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)
Presentation Overview
•
Research Questions
•
Quantitative and Qualitative
Strands
– Design Choices – Findings – Implications•
Analytical Limitations
•
Manuscripts to submit
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
Student, College, and State Factors Influencing 2/4 Transfer
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?
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 guideQUAL 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
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
• 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?
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
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 ModelPrimarily 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
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 0.000 = 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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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,
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
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’