Diving Into the Pool: An Analysis of Texas Community College Students’ Transfer Institution Choice Sets
by Huriya Jabbar, Eliza Epstein, Wesley Edwards & Joanna D. Sánchez - 2019
Background/Context: Community colleges are drawing renewed attention from policy makers and advocates seeking to increase college attendance and completion. Nearly half of all students awarded a bachelor’s degree attended a community college. However, we know little about how community college students decide where and how to pursue postsecondary education, or how they select a four-year institution—choices that have significant implications for student outcomes.
Focus of Study: This study examines transfer-intending community college students’ choice sets, or the list of institutions they are selecting from. Specifically, we ask: What kinds of colleges and universities are in transfer-intending students’ choice sets, and how are these choice sets shaped by individual and structural barriers?
Setting: The research took place in two community college systems in Central Texas.
Research Design: Drawing on data from 95 interviews with transfer-intending community college students in Texas—the majority of whom are first-generation college-goers, low-income, or students of color—we examine their choice sets, the institutions to which they considered transferring.
Conclusions/Recommendations: Our findings suggest significant heterogeneity among our sample of community college students seeking transfer to four-year institutions. We find that geography, financial concerns, and quality of institution all play a role in student considerations—though these mechanisms operate differently for groups of students. Students’ choices are bounded, but in different ways. We identify five approaches to choice-set construction among our sample that have differential implications for programs and policies that help students successfully apply and transfer to high-quality four-year institutions.
Community colleges have received renewed attention from policy makers and advocates seeking to increase college attendance and completion rates (Atkinson & Geiser, 2009). Community colleges now account for 42% of first-time freshmen enrollment in the United States (Ma & Baum, 2016), and they serve a large number of low-income students and students of color. Forty-four percent of all African American undergraduates and 56% of Latinx undergraduates attend community colleges (Ma & Baum, 2016). Although community colleges have multiple institutional goals and serve a broad range of nontraditional students, a key, ongoing aim of community colleges is to facilitate transfer to four-year institutions (Cohen, Brawer, & Kisker, 2014; Grubb, 1991; Wyner, Deane, Jenkins, & Fink, 2016). Almost half of all bachelors degrees awarded in the United States are earned by students who attended a community college (National Student Clearinghouse Research Center, 2013).
Several studies over the past three decades have examined transfers from two- to four-year institutions, focusing on either the factors that predict student transfer to a four-year college (Crisp & Nuñez, 2014; Cuseo, 1998; Doyle, 2009; Shaw & London, 2001; Wassmer, Moore, & Shulock, 2004) or the effects of attending community college on four-year college completion rates and outcomes (Gonzalez & Hilmer, 2006; Grubb, 1991; Hilmer, 1997; Leigh & Gill, 2003; Lockwood Reynolds, 2012; Long & Kurlaender, 2009; Surette, 2001). However, despite the large number of studies examining high school students initial choices of postsecondary institutions (Beattie, 2002; Grodsky & Jones, 2007; Long, 2007; Manski, 1993; Tierney, 1983; Turley, 2009) and students decisions about whether to attend community colleges in the first place (Bers & Galowich, 2002; Somers et al., 2006), very little research has been conducted on how community college students choose among four-year institutions (for a recent exception, see Backes & Velez, 2014). To date, no study has explored the actual schools that transfer students consider; instead, researchers have typically inferred choice sets by assuming that students consider all available options, sometimes within a given geographic radius. Understanding the choice sets of students can help policy makers to provide relevant and supportive information to students interested in transfer that could positively impact transfer rates and baccalaureate degree completion.
This study draws on theories of college choice and decision making to explore where community college students in Texasthe majority of whom are first-generation college-goers, low-income, or students of colordecide to pursue postsecondary education. To understand their decision-making process and their preferences regarding transfer, we drew on data from 95 interviews with Texas community college students at two institutions in Central Texas. In particular, we examined their choice sets, or the list of transfer institutions from which they are selecting. Using choice-set analysis (Bell, 2009; Flint, 1992; Tierney, 1983), we examined the types of four-year institutions that community college students choose from.
We found that most students were considering a relatively bounded set of transfer institutionsnot all universities in the state, and not even all universities nearby. We used the data to develop a typology of ways in which students approached the transfer decision, and we identified several types of transfer choosers. Students constructed these choice sets of prospective universities in different ways, and there was significant heterogeneity, both within and across the chooser types, among our sample of community college students seeking transfer to four-year institutions. Our findings align in many ways with current literature but also offer new insights about transfer choice, an understudied dimension of an important populationcommunity college students seeking transfer to four-year institutions. By extending existing knowledge about the choices that students make during the transfer process, our work can inform the development of targeted interventions to improve college access and completion for low-income, first-generation community college students in Texas and beyond.
CONSTRUCTING THE CHOICE SET AND MAKING CONSTRAINED DECISIONS
BOUNDED RATIONALITY AND DECISION MAKING
Economists have typically relied on theories of rational choice, human capital, and expected value to explain the decisions of adolescent econometricians, who evaluate the complex probabilities, costs, and benefits of college attendance (Manski, 1993). Attending college often involves risk and high stakes, owing to the uncertainty of returns on the investment. These risks and uncertainties might be greatest for the most underserved students, particularly low-income and first-generation college students. For community college students seeking to transfer, the risks are even greater because they choose community colleges knowing that, later on in their postsecondary trajectories, they are not guaranteed admission to the four-year colleges of their choice (Hilmer, 1997).
Despite the assumptions from economics regarding decision making, research from psychology and organizational theory has consistently demonstrated that humans make consistent and systematic mistakes when choosing (Kahneman & Tversky, 1979) and are unable to consider all the possibilities available to them. Instead, people use heuristics and shortcuts to make decisionswhat Simon (1955) has called bounded rationality (as quoted in Kahneman, 2003, p. 1449). Furthermore, peoples decisions are shaped by social context, as sociological critiques of the rational decision-making model have shown (Cox, 2016; Freeman, 1997; Gildersleeve, 2010). Researchers have identified differences in predictions of college costs and labor-market benefits based on race and class (Beattie, 2002; Bridge & Wilson, 2015; Grodsky & Jones, 2007). These dynamics are important to understand because policies that do not take these understandings into account can perpetuate inequities in college access and in the economy, resulting in a pattern where the academically and socioeconomically rich get richer, and the poor get poorer (Hearn, 1984).
In reality, low-income, first-generation college students are likely neither econometricians nor cogs in a predetermined and fixed structure of race and class patterns. The selection of a college is not solely an issue of individual choice; it also has social and organizational dimensions, including patterns of historical disadvantage, bias, discrimination, complexity, privilege, and entitlement, all of which shape where students end up. Students choices are bounded (Perna, 2006), and theory and empirical research suggest that college application behaviors differ by race, ethnicity, and income, which can reinforce inequities in access to higher education. In this study, we use concepts from economic models of decision making (i.e., the choice set), keeping in mind that rationality is bounded (Bell, 2009; Kahneman & Tversky, 1979; Simon, 1955, 1997) and that students face structural barriers as they navigate the transfer path.
THE CHOICE SET
To frame our study, we drew on existing theories of the college-choice process, which have focused on the high school-to-college transition. Researchers have suggested that the higher education choice process has three stages (Hossler & Gallagher, 1987). The first stage is the predisposition stage, in which students decided, in our case, whether they intended to transfer. Next, a key part of the decision-making process involves the formation of a choice set, which occurs during the search stage; in this stage, students search for schools, obtaining information about institutions of higher education and developing criteria for judging schools before they actually decide (the choice stage) on a college or university to attend, which constitutes the termination of a sequential choice process (Bell, 2009; Castleman, Schwartz, & Baum, 2015; Tierney, 1983). Our study focuses on the search stage of the theorized choice process, during which students develop a choice set.
The particular set of choices considered by an individual when making a decision is important because the decision to apply, and where to apply, may be more consequential than college admissions in determining student attendance, particularly given that most applicants are accepted to their first-choice institution (Long, 2007; Manski & Wise, 1983). However, the actual choice sets of decision makers are often not available to researchers, who therefore rely on expressed preferences, assuming that these are identical to actual preferences (Beshears, Choi, Laibson, & Madrian, 2008). Economists thus typically infer students choice sets based on their ultimate decision, in part because economists do not view self-report data as reliableonly choice data (Bernheim & Rangel, 2007).
A key assumption of choice behavior is that studentsor, in the case of K12 school choice, parentschoose from a range of schools (Bell, 2009); yet research suggests that students decisions about where to apply are largely random and may be dependent primarily upon the haphazard information that students encounter (Tierney, 1983, p. 272). Bell (2009) used the choice set as an analytic tool that describes and quantifies [choosers] bounded rationality (p. 193). This concept is also similar to the idea of pragmatic rationality, where decision makers are limited by their experiences, constrained by the opportunities in the local labor market most familiar to them and affected by others . . . choosing for them (Finkelstein & Grubb, 2000, p. 615). In other words, students choice sets are bounded.
It is important to unpack the actual choices they consider, the bounded set, and the processes by which they construct choice sets and, ultimately, make decisions. Decision makers also do not consider all choices simultaneously. Instead, they narrowly bracket options, considering one decision or aspect at a time rather than considering all options simultaneously (Kahneman, 2011; Rabin & Weizsäcker, 2009; Read, Loewenstein, & Rabin, 1999; Thaler, 2000). This dynamic can lead students to make less than optimal decisions. In this study, rather than examine revealed preferences, as other studies have done (e.g., Backes & Velez, 2014), we examined students stated preferences and the actual sets of schools they reported considering.
FACTORS INFLUENCING COLLEGE CHOICE
Studies of college choice have examined the importance of various factors (e.g., geography, tuition, selectivity) in high school students decisions about higher education institutions. Tierney (1983) examined the actual alternatives considered by students in a county in Pennsylvania and found little variation in students choice sets; rather than comprising a combination or mix of wish and safety schools, most schools in a students choice set had similar costs and selectivity levels. In Tierneys study, the dominant cluster consisted of 86% of the students, who selected institutions of moderate to high cost and quality, generally no farther than 150 miles from home. Students selecting community colleges tended to cluster together, as did those applying to smaller and more expensive schools, and prestige was a key factor in differentiating clusters of students.
Proximity to home is one of the most important factors for students selecting a college (Hillman & Weichman, 2016; Long, 2004; Tierney, 1983), especially for students of color and low-income students (Turley, 2009). Therefore, decisions about which university to attend are not independent of location, and existing conceptions of college choice do not always address the fact that many students consider only a limited set, bounded by geography. Black, Cortes, and Lincove (2015) examined students application behaviors and found that in Texas, Latinx students are most sensitive to distance, although students of all races and ethnicities are less sensitive to distance as income level increases. In our prior research, we also found that Latinx community college students in Texas were more sensitive to distance at one community college, but less sensitive to distance and willing to travel farther at another community college, suggesting that the local context plays a role in shaping students preferences (Jabbar, Sánchez, & Epstein, 2017). Some studies have found that students are becoming increasingly sensitive to distance (Long, 2004; Skinner, 2016). Although these studies examine general college choices, geographic proximity may be even more important to community college students, many of whom are low income.
Another key factor is tuition costs, including students perceptions and understandings of financial aid packages (Goldrick-Rab, Harris, & Trostel, 2009). Low-income students of color, and their parents, tend to be more likely to overestimate the costs of attending college than middle-class or White parents (Grodsky & Jones, 2007). Small price changes have a greater impact on the decisions of low-income students than on those whose income is higher (Mundel, 2008). Price and financial aid play an important role in how students choose among colleges (Fishman, 2015; Long, 2004). However, some recent studies have found that the importance of cost in college choice is declining over time (Skinner, 2016).
School quality or rankings also play a role in students decisions. Students social capital shapes the extent to which students are aware of school rankings or actively use those rankings in their choice-making process (Ball, Davies, David, & Reay, 2002). Researchers have identified the problem of undermatching, whereby students apply to schools that are less selective than the schools they could have received admission to based on their academic records (Bastedo & Jaquette, 2011; Hoxby & Avery, 2012; Smith, Pender, & Howell, 2013). For example, the choice sets for high-achieving, low-income students resemble those of peers who are socioeconomically rather than academically similar (Page & Scott-Clayton, 2016, p. 10). When students attend schools that have worse conditions and supports, their persistence and completion may be impeded (Castleman et al., 2015). Furthermore, all students benefit from attending high-quality four-year institutionsthose that are better funded, with higher skilled peersregardless of their own performance (Goodman, Hurwitz, & Smith, 2016), and achieving equity in higher education requires explicit practices that work to deconstruct historic barriers to success that low-income students and students of color have faced (Castro, 2015). Such evidence further highlights the importance of where students actually applythe schools they are consideringand of those schools features.
CHOICES OF COMMUNITY COLLEGE STUDENTS
Although there is a robust body of research examining the choices of high school students, there is almost no research on the search and decision-making processes of community college transfer students. Indeed, researchers have argued that existing models for choice are less effective in predicting nontraditional or delayed-entry students search and choice processes than they are of traditional-aged students (Hurtado, Inkelas, Briggs, & Rhee, 1997, p. 45). Community college students represent a different population, one that is arguably more financially and geographically constrained (Backes & Velez, 2014) and one that has already had some experiences in higher educational environments. Studies examining transfer outcomes provide some guidance about what factors matter to community college students in particular. For example, Crisp and Nuñez (2014) examined the likelihood of students transferring to a four-year university and found, in agreement with Surette (2001), that being female reduces the probability of transfer, as does having a dependent. Latinx and Black students are more likely to transfer to universities that are racially segregated and receive less state funding (Gándara, Alvarado, Driscoll, & Orfield, 2012).
One recent study did focus on the decisions of community college students seeking to transfer. Using a comprehensive longitudinal data set from Florida, Backes and Velez (2014) examined the decisions of community college students using observed transfers. They found that community college students were more sensitive to distance than students who had recently graduated from high school. They found that course-taking patterns and distance to the nearest four-year college were very predictive of whether a student transferred. Students who earned more income were also less likely to transfer. Importantly, Backes and Velez found that students, despite being geographically constrained, were still responsive to measures of quality. Students who lived near four-year universities with higher instructional expenditures, lower student-faculty ratios, and more financial aid were more likely to transfer.
Although these studies help us to understand the particular decisions of transfer students, we extend this work by examining students actual choice sets through a smaller qualitative sample. The studies that have examined community college students decisions about transfer institutions have focused on the choice phase rather than the search phase, often making strong assumptions about the schools students choose among. While Backes and Velez (2014) have rich statewide data to establish overall patterns in student transfers, they must create assumed choice sets for students. Indeed, researchers have suggested that in settings where the choice process is complex (i.e., involves a large number of colleges or alternatives), and where personal experience with the goods or services being considered is limitedwhich may be the case for first-generation college studentsrevealed preferences may not represent the actual preferences of decision makers (Beshears et al., 2008). Using qualitative methods, we can unpack students choices in efforts to understand how and why they selected the schools that made up their choice sets. Although some researchers disregard self-reported preferences as cheap talk, students self-reports may in fact be an indication of their hopes and values. Our qualitative approach, complementing prior quantitative research, may help to explain the mechanisms by which students end up transferring to particular types of institutions, with implications for whether they end up at the institutions they hope to attend.
DATA AND METHODOLOGY
This study illuminates patterns of inequality in access and persistence in higher education by looking deeply at how community college students make decisions about when to transfer and which four-year universities to attend. Specifically, we ask: What kinds of colleges and universities are in transfer-intending students choice sets, and how are these choice sets shaped by individual and structural barriers?
We selected two public community college systems located in Central Texas. Our goal was to find systems that were relatively close to one another, such that the choices of four-year transfer institutions could, in theory, be roughly similar in terms of geographic distance and opportunity but have institutional variation (Tierney, 1983). Given the variations in community college contexts and their role in shaping student transfer, it seemed necessary to capture at least two different contexts (Shaw & London, 2001). Although these were not perfect points of comparison, our goal was to explore patterns and themes that might hold across the two different contexts, as well as places where they differ.
Community College A served 41,574 students on 11 campuses in the fall of 2015, the semester under way during primary data collection. There were more females than males (52.7% and 47.3%, respectively). The majority of students (82.5%) reside within the boundaries of the district, while just under 15% are Texas residents living outside the six-county limits; fewer than 2% are out of state or international students. Nearly four fifths of students (78.28%) attended classes part time, with an average of 6 course hours attempted, and the remaining 21.72% attended full time with an average of 13 course hours attempted. White students made up 44% of the student body, Latinx students 32%, Black students nearly 7%, and Asian students 4.3%. Two thirds of students (66.29%) are younger than age 25, while the balance (33.7%) are age 25 and older. At Community College A, 53% of full-time students received any form of financial aid, and 33% of those students received a Pell grant.
At Community College B, we recruited students from two campuses that are a part of a larger network of schools within a metropolitan area. Together, they served 31,857 students. The campuses in this district have some variation in student demographics, though both enroll more women and more students of color than Community College A. On one campus, female students represent 56% of the student body, while on the other, they represent 58%. Students from historically underrepresented groups made up 68% of the student body on one campus and 65% on the other campus, while White students made up 26% and 28%, respectively. In terms of geography, Community College B had nearly identical statistics as Community College A, with mostly (96%) students who were Texas residents. At Community College B, like Community College A, approximately two thirds of all students are younger than 25 years of age, and one third are older than 25. At one campus from the Community College B network, 63% of full-time students received any form of financial aid, and 50% of those students received a Pell grant. At the second campus from the network, 78% of full-time students received any form of financial aid, and 65% of those students received a Pell grant.
In the fall of 2015, we targeted students who were already predisposed to making a choice (i.e., they had already decided they wanted to transfer to a four-year institution; Hossler & Gallagher, 1987). We identified students who were transfer-intending, meaning they had expressed that they intended to transfer in the next 12 months, so that we could capture the search stage of their decision-making process, during which students actually obtain information and assess the different options in their choice set.1
At each community college system, we worked with staff to e-mail listservs, sometimes targeting ones consisting only of students who had declared an intention to transfer. We also reached out to students through Facebook by posting on campus group home-pages and through Twitter by tweeting at the institutions. At Community College A, a staff member e-mailed more than 6,000 students who were intending to transfer, and we also attended six transfer events to recruit participants. At Community College B, a staff member e-mailed the student advising listserv, and we tabled twice a week for two months, handing out flyers and sign-up sheets in the main advising/resource building. We worked to recruit a large pool of students who were from racial groups that historically have been marginalized from higher education, were living in poverty, and/or were the first in their families to attend college; however, any student intending to transfer within the next 12 months was eligible to participate. See Table 1 for a description of our participants.
Table 1. Description of Sample
These data come from a larger, ongoing study of community college students transfer decisions in Texas. All data for this analysis were collected between mid-September and mid-November 2015.
We interviewed 103 community college students across two community college systems over two months about the four-year schools in their choice sets to see how their decision making was constrained, what heuristics were used, and how these sets differed across students from different racial and socioeconomic backgrounds. Interviews were semistructured (Patton, 1990), lasting about 60 minutes each, and all were recorded (with consent) and transcribed. For consistency across interviews, we created protocols based on Pattons (1990) framework, using informal, open-ended, and more formulated questions (e.g., Describe your ideal college/university experience. What types of schools do you expect to apply to? In what areas are you looking for universities?). We asked participants about the schools they were choosing, examining the narratives of reasons for those choices being added and rejected (Ball et al., 2002).
Using the list of schools that participants provided, we probed more deeply, asking questions such as: Why did you select those schools? Where did you find information about those schools? What about these schools is most important to you? Here, we probed for tuition, housing, location, flexibility, major, supports, and prestige/reputation. We also explored options they had ruled out, asking: Which schools, if any, did you hear about but decide not to apply to? Are there any other schools you would not consider?
We considered using only surveys to capture students choice sets, but we wanted to understand why students chose each school (which would have made for a burdensome survey), examine what they knew about each school, and construct a meaningful and more credible choice set for each student (rather than just relying on a list of schools they might have checked off).
As part of each interview, students completed a short online survey using Qualtrics, which took 1020 minutes to complete. This survey was conducted about three quarters of the way through each interview so that the interviewer could ask the respondent to elaborate on key portions of the survey. Of the 103 students interviewed, 100 completed a survey regarding their choice sets. Three others did not complete the survey, but we were able to construct their choice sets from their interview responses. On the survey, students were asked about the factors important to them in the transfer process and to list the schools they had heard of, were considering, or had applied to from an initial start list of schools to which they could add. They were also asked to rank those schools they were considering in order of preference. To aid recall, we constructed lists of schools for each community college system using lists of public and private institutions in Texas. We generated a list of four-year colleges and universities within a 200-mile radius from each community college by creating a buffer in ArcGIS. We then examined transfer data from the Texas Higher Education Coordinating board from 2011 through 2013, the most recent years available, to see the institutions to which students from each institution actually transferred. We added to the list of options both the most popular schools and schools where the institution had posted articulation agreements on its websiteup to 30 schools maximum, to keep the list manageable. Additional spaces were available for students to list other schools they were considering, with an accompanying prompt for any out-of-state institutions. These responses were combined with the participants narrative responses to construct their choice sets. To provide greater context for each institution selected, we also drew on publicly available data from 2014 through 2015 (the year before their applications) from the Integrated Postsecondary Education Data System (IPEDS), which we used to discern the institutional features associated with students choices.
The analysis was conducted in several stages. First, we examined students choice sets, the sets of schools they considered. Next, we examined the qualitative data to help explain the choices they made and the factors most important to them. Using these combined sources of data, we developed a typology of transfer students choice sets. We describe each stage next.
Following the method of choice-set analysis (Bell, 2009; Flint, 1992; Tierney, 1983), we operationalized the choice set by including any four-year institution reported by the student as one they were considering or to which they had applied. (We combined those they were considering or had applied to, given that the application process was still under way at the time of the study.) We indicated whether the student had heard of, considered, or applied to each school in the list of 30 schools we provided, and we added any additional schools they were considering to this file.
We coded the data in the qualitative software program Dedoose using a hybrid coding method (Miles & Huberman, 1994), where we first developed deductive codes from the literature on college choice. Through our team meetings and discussions, we identified other themes inductively throughout data collection. While coding, we defined boundaries between subcategories through a constant-comparative method (Glaser & Strauss, 1967). Through dialogue between the data and literature, we modified and omitted deductive codes as necessary, replacing or expanding on them. We began with broader codes (e.g., Transfer-Choice-Why, to indicate any reasons students stated for selecting a school) and then created subcategories inductively, based on participants actual responses (e.g., Major/Field Availability, Distance Learning, etc.).
After data collection was complete, two team members coded the first transcript and discussed the process and identified any revisions to the coding scheme. Two coders coded one transcript, with 70% agreement, and they discussed discrepancies in code application. Next, they coded another transcript with 82% agreement, where differences were relatively minor (e.g., including or not including a parent code). We therefore decided to proceed with the coding.
We synthesized findings across our individual students and data sources to build or extend theory about how students make decisions (Eisenhardt, 1989). Using the coded data, we created memos for each student to address the studys central questions about students decision making about higher education, drawing from all data sources. To explore differences among students in the characteristics of their choice sets, we used the qualitative data to create matrices and categorize students approach to choice-set construction into five types, which we describe next in more detail: a purposeful mix, casting a wide net, ambitious, a lower bar, and all eggs in one basket (see Table 2 for a distribution).
Table 2. Distribution of Choice Set Types
During the coding process, we identified eight participants who were not far enough along in their choice process to have a choice set, so our analysis focuses on just 95 of the 103 students interviewed.2 Two coders independently classified each student into a category and then met to discuss and come to consensus. We also examined the drivers of students choices and explored themes related to cost/financial considerations, particular program features, and geographical and family constraints. We triangulated data among the interviews, the surveys, and our field notes, reconciling seeming contradictions in students choice sets. Where additional information was needed, we followed up with the student to clarify. We wrote analytic memos about patterns and themes while coding and when examining the matrices, using these memos to draw out major findings.
Although we sought to reach a broad range of students through multiple means (listservs, tabling), the students who responded and were willing to participate may not have reflected the general population of community college students because they may have had more time or stronger opinions about the transfer experience. Therefore, as is true of most qualitative research, our results are not generalizable to the broader community college population, but we illuminate processes and patterns that generalize to theory, finding connections to existing theories of college choice. Furthermore, our sample does include a diverse group of students that reflects the varying categories of community college students, including many students of color, first-generation college students, and students with a range of ages and work experience, but we do not have data on students income or a sampling procedure that would allow us to draw conclusions about particular demographic groups. Our study is limited to two college campuses, selected because they are located in the same geographic area, in Central Texas. However, these choice dynamics may be different in settings that are more rural or have fewer colleges and universities nearby. Finally, with one year of data, we were able to capture only students intentions regarding the schools they were considering. We do not know whether they applied to those schools or selected them; therefore, these choice sets represent their search process at a delimited time only.
COMMUNITY COLLEGE STUDENTS CHOICE SETS
Overall, we found significant variation in the choice sets of community college students. Because where students choose to apply significantly influences their future academic outcomes and success in college, we look deeply at the ways in which students construct their choice sets and at the types of schools they consider.
CONSTRUCTING CHOICE SETS: MANY TYPES OF CHOOSERS
For students to be able to consider a university for transfer, they must first be aware of the institution. On average, students had heard of approximately 18 of the universities (about half) that either were within 200 miles or were those to which students from their college typically transfer, but this varied by student. Students in our sample reported considering or applying to only a subset of these schools, just under five on average. Therefore, most students were considering a relatively bounded setnot all universities in the state, and not even all universities nearby (see Table 3).
We identified five different types of choosers. Out of 95 students for whom we could generate choice sets, 41 had a purposeful mix of schools, including selective and less selective institutions, public and private, and so forth; 23 students had all eggs in one basket, meaning they had only one school in their choice set; 16 were casting a wide net, with a long list of schools but without deep knowledge of each option; five were ambitious, seeking only highly selective universities; and 10 had a lower bar, intentionally choosing only among less selective universities. Next, we explore each of these categories in more depth.
Table 3. Mean Characteristics of All Student Choice Sets
A Purposeful Mix
For a choice set to be categorized as a purposeful mix, it needed to contain at least two schools, and the students who formed the choice sets had to have shown evidence of considered thought about their selections. The schools in a purposeful mix choice set were varied in some way (e.g., through their public/private status, their selectivity level, their geographic location, or their vibe) but usually included institutions with a range of selectivity. A purposeful mix was the most frequent category for students in our sample, with 41 out of 95 students (43%) developing this type of choice set.
The students with a purposeful mix all talked about the reasons for selecting the schools in their choice set, which included the schools geographic locations, the quality of the college experience, cost, institutional prestige, and availability of major. The degree to which the students had researched and evaluated the options varied among the 41, though each student provided justification of some type for the schools in their choice sets. For example, one student had a choice set that included the University of Texas at Austin, the state flagship, and St. Edwards, a small, private, religious school in the same area. He reported that he was open to more schools but said he wanted to stay in the area and was waiting to see what grades he received. On the other end of the spectrum, a student described how she had whittled the options down to arrive at her current choice set: There were 77 schools in Texas that offered the major. So then I looked into how focused on it they wereif they just offered it or whatever. So, yes, I narrowed down my list to 22 and then I started visiting them.
Many of these students had arrived at a purposeful mix because they had included their top choice(s) along with at least one backup or Plan B school. For some, the choice set was small; it contained only their top choice and one other school that they believed would accept them. For example, one student, whose top choice was the state flagship, commented, UT Austin is the main goal. I think Texas State is more of a backup. Another student, whose top choice was also UT Austin, talked about his inclusion of additional public schools in the state in his choice set:
Secondary would be [Texas] A&M and third would be [Texas] Tech. Because I didnt try and keep my GPA up to try and go to Tech. I kept it up to go to UT Austin. And if I cant get in, it will get me into a lower school. But I have kept it up for a reason. I want to go to UT.
For these students, the academic rigor of their top choice was appealing but also daunting, leading them to add other schools to their choice sets. These students knew where they wanted to attend but had prepared themselves with an alternative plan in case they were not admitted. The choice sets of these students suggested that they would probably have at least one acceptance and potentially multiple acceptances to choose from when the time came to transfer because of the mix of institutions in their choice sets. Although all these choice sets were relatively bounded, with the exception of one student who considered all institutions in the state, they included a range of institutional types or selectivity levels.
Casting a Wide Net
Students who were casting a wide net had a large and varied choice set, but unlike students who had a purposeful mix, there was little evidence that they had carefully planned or considered their options. In our sample, 16 of the 95 students (17%) cast a wide net in constructing their choices. These students typically had larger choice sets. For example, one student was considering 10 universities, including John Paul University in California and Seton Hall University in New Jersey, as well as various Texas public and private universities. He noted he was all over the place with his goals, and some of his choices were therefore not carefully considered. He said, Every time that I meet with an academic advisor I still . . . cant explain what it is that I want to do, which they all hate. Therefore, rather than shut down opportunities when he was still somewhat undecided, he maintained a large choice set that captured his varying interests and goals; he had not looked into all options in depth. Similarly, the choices of other students who fell into this category lacked clear patterns, even if some were more considered than others. One student with a lot of schools on his list noted that since he had heard it was free to apply to all the University of Texas campuses once you paid one application fee, he was planning to apply to many of them, driving up the numbers of schools in his choice set even though he had not researched all of those options. This inaccurate information caused the student to delay a careful evaluation of transfer opportunities. What he perceived as keeping his options open could lead to a hurried and ill-informed selection when he begins the application process and encounters higher fees than he expected.
Sometimes students casting a wide net added schools to their choice sets in haphazard waysfor example, through the recommendation of a friend or teacher. One student added several out-of-state schools, including Ohio State University, explaining, My choir teacher in high school went there. She is featured in a lot of things, so that is what I was going for. One student had a list of 40 schools that she had started with. She had narrowed the list but still had a large choice set of in-state and out-of-state universities. She selected some for their names and the prestige and chose others because they had reached out to her directly (e.g., South Dakota School of Mines, Brigham Young University), even though they were not highly ranked in her majorsomething she specifically noted was of interest to her. Her choice set also seemed to be growing, rather than shrinking, as she heard of new schools and added them to her list. We spoke with one student who was considering 19 universities, another who was considering everybody right now, and one who planned to reapply to the University of Toronto and to some Texas schools, including UT Austin, and to a couple of schools in California. The process of trying to narrow the choice set was daunting for one student:
Ive had ample time to think about this, and its just been easier to put it off. Now that Im here, its like, okay, crap. Now what? But even then, I wasnt entirely sure what I was going to do. Its hard to say, okay, youre going to make a decision that impacts the rest of your life. Do you choose option A, or do you choose option B? Its almost a paralyzing thought, and just the anxiety and everything else that comes along with that, its a lot, its substantial.
These students thus ended up having a varied choice set with many options, but they were not well-considered or intentional options. These students still had bounded choice sets in that none of them considered all institutions, but they were broader than other students choice sets. In a sense, however, these students choice sets were also bounded because of how broad their choices were; they did not have sufficient information about each choice, which could cause them to make less optimal decisions.3
All Eggs in One Basket
There were 23 students in our sample (24%) who had placed all eggs in one basket, selecting only one school for their choice set. We found that they constructed these choice sets in different ways.
In most cases, participants with all eggs in one basket did not pick the first university that came along; nevertheless, they had come to the conclusionbefore applyingthat there was only one option for them. Some of these participants knew they had other options but were determined to get into a particular school. For example, one student explained that although she had been admitted to a nearby private four-year institution in earlier application rounds, she had her heart set on UT Austin. She said that if she was not accepted in her first transfer attempt, she would wait until the next cycle and apply again. Although this student expressed an understanding of what it meant that she had only one school in her choice set, it is not clear that other students understood the implications of that kind of limitation. One student had considered three schools but had decided that she was going to apply only to the University of Texas at San Antonio. She noted that one of the schools she had excluded was not for her because it was a relatively new institution, and that the other was out of the question because she would need to rent an apartment.
A subset of students seemed to arrive at their single choice without seriously considering other options. These students encountered an institution, checked its compatibility with their needs, found a match, and closed their search. For example, one student who was committed to Texas A&M College Station stated that A&M checked off his boxes; it was highly ranked in his major and would allow him to establish distance between him and his parents. Another student, interested in staying in San Antonio for nursing, explained her process, which was guided by her boyfriends enrollment at UT Health Science Center:
I go to Google, and I just type in San Antonio nursing bachelors. So I went to [the UT Health Science] website and looked at their program because [my boyfriend] was telling me about it, so I looked on there. I just kind of looked around at what their program does, what they offer. And I was impressed because their site actually had statistics of their passing rate and how many of their students are employed and things like that, so they have statistics to back up their program . . . I looked at that. Ive tried to look at other colleges, but I cant remember. I do remember that one for sure.
This student found a school that offered her major and satisfied her geographic preferences, and ended her search. Another student mentioned that she knew of a few schools in her area, but after an advisor showed her a career plan for Texas A&M San Antonio, she decided that was the choice for her. She stated,
Im actually feeling great about it. A lot of people that I do talk to like where I work at, and they found out Im in school, theyre like, You know, Texas A&M is a really good school. . . . Ive always heard really good things about it, so Im ready.
Another student echoed this sentimentthat the school ticked all of her boxesbut said she landed on UT Austin because its a great school and she had a lot of friends there. Whereas the first student described here had chosen a less selective institution, the second student had placed only UT Austina highly selective schoolin her choice set, which reduced the likelihood that she would be accepted and able to enroll.
It is important to note that of the eggs in these students baskets, all were in the state of Texas, and almost all were within the metropolitan areas of the two community college sites. The only student who had selected a school located more than 100 miles away had done so out of anger and frustration with the counseling office:
Well, I chose the school because Ive always been very unique in my choices. I didnt like going with everybody else. I was always the opposite. All my advisors thought that I was going to go to Texas State and they even put it on my record, and the more I saw that, the more it challenged me. Then one day I just woke up and I told one of my advisors, Who says Im supposed to go to Texas State? Its my decision and I think that I need to be allowed to decide where. Dont just put me there just because everybody else does that. Everybody is supposed to graduate in two years and then go to Texas State.
None of the participants who had thrown their eggs in one basket were geographically constrained to the area. Many preferred these cities, but they could have been encouraged to explore colleges and universities beyond their local areas.
Ambitious Choice Sets
A small subset (5 out of 95) students in our sample had ambitious choice sets, meaning that all the schools they named were highly selective institutions, and they mentioned including those schools because of their academic quality or reputation. All five students stated explicitly that school quality was important in their selection of a transfer institution. One student noted that she sought prestigious schools not just for their reputation but also for what she believed that was correlated with: job outcomes. As she put it, The reputation, of course, is important, but also because the students who [graduate from] the college or the university, they get good jobs. They do much better, the graduates from UT Austin, than a lesser four-year university. That matters. Students commented that they go by a lot of the national rankings or used rankings to identify the top schools in Texas or in their major. One of these ambitious students with a national outlooka first-generation college goerhad received information and confidence about the transfer process from his siblings, who had already successfully navigated a four-year school.
Another participant, who had already been accepted to Baylor University in Texas, reported that her professors at community college had influenced her broad and ambitious choice set. She stated,
Since I started coming here, a lot of the professors said theyve seen potential in me and theyve started to talk to me about different schools and stuff like that . . . I actually started looking at Stanford, but I dont know. I have a 4.0 GPA right now. I think I could get in, but I also know that Stanford takes into consideration all of your work experience and all of your internships and volunteer work and all of that stuff. I dont know if I would get accepted, but I sure do want to try at the end of this year. If not, then I still have Baylor to fall back on.
In these cases, students choice sets were also bounded, but primarily by the reputation of the school, based on rankings or selectivity.
A Lower Bar
Although many students in our study included a highly selective school in their choice set, slightly more than 10% of the participants (10 out of 95) expressed the feeling that these institutions were out of reach for them and said that they had decided to, as one participant put it, [set] the bar a little lower. These participants had choice sets that contained only less selective schools rather than a range of schools. They often ruled out schools, or shaped their choice sets, based on where they believed they could get in, sometimes lowering their expectations in light of new information. For example, when one student enrolled in community college, she came in with the mindset that she would transfer to a selective public flagship university after she completed her basics. But she said that on hearing other students say how competitive the institution was and how difficult it could be to transfer to, she made the choice to move a less selective institution to her first choice because she knew getting in to that university was easier. At the time of the interview, she had not yet completed her basics, but she was already lowering her expectations. As she said,
I knew it was going to be a challenge to get into UT Austin, but I didnt know how competitive it was and I didnt know how stressful it might actually be. While I know I can do it, I just dont want to put myself in that position that is overwhelming or so overwhelming that I am not actually benefiting from it. That it is just causing stress in my life.
This student, and others like her, did not see flagships as out of their league but knew that they would have to invest time and money to raise GPAs or be required to do additional coursework after transfer. Many shared the feeling expressed by onethat they barely have time to eat and breatheand were looking for a quicker route to a bachelors degree.
CONSTRAINED CHOOSERS: THE DRIVERS OF CHOICE
Across the students we interviewed, several common themes emerged regarding the factors that drove their decisions. Previously, we described students resulting choice sets and the types and quantity of institutions they selected. Now we turn to the major drivers of those decisions. Specifically, we focus on three themes: financial and cost considerations, specific program or feature availability, and constraints arising from geography or family. Overall, we note that students choices are contextualized, and although they are making choices, they do so within constraints.
Many students considered tuition, loans, and scholarships when weighing their options. Across all students, the majority of universities that students were considering were public and in state, because of the lower cost of in-state tuition relative to out-of-state tuition. Even students who would have liked to consider out-of-state universities constrained their choice sets in this way, based on their perception of school costs. One student said, I know out-of-state tuition is more expensive. I dont want to go anywhere out of Texas. Im not even thinking out of Texas. UT Austin is good for me in terms of financial aid. Another student said, I dont want to get stuck in out-of-state tuition. That adds a whole other element to it. Students thus initially considered out-of-state institutions but often ruled out these schools because of their higher costs related not only to tuition but also to travel and room and board.
For international community college students or students using military education benefits, additional financial constraints and considerations were present. As one international student said,
I made those decisions very deliberatelyI think thats the word Im looking for. Because I have a lot of constraints, mainly economic. And there is [sic] not as many scholarships for international students as for other students . . . that is one of my limitations.
He restricted his search to Texas because students who have lived in the state for a certain period of time receive a break on tuition. A student on the GI Bill discussed how his benefits shaped his decision to move to Texas:
How the GI Bill works, it pays in-state tuition rate, period. So if you go to a state that does not give you in-state tuition, then they will either have to use the Yellow Ribbon program, which means the school gives some money and the VA will match that to cover the cost, but those are all space available and it is not 100 percent guaranteed. You could have it one year and then the school decides We are not offering this program anymore. So, it was really big for me to have the stability that I would have in-state residency. Texas automatically gives in-state residency to veterans.
Private institutions can also participate in the Yellow Ribbon program, matching the funds offered by veteran benefits. As noted by the student just quoted, however, these collaborations are voluntary and are not guaranteed year to year. Under the Hazlewood Act, Texas veterans and their families are provided additional military education benefits beyond those available through the GI Bill; these benefits apply only to public institutions of higher education in the state of Texas. Nearly all military-affiliated students in our study were focused on public schools because of these financial considerations.
Many students considered tuition in their decision-making process but also looked at scholarships and financial aid. Several students examined tuition plans for each school in their choice set, including the cost per credit hour. As one student said, The number one consideration is money. Some students said they had considered private schools, attractive because of their smaller class sizes, but could not afford costs that were twice the amount of the tuition at the local public university. As one student said, Any private school [I] would love to [attend] because the small classes. . . . But [private schools] are pretty expensive, despite scholarships . . . I still have to pay thousands. These were widely held perceptions. While these and other students ruled out private schools altogether (My parents cant afford a private school), in part because of sticker shock (I wanted to apply to Baylor, but I know it is $60,000 and thats way too much), others hoped to receive financial aid and scholarships so that [tuition] doesnt play such a big factor. Most students thus considered both tuition and financial aid opportunities in their decisions, but few had a good sense of how much aid they would receive, if any.
Several students were hesitant to take out a loan because of repayment concerns. As one student said, It is going to hurt later on after I finish my education. Similarly, another student said that he didnt want to take out any loans because of how his friends had struggled with repayment:
I didnt want to take out any loans . . . I know the majority of college students take out loans, but on a personal level, I have a lot of friends who, by the time they are done, they are going to be . . . I dont want that for me. My brother actually took out a lot of loans as well and now he is working. I want to avoid all of that.
Yet other students believed that loans were necessary or a good investment. They said things like, Youve got to spend money to make money, and Im already $20,000 in debt for student loans, so whats another $100,000? One student believed that cost was correlated with quality, and loans were thus necessary:
This is how I see it: If I am expecting to have amazing teachers and those teachers usually require a healthy income . . . to teach. The school is expensive for that reason. That is how I feel. UT Austin is a good school. It is a good school because of the teachers. The teachers are going to continue to stay there and they are going to make more money because the school is good. I just want to go there because I am expecting to have good teachers.
Costs were a consideration for many students we spoke with, which is not surprising given that community college students tend to be lower income, on average, than students at four-year institutions.
Some students defined their choice sets based on their constraints, which were usually geographical, resulting from a job, a partner, or family support with child care, particularly if they had dependents for whom they were the primary caretakers.
Work or employment was one geographical constraint that emerged in the interviews. Some students could not relocate because of their jobs. They therefore sought nearby universities or those with distance-learning programs. One student, for example, did not want to move to another part of Texas or out of state because she wanted to continue her employment with a particular company. Students were also geographically constrained because of their partners jobs or current educational commitments. For example, one student was moving to Washington, DC, for her husbands career, and that city had only one public school option. Another student had to stay in Austin because his wife was attending college there. Others were geographically constrained by dependents and their reliance on family help to care for them: I didnt really want to go that far because my son is still young, hes three, so its either here or Texas State. I didnt really want to go, venture off too far. Students sometimes had to sacrifice program quality because of this constraint. As one explained,
UTSA doesnt have the greatest architecture program but I feel like I have to go there because of my son. Like, I cant just pick up and move. I have thought about it a lot . . . Im basically forced to stay here because I cant just take my son and I have a lot of help here, too, with school and stuff like that. My mom and then the other grandparents, they help me out a lot.
This example illustrates how students who were geographically constrained factored in program quality but eventually had to prioritize schools based on other factors. For choosers constrained by geography, all the schools in their choice set were within driving distance of their current location. These findings are in line with existing research on the geography of educational opportunity and the role of distance in students decisions about higher education (Backes & Velez, 2014; Flint, 1992; Hillman & Weichman, 2016; Tate, 2008; Tierney, 1983; Turley, 2009), particularly for students with dependents and for nontraditional students, who may be more established in their places of residence because of work or family (Fishman, 2015; Jepsen & Montgomery, 2009).
Other students were not necessarily constrained by geography but simply preferred their current location. They loved their city (a term that several students used), or believed it provided the necessary amenities and was well equipped. Several other students had actually already moved to the area from elsewhere to attend community college, having already indicated their geographic preference. For that reason, they did not want to move again and hoped to gain acceptance to a nearby university. Other students prioritized universities with a distance-learning option in their field. One student described how she narrowed her choice set: I guess when I really delve into details, my determining factors would be how many classes I could do, you know, distance learning, because that really helps me.
For a subset of participants, college could be an opportunity to branch outeither a push factor, a desire to leave where they were from, or a pull factor, to explore other places. A more distant institution meant that they could get out of this city: I love my family, but I love them a little bit more when I have some time to get away from them.
Focused on a Feature
The availability of specific courses, curriculum, or majors drove choice-set construction for some students. The key feature that they sought often related to their program of interestespecially in the arts.
Two students sought universities with particular music programs, one sought a film program, and two were searching for institutions with a premed option or medical school affiliation. One student, who was considering only institutions that had a film program, said,
My career is the center of my world. Im really passionate about the film industry. I want to be a director. . . UT Austin has a brilliant film program . . . I dont want to be thrust into a basic filming class. I want a decent education on this stuff.
Another student described how the presence of an ethnomusicology program with a focus on Latin and Mexican music moved one school to the top of her list.
Students also considered graduation rates and universities records of placing graduates in jobs in their field. For example, one student said that a key factor was having a good history of kids graduating and getting good jobs. Two other students cited graduation rates as important in the interviews. However, one student did not believe that where his degree came from was important. As he said,
Its not a big deal in the private sector where you got your education from. It is more like, you have that box checked. You have your education and you have the connections that you can get in. Thats what it is all about really.
This student noted that network connections established in college were more important than the reputation of the school. This role of networks was echoed in other students decision-making processes as well; as one student put it,
If I am trying to get into an amazing job or an amazing career, then they have the connections. . . . Maybe I will meet somebody who will come up with a good idea and we will start our business together.
People who were currently working also talked to their current bosses or colleagues and were referred to programs that were viewed as good in their fields.
Several students described the importance of getting real-world experience during their college experience. As one student said,
I prefer [Wayland] Baptist [University] only because they say they integrate into the job system and they help them with their job. That is perfect. It is more than education, you have got to have real life experience. You can memorize the book, but . . .
Similarly, other students described wanting opportunities for part-time internships in their fields of interest, or schools that had strong career support.
Generally, students we interviewed perceived public schools to be larger, with larger classes, and more affordable. Although most students described wanting smaller classes, many also desired the benefits of a large public university, because, as one student said, In a big school I feel like you have more opportunities for the future and meet more people from everywhere. Other students shied away from private religious institutions because of curricular requirements or because of the environment. One student said of a religious school, I was raised Catholic . . . I still believe, but I dont follow any of the rules. He felt that the university was therefore not his style. On the other hand, some students viewed the religious aspects of these universities as a draw. For example, one student believed that faith-based institutions would be more likely to have his interests at heart: Somebody with a faith-based perception . . . they are going to give you more helpful advice.
This study contributes to the empirical literature on higher education decision making, community colleges, and college completion by unpacking transfer students choice sets, examining the types of schools they consider, and discerning the factors most important to them. By focusing on community colleges, which serve large proportions of low-income, Black, and Latinx students, our work also yields insights that can be used to improve access and equity in higher education.
We elaborate and extend theories of decision making by building on concepts from existing theories about the choices students make (McDonough, 1997; Perna, 2006; Tierney, 1983; Turley, 2009). Researchers have noted that college choices are bounded, particularly by social class (McDonough, 1997), and our work both illuminates the ways in which they are also cognitively bounded and identifies some ways in which students bound their choices. Furthermore, we deepen knowledge of the search stage (Hossler & Gallagher, 1987), illuminating the various processes through which students arrive at a choice set. Previous studies have examined community college students revealed preferences. Revealed preferences, however, may not reflect the real preferences of the decision makers in cases in which the search process is complex or where the consumer, in this case the student, has limited experience with the goods or services (Beshears et al., 2008). In the region we studied, there is an abundance of higher education institutions, which creates complexity, and because most of our students are low income or first generation, they may have limited experience with or knowledge about higher education.
Our work also builds on prior literature regarding the nature of choice sets. In his analysis of high school students choice sets, Tierney (1983) found little variation in students choice sets, rather than a combination or mix of wish and safety schools, and most schools in students choice sets had similar costs and selectivity levels. Although we found that some students had similar schools in their choice sets (i.e., all ambitious or highly selective schools, all low-cost institutions because of sticker shock, or all nonselective institutions), many students, over 40%, did aim to have a purposeful mix of institutions that ranged in selectivitystriving for a first-choice, often selective school but including a backup option. Our qualitative approach allowed us to unpack the search process of these students in an effort to understand how and why they selected the schools that make up their choice sets. The construction of students choice sets is important because students with poor information or narrow choice sets may make suboptimal decisions (Rabin & Weizsäcker, 2009; Read et al., 1999), leading to racially and socioeconomically disproportionate outcomes. Students may fall into default patterns of transfer, pursuing universities to which students from their community college typically transfer or where there are articulation agreements or other partnerships; yet, these institutions may not be best suited to a particular students needs.
This study aligns in many ways with current literature but also offers deeper insights about college choice for community college students seeking transfer to four-year institutions. Our findings suggest significant heterogeneity among our sample of community college students seeking transfer to four-year institutions, particularly in how they navigate the decision-making process. The nontraditional or community college student is a very broad and varied category, encompassing students ranging from those who went straight to community college from high school, to those who might have taken a year or two off, to those who are coming back to school after many years of working, parenting, or serving in the military. Among our sample, we identified five different types of choosers, including those who had a purposeful mix of reach and safety schools, as well as those who had all eggs in one basket, or believed they had only one choice of transfer institution. For some students, their constrained, narrow, or bounded choice sets were still very heavily researched, and for others, even those who were considering many schools (e.g., casting a wide net), they were not. In other words, it isnt the size of the choice set that matters, but the quality.
In general, students choice sets were relatively small, and some students even sought to broaden them but did not always have sufficient information about alternative options. These choices can have important implications for student outcomes. Research suggests that when students attend lower quality schools, those with fewer supports and resources, their persistence and degree completion can be impeded (Castleman et al., 2015). Indeed, despite the focus on match of student to institution in the literature, recent studies suggest that all students benefit from attending high-quality four-year institutionsthose that are better funded, with more skilled peersregardless of their own performance (Goodman et al., 2016). Therefore, there is a role for policy makers and practitioners to develop information-based solutions to aid students in their decision making to ensure they include high-quality options.
Previous research shows that community college students are strongly influenced by geography when considering higher education opportunities (Backes & Velez, 2014). Students in our sample were similarly concerned about the location of prospective transfer institutions; qualitative inquiry, however, reveals that many who were not geographically constrained still limited their options to just one school, often a nearby one, suggesting opportunities for information or intervention. Earlier studies find that college cost is an important factor in college choice, particularly for students of color (Grodsky & Jones, 2007; Mundel, 2008); in our study, students weighed financial considerations while creating their choice sets. Some students seemed to lack information about financial aid or loans, instead basing their decisions on the sticker price of institutions. Therefore, more tailored advising or better estimates of their out-of-pocket costs might help students make more accurate assessments about their options.
We found that students are making bounded choices, but they are bounded in different ways. Their choices are positioned based on how privileged, resourceful, and powerful they are, which affects their ability to navigate and succeed within the dominant social structure (Andre-Bechely, 2005; Cooper, 2005). Broader structural inequities drive students options. Constraints arising from finances, availability of resources, and family responsibilities, as well as preferences related to geography, for example, may affect students differently. For example, some of the women we spoke to, because of gendered family responsibilities and the division of labor inside the home, were less able to travel farther (even as a commuter) to a campus of their choice. Within this group, students sometimes had considered a broader range of options or as many institutions as possible, sometimes even momentarily considering universities farther away; however, they had to stay local because of family or work constraints. As they made these complex decisions, they weighed the quality of the programs alongside geography and knew they had to make tradeoffs. This situation also points to the broader geographic inequities in educational opportunity (e.g., Hillman & Weichman, 2016) that exist in the higher education landscape. By recognizing that students choices are contextualized and constrained, and showing how these influences play out by typology, policy makers and practitioners can better help with two- to four-year transitions.
IMPLICATIONS FOR POLICY AND PRACTICE
Our work also has implications for practice. An understanding of students choice sets and the factors that matter to students may help to explain the mechanisms through which community college students door do nottransfer to four-year institutions, and it has implications for programs and policies that help low-income, first-generation students in particular to successfully apply and transfer to high-quality four-year institutions. In most instances, the community college staff on the ground play the most pivotal role in enabling students to move forward with their transfer plans. Therefore, by understanding the students they serve and how they go about making decisions about higher education, staff will be better able to help students. In the case of these two community college systems, which have a student population consisting of high numbers of students of color, many of whom are first-generation, nontraditional, or Pell-eligible students, staff need to understand how various factors and constraints influence students choice-set construction differently.
The type of bounded choices a student makes also has implications for that students future outcomes. For students with a purposeful mix, the transfer outcome is likely more optimal. These students choice sets suggested that they would be likely to have an option when the time came to transfer because of the mix of institutions in their choice sets, ranging from selective to nonselective. This is ideally what staff would want for all students who transfer, but as our other four typologies suggest, not all students approach their choice-set construction in the same way. Identifying what type of chooser a student is while the student is still in the search phase is critical to making sure that the proper conversations take place and the right supports are provided. Although it may be desirable for students to cast a wide net and broaden their choice sets, those who have large choice sets that were generated in a general or haphazard way may still end up at transfer institutions that are not a good match for them. Although having ambitious higher education goals and aiming for reach schools should be supported, encouraging students to have a balanced choice set that includes highly selective and less selective institutions may improve their chances for successful matriculation. In addition, counselors and advisers should ensure that students receive high-quality information about schools so that students do not mis-project their chances of admission. Similarly, counselors should guide students who have put all eggs in one basket and are focused on only one school to broaden their searches, both to avoid the potentially catastrophic delays that could come with a rejection and to avoid undermatching. Finally, in the case of students who fall in the lower bar category, guidance counselors should encourage students who are on the borderline of admissions or GPA to submit at least one application to a reach school. These students may be making realistic choices, targeting universities where they are likely to be successful in admissions; however, it is important for them to make these decisions in conjunction with an adviser or staff member who can ensure that they have accurate information about admissions processes and that they are not undermatching. Students who are selecting institutions based on a particular program or major might require more tailored guidance, perhaps from faculty in the field they intend to pursue, about the reputation of each program, its quality, and its selectivity. Advisors and guidance counselors at community colleges can have these conversations with students and help them identify both how the barriers they face are affecting their choice set construction and why the college choices they make matter. These conversations will look different for every student.
Although information-based interventions may help shape students choice sets, we also acknowledge that this is simply one piece of a larger system that structures opportunities for students higher education success and must be paired with institutional and policy supportssuch as clear articulation agreements between community colleges and four-year universitiesto expand access and ease the transition. Transfer hurdles, or challenges in transferring coursework, may ultimately limit students options. The way in which students receive information currently is important to examine in relation to potential interventions. For example, students may receive information based on word-of-mouth through friends, family, and sometimes counselors or advisers, and they may often find this information to be more reliable (Schudde & Goldrick-Rab, 2015). Future interventions might focus on fostering networks or connecting students with students who are attending the transfer institutions they are considering.
IMPLICATIONS FOR FUTURE RESEARCH
Our findings are limited to community college students in urban settings in Texas; the nature of students choice sets might look vastly different in rural settings, or educational deserts (e.g., Hillman & Weichman, 2016). Furthermore, we captured students choice sets only in terms of the institutions they considered at one point; future research might explore how they eventually decided among these institutions, where the reality of dimensions such as costs and distance may play an even greater role, and how stable these choice sets are over time. In other words, research that examines the process of community college students seeking transfer to and enrolling in baccalaureate-granting institutions could be productive. Future studies could also examine community college students choice sets using administrative data, perhaps examining application patterns, to see how these types of choice sets are represented in the broader population of community college students.
1. Other than an intent to transfer within the next year, there were no other eligibility requirements. While we acknowledge that students may intend to transfer, but not in fact be on track to transfer, because of a lack of awareness of additional coursework, a key goal of the larger study was to understand these potential barriers and any miscommunication that students experienced as they sought to transfer. We also wanted to capture students who intended to follow a two-by-two plan, in which they take two years of coursework at a community college before completing two years at a four-year university, as well as those who sought to transfer as soon as possible (i.e., after just one year).
2. That almost all the participants responded to a call for students intending to transfer within a year raises concerns about why they have not started to consider the transfer institution, whether their adviser has put them on a degree or transfer plan (perhaps without their knowledge of which institution it is targeted toward), and why the timeline for this decision has not been made clear. This is important because otherwise, students can take many classes at the community college that will not transfer to their major or institution of choice.
3. We thank an anonymous reviewer for suggesting this interpretation.
Andre-Bechely, L. (2005). Public school choice at the intersection of voluntary integration and not-so-good neighborhood schools: Lessons from parents experiences. Educational Administration Quarterly, 41(2), 267305. doi:10.1177/0013161X04269593
Atkinson, R. C., & Geiser, S. (2009). Reflections on a century of college admissions tests. Educational Researcher, 38(9), 665676. doi:10.3102/0013189X09351981
Backes, B., & Velez, E. D. (2014). Who transfers and where do they go? Community college students in Florida. National Center for Analysis of Longitudinal Data in Education Research. Retrieved from https://caldercenter.org/sites/default/files/WP%20126.pdf
Ball, S. J., Davies, J., David, M., & Reay, D. (2002). Classification and judgement: Social class and the cognitive structures of choice of higher education. British Journal of Sociology of Education, 23(1), 5172. doi:10.1080/01425690120102854
Bastedo, M. N., & Jaquette, O. (2011). Running in place: Low-income students and the dynamics of higher education stratification. Educational Evaluation and Policy Analysis, 33, 318339. doi:10.3102/0162373711406718
Beattie, I. R. (2002). Are all adolescent econometricians created equal? Racial, class, and gender differences in college enrollment. Sociology of Education, 75(1), 1943. doi:10.2307/3090252
Bell, C. (2009). All choices created equal? The role of choice sets in the selection of schools. Peabody Journal of Education, 84(2), 191208. doi:10.1080/01619560902810146
Bernheim, B. D., & Rangel, A. (2007). Behavioral public economics: Welfare and policy analysis with nonstandard decision-makers. In P. Diamond & H. Vartiainen (Eds.), Behavioral economics and its applications (pp. 777). Princeton, NJ: Princeton University Press.
Bers, T. H., & Galowich, P. M. (2002). Using survey and focus group research to learn about parents roles in the community college choice process. Community College Review, 29(4), 6782. doi:10.1177/009155210202900404
Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. (2008). How are preferences revealed? Journal of Public Economics, 92(89), 17871794. doi:10.3386/w13976
Black, S. E., Cortes, K. E., & Lincove, J. A. (2015). Academic undermatching of high-achieving minority students: Evidence from race-neutral and holistic admissions policies. American Economic Review, 105(5), 604610.
Bridge, G., & Wilson, D. (2015). Towards an interactive sociological rational choice approach to theorising class dimensions of school choice. Policy & Politics, 43(4), 493507. doi:10.1332/030557314X13923920087710
Castleman, B. L., Schwartz, S., & Baum, S. (Eds.). (2015). Decision making for student success: Behavioral insights to improve college access and persistence. New York, NY: Routledge.
Castro, E. L. (2015). Addressing the conceptual challenges of equity work: A blueprint for getting started. New Directions for Community Colleges, 2015(172), 513. doi:10.1002/cc.20159
Cohen, A., Brawer, F. & Kisker, C. (2014). The American community college. San Francisco, CA: Jossey-Bass.
Cooper, C. W. (2005). School choice and the standpoint of African American mothers: Considering the power of positionality. The Journal of Negro Education, 74(2), 174189.
Crisp, G., & Nuñez, A.-M. (2014). Understanding the racial transfer gap: Modeling underrepresented minority and nonminority students pathways from two-to four-year institutions. Review of Higher Education, 37(3), 291320. doi:10.1353/rhe.2014.0017
Cuseo, J. B. (1998). The transfer transition: A summary of key issues, target areas and tactics for reform. (ERIC Document Reproduction Service No. ED425771) Retrieved from http://eric.ed.gov/?id=ED425771
Doyle, W. R. (2009). The effect of community college enrollment on bachelors degree completion. Economics of Education Review, 28(2), 199206. doi:10.1016/j.econedurev.2008.01.006
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532550. doi:10.2307/258557
Finkelstein, N. D., & Grubb, W. N. (2000). Making sense of education and training markets: Lessons from England. American Educational Research Journal, 37(3), 601631. Retrieved from https://pdfs.semanticscholar.org/cac7
Fishman, R. (2015). 2015 college decisions survey: Part 1: Deciding to go to college. New America. Retrieved from https://www.luminafoundation.org/files/resources/deciding-to-go-to-college.pdf
Flint, T. A. (1992). Parental and planning influences on the formation of student college choice sets. Research in Higher Education, 33(6), 689708. doi:10.1007/BF00992054
Gándara, P., Alvarado, E., Driscoll, A., & Orfield, G. (2012). Building pathways to transfer: Community colleges that break the chain of failure for students of color. Civil Rights Project/Proyecto Derechos Civiles, UCLA. (ERIC Document Reproduction Service No. ED529493) Retrieved from https://eric.ed.gov/?id=ED529493
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. London, England: Aldine.
Goldrick-Rab, S., Harris, D. N., & Trostel, P. A. (2009). Why financial aid matters (or does not) for college success: Toward a new interdisciplinary perspective. In Higher education: Handbook of theory and research (pp. 145). Dordrecht, The Netherlands: Springer.
Gonzalez, A., & Hilmer, M. J. (2006). The role of 2-year colleges in the improving situation of Hispanic postsecondary education. Economics of Education Review, 25(3), 249257. doi:10.1016/j.econedurev.2004.12.002
Goodman, J., Hurwitz, M., & Smith, J. (2016). Access to 4-year public colleges and degree completion. Journal of Labor Economics, 35(3), 829867. doi:10.1086/690818
Grodsky, E., & Jones, M. T. (2007). Real and imagined barriers to college entry: Perceptions of cost. Social Science Research, 36(2), 745766. doi:16/j.ssresearch.2006.05.001
Grubb, W. N. (1991). The decline of community college transfer rates: Evidence from national longitudinal surveys. Journal of Higher Education, 62(2), 194222. doi:10.2307/1982145
Hearn, J. C. (1984). The relative roles of academic, ascribed, and socioeconomic characteristics in college destinations. Sociology of Education, 57, 2230. doi:10.2307/2112465
Hillman, N., & Weichman, T. (2016). Education deserts: The continued significance of place in the twenty-first century. Viewpoints: Voices from the Field. Washington, DC: American Council on Education.
Hilmer, M. J. (1997). Does community college attendance provide a strategic path to a higher quality education? Economics of Education Review, 16(1), 5968. doi:10.1016/S0272-7757(96)00018-0
Hossler, D., & Gallagher, K. S. (1987). Studying student college choice: A three-phase model and the implications for policymakers. College & University, 62(4), 207221.
Hoxby, C. M., & Avery, C. (2012). The missing one-offs: The hidden supply of high-achieving, low income students (NBER Working Paper No. 18586). Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w18586
Hurtado, S., Inkelas, K. K., Briggs, C., & Rhee, B. S. (1997). Differences in college access and choice among racial/ethnic groups: Identifying continuing barriers. Research in Higher Education, 38(1), 4375. doi:10.1023/A:1024948728792
Jabbar, H., Sánchez, J., & Epstein, E. (2017). Getting from here to there: The role of geography in community college students transfer decisions. The Urban Review, 49(5), 746776.
Jepsen, C., & Montgomery, M. (2009). Miles to go before I learn: The effect of travel distance on the mature person's choice of a community college. Journal of Urban Economics, 65(1), 6473.
Kahneman, D. (2003). Map of bounded rationality: Psychology for behavioral economics. The American Economic Review, 93(5), 14491475.
Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263291. doi:10.2307/1914185
Leigh, D. E., & Gill, A. M. (2003). Do community colleges really divert students from earning bachelors degrees? Economics of Education Review, 22(1), 2330. doi:10.1016/S0272-7757(01)00057-7
Lockwood Reynolds, C. (2012). Where to attend? Estimating the effects of beginning college at a two-year institution. Economics of Education Review, 31(4), 345362. doi:10.1016
Long, B. T. (2004). The impact of federal tax credits for higher education expenses. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 101168). Chicago, IL: University of Chicago Press.
Long, B. T. (2007). The contributions of economics to the study of college access and success. Teachers College Record, 109(10), 23672443. doi:10.1016/j.jeconom.2003.10.004
Long, B. T., & Kurlaender, M. (2009). Do community colleges provide a viable pathway to a baccalaureate degree? Educational Evaluation and Policy Analysis, 31(1), 3053. doi:10.3102/0162373708327756
Ma, J., & Baum, S. (2016, April). Trends in community colleges: Enrollment, prices, student debt, and completion (Research brief). College Board Research. Retrieved from https://trends.collegeboard.org/sites/default/files/trends-in-community-colleges-research-brief.pdf
Manski, C. F. (1993). Adolescent econometricians: How do youth infer the returns to schooling? In C. T. Clotfelter & M. Rothschild (Eds.), Studies of supply and demand in higher education (pp. 4360). Chicago, IL: University of Chicago Press.
Manski, C. F., & Wise, D. A. (1983). College choice in America. Cambridge, MA: Harvard University Press.
McDonough, P. M. (1997). Choosing colleges: How social class and schools structure opportunity. Albany: State University of New York Press.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: SAGE.
Mundel, D. S. (2008). What do we know about the impact of grants to college students? In S. Baum, M. McPherson, & P. Steele (Eds.), The effectiveness of student aid policies: What the research tells us (pp. 938). New York, NY: The College Board. Retrieved from https://www.researchgate.net/profile
National Student Clearinghouse Research Center. (2013). Baccalaureate attainment: A national view of the postsecondary outcomes of students who transfer from two-year to four-year institutions (Signature Report 5). Retrieved from https://nscresearchcenter.org/wp-content/uploads/SignatureReport5.pdf
Page, L. C., & Scott-Clayton, J. (2016). Improving college access in the United States: Barriers and policy responses. Economics of Education Review, 51, 422. doi:10.1016
Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: SAGE.
Perna, L. W. (2006). Studying college access and choice: A proposed conceptual model. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 21, pp. 99157). Dordrecht, The Netherlands: Springer.
Rabin, M., & Weizsäcker, G. (2009). Narrow bracketing and dominated choices. American Economic Review, 99(4), 15081543. doi:10.1257/aer.99.4.150
Read, D., Loewenstein, G., & Rabin, M. (1999). Choice bracketing. Journal of Risk and Uncertainty, 19(1), 171197. doi:10.1023/A:1007879411489
Schudde, L., & Goldrick-Rab, S. (2015). On second chances and stratification: How sociologists think about community colleges. Community College Review, 43(1). Advance online publication. doi:10.1177/0091552114553296
Shaw, K. M., & London, H. B. (2001). Culture and ideology in keeping transfer commitment: Three community colleges. Review of Higher Education, 25(1), 91114. doi:10.1353/rhe.2001.0015
Simon, H. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99118. doi:10.2307/1884852
Simon, H. A. (1997). Models of bounded rationality. Vol. 3: Empirically grounded economic reason. Cambridge, MA: MIT Press.
Skinner, B. T. (2016, March). Modeling college enrollment via conditional logic: How has college choice changed? Paper presented at the annual conference of the Association for Education Financial and Policy Annual Meeting, Denver, CO.
Smith, J., Pender, M., & Howell, J. (2013). The full extent of student-college academic undermatch. Economics of Education Review, 32, 247261. doi:10.1016/j.econedurev.2012.11.001
Somers, P., Haines, K., Keene, B., Bauer, J., Pfeiffer, M., McCluskey, J., & Sparks, B. (2006). Towards a theory of choice for community college students. Community College Journal of Research and Practice, 30(1), 5367. doi:10.1080/10668920500248886
Surette, B. J. (2001). Transfer from two-year to four-year college: An analysis of gender differences. Economics of Education Review, 20(2), 151163. doi:10.1016/S0272-7757(00)00013-3
Tate, W. F., IV. (2008). Geography of opportunity: Poverty, place, and educational outcomes. Educational Researcher, 37(7), 397411.
Thaler, R. H. (2000). From homo economicus to homo sapiens. Journal of Economic Perspectives, 14(1), 133141. doi:10.1257/jep.14.1.133
Tierney, M. L. (1983). Student college choice sets: Toward an empirical characterization. Research in Higher Education, 18(3), 271284. doi:10.1007/BF00979600
Turley, R. N. L. (2009). College proximity: Mapping access to opportunity. Sociology of Education, 82(2), 126146. doi:10.1177/003804070908200202
Wassmer, R., Moore, C., & Shulock, N. (2004). Effect of racial/ethnic composition on transfer rates in community colleges: Implications for policy and practice. Research in Higher Education, 45(6), 651672. doi:10.1023/B:RIHE.0000040267.68949.d1
Wyner, J., Deane, K. C., Jenkins, D., & Fink, J. (2016). The transfer playbook: Essential practices for two-and four-year colleges. Aspen Institute. Retrieved from https://www.aspeninstitute.org/publications/transfer-playbook/