Executive Officers (SHEEO)

from each state and Aspen Prize Top 120 data

Used SHEEO advice and college’s participation in Achieving the

(20)

Qualitative Methods

Interviews with state policy officials in articulation and

transfer (N=20)

Interviews with college administrators, faculty, and student

affairs staff (N=110)

Individual interviews and focus groups with students (N=49)

N=179 overall

(21)
(22)

Findings – Advising in Above-Average Performers

Transfer not a universal outcome or push for all students…

College-level systems of support for transfer generally constrained…

Above-average colleges generally have:

Academic leaders who champion students’ transfer and successfully engage others in this work

Mandatory student advising models

(23)

Findings – Advising in Above-Average Performers

Above-average colleges also tend to have:

Faculty contracts which include student advising hrs.

Faculty and staff engaged in planning out-of-class supports and enrichment experiences for students that aid transfer

Campus supports for TRIO and similar STEM programs for low-income, minority, and first generation students

Key Support for Stronger Advising: Active communication/coordination with public and private four-year institutions within major fields by administrators and faculty

(24)

Findings – State Policy as a

Context for Colleges’ Innovation

Creating a stronger system of support for students’ upward transfer—

• State-college collaboration on policy design

• 2-yr to 4-yr collaboration on articulation and transfer…

…..robust communications and

Data-based problem solving focused on increasing step-by-step outcomes to BA attainment… supports

(25)

Common Course Numbering:

Lessons Learned from Florida

Moderating positive influence of common course numbering (CCN) for first-generation college students from quantitative inquiry…

CCN Proxy for a more robust transfer policy context?

CCN built from communication across lower and upper

division faculty and programs

Florida: CCN in place for 30 yrs; created when 2 yrs and 4 yrs

(26)

Some Implications:

Qualitative Inquiry

States:

Incentives and support for college-level innovation Support for measuring innovation effectiveness Build transfer into performance accountability Colleges:

Collaborative problem-solving re: transfer

Broad implementation of personalized learning & transfer advising Incentives to be transfer champions

States and colleges:

More efficient, accessible processes to using data for decision support about students’ transfer

(27)

Analytical Limitations - Quantitative

Data Limitations

BPS measures of academic and social integration

State policy measures binary coding

No adequate measure of policy strength for the period

Available college-level data mostly not predictive of transfer

Not a causal inference multi-level model

Does not examine reasons for stopping out or mixed

(28)

Analytical Limitations - Qualitative

Examined broad scope of practices affecting transfer

probability (from pre-college to graduation check/final term advising) rather than one or two specific

innovations

Used analytical memo writing not software-based

coding methodology

Inductive approach to claim formulation rather than

deductive hypothesis-testing

Different framing literatures inform each strand,

(29)

With Appreciation

To all the participants in my study

To Debra Bragg and OCCRL for post-doc support

To NISTS for the honor of the award and presentation with you

To my chair, Bill Zumeta

To my co-advisor, Marge Plecki

To my committee members:

Mike Knapp

Bob Abbott

Jennie Romich

To my fellow doctoral students

And to IES and AIR for funding

Sponsored by the US Department of Education, Institute of Education Sciences (#R305B090012) and the Association of Institutional Research Dissertation Grant

(30)

Manuscripts to be submitted

What Matters In Increasing Community College Students’

Upward Transfer to the Baccalaureate Degree: Findings from the Beginning Postsecondary Study 2003-2009 (Research in Higher Education/AIR)

Supports and Barriers for Data-Based Decision-Making to

Improve Students’ Upward Transfer (Review of Higher Education/ASHE)

How CC Leaders Engage in Innovation to Improve Transfer

(Journal of Community College Research and Practice)

Mixed Methods Design Challenges and Opportunities: A

Sequential, Explanatory Approach to Studying Students’

References

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