Predictors of transfer behaviours in adult university students

Abstract

Adult students are critical to addressing the college completion crisis. Retention and completion for adults lags behind students who enter college directly from high school. However, higher education has largely been built around service to younger high school graduates, and institutions are slow to change. A shift in focus to accommodate the needs and interests of adult learners will require additional research regarding the enrollment patterns and behaviours of adult students. This study uses quantitative methods and the Beginning Postsecondary Students 12/14 dataset to consider the role of transfer in the experience of the adult learner, with specific attention to the characteristics, demographics and experiences of adult transfer students as well as the predictors of reverse and lateral transfer behaviour in adult student populations.

Keywords

Adult education higher education transfer retention

Transfer activity is an important indicator of success or struggle for college students and institutions of higher education. Nearly 40% of students engage in transfer within six years of enrollment (Shapiro et al., 2015). For students who begin at a community and/or technical college and seek to earn a baccalaureate degree, transfer is necessary for success.

However, transfer behaviours can also indicate challenges for students and institutions. Transfer from bachelor’s granting institutions may suggest that students are not well matched to a college or that colleges are not appropriately supporting students. Little is known about why students who begin at bachelor’s granting universities choose to transfer; unlike transfer activities from most community and/or technical college programs to bachelor’s granting programs, reverse and lateral transfer among students originally enrolling at bachelor’s granting institutions is not required in order to obtain a baccalaureate degree. As transfer behaviours become more complicated, the research must attempt to understand their increasing complexity alongside ongoing demographic changes in student populations. For these reasons, understanding what contributes to a student’s decision to transfer is critical to improving college completion outcomes (Jenkins & Fink, 2016). If colleges gain a strong understanding of what contributes to positive and negative transfer outcomes, they can better support students, strengthen enrollments and increase their completion numbers.

Being able to predict and support transfer success is especially important for the success of adult students, who compose an ever-larger percentage of college and university enrollments (Soares, 2013). Degree attainment for adult students is currently declining, while adult student enrollments are growing . As populations of traditional-age college students decrease (Centers for Disease Control and Prevention, 2017; Cruce & Hillman, 2012; Grawe, 2018), the success of adult students will be central to college enrollment and completion goals (National Adult Learner Coalition, 2017). Both transfer (for community and/or technical colleges and their students) and retention (for bachelor’s granting colleges and their students) are elements critical to supporting the success of adult students, who compose an increasing proportion of the 38% of students who transfer each year.

Unfortunately, much attention in transfer literature is paid to students of traditional college age who transfer vertically (Dougherty, 2009; Ivins et al., 2017; Monroe, 2006). Typically, these students attend college directly or nearly directly after high school. Historically, traditional-age college students were the primary population of individuals attending college. This statistic has changed, and economic and social factors suggest that older students have increasing access and desire for additional postsecondary education (National Center for Education Statistics, 2015; Wyatt, 2011). In fact, adult students now compose the majority of students in many institutions of higher education (National Adult Learner Coalition, 2017; Soares, 2013).

Moreover, very little research exists about reverse and lateral transfer behaviors, even for traditional-age student populations (Goldrick-Rab, 2006). Baccalaureate degree-seeking students who elect to transfer laterally or in reverse are leaving institutions when they do not necessarily need to do so; conversely, in vertical transfer, a move from one institution to another gets a student closer to a baccalaureate degree. The high numbers of adults with some college and no degree underscore the need to pay attention to adult student retention and transfer behaviour. The changing realities and economic needs noted above make the absence of information regarding adult student transfer choices—especially traditionally understudied transfer outcomes such as reverse and lateral transfer—an important area for research.

For these reasons, a better understanding of the predictors of these lateral and reverse transfer behaviours in the adult student population could provide insight into enrollment patterns, student success and retention conversations, and student success operations. Building upon previous transfer prediction studies (e.g; Crisp & Nuñez, 2014; Goldrick-Rab, 2006), this study considered the complex relationships of adult student characteristics, college experiences and institutional characteristics to the reverse and lateral transfer behaviours of adult students using hierarchical generalised linear modelling (HGLM) and the Beginning Postsecondary Students Longitudinal Study (BPS: 12/14) dataset. Additionally, this research identified implications for policymakers and practitioners related to the factors contributing to the transfer choices of adult students in increasingly-complex college environments.

Background and literature review

Literature relevant to this study falls into several categories: adult student learning and success, transfer behaviours and adult student transfer. Typically, literature on adult student learning addressed student outcomes, retention and persistence, as well as perspectives related to higher education. Research on transfer behaviours tended to focus on factors, characteristics and actions that predict and/or correlate with specific transfer decisions. Although few studies have considered specifically the transfer behaviours of adult students, some relevant research that looks at both issues—transfer and adult student outcomes—was found.

Research on adult student learning and success

Adult student learners are the population most prone to attrition (Ma et al., 2016; Serowick, 2017; Soares, 2013). Previous studies (Gast, 2013; Osam et al., 2017) have suggested that time and finances are the most common situational barriers to degree completion. However, at least one study (Lundberg, 2003) found that working and commuting do not negatively influence learning for adult students, suggesting that adult students are confident in their time management abilities. Lack of confidence was also noted as a barrier to success (Osam et al., 2017; Samuels et al., 2011). Institutional barriers also inhibit the success of adult students. Previous research has also suggested that confusing enrollment, remediation and financial aid programs and policies can all discourage student retention and persistence (Gast, 2013; Osam et al., 2017; Soares, 2013). Additionally, individualistic, cognitive and Eurocentric educational approaches have been shown in several qualitative studies to impede adult student success (Buckmiller, 2010; Guy, 1999; Kasworm, 2002; Peterson, 1999). For example, Buckmiller (2010) conducted a qualitative study in order to understand the lived experiences of Native American adult students on a predominantly White college campus. This study suggested that Native American students feel disenfranchised by the prevalence of White “ways of knowing.” Similarly, Peterson (1999) used critical race theory to emphasise the importance of culturally-relevant learning opportunities for African American adult students.

Conversely, educational aspirations, institutional responsiveness and familial encouragement have all been found to impact retention and completion positively for adult students (Bergman et al., 2014; Ray, 2012; Samuels et al., 2011; Serowick, 2017). For example, a logistic regression conducted by Bergman et al. (2014) on data on returning adult students found that “campus environment accounted for more of the variation in adult student persistence than student entry characteristics or external factors” (p. 98). Using narrative inquiry, Samuels et al. (2011) had similar findings that emphasised the overall importance of campus supports to adult students. This study also noted the importance of student attitudes regarding education and “[s]upports in the life-world” environment (p. 368). Student support services have also been found to improve student experiences (Ray, 2012; Ross-Gordon, 1998). Descriptive statistics and a multiple linear path model offered by Lundberg (2003) revealed that adult student learning may be enhanced by peer learning and relationships on campus (Lundberg, 2003). Intrinsic motivation was also found to be positively associated with adult student success (Bergman et al., 2014; Shillingford & Karlin, 2013). Shillingford and Karlin (2013), for example, conducted analyses of variance on adult student responses to the Academic Motivation Scale survey and found a possible relationship between intrinsic motivation and the decision to enroll in college.

Demographic factors have also been shown to be related to adult student success. A phenomenological study of adult students by Serowick (2017) suggested that women are less likely to return to college as adults but more likely to succeed than adult men. However, a separate mixed-methods study suggested that gender was shown not to predict adult student persistence (Markle, 2015). Income and socioeconomic status also matter (Bowers & Bergman, 2016; Chen & Hossler, 2017), and higher earners were found to be more prevalent in college classrooms (Serowick, 2017). Gaps were also found to exist in the persistence of adult students according to race, and White students were found to compose 85% of the adult student population (Goings, 2018; Paulson, 2012; Schatzel et al., 2011). Using hierarchical linear modelling, Stern (2016) found that age has also been shown to influence transfer behaviour. As age increased, the likelihood of vertical transfer was found to decrease (Stern, 2016), while this study focused solely on vertical transfer, its findings raise interesting questions about the relationship between age and other transfer outcomes.

Enrollment patterns may also relate to adult student success. Lower persistence and completion rates have been found to be associated with attendance at numerous colleges (or “swirl”) throughout an academic career (Ishitani, 2006; Kuh et al., 2008; Li, 2010; Selingo, 2013; Sinha, 2010; Yang, 2007).However, no research was found that specifically addressed the issue of adult student co-enrollment or swirl. A meta-analysis of graduation rate data by suggested that enrollment intensity may be associated with adult student success. specifically found that full-time adult students were more likely to graduate. The transferability of credit has also been previously shown to be an impediment to success (Gast, 2013; Monaghan & Attewell, 2015), but credit for prior learning and prior learning assessments was found to increase the odds of graduation (Gast, 2013).

Research on transfer behaviours

As previously suggested, recent transfer-related research in the field of higher education has focused on vertical transfer outcomes and traditional-age students (Cohen et al., 2014). While not necessarily applicable to reverse and lateral transfer, this research is important to consider because it provides some information about what might help or impede students looking to move between institutions. Much of this research (Crisp & Delgado, 2014; LaSota & Zumeta, 2015; Wang, 2009, 2012) focused on the persistence and success of transfer behaviours of community and/or technical college students who pursue baccalaureate degrees at bachelor’s granting institutions. Most research on vertical transfer considered student-level characteristics such as socioeconomics, previous academic preparation, academic standing and employment status (Adelman, 1999; Bahr, 2008; Dougherty & Kienzl, 2006; Wang, 2012). The prevailing finding of research on vertical transfer related to student-level characteristics was that positive outcomes are less common for historically underserved populations (Cohen et al., 2014; Crisp & Nuñez, 2014; Dougherty & Kienzl, 2006; Dowd, 2012; Wang, 2012; Urias et al., 2016). Specifically, Black and Hispanic students were found to have lower rates of vertical transfer (Dougherty & Kienzl, 2006; Urias et al., 2017; Wood & Palmer, 2013). Part-time enrollment, employment while in college and previous academic preparation were also associated with poor transfer outcomes (Cohen et al., 2014; Dowd, 2012; Wang, 2012).

Institutional factors also mattered when considering vertical transfer behaviour, although this area needs additional study. Several quantitative studies have considered the role of institutional selectivity in transfer success (Crisp, 2017; Porchea et al., 2010). Characteristics of specific institutions were also found to contribute to successful transfer for students. Specifically, student support services (Cabrera et al., 2005) and faculty support (Cabrera et al., 2005; Eagan & Jaeger, 2009) have been found to contribute to vertical transfer success.

As previously noted, reverse and lateral transfer are not as well understood as vertical transfer. Some quantitative research (e.g. Crisp, 2013; Johnson & Muse, 2012; Wang & McCready, 2013; Wang & Wickersham, 2014) attempted to explain co-enrollment, defined as enrollment at more than one institution at the same time, and swirl, defined as the process of enrolling at a second institution with the intention of returning to the institution of origin.

Although these concepts are not the same as lateral or reverse transfer, co-enrollment can help researchers understand some behaviours and characteristics that might also motivate lateral and reverse transfer. A few quantitative studies explored reverse and lateral transfer specifically (Aulck & West, 2017; Bahr, 2012; Goldrick-Rab, 2006; Goldrick-Rab & Pfeffer, 2009; Hillman et al., 2008; Hossler et al., 2012; Winter et al., 2001). Even fewer (Goldrick-Rab, 2006) attempted to use multivariate statistical modelling to understand reverse and lateral transfer behaviour.

Descriptive findings on reverse and lateral transfer suggested that, as with vertical transfer, underserved populations were less likely to be successful. For example, regression analyses by Goldrick-Rab and Pfeffer (2009) suggested that students with low socioeconomic status, first-generation students and working-class students are more likely to reverse transfer. Historically underserved students also appeared to reverse transfer at higher rates . College experiences were also found to impact transfer behaviours. For students of traditional age, financial concerns, underpreparation for college (Hillman et al., 2008), lower social capital (Goldrick-Rab & Pfeffer, 2009) and unclear degree aspirations (Hillman et al., 2008) were all associated with reverse transfer. Poor academic performance has also been shown to predict transfer from bachelor’s granting institutions (Goldrick-Rab & Pfeffer, 2009; Hillman et al., 2008; Kalogrides & Grodsky, 2011;.

Contradictions and omissions also emerged in the small body of research on reverse and lateral transfer. For example, a regression analysis by Hillman et al. (2008) suggested that males are less likely to reverse transfer, while a case study conducted by found males to be more likely to transfer from bachelor’s granting institutions. Additionally, although many academic and social experiences also served to connect students with their institution and thereby decrease transfer behaviour, these factors have not been well studied in reverse and lateral transfer research. These discrepancies and omissions further underscore the need to better understand the differences in and predictors of various transfer behaviours.

Research on adult student transfer behaviour

Despite the ever-increasing population of adult learners in institutions of higher education, adult student transfer behaviour is underrepresented in literature on student transfer behaviours. Much of the literature that addressed adult student populations did so obliquely. For example, Milsom and Sackett (2018) published a phenomenological study on students with disabilities who vertically transfer; their study included adult students, but these students were not the specific focus of their research. Other studies (List & Nadasen, 2017; McCormick, 2003; Reyes, 2011; Stern, 2016; Winter et al., 2001) on transfer also included adults but fail to disaggregate results by age; the lack of disaggregation in the research makes identifying meaningful findings for adult students difficult. Notably, only one study considered specifically the reverse and lateral transfer behaviours of adult students. A discriminant analysis by Winter et al. (2001) found that older reverse transfer students were less likely to complete college, suggesting that “age is the only significant discriminator between completer and non-completer reverse transfer students” (p. 279). However, the study specifically focused on outcomes, rather than predictors, of transfer.

At the time of this study, only two articles were found that addressed adult student transfer as the primary subject of research. Austin (2006) published a qualitative study that considered the attributes and outcomes of a scholarship program for adult female community and/or technical college transfer students. The study found that scholarships, mentoring and access to academic resources and counselling improve transfer outcomes of adult women who transfer. Monroe (2006) also completed an ethnographic study of adult student attrition that attempted to understand why adult students choose to attend and then leave institutions. Although this study did not focus specifically on transfer, it addressed transfer as a component of attrition. Monroe (2006) found that poor program design, customer service, institutional fit and academic integration may all contribute to student attrition.

In sum, the lack of attention on adult student transfer represents a significant gap in the literature. Some of the research (e.g. Crisp & Nuñez, 2014; Urias et al., 2017) has attempted to use critical and creative lenses to better understand transfer. In other words, specific attempts to study the relationship between “non-traditional” students and unconventional enrollment behaviours are becoming more common in the literature. Unfortunately, a major gap still exists in research on adult student transfer behaviour. More specifically, only two studies have been conducted that specifically consider adult student transfer; both studies were qualitative, which underscores the need for a quantitative approach to this topic. As Laanan and Jain(2016) have suggested, additional critical perspectives are necessary in the literature on transfer. In other words, transfer literature needs new approaches, perspectives and voices. The inclusion of adult students into transfer studies is one strategy for accomplishing this goal, as it incorporates previously-understudied populations into the collective knowledge base of transfer. This inclusive approach also stands to benefit institutions looking to retain students and improve equity. Adult student transfer behaviours of all types need additional attention and understanding so that this important and underserved student population may find greater success.

Theoretical overview and research questions

Questions about the relationship between adult student success and transfer can be situated at the intersection of nontraditional student development theory, persistence theory and student/institution engagement theory. Each of these theories informs hypotheses about what may influence an adult student to make choices that result in transfer and, eventually, persistence and/or completion. Development theory (Kasworm, 2010; Soares et al., 2017) takes a holistic and psychological approach to adult student decision making, persistence theory (Bean & Metzner, 1985; Tinto, 1993) examines obstacles and factors that contribute to persistence or attrition, and student/institution engagement theory attempts to explain the specific role of the institution on transfer behaviour and other outcomes. In this way, the models for this study consider overlapping trends: student persistence in higher education institutions in the form of transfer and student decision making (both developmental and environmental) about what kind of transfer activity in which to engage.

Kasworm (2010) created a developmental framework for understanding the decisions made by adult student populations, noting that learner roles, life roles, life experience and knowledge mastery are the central constructs that apply to adult students. In addition, these forces are influenced by academic programs, policies and practices, institutional clusters and systems and faculty/staff relationships. Kasworm (2010) suggested that learner roles include the positionality of the student and their autonomy as learners. Life roles encompass the activities and priorities a student has outside of school. Life experiences refer to the information and knowledge gained by being a more mature student; these experiences include world view and beliefs about education. Knowledge mastery describes the unique knowledge and skills adult students often bring to the classroom as a result of having already been participants in the workforce. Each of the external factors (academic programs, policies and practices, institutional clusters and systems and faculty/staff relationships) interplays with the central constructs and influences student outcomes.

Soares et al. (2017) built on Kasworm’s research to create an additional framework to describe the experiences of adult students and argued that institutions “tend to focus too narrowly on … learner and life roles” at the expense of life experience and knowledge mastery (Soares et al., 2017). In response to this concern, the learning ecosystem modelled by Soares et al. included informal learning, nonformal learning and formal learning. Informal learning describes the knowledge acquired through life experiences and work, while nonformal learning relies on knowledge gained by cultural experiences and human relationships. Formal learning refers to traditional, “school-based” knowledge. While traditional-age students also bring learning experiences from each of these areas to college (and this is an area worthy of additional research and consideration for traditional-age students), they generally have fewer years of work experience and life experience due to their age.

Deil-Amen (2014) offered a similar model that notes the interplay of dimensions of diversity in the life of a student. Age is only one dimension, and other life factors (e.g. support networks, work history, health) all matter to student success. Moreover, she noted that each of these factors can be marginalising for students, who are often expected to fit into a very specific mold of what a college student should be. These factors are especially true when one considers adult student development and the ways that adult students have had more time to accrue life experiences, relationships and hardships than those of traditional age.

While development theory attempts to explain the ways that psychology and life experience interface with college decisions and success, persistence theory seeks to understand the reasons why students choose to stay in college, despite obstacles they encounter along the way. Tinto’s (1993) model of student departure was the foundation for most of the theories in this category. Student departure theory seeks to understand the reasons that students persist and succeed at the institutions they choose. Tinto created and popularised a specific model of student departure that suggested that attrition could be attributed to combined factors associated with student characteristics and institutional factors. Tinto (1993) emphasised the importance of commitment: in order for students to be retained, students must commit to institutions, and institutions must commit to students. Integration and acclimation of students (and the support of integration and acclimation by the institution) were identified as key contributors to retention.

Bean and Metzner’s (1985) non-traditional undergraduate student attrition model, based on Tinto’s institutional departure model, is an example of the application of Tinto’s work to adult students. Tinto (1993) suggests that, in addition to variables specific to the student, institutional and social variables contribute to whether a student persists. The theory of institutional departure addresses several of these institutional and social variables (e.g. the influence of faculty and staff interactions on a student’s experience). Bean and Metzner (1985) also specifically assessed the relationship of external variables to adult student success. As the characteristics of adult and traditional-age students become more similar (due to increased access to college and flexible course modalities, among other variables), the adult undergraduate student attrition model may have increasing applicability for all student types.

While student departure and persistence models are valuable to this research, numerous studies have both supplemented and critiqued them (e.g. Metz, 2004 provided a comprehensive, article-length overview of the far-reaching influence of student departure theory) in order to account for issues related to accuracy and equity. In addition, several studies have sought to better understand the specific institutional attributes that support student success. In fact, Tinto (1993) revised his previous model to emphasise the importance of the institution in understanding student success. Additionally, although Tinto originally focused on the baccalaureate college experience, several researchers (Bean & Metzner, 1985; Elkins et al., 2000) have applied variations of student departure theory to community and/or technical colleges.The findings have been mixed. Student demographics and characteristics have been heavily considered in student departure models, resulting in a chicken–egg situation wherein one cannot easily tell whether the student’s characteristics create an environment where success is not possible or whether institutions make success impossible for students with specific characteristics.

Nevertheless, questions of institutional selectivity and control were central to the research included in this study, which focuses, in part, on the institutional pathways that students choose when they make transfer decisions. For example, what factors influence a student’s decision to attend or remain at a specific institution? Similarly, student departure theory attempts to explain, at least in part, the influence of institutional characteristics on student success and decision making. Vertical transfer has traditionally been viewed as evidence of student success: a student seeks to further their education at an institution with more comprehensive offerings. While neither reverse or lateral transfer are inherently successful or unsuccessful moves, the research in this study explored the ways that these choices are influenced by other factors that are associated with student success, especially for adult students.

The limitations of Tinto-oriented persistence theories benefit from the addition and consideration of a theory such as student/institution engagement model. This model attempted to account for the factors that influence transfer behaviour by considering the attributes students possess when they attend college (e.g. financial circumstances) as well as the environmental factors (e.g. employment, parenting) that contribute to and/or compromise their ability to engage in collegiate endeavors. These factors can be used to “map” and understand transfer decisions. In other words, model observed that, for example, environmental pull factors could influence transfer behaviours.

Persistence and engagement theories also overlap with the development theory work of Kasworm (2010) and Soares et al. (2017). Specifically, Bean and Metzner (1985) attempted to outline the departure-related factors influencing adult students. Like Kasworm (2010), Bean and Metzner (1985) identified variables that impact retention. Study habits, academic advising, absenteeism, major selection, course availability, finances, hours of employment, outside encouragement, family responsibilities and opportunity to transfer are all included in their model. Social integration variables, although less well defined, are also considered. model considered similar factors but reorganises them to emphasise characteristics and pull factors. In these ways, this study attempts to create cohesion across models of adult student development, persistence and student/institution engagement by employing them together as tools for understanding adult student behaviours. Additionally, the research included here provides ideas about how these models might be supplemented through a greater focus on and understanding of adult student needs and choices.

Research questions

In order to better understand the transfer behaviours of adult students, this study addressed the following research question (RQ), within the larger frameworks of student departure theory, adult student development theory and student/institution engagement theory: Which demographic characteristics, institutional contexts, early college experiences and student supports help to predict adult student lateral and reverse transfer behaviours before the third year of college for students with the intent of earning baccalaureate degrees (RQ1)? After accounting for student characteristics and experiences, which aspects of the institutional context help to explain differences in the transfer choices and behaviours of adult students (RQ2)?

Methods

Data source and sample

Follow-up data from the current cohort of the BPS: 12/14 were used for this research. These data were taken from the 2011–2012 National Postsecondary Student Aid Study (NPSAS:12), Integrated Postsecondary Education Data System (IPEDS:10–11) and other data sources (Hill et al., 2016). BPS data have been available for many years, and this data cohort allowed for comparison with previous cohorts. Additionally, new variables were added to the 12/14 dataset, including data on student services, institutional factors and enrollment activities. Each of these new data points were considered in this study and subsequently provided descriptive information previously unavailable about adult student preferences, activities and behaviours.

Specifically, this study used a nationally-representative sample of adult students (defined as first-time students who are 21 and older) who began college during the 2011–2012 academic year at a Title IV eligible college or university in the United States. Although many studies consider “nontraditional students” to be those over 24, previous studies using the BPS: 12/14 dataset (Potter, 2020) found that significant differences were not found in transfer behaviours of students aged 21–23 and those ages 24 and older. According to that study, transfer outcomes for students aged 21–23 closely resembled those of students ages 24 and older. These findings square with aforementioned research on how “post-traditional” students are defined, suggesting that those between ages 21–23 possibly have more in common with their older counterparts than students entering college directly from high school.

The analytic sample for this research included the 3680 adult students who initially enrolled at a community and/or technical college or bachelor’s granting institution. The study modelled the adult students who laterally transferred to another similar institution (n = 220) and the students who reverse transferred from a bachelor’s granting college to a technical or community and/or technical college by the third academic year (n = 140).¹ Definitions for the coding of institutional types were derived from IPEDS definitions. Community and/or technical colleges were, for the purposes of this study, defined as institutions that offered programs of at least two but less than four years’ duration. Baccalaureate granting colleges were defined as institutions offering baccalaureate degrees. Students who only took courses at another institution to transfer credit back to their original institution were excluded.

Variables/conceptual model

As indicated in the literature review, several previous studies (Crisp, 2017; Goldrick-Rab, 2006; Goldrick-Rab & Pfeffer, 2009) suggested a relationship between sociodemographics and transfer behaviour, although none of them directly addressed adult transfer behaviours. Similarly, pre-college experiences and supports are thought to influence traditional-age student transfer (Hillman et al., 2008; St. John et al., 2000), but the effect of these experiences on adult students is not well understood. Early college experiences, some of which are new to the BPS dataset, were also thought to play a role in transfer for traditional-age students (Hu, 2011; Hurtado & Carter, 1997; Kuh et al., 2008); for that reason, this study considered their influence on adult students, as well. Institutional characteristics such as urbanicity, percentage of students of color and institutional control have also been found to influence traditional-age student transfer behaviour (Crisp, 2017) and were considered in this study of adult students. All specific variables used in this study appear in Appendix 1.

Using research on transfer, adult student behaviours and the frameworks of student development and student departure as a foundation, this study analysed independent variables from the following BPS categories: sociodemographic characteristics, early college experiences and institutional characteristics. These categories were identified in a previous study as having relevance for transfer behaviours for traditional-age student populations . Additionally, this study combined two related variables into a dependent variable: lateral transfer to another institution before the third year of college and reverse transfer to a community or technical college before the third year of college. The comparison group for these variables was composed of adult students who did not transfer. Drawing from the work of Bean and Metzner (1985) and Kasworm (2010), this study hypothesised that a combination of demographics, motivations, early college experiences and institutional contexts influences transfer decisions.

Demographic data were important to this analysis. This model considered race/ethnicity, socioeconomic status, gender, age and generational status (defined as whether or not a student’s parent has earned a baccalaureate degree). Each of these characteristics was known to influence traditional-age student transfer behaviours (Soares, 2013). It was expected that these variables would similarly impact adult student behaviours. Stern (2016) also found that minoritised populations are less likely to transfer. The relationship between race and adult student success has been explored in at least one qualitative study (Goings, 2018), which suggested that social factors were associated with success for Black male adult students. In another study, gender was shown not to predict adult student persistence (Markle, 2015). However, this study suggested that, for each gender, the factors that result in persistence are different; for example, grade point average (GPA) and self-confidence matter for both men and women, while part-time status affected persistence positively in women only.

Using the aforementioned student departure and development models, one can also hypothesise that financial factors and student motivations influence adult student transfer decisions. Previous studies (Bowers & Bergman, 2016; Chen & Hossler, 2017) have explored the importance of financial factors in adult student success. For this reason, financial factors were considered, including the average hours a student worked per week, the total amount of financial aid received, financial support from family or friends and social capital (defined as support from friends about postsecondary education). Greater financial need was expected to relate to decisions to reverse transfer. Additionally, the highest degree a student expects to earn and a student’s academic self-concept at the beginning of college were expected to be related to a decision to transfer; intrinsic motivation factors have been previously associated with adult student success (Bergman et al., 2014; Shillingford & Karlin, 2013), and high intrinsic motivation was expected to predict against reverse and lateral transfer. For adult students, motivation has been previously shown to be correlated with GPA ; this model attempted to account for this relationship and predicts that higher GPAs will predict against reverse and lateral transfer.

Support services (academic advising, academic services and career services) and measures of engagement (social satisfaction and sense of belonging) were included in the model (Goings, 2018; Hurtado & Carter, 1997). Enrollment intensity, first-year GPA, developmental coursework and co-enrollment behaviours were also examined, due to findings from previous transfer studies (Crisp & Delgado, 2014; Kalogrides & Grodsky, 2011; Kuh et al., 2008; Hillman et al., 2008; Hu, 2011). found that enrollment intensity was correlated with improved graduation rates in adult populations, and full-time status was expected to correlate with lateral transfer. Part-time enrollment and developmental coursework were not expected to be associated with reverse transfer, as is the case for traditional-age students, due to the complexities of adult student lives as noted in the aforementioned conceptual models.

Institutional characteristics were also included in the model. Institutional control, the cost of attendance, the school’s urbanicity, the percent of Underrepresented Minority (URM) students enrolled and institutional selectivity were all considered. Specifically, it was hypothesised that institutional contexts would have a unique effect for adult students. While traditional-age students were less likely to transfer from private non-profit institutions (Goldrick-Rab, 2006), it was expected that the data will show the opposite effect for adult students based on the influence of socioeconomic factors in the lives of adult students (Bean & Metzner, 1985; Soares et al., 2017). Urban college students were expected to reverse and lateral transfer more frequently, due to issues related to immobility and institutional access (Bean & Metzner, 1985). However, urbanicity was also a factor in vertical transfer, with a higher percentage of rural students transferring than urban students (Stern, 2016). Additionally, the percentage of URM student enrolled was expected to influence reverse and lateral transfer decisions for adult students in ways similar to traditional-age students (Crisp, 2017; Goings, 2018; Ishitani, 2006; Titus, 2004).

Analysis

HGLM was used for the analysis. This study was interested in the influence of student and institutional characteristics on the transfer behaviours of adult students. The dependent variable was dichotomous (transferred or did not transfer), and students were nested within particular types of higher education institutions. For these reasons, HGLM was an appropriate inferential technique (Raudenbush & Bryk, 2002). In other words, the random effects at each level will not be normally distributed, due to the dichotomous nature of the dependent variable. The multiple levels (students and institutions) required a hierarchical model. Moreover, variance in the random effects relied upon predicted values, which are restricted in a dichotomous scenario only to values of 0 and 1. These were not real values; they were probabilities, which makes an HGLM the model of choice (Raudenbush & Bryk, 2002). Student-level predictors were added to within-institution models in order to interpret the influence of student-level variables on transfer behaviours. An additional level of variables was added to model the hypothesised contextual predictors of transfer.

Logit-model HGLM analyses were run using STATA 13. Missing data (3% across all variables) were handled using multiple imputations with LISREL (Manly & Wells, 2015). Data were cleaned prior to analysis, and multicollinearity among the predictor variables was analysed according to variance inflation factors (VIF) (Warner, 2013). All variables were shown to have a VIF less than 10. Descriptive statistics were provided in order to provide disaggregated information about reverse and lateral transfer as well as key information about student and institutional characteristics associated with transfer frequencies.

Limitations

This study only considered the transfer behaviours of students aged 21 and older. The study did not specifically account for broader definition of non-traditional or post-traditional students, although demographic data and environmental pull factors were analysed in the model. Additionally, pre-college experiences were not collected in the dataset for adult students. This limitation reduced the ability of the study to measure the impacts of these experiences on transfer behaviour and institutional engagement, although they may indeed be significant .

The predictive modelling in this study did not consider lateral and reverse transfer separately. Due to sample size concerns, the data could not be disaggregated. However, the findings will still be instructive for colleges seeking to reduce attrition related to transfer behaviours more generally. Additionally, this research only considered students who transfer at least one time during their first three years of college enrollment. Students may transfer multiple times. Although these transfers beyond the first were not included, they are worthy of additional consideration in future studies.

Results

This section provides a summary of key descriptive and inferential findings. A discussion of the significant student and institutional variables of adult students who reverse or laterally transfer before the third year of college is followed by HGLM findings that consider the variables that increase or decrease odds of reverse and lateral transfer in adult student populations. Full sets of descriptive and HGLM findings are found in Tables 1 and 2.

Table 1.

Salient characteristics of students aged 21 and older who intend to earn a baccalaureate degree and reverse or lateral transferred within three years.

Characteristic	Students who did not transfer (n = 3320)^a	Students who reverse or laterally transferred (n = 360)	Sig. Chi square or ANOVA
Sociodemographic characteristics
Race/ethnicity			29.84*
White	52.3	51.5
African American	22.9	26.3
Latinx	17.1	15.2
Asian American	2.3	1.1
American Indian, Hawaiian and Pacific Islander	2.0	2.2
Biracial	3.4	3.6
Socioeconomic status (SES)			9.04
High	26.8	26.3
Middle high	26.4	25.8
Low middle	23.3	21.1
Low	23.5	26.9
Gender			3.01
Male	50.4	45.7
Age	29.4 (SD = 8.47)	27.9 (SD = 7.32)	4.56**
Employment, financial aid, social capital
Work commitments			19.78**
Did not work	58.6	64.3
20 hours per week or less	5.6	4.7
More than 20 hours per week	35.9	31.0
Financial aid	$10,114.01 (SD = 7635)	$11,393.81 (SD = 9,129.38)	7.45***
Received financial support	8.7	7.2	2.81
Social capital	4.26 (SD = 1.09)	4.20 (SD = 1.18)	.73
Motivations
Degree expectations			9.05
Doctoral or professional degree	10.2	8.6
Post bac or master’s degree	28.9	30.7
Bachelor’s degree	60.9	60.7
Self-efficacy	4.42 (SD = 1.00)	4.16 (SD = 1.25)	11.72***
Early college experiences and institutional supports
Accessed advising services	49.5	44.9	5.33
Accessed academic services	31.1	31.3	6.08
Accessed career services	18.4	17.5	3.74
Engagement	4.14 (SD = 1.10)	3.88 (SD = 1.25)	10.53***
Sense of belonging	4.18 (SD = 1.10)	3.91 (SD = 1.27)	9.26***
Enrollment intensity			76.01***
Always full time	60.3	54.8
Always part time or mixed	39.7	45.2
In-state attendance			18.78***
In-state student	74.5	68.4
Out of state or international student	25.5	31.6
Took developmental courses	24.8	23.0	5.52
First-year GPA	3.08 (SD = .88)	2.82 (SD = 1.06)	12.35***
Co-enrolled	1.3	19.9	384.85***
Institutional context and characteristics
Control			148.60***
Public	33.5	21.6
Private not-for-profit	4.6	5.8
Private for-profit	62.0	72.6
College cost	$18,311.65 (SD = 9656)	$19,969.15 (SD = 10,235)	9.62***
Urbanicity			7.09
City	57.6	56.2
Suburb	24.4	27.1
Town or rural	18.1	16.6
Exposure to diversity	33.42 (SD = 20.48)	32.28 (SD = 20.37)	.75
Institutional selectivity			7.48
Very selective	0.4	0.0
Moderately or minimally selective	28.6	50.9
Open admission or community/ technical college	71.0	49.1

Source: U.S. Department of Education, National Center for Education Statistics, 2011–2012 Beginning Postsecondary Students Longitudinal Study, first follow-up (BPS:12/14).

ANOVA: analysis of variance.

^aData are rounded to the nearest 10 per IES guidelines.

*p < .05, **p < .01, ***p < .001.

Table 2.

Predictors of reverse and lateral transfer among adult students seeking a baccalaureate degree.

	Beta Coef. (SE)	Odds ratio
Sociodemographic characteristics
African American	.05 (.18)
Latinx	–.31 (.21)
Asian American	–1.18 (.74)
Native Indian, Hawaiian or Pacific Islander	.09 (.46)
Biracial	.05 (.36)
High-middle income	–.04 (.19)
Low-middle income	–.31 (.21)
Low income	–.14 (.21)
Female	–.19 (.14)
Age	.03** (.01)	1.03**
First generation	.32* (.15)	1.37*
Employment, financial aid, social capital
Worked 20 hours or less	–.19 (.32)
Worked more than 20 hours	–.38* (.16)	.67*
Financial aid	–9.64e-06 (.00)
Family financial support	.16 (.26)
Social capital	.02 (.06)
Motivations
Expects graduate degree	.42 (.29)
Expects bachelor’s degree	.43 (.28)
Self-efficacy	.04 (.08)
Early college experiences and institutional supports
Advising services	.35* (.15)	1.41*
Academic services	–.22 (.16)
Career services	.13 (.19)
Engagement	.00 (.09)
Sense of belonging	.17* (.09)	1.19*
Mixed enrollment	.62*** (.15)	1.86***
Part-time enrollment	–1.47** (.53)	.23**
Instate enrollment	.03 (.18)
Developmental courses	.03 (.17)
First-year GPA	.00*** (.00)	1.00***
Co-enrolled	2.44*** (.35)	11.43***
Institutional context and characteristics
Private not-for-profit	.20 (.39)
Private for-profit	.16 (.28)
College cost	–.00 (.00)
Suburb	.16 (.17)
Town	.51 (.29)
Rural	–.32 (.26)
Exposure to diversity	.00 (.00)
Moderately selective	.35 (.51)
Minimally selective	.45 (.26)
Open admission	.50* (.24)	1.65*

Source: U.S. Department of Education, National Center for Education Statistics, 2011–2012 Beginning Postsecondary Students Longitudinal Study, first follow-up (BPS:12/14).

*p < .05, **p < .01, ***p < .001.

Students who did not transfer

Bivariate results appear in Table 1, and notable findings are outlined below. Demographically, 52% of adult students who did not transfer were White. African American students represented the second largest group (23%). Fifty percent of adult students who did not transfer were male. The average age was 29.4 (SD = 8.47), and 47% of students who did not transfer were of low-middle or low socioeconomic status.

Analysis of financial variables revealed that most adult students who did not transfer (59%) did not work, but those who did work typically worked more than 20 hours per week (36%). On average, their financial aid packages were $10,114 (SD = 7635). Nine percent reported receiving financial support from family members.

In terms of motivations and early college experiences, most students who did not transfer sought a baccalaureate degree (61%). They averaged 4.42 (SD = 1.0) on a five-point scale of self-reported self-efficacy, with five being the highest rating. On another five-point scale, students who did not transfer reported similar levels of engagement (4.14, SD = 1.10) and sense of belonging (4.18, SD = 1.10). These students were most frequently enrolling full time (60%) and attending in-state (75%). They rarely co-enrolled (1%). Their first-year GPAs averaged 3.08 (SD = 0.88), and 25% took developmental courses.

Institutional variables suggested that most (62%) students who did not transfer were attending for-profit institutions, and 5% attended private not-for-profit colleges. Most students (71%) attended open admission or community/technical colleges. More specifically, 61% began at community/technical colleges.

Students who reverse or laterally transferred

Of the bachelor’s degree-seeking students aged 21 and over in the national sample, 10% reverse or laterally transferred. Demographic analysis revealed that 52% of adult students who reverse or laterally transferred were White. The second largest race/ethnicity category was African American (26%). Forty-eight percent were classified as low or low-middle socioeconomic status. The average student age was 27.9 (SD = 7.32), and most were female (54%).

In terms of employment and financial support, most adult students who reverse or laterally transferred were not working (64%). Thirty-one percent, however, worked more than 20 hours per week. Their financial aid packages averaged $11,394 (SD = 9129). Seven percent reported receiving financial support from family members.

Students who laterally or reverse transferred most commonly expected to earn a baccalaureate as their highest degree (61%). Their self-reported self-efficacy was 4.16 (SD = 1.25) on a five-point scale. Forty-five percent reported accessing advising services, and lower percentages reported accessing academic (31%) and career (18%) services. On average, these students reported levels of engagement of 3.88 (SD = 1.25) and sense of belonging of 3.91 (SD = 1.27). They most commonly enrolled full time (55%) and attended in-state (69%). Twenty-three percent took developmental courses, and their average first-year GPA was 2.82 (SD= 1.06). Twenty percent co-enrolled.

Institutionally, these students frequently attended for-profit institutions (73%). Their college costs were, on average, $19,969 (SD = 10,235). No students attended very selective institutions, and most students attended moderately or minimally selective colleges (51%). Seventy-seven percent began at baccalaureate granting institutions.

Predictors of reverse and lateral transfer for adult students

As noted in Table 2, findings indicated that the odds of transfer varied significantly across institutions (p < .001), verifying the appropriateness of the HGLM analysis. Analysis of demographic and employment/financial variables revealed that age was a significant predictor of transfer behaviour (p < .01). As age increased, reverse and lateral transfer became slightly more likely, when compared with non-transferring adult students. Working more than 20 hours per week was a predictor that adult students would not lateral or reverse transfer (p < .05), when compared with students who did not transfer.

Most of the significant findings related to early college experiences. When compared to students who did not transfer, students who reported seeking advising services in their first year were also more likely to reverse or lateral transfer (p < .05, odds ratio 1.41). Enrollment behaviours were also significant predictors of reverse and lateral transfer. Mixed enrollment (p <.001) greatly increased the odds of reverse or lateral transferring (odds ratio = 1.86), as did co-enrollment (p < .001, odds ratio = 11.43). Relatedly, sense of belonging was also a predictor of reverse and lateral transfer (p < .05), with higher senses of belonging being correlated with a slight increase in the odds of transfer. First-year GPA was also found to predict transfer (p <.001), with higher GPAs suggesting slightly higher odds of transfer. Enrollment in open admission colleges also predicted reverse and lateral transfer (p < .05, odds ratio = 1.646). Similarly, students who enrolled part time were less likely to reverse or lateral transfer as compared to adult students who did not transfer (p < .01).

Discussion and conclusions

This study found that the rate of adult student lateral and reverse transfer (10%) was identical to that of traditionally aged students . Additional comparison with previous studies revealed numerous similarities and discrepancies. Specifically, no predictive relationship between race/ethnicity and transfer was found, but age predicted reverse and lateral transfer when compared to adult students who did not transfer. Enrollment variables predicted transfer in unsurprising ways, but financial factors were not found to predict transfer. Additionally, advising and sense of belonging findings contradicted previous studies in several key ways. Finally, of the institutional variables studied, attendance at an open admission college was the only one to predict reverse or lateral transfer for adult students.

While previous studies of traditional-age populations suggested that race/ethnicity may predict transfer (Stern, 2016), this study found no relationship for adult students. Age, however, was found to be a significant predictor of transfer; this was possibly the first study to consider adult student age specifically as a potential contributor to transfer. Other studies (List & Nadasen, 2017; McCormick, 2003; Reyes, 2011; Stern, 2016) have assessed adult student transfer and evaluated age as a factor in completion of students who reverse or lateral transferred (Winter et al., 2001), but they did not consider age specifically as a predictor of reverse and lateral transfer behaviour.

The inferential findings of the study revealed that several enrollment-related factors increased the likelihood of reverse and lateral transfer in adults, when compared with adult students who did not transfer. While mixed enrollment and co-enrollment increased the odds of reverse and lateral transfer for adult students when compared to students who did not transfer, part-time enrollment decreased transfer odds. These findings were expected and aligned with previous studies related to adult students and traditional-age students (Crisp & Delgado, 2014; Kalogrides & Grodsky, 2011; Kuh et al., 2008; Hillman et al., 2008; Hu, 2011).

This study did not align with previous findings in research on traditional students that financial concerns might influence transfer in adult students (Bowers & Bergman, 2016; Chen & Hossler, 2017). Although college costs and financial aid packages were found to be higher for adult students who reverse and lateral transferred than for students who did not transfer, the relationship was not predictive. One potential explanation for this finding may be that the high percentage of adult students enrolling at for-profit institutions inhibits transfer (Iloh, 2016), regardless of college cost, due to potential credit loss/lack of transferability of programs. Another explanation may be that disaggregation of reverse and lateral transfer populations would yield different results, although this was not possible in this study due to sample size.

Of particular interest was the finding that, for adult students, seeking advising in the first year predicted reverse and lateral transfer. The inverse was true for traditional-age students (Crisp, Potter, & Taggart, 2020), and previous research on advising (Deil-Amen & Rosenbaum, 2003) suggested that participation in this activity contributed positively towards adult student success. Several explanations for this finding are possible. For example, advising may be inappropriate or unhelpful for the adult or post-traditional experience and/or focused primarily on concerns of traditional-age students. Another potential explanation is that adult students might gain information during an advising session that makes them realise that their academic ambitions may be better served at a community and/or technical college or at another institution. It is possible that adult students who attend advising appointments receive less pressure and/or attention related to their academic goals, given their age or life experience.

The study also resulted in one especially puzzling finding. Sense of belonging has long been found to correlate with positive outcomes for students (e.g. Bergman et al., 2014; Hu, 2011; Kuh et al., 2008). Although the significance and relationship are not strong, this study suggests that the opposite may be true for adult students. Adult students who reported higher senses of belonging were slightly more likely to transfer than those who did not transfer. The descriptive data in the study were relatively inconclusive in this regard. College may be a more transactional experience for adult learners, and/or such learners may bring more established relationships into their college experience and therefore sense of belonging factors less into their decisions about whether or not to transfer.

Only one institutional characteristic or context was found to predict adult student transfer behaviour: attendance at an open admission college. This finding contradicted numerous previous studies (e.g. Crisp, 2017; Goings, 2018; Goldrick-Rab, 2006; Ishitani, 2006; Stern, 2016; Titus, 2004) that connected traditional-age student transfer with institutional factors such as diversity, cost, control and urbanicity. Perhaps this finding could be attributed to the lack of difference in adult student attendance by institutional selectivity (with 71% of students who did not transfer attending open admission/community colleges) and institutional control (with 73% of students who transferred attending for-profit institutions).

Implications and recommendations

Adult students are increasingly critical to colleges and the communities they serve. For this reason, understanding adult student retention and success of college students is important for the success of colleges and the communities they serve. Transfer outcomes are an important aspect of this conversation, and lateral and reverse transfer behaviours are especially crucial to understand. This study illuminated some of the predictors of these behaviours, and implications exist for policymakers, colleges and researchers seeking to improve adult student outcomes.

Recommendations for policymakers and practitioners

The findings of this study underscored the significance of understanding enrollment behaviour and accommodating adult student enrollment needs. Colleges should consider whether their course offerings and schedules meet the needs of adult students in particular (Gast, 2013; Spellman, 2007), especially those who are working more than 20 hours per week. Identification and support for mixed enrollment students appears to be especially important, and services directed at mixed enrollment students could reduce transfer.

Open admission and community/technical colleges should pay particular attention to adult students, as attendance at these institutions predicts reverse and lateral transfer in this population. More research is needed to consider the reasons why adult students attend open admission colleges (e.g. geography, academic preparation). Such research would help open admission colleges better tailor their services to adult students. Regardless, open admission institutions should consider ways of retaining adult students. For community and technical colleges, this finding is especially important, as lateral transfer is a confusing choice for community college students seeking baccalaureate degrees. Existing best practices include design of services (e.g. tutoring and counselling) specifically for adult students (Gast, 2013; Wyatt, 2011), development of communication and marketing materials specifically for adults (Wyatt, 2011) and the establishment of programs that support peer and mentoring relationships for adult students (Lundberg, 2003).

Similarly, institutions should consider reviewing their advising offerings to ensure that they meet adult student needs and that they treat adult students equitably in order to ensure that retention goals are met (Deil-Amen & Rosenbaum, 2003; Spellman, 2007). While the findings around advising in this study raise more questions than answers, they do suggest that current advising practices may need review and revision in order to confirm that they meet the goal of retaining adult students.

Implications for researchers

Much additional research on the adult student experience is warranted. Specifically, additional studies related to adult student transfer, especially from four-year institutions, are needed (Goldrick-Rab, 2006). Very few other studies exist that consider reverse and lateral transfer, and even fewer consider the adult experience. Studies that can consider these two phenomena separately are especially important, which also highlights the need for increased sampling and data collection related to adult students.

Sense of belonging is an area that is especially suitable for additional research, given the predictive but inconclusive findings of this study and the wealth of research on sense of belonging for traditional-age students. Qualitative study would be especially valuable in this area. Research is needed that better explicates the relationship between belonging and the adult student experience. Similarly, additional qualitative work related to adult student enrollment, retention and success would improve the overall understanding in the field of education of the ways that colleges might better serve this growing segment of the student population.

More research is also needed to better understand the reasons that adult students co-enroll. Additionally, an improved understanding of the difference between mixed enrollment and part-time enrollment is crucial for understanding adult student transfer behaviour. Mixed enrollment predicts reverse and lateral transfer for this population, and part-time enrollment predicts against it. Qualitative research on the factors that contribute to mixed versus part-time enrollment would be useful to improving the literature in this area .

Finally, additional research and datasets that allow for better disaggregation between transfer types and institution types is critical, as the sample in this study was too small for such analysis. Multinomial regression was not possible due to sample size limitations. Disaggregated analysis of the differences in transfer between community/technical colleges and baccalaureate granting institutions is warranted. Also, the predictors of reverse and lateral transfer may actually be different; for this reason, additional research is needed to better understand how these behaviours compare. Stronger data collection and sampling of adult populations is also critical to future research on adult students.

Conclusions

As the number of adult students in colleges and universities continues to grow, an improved understanding of the factors that contribute to reverse and lateral transfer behaviours for those seeking baccalaureate degrees can help colleges, policymakers and researchers improve outcomes for this critical population. This study looked at predictors of these transfer behaviours for adult students and found that certain demographic factors, early college experiences and institutional characteristics predicted reverse and lateral transfer, when compared to adult students who did not transfer. For example, age predicted transfer behaviours, as did mixed enrollment, co-enrollment, first-year GPA and attendance at open admission and community/technical colleges. Conversely, working full time predicted against transfer, as did part-time enrollment. Surprisingly, having a higher sense of belonging also predicted reverse and lateral transfer for adult students, when compared to students who did not transfer.

Numerous conclusions can be drawn from this study. These findings suggested a need for enhanced and specialised services catered towards adult populations, efforts to prevent and understand credit loss and improved course offerings that cater to adult student needs.

Additionally, this project underscored the importance of additional study on sense of belonging in adult student populations, further exploration of enrollment behaviours and intensity with specific attention to mixed enrollment and increased research on adult students more generally as well as improved datasets on adult students.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Charlie Potter

Note

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