Abstract
This research uses the nationally representative Beginning Postsecondary Study: 2003–2009 to investigate the relative significance in upward transfer of individual and institutional factors for different groups of students, considering their state policy contexts of variable support for improved articulation and transfer between 2-year and baccalaureate-granting colleges. Layered analyses of hierarchical generalized linear model population-average results found that a few community college characteristics and state transfer policy components (such as a state articulation policy, cooperative articulation agreements, transfer data reporting, etc.) demonstrated a statistically significant association with individual upward transfer probability within 6 years of community college entry. Student characteristics found to be influential and positive for increasing upward transfer probability included: having an intention for upward transfer at entry, attending primarily full-time, working between 1 and 19 h per week (not more or less), and declaring a transfer-oriented major in STEM (science, technology, engineering, or mathematics), Arts and Social/Behavioral Sciences, or Education.
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Notes
Upward transfer is a term often used to describe a student’s transition from a community college or primarily associate’s degree-granting institution to a baccalaureate degree-granting institution or program.
See: Rosenbaum and Rosenbaum (2015).
While not all such institutions call themselves community colleges, this term is used here to describe primarily associate’s degree granting institutions or “two-year” institutions.
See notes for 4 and 5 in Deil-Amen (2011) for relevant citations on how these constructs are typically defined.
In the 2003–2009 BPS, the overall response rate was 89 percent for the 6-year time period.
See Smith (2010) for definitions of the above terms.
“Majors” have been consolidated into three main categories based on the first author’s previous regression analyses, as well as other scholars’ research (Dougherty and Kienzl 2006) showing similar degrees of association within categories with upward transfer probability. Specifically, humanities, social sciences, STEM, and education majors have similar positive associations with upward transfer probability (relative to the reference group of an undeclared major), and are grouped together as transfer-oriented majors. Health, vocational, technical, and professional fields have similar negative associations with upward transfer probability and are grouped together as non-transfer oriented majors. Declaring a major in business was not significantly related to upward transfer probability compared to the reference category, so these two categories were grouped together for parsimony.
In IPEDS, transfer out-rate is measured for full-time, first-time students (although these are only 40 % of students enrolled in community colleges) who first enroll in summer or fall only and includes lateral transfer to other associate’s degree granting institutions (about 36 % of transfers in BPS), as well as upward transfer. Offenstein and Shulock (2010) note these limitations in using this rate in analyses of upward transfer.
The intraclass correlation measures the proportion of the variance in the outcome that is between groups, such as states or colleges, and applies only to random intercept models. The explained variance at level 2 (i.e. between community colleges) can be calculated as p = τ 200/(σ + τ00), where τ00 is the level 2 variance, and 2σ generally represents the variance at level 1 (Raudenbush and Bryk 2002, p. 36).
According to NCES (2011), two factors, stratification by tracing outcome (whether the student was located or not) and the likelihood of being a first-time beginner (FTB) in college, were used to oversample the students most likely to be located and eligible for BPS.
From: www.bls.gov/lau/#tables, “Labor force data by county, annual average.”
Demographic characteristics of students in community colleges in BPS data were compared to data from the American Association of Community Colleges (AACC 2013) and are very similar. This data shows that community college students as a group are different from and face more challenges than typical students attending most traditional BA-granting institutions, and the BPS sample represents the population fairly well.
Available state- and college-level predictors were tested one by one to determine which had potential significance in the multi-level model.
Sub-populations of students that vary in upward transfer probability by college and state are: (1) low-income, first in family to earn a BA; (2) first-generation but not low-income students (both compared with not low-income and not first generation students); and (3) students who declare a health/vocational/technical-oriented major (vs. business or undeclared). Subpopulations that vary in upward transfer probability by state but not college are: students who planned to transfer at entry compared to students who did not so plan. Results were derived from multi-level regression with random intercepts and random slopes with all student characteristics included (varied one at a time with state policy characteristics and community college characteristics at the intercepts).
Population-average results define regression coefficients to be interpreted as the expected change in the outcome associated with a one-unit increase in the relevant predictor, holding constant other predictors but without controlling any random effects. The unit-specific or subject-specific model holds constant the other predictors and controls random effects (Raudenbush and Bryk 2002, p. 334), and estimated effects are adjusted for individual differences (Hu et al. 1998). Subject-specific results are selected in order to present findings in terms of the change in transfer probability due to the covariates for a specific subgroup of individuals (e.g. students planning to transfer).
Similar to our findings, Stange (2012) did not find evidence that higher per-student instructional expenditures at two-year colleges improved transfer probability.
Contrary to the hypothesis that higher county unemployment would increase students’ likelihood of pursuit of a bachelor’s degree to boost their long-term earning potential, Kienzl et al. (2011) also found that a higher local unemployment rate seemed to be associated with a negative effect on transfer, though the relationship was not significant. They theorized that students may be unwilling to continue on to a four-year institution due to other economic impacts—which may be associated with higher unemployment, state/county economy, etc.—such as reduced financial aid, cutbacks in services at community colleges, etc.
The other four states with common numbering (AK, ND, SD, ID) have only a few or no students represented in the BPS data.
In order to derive proportion of associate’s degree completions in health and vocational fields, IPEDS data were used and health and vocational fields were categorized as: communications technologies, computer and information sciences and support services, personal and culinary services, engineering technologies/technicians, legal professions and studies, military technologies; parks, recreation, leisure, and fitness studies; science technologies/technicians; security and protective services; construction trades; mechanic and repair technologies/technicians; precision production; transportation and materials moving; and health professions and related clinical sciences.
Source: NCES (2012, p. 253, November). Community college student outcomes: 1994–2009. Web Tables. Table 6. “Transfer status after six years: Percentage of 2003–04 beginning students who transferred by 2009 and direction of transfer, by sector of first postsecondary institution: 2004–2009.” Online: http://nces.ed.gov/pubs2012/2012173.pdf.
NCHEMS’ analysis used data gathered in 2007 (which was very close to the mid-year of the BPS period) to indicate policies for each state: (1) presence of a transfer policy, (2) institutional coverage of the policy, (3) state has transferable general education curriculum, (4) the AA/AS degree satisfies the general education curriculum requirement at the covered four-year institutions, and (5) the policy allows specific courses to transfer. State articulation/transfer policies generally cover these themes. NCHEMS’ analysis does not include presence of: (1) state cooperative agreements, (2) transfer data reporting, (3) transfer-related incentives, (4) common course numbering to support upward articulation across state, or (5) state transfer guide. Both inventories include presence of a statewide articulation/transfer policy and transferable general education curriculum.
From: Provasnik and Planty (2008).
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Acknowledgments
The authors extend their appreciation to Robert D. Abbott (Professor, Educational Measurement, College of Education, University of Washington), Mark C. Long (Associate Professor of Public Policy and Governance, University of Washington), Christopher Adolph (Associate Professor, Center for Statistics and the Social Sciences, University of Washington), Margaret Plecki (Professor, College of Education, University of Washington), Xueli Wang (Associate Professor, Educational Leadership & Policy Analysis, University of Wisconsin-Madison), and David Kaplan (Professor, Educational Psychology, University of Wisconsin-Madison) for their guidance and assistance in these research analyses. The authors gratefully acknowledge funding for this research provided by the US Department of Education, Institute of Education Sciences (#R305B090012), and the Association of Institutional Research. We thank the fellow faculty and students in the Collaborative Researchers for Education Sciences Training (CREST) program who provided valuable input throughout this research project.
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Appendices
Appendix 1: Means and Standard Deviations
See Table 3.
Appendix 2: Summary of Variables Listing and Notes
Outcome: CCSTAT6Y (Coded as 1 if “Transferred to 4 year without AA” or “Transferred to 4-Year with AA”)
BPS student-data weight variable used: WTB000, which is the base panel weight for the 2003–2009 data.
College panel weight variables based on first institution attended, not clustered by community college attended for the longest period, so the college weight variables are not perfectly aligned. However, most students in the sample attended their primary community college as the first institution.
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A.
Student background (BPS)
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1.
Income group 2003–2004 and Parent’s Education Level in 2003–2004 (TRIO) Based on TRIO eligibility by CINCOME and PAREDUC in 2003–2004 (Dumour variables are: (1) first generation/not low income, (2) first generation/low income, (3) not first generation/low income, reference = not first generation/not low income)
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2.
(AGE) as of 12/31/03 (ages 15–19 vs. 20 or older at time of college entrance)
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3.
Gender (female = 1; male = 0)
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4.
Race/Ethnicity [African-American (RABLACK), Latino (HISPANIC), Asian (RAASIAN), Native American or Pacific Islander (RAINDIAN or RAISLAND), Reference = Caucasian (RAWHITE)]
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5.
Single (Single, never married = 1 vs. married/widowed/divorced/separated=0) (MARITAL09)
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1.
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B.
Precollegiate academic aspirations and experiences (BPS)
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1.
4-Year Institution Transfer Plans 2003–2004 (1 = plans to transfer to 4-year institution in 2003–2004; 0 = not) (TRANSPLN)
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1.
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C.
Risk Factors Associated with Retention and Persistence (BPS)
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a.
Have dependents (DEPNUM09)
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b.
Work hours (averaged between 2003–2004 and 2005–2006—calculated as the average hours worked from JOBHOUR (2004) and JOBHR06)
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c.
Attended primarily part-time 2004–2006 (calculated from ENMNPT3Y/TOTMN3Y, if 60 % or higher, coded as primarily part-time) versus attended primarily full-time 2004–2006 (calculated from ENMNFT3Y/TOTMN3Y, if 60 % or higher, coded as primarily full-time) versus attended primarily mixed full and part-time 2004–2006 (calculated as mixed if the proportion of full-time/part-time enrollment were both between 40–60 %)
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a.
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D.
College experience, integration, and performance (BPS)
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1.
Academic Integration 2004 variables 1–4:
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a.
social contact with faculty (FREQ04A) coded as 1 if often or sometimes
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b.
participation in study groups (FREQ04G) coded as 1 if often or sometimes
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c.
met with an academic advisor (FREQ04C) coded as 1 if often or sometimes
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d.
talked with faculty about academic matters outside of class (FREQ04B) coded as 1 if often or sometimes
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a.
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2.
Social integration 2004 variables 1–3:
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a.
attended fine arts activities (FREQ04D),
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b.
participated in intramural or varsity sports (FREQ04F), or
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c.
participated in school clubs (FREQ04E)]
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a.
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3.
Any remedial course taken in 2003–2004 (Yes = 1, No = 0) (REMETOOK)
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4.
College GPA in the first year (2003–2004) on 4.0 scale (measured from zero to 400) (GPA) recoded to tenths (e.g. 4.0)
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5.
College Major: (based on MAJORS12 in 2003–2004)—Categories are: Humanities and Social Sciences (1, 2), Math or Scientific (3, 4, 5, 6, 7), Education (8), Business (9), Health (10), Vocational/Technical (11 or 12) versus reference= undeclared]—recoded as Transfer-oriented major (1, 2, 3, 4, 5, 6, 7, 8) versus health/vocational/technical (10,11,12) versus business/undeclared (reference)
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1.
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E.
Community college institutional characteristics
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1.
Size/Locale of 2-year institution (Categories are: Public Community Development and Career Institution/less than 2000 students; Public Community Connector Institutions 2000–9999 students; Public Community Mega Connector Institutions/at least 10,000 students)
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2.
Total enrollment (average undergraduate fall enrollment 2003–2008) in thousands
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3.
Faculty to Student ratio averaged from 2003–2008 [linked from IPEDS, calculated as: (e.g. for 2003, All full time faculty status + all part time faculty status * 0.334)/Undergraduate fall enrollment total 2003] This is an approximate weight for part-time faculty in the calculation, used by IPEDS in calculating student-to-faculty ratio.
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4.
Per-student (FTE) expenditures for instruction and student services (linked from IPEDS) (standardized) based on fall enrollment data
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5.
Per-student (FTE) total expenditures (linked from IPEDS, standardized) based on fall enrollment data
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6.
Proportion of full-time students (6 year average based on IPEDS fall enrollment data, full-time undergraduate students/total enrollment of undergraduates)
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7.
Proportion of full-time faculty (6 year average based on IPEDS faculty data, full-time faculty/total full-time faculty)
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8.
Minority-serving institution status (Historically Black College or University or Hispanic-Serving Institution in 2003)
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9.
% of tenured and tenure track faculty (linked from IPEDS, calculated as (All faculty status employees with Tenure + All faculty status on tenure track that do not yet have tenure)/All faculty status employees total; there are too few part-time tenured faculty, so FT/PT was not weighted here)
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1.
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F.
State Policy Variables in 2005–2006 (imputed from ECS 2001, 2010, are coded as 1 = policy is present, 0 = policy is not present)
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1.
Statewide articulation and transfer policy Legislatures and higher education systems adopted articulation policies at the state level.
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2.
Cooperative agreements Cooperative agreements between postsecondary institutions allow articulation on course-to-course, department-to-department, or institution-to-institution basis, oftentimes in situations where no state or system policy exists.
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3.
Transfer data reporting: States that collect data on transfer and student persistence currently have or are developing the capacity to monitor the success of articulation programs.
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4.
Incentives and rewards: In an effort to encourage upward transfer, some states provide extra incentives by offering financial aid, guaranteed transfer, or priority admission.
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5.
Statewide articulation guide: Provides concrete descriptions of these requirements and answer questions students have about the transfer process.
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6.
Common core curriculum: Streamlines articulation process by establishing a general education core curriculum that fulfills BA graduation requirements.
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7.
Common course numbering: Identical course numbering for similar courses between 2-year and 4-year institutions facilitates ease of transfer, and reduces number of students taking non-transferable credits.
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1.
-
G.
College and State Context Variables
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1.
Average county level unemployment rate (averaged over 2003, 2004, 2005, and 2006) of county of student’s primary community college attended (Bureau of Labor)
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2.
Percent of state population with bachelor’s degree or higher in 2003 (U.S. Census)
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3.
Gross State Product per capita in 2003 (Bureau of Economic Analysis)
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4.
% of state postsecondary enrollment in community colleges relative to state population of 18–24 year olds in 2005–2006
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5.
Ratio of 2-year tuition to 4-year tuition for in-state public institutions (averaged over the period)
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6.
Distance from primary community college attended to nearest public, 4-year institution (use of latitude and longitude data from IPEDS)
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7.
Distance from primary community college attended to nearest non or less-selective public, 4-year institution (use of Barron’s selectivity data from NCES)
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Additional Variable Descriptions
College-Level Variables
Data on public 2-year college (or community college) characteristics comes primarily from the federal Integrated Postsecondary Education Data System (IPEDS). In the prior analysis (as reported in LaSota 2012), community college characteristics that did not explain variance in transfer probability were: proportion of full-time faculty, proportion of full-time students, full-time faculty to student ratio, minority-serving institution (as a level-two, college-level characteristic of primary community college attended), average enrollment in thousands, and college locale (Urban, rural, or suburban). In prior analyses with this data (reported in LaSota 2012), higher per-student spending on instruction and student services (standardized) in community colleges were both unexpectedly negative predictors of transfer probability. One hypothesis for this relationship is the confounding influence of a college’s spending and relative emphasis on training for health and/or vocational/technical fields, which are student majors negatively associated with transfer and that also tend to be relatively expensive to provide. Therefore, this project investigated the effects of proportion of health and vocational/technical degrees conferred at the community college (averaged over the six-year period)Footnote 20 as an institutional variable alongside per-student instructional expenditures and per-student expenditures on student services.
Average transfer-out rate from IPEDS was utilized as a college-level predictor, despite its limitations. Transfer out-rate is measured for full-time, first-time students who first enroll in summer or fall only rather than all students and includes upward transfer as well as lateral transfer to other associate’s degree granting institutions, which would ideally be excluded. Full-time, first-time students comprise less than half of the public 2-year college population, and approximately 36 % of community college students who transferred, completed lateral transfers to another associate’s granting college (NCES 2012).Footnote 21 Average transfer-in rate for public 4-year institutions was also extracted from IPEDS, and used with the distance measures. IPEDS defines this rate as “total number of full-time degree/certificate-seeking undergraduate students entering the reporting institution for the first time but known to have previously attended a postsecondary institution at the undergraduate level. These students may or may not have transferred credit(s).” This does not specify that transfer-in students have to be from 2-year institutions, however. According to NCES analyses of BPS data, 26 % of those who “transferred in” transferred from public and private 4-year institutions, which indicates that approximately 74 % transferred-in from 2-year institutions (which would be mostly public, but some private). As it turned out, neither the distance to the nearest public 4-year institution, nor its transfer-in rate turned out to be a significant predictor of upward transfer in the multi-level analysis.
County-Level Unemployment Rate
County-level unemployment data from the Bureau of Labor Statistics (BLS), mapped with the county of the primary community college attended zipcodes, is used (average unemployment rate from 2004–2008, during the potential transfer period), as an economic factor that may influence transfer to a 4-year institution. One hypothesis may be that higher local unemployment would influence students to continue with their education to maximize long-term earnings while incurring lower opportunity costs. But a competing hypothesis may be that higher unemployment rates constrain students’ ability to pay for college and their perceptions of their capacity to afford and benefit from continuing in college, particularly because the ability to work at least 1–19 hours per week contributes to increased transfer probability. Kienzl et al. (2011) found that a higher unemployment rate produced a negative effect on transfer, and theorized that students may be unwilling to stay in postsecondary education due to other economic impacts associated with high unemployment such as reduced financial aid, cutbacks in services at community colleges, etc.
Selectivity of Nearest Public 4-Year Institution
Another important factor that has been investigated by scholars is the proximity of a nearby public 4-year institution, particularly one that is less or non-selective (similar to the community college) (e.g. Rouse 1995; Calcagno and Alfonso 2007; Clotfelter et al. 2013) for transfer and baccalaureate degree completion. Therefore, data on the selectivity of public 4-year institutions from the National Center for Education Statistics (NCES) was used (the combined lists of 2004 and 2008 public 4-year institutions and their Barron’s selectivity rankings, since some institutions (less than 20) were added or changed status in that time). Distance to the nearest 4 year institution was calculated using latitude and longitude of the individual’s primary public 2-year college for the BPS sample and the latitudes and longitudes of the public 4-year institutions with selectivity data to find the nearest public 4-year institution from that data set.
Barron’s selectivity rankings fall into seven categories: (1) most competitive, (2) highly competitive, (3) very competitive, (4) competitive, (5) less competitive, (6) noncompetitive, and (7) special. Research by Dowd and Cheslock (2006) showed that transfer access from both two and 4-year colleges to elite, highly selective institutions became constricted between 1984 and 2002. The proportion of transfer students to the total entering student class reduced from 10.5 to 5.7 % over the period in highly selective private institutions and in public selective institutions was down to 18.8 % in 2002 from 22.2 % in 1984. Only a small proportion of community college students would transfer upward to a private or public selective institution (Dowd et al 2008), so we tested the influence of proximity to nearest public 4-year institution as well as nearest non or less selective public 4-year institution. Due to changes in institutions offering bachelor’s degrees of various types, 183 public 4-year institutions were identified in IPEDS in 2004 and 2008 that did not have Barron’s selectivity data (so the sample is somewhat constricted in this analysis).
In order to create parsimony in the multi-level regression, these were recoded into three categories: (1) most, highly, or very competitive, (2) competitive, or (3) non or less competitive. Of the 1530 4-year institutions with Barron’s selectivity data from NCES and latitude and longitude data from IPEDS, 520 were public 4-year institutions. The resulting n’s rounded to the nearest tens (due to data security regulations from the Institute of Education Sciences), were then: 10 most competitive, 30 highly competitive, 80 very competitive, 240 competitive, 90 less competitive, 60 non-competitive, and 10 special focus (such as arts and music focused institutions). Special focus institutions were excluded as possible transfer destinations for this analysis, as being too specialized and serving a smaller number of special-interest students. After re-coding, the resulting n’s were: most, high, or very competitive = 120; competitive = 240; and non- or less-competitive = 150. The average distance to the nearest public 4-year institution was 22 miles, using this data and approach, and the average distance to the nearest non- or less-selective public 4-year institution was much greater, i.e. 84 miles.
State Policy Variables
Similar to Roksa’s (2009) findings, we found differences between how ECS and NCHEMS categorized the presence of a statewide articulation/transfer policy. Roksa (2009) compared policy designations from ECS (2001), Roksa and Keith (2008), Ignash and Townsend (2000) and found agreement across the three studies for only 21 states with policies of a possible total of 34 states designated by Ignash and Townsend (2000) as having such a policy (Roksa 2009, p. 2449). ECS has comparable data for both 2001 and 2010, and the 2010 policy chart indicated in which states and which policies were updated from 2001. This information was used to determine transfer and articulation policies that were in place by 2005–2006.Footnote 22 Due to differences in coding of policies, data validity is strengthened by using one coding framework (ECS or NCHEMS) to assess relative influence of policy components. ECS’ framework is more comprehensive, and is therefore used in this analysis.
State Context Factors
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Gross state product per capita (in millions/population in millions) at the base year of the period (2003) is available from the Bureau of Economic Analysis (BEA). The reason to control for gross state product per capita relates to research by Anderson et al. (2006a) that documented an association between the increase of gross state product (in constant dollars) and a decline in state expenditures on higher education per capita and the expansion of comprehensive articulation agreements. As stated by Anderson et al. (2006a), “in an effort to help manage a somewhat inevitable fiscal crisis in public higher education—which is attributable in part to the state underfunding—statewide articulation agreements were adopted or modified during this period [1978–2000] to generate new cost-effective pathways for states to educate baccalaureate-bound students,” (p. 434). Gross State Product per capita has a high correlation with the percentage of the state’s population with bachelor’s degrees or higher, which has been found to have a significant and positive association with higher levels of upward transfer probability.
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Need-based aid per undergraduate student by state in 2005–2006 comes from the National Association of State Student Grant and Aid Programs (NASSGAP) Annual Survey from: http://www.nassgap.org. This variable was included as a measure of state investment in higher education access, particularly for low-income students, who disproportionately attend community colleges and may be deterred from transferring due to the cost differential between two and 4-year colleges. Future analyses may consider the influence of “average net price” at public 4-year institutions and measures of financial aid packaging for low-income students, however, this analysis does not include this factor.
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Proportion of 2-year tuition to 4-year tuition for in-state public institutions, averaged from 2003–2008, from Delta Cost Project data. Community college students with a reduced gap in tuition between public 4- and 2-year institutions may have an increased probability of upward transfer because students may perceive greater fiscal manageability (Kienzl et al. 2011). However, Kienzl et al. (2011) found that the tuition difference was not a factor in predicting upward transfer probability in the BPS 2003–2009, only for the BPS 1996–2001 period. The proportions are calculated for each year (2003, 2004, 2005, 2006, 2007, and 2008) and then averaged. If the ratio of 2-year tuition to 4-year tuition is higher, then students may feel less “sticker shock” when transitioning to the baccaulaureate-granting institution, which may increase upward transfer probability.
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Ratio of community college enrollment to adult population (over 18) in 2005–2006.Footnote 23 States vary “by as much as five to one in the portion of their population that’s attending a community college,” (Shaffer 2008). Wellman (2002) cited Orfield and Paul’s (1992) study which reported that “states that relied least on community colleges had higher rates of bachelor’s degree attainment,” which may suggest that having a higher proportion of the state’s postsecondary enrollment in community colleges does not mean that a higher proportion accesses and succeeds in baccalaureate-granting institutions. The upward transfer mission in community colleges encompasses a large segment, but is not the only mission of community colleges (Cohen and Brawer 2008), and state contexts show wide differences in how upward transfer is emphasized and supported among 2-year institutions (Wellman 2002). Among U.S. community college students, however, the most prevalent transfer destination is a 4-year institution (60.8 %) (National Student Clearinghouse 2012).
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LaSota, R.R., Zumeta, W. What Matters in Increasing Community College Students’ Upward Transfer to the Baccalaureate Degree: Findings from the Beginning Postsecondary Study 2003–2009. Res High Educ 57, 152–189 (2016). https://doi.org/10.1007/s11162-015-9381-z
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DOI: https://doi.org/10.1007/s11162-015-9381-z