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Running head: PREDICTORS OF TRANSFER SHOCK 1

Running head: PREDICTORS OF TRANSFER SHOCK

STEMing the shock: Examining GPA “Transfer Shock” and its Impact on STEM Major and

Enrollment Persistence

Joni M. Lakin

Auburn University

Diane C. Elliott

Immaculata University

Author Note

Joni M. Lakin, Ph.D., Department of Educational Foundations, Leadership, and

Technology, Auburn University. Diane C. Elliott, Immaculata University.

Correspondence concerning this article should be addressed to Joni Lakin, Department of

Educational Foundations, Leadership, and Technology, 4036 Haley Center, Auburn University,

AL 36849. Phone: +1-334-844-4930. Email: [email protected]


Abstract

Students who transfer between institutions of higher education often experience “transfer shock”,

a temporary decrease in academic performance (GPA) hypothesized to be due to changes in

academic expectations between institutions. This study used university institutional records to

explore the magnitude of transfer shock, what factors impact the GPA effects of transfer shock,

and the impact of shock and other student characteristics on important academic outcomes

including persistence in a STEM and baccalaureate degree. We found that STEM majors and

students transferring from two-year institutions experienced the largest degree of transfer shock

and that shock was a significant predictor of change of major. Most intriguingly, we found

interaction effects where shock had a greater impact on institutional retention for two-year

transfers and Science/Mathematics majors. Implications for future research and academic

policies and interventions are discussed.


STEMing the shock: Examining GPA “Transfer Shock” and its Impact on STEM Major

and Enrollment Persistence

Community colleges serve as a critical point of entry to higher education for many

students. According to the American Association of Community Colleges (2012), nearly half of

all beginning undergraduate students enroll in a community college. Although many community

college students enter with intentions to transfer to a four-year college, few successfully do.

National surveys have shown that although intention to transfer to a 4-year institution may be

high at 2-year institutions, only a fraction of those students successfully transfer (Bailey et al.,

2005; Goldrick-Rab, 2010; Hoachlander, Sikora, & Horn, 2003; Laanan, 1996). Recent evidence

suggests less than one-tenth of transfer students earn a baccalaureate degree (Nevarez & Wood,

2010). Such poor rates of attainment have prompted much debate and concern on the part of

policy makers, researchers, and practitioners. In particular, as the US faces an increasing need to

remain globally competitive, especially in STEM fields, community college students are viewed

as the greatest means for improving postsecondary attainment levels (Bahr, Toth, Thirolf, &

Masse, 2013).

Clearly, a key milestone for community college students on the path to a STEM

baccalaureate degree is transferring to a four-year institution. A considerable amount of literature

has studied the effects of transferring on student attainment (e.g. Duggan & Pickering, 2007-

2008; Glass & Harrington, 2002; Johnson, 2005; Laanan, 2004, 2006, 2007; Monaghan, &

Attewell, 2014), though little has been done specifically on STEM persistence. While findings in

the general literature are inconsistent, and it is difficult to control for pre-existing group

differences, the literature is generally in agreement that native students (students who begin their

studies at a four-year institution) exhibit greater academic success, especially in the form of

grades, than do transfer students (Duggan & Pickering, 2007-2008; Laanan, 2004; Townsend &

Wilson, 2008-2009). The literature is also divided in terms of persistence and degree attainment

where some findings have shown that transfer and non-transfer students graduate in comparable

levels (e.g., Glass & Harrington, 2002; Ishitani, 2008) while others have shown transfer students

are at greater risk for delayed graduation and dropout (Duggan and Pickering, 2007-2008).

Evidence on the differential attainment rates of transfer students suggest academic issues

are, in part, at the root. Transitioning between institutions can be a challenge in itself. Transfer


students, especially those from two-year institutions, often enter the university environment

having shown excellent past performance in their classes. Yet, when challenged by the new

institution’s academic norms in the form of increased pace of instruction, coursework intensity,

and larger class size, class performance suffers (Cejda, 1997; Johnson, 2005). Documented

declines in academic performance (i.e., dips in grade point average; GPA) are referred to as

“transfer shock” and may be related to these changing academic norms (Hills, 1965; Cejda,

1997; Townsend, McNerney, & Arnold, 1993). In a meta-analysis examining this phenomenon,

Diaz (1992) uncovered that over three-quarters of community college students experienced

transfer shock. The degree of shock experienced seems to vary by student. For example, Cejda,

Kaylor, and Rewey (1998) showed evidence of greater transfer shock for students who were

majoring in math and sciences than in other fields (see also Cejda, 1997).

Research suggests transfer students experience shock predominantly during their first

transfer semester, suggesting its impact is temporary. However, other research points to lasting

consequences of even one semester of apparent transfer shock. For instance, Ishitani (2008)

found that students with lower GPA were significantly more likely to withdraw from college,

suggesting transfer shock plays a pivotal role in degree persistence behavior. Similarly, Glass

and Harrington (2002) showed that transfer shock in the first semester impacts student

withdrawal and drop out behaviors. Despite research demonstrating the existence and impact of

transfer shock, remarkably little literature has been devoted to understanding the range of

contextual and student factors that impact shock and, specifically, how it impacts particularly

challenging majors, such as STEM. Accordingly, the purpose of this study was to examine the

magnitude of transfer shock, what student and contextual factors affect shock, and understand

the role shock plays in general and STEM-specific major persistence and institutional retention.

Review of the Literature

Extensive research has examined factors that contribute to the adjustment and persistence

of college students (e.g. Astin, 1984, 1993; Bean, 1983; Tinto, 1987; Nora, Barlow, & Crisp,

2005), including students who transfer to a four-year college from a two-year institution (e.g.,

Sorrey & Duggin, 2008; Wang, 2009). Commonalities across these studies emphasize the

importance of sociodemographic characteristics, pre-college academic experiences in the form of

grades and high school curriculum, and individual attributes that influence educational


aspirations and college readiness (Braxton & Hirschy, 2005; Braxton, Hirschy, & McClendon,

2011). Most important to discussions of the transfer experience, this research conceptualizes

persistence as a function of student adjustment, assimilation, and integration in academic and

social spheres of a college (Bean, 1983; Tinto, 1987; Sorrey & Duggin, 2008; Wang, 2009).

Persistence, essentially, results from a longitudinal process of interactions between students and

faculty, staff, and peers in academic and social settings in which students are socialized to

internalize academic and social norms. (Sorrey & Duggin, 2008; Tinto, 1993)

We relied specifically on a re-conceptualized and updated model of student retention by

Bean and Eaton (2002) that incorporates psychosocial characteristics such as motivation,

attribution, and self-efficacy which have been empirically linked to a host of student outcomes

(Robbins, et al., 2004). In addition, the model has been applied to non-traditional students who

are typically over-represented in community colleges. Briefly, the model hypothesizes that pre-

entry characteristics interact with environmental factors in the form of academic and social

interactions and psychosocial characteristics that form a feedback loop. Positive interactions and

involvement in academic and social settings provide students with the means to integrate and

assimilate to institutional norms leading to a heightened commitment to completing college and

to the institution itself. Negative experiences and factors that limit campus involvement weaken

intentions and commitments and increase the likelihood of departure.

Academic interactions may be a particular obstacle for transfer students. Transfer

students, particularly those from two-year institutions, have usually done well in high school and

community college and come to the new institution “knowing” that they can handle their

academic demands. However, Berner (2012) cited challenges in relation to academic interactions

include accessing academic support, fit to major, adjusting to include larger classes, limited

access to instructors, greater independence in class, and less flexibility from instructors. For

STEM students, these differences are often more dramatic (Packard, 2011). For example, in our

focus group research (Lakin & Elliott, 2013), STEM students cited heavy, lab-dominated course

loads and challenging gateway courses as particular stressors in their first semester on a four-

year campus. Prior research has also shown that transfer students report not using group study

strategies before coming to the university and have some difficulty finding study groups once

they arrive (Packard et al., 2011).


Transfer students also experience unique challenges in integrating socially. Opportunities

to interact with faculty in informal situations about non-academic topics – for instance careers –

can be very important for future success. Yet, transfer students have fewer opportunities to

establish a social network and connect with faculty because of their shortened graduation clock

at the four-year institution. Transfer students also experience social challenges including include

developing social supports and friends and adjusting to a new campus culture which theoretically

may impact transfer shock (Berner, 2012).

Despite their widespread use, contemporary models of persistence have been criticized

because usage of integration perspectives stress an underlying notion that acculturation is

necessary (Hurtado & Carter, 1997) and assume there is a single uniform set of values and

attitudes in an institution (Tierney, 1992). Thus, the central premise of integration is that students

must relinquish previously held values and adopt the dominant values of an institution. Such a

perspective can isolate students whose beliefs and attitudes may run contrary to the dominant

values (Hurtado & Carter, 1997). In addition, existing frameworks have predominately been

devoted to understanding the high school to college transition of first-year students.

Considerably less research has utilized these frameworks for understanding transfer behaviors

which is distinctly different from the first-year transition. Accordingly, we incorporate elements

of Transition Theory (Schlossberg, 1984)

Transition Theory

Transition Theory emphasizes the meanings attributed by individuals to transitions

accounting for the context, impact, and the type of transition (Schlossberg, 1984). Context and

impact refer to situational factors such as how and where a transition occurs, whether a transition

directly or indirectly impacts an individual, and how central the transition is to daily life,

routines, perceptions of self and relationships, and roles (Schlossberg et al., 1995). Generally

speaking, the more impactful a transition, the longer the assimilation period (Sargent &

Schlossberg, 1988). Transition theory posits three types of transitions exist: anticipated

transitions, such as college attendance, unanticipated such as loss of a job or a family member,

and nonevents which are anticipated transitions which fail to occur such as failure to be admitted

to a college of choice (Chickering & Schlossberg, 1995).

Adaptation and adjustment to a transition rests on four elements: (1) situational

circumstances such as causes of the transition, timing, duration and previous experience with a


similar transition, (2) personal, demographic, and psychosocial characteristics that affect outlook

and perspective on the transition, (3) the availability of social support systems including family

and friends, and (4) coping strategies that help modify and manage stress associated with the

transition (Schlossberg, 1984).

Although Transition Theory has been applied to college transitions (e.g. Flowers,

Luzynski, & Zamani-Gallaher, 2014; Griffin & Gilbert, 2015; Milsom & Hartley, 2005) much of

this literature focuses on transitions from high school to college. Transfer students have unique

experiences compared to native freshmen in the college transition because they come with

greater experience with the transition to college and a history of high or low integration, support,

and coping strategies at their prior institution(s) that inform their experience of the transition to

the new institution. Two small-scale research studies have explored the community college

transfer experience in the context of Transition theory (Lazarowicz, 2015; Rodriguez-Kiino) and

found themes consistent with Schlossberg’s theory. Both found that support systems of varying

kinds (family, institutional, financial) were critical to the success of transfer students.

Lazarowicz (2015) also emphasized the need for time to adjust and found that many of the

students he interviewed felt overwhelmed by the new institution. He concluded that adjusting to

the transition takes at least one semester and perhaps longer suggesting the act of transferring

may be a longitudinal process. Incorporating transition theory and contextual factors (type of

transfer institution, length of prior study, major, etc.) that impact perceptions of the transition is

therefore critical in this study.

Methods

This study uses institutional records from a large, research-intensive university in the

southeastern United States. The university serves over 20,000 undergraduate students per year in

10 academic colleges (Architecture, Human Science, Forestry, Nursing, Liberal Arts, Business,

Agriculture, Education, Math and Science, and Engineering). STEM majors comprised students

in the College of Engineering (COE) and the College of Math and Sciences (CMS; which

includes biomedical majors as well as chemistry, physics, and mathematics). We utilized

institutional data to obtain student characteristics and academic records for all transfer students

(n=14,159) first enrolling at the institution between 2004 and 2013. Transfer students from two-

and four-year institutions were specifically included in analysis for comparative purposes. The


institution admitted roughly equal numbers (54%/46%) of students from 2-year and 4-year

institutions. See Table 1 for descriptive statistics of the sample.

It is important to note that transfer students in science and math tended to be closer to

traditional age native students (18-22) with 86% of transfer students in the range of 18 to 25

years old according to institutional records. In contrast, engineering transfer students were

somewhat older with 32% aged 18-25 and 68% aged 26 or older. Two-year transfer students had

a larger proportion of white students and other small differences compared to four-year and

native students as shown in Table 1.

Transfer students varied considerably in terms of the number of credit hours they brought

to the institution (M=59, SD=18 for 2-year; M=61, SD=30 for 4-year transfers). Sophomore

status was by far the most common for incoming transfer students (49% and 44% for 2-year and

4-year). GPAs at the transfer institution were on average B-level (M=3.1, SD=.5). Admission to

the focal institution is primarily based on transfer GPA if the student has earned at least 30 credit

hours at other institutions.

[Insert Table 1 about here]

Defining Dependent Variables

This study utilized two dependent variables: transfer shock and persistence. Transfer

shock was calculated as the difference between the student’s cumulative GPA at the most recent

transfer institution compared to their first semester GPA at the focal institution consistent with

prior research (e.g., Cejda, 1997). For example, a student transferring from a community college

with a GPA of 3.9 who earns a GPA of 3.1 in their first semester at the four-year institution

would have a “shock” value of 0.8. Persistence was operationalized in three distinct ways:

persistence within a college of enrollment to allow comparison across different colleges and

majors and provide baseline data for contextualizing possible differences in STEM majors,

persistence within a STEM degree (essentially the first variable limited to those starting in

STEM), and institutional retention. Change in terms of college of enrollment rather than specific

major was purposefully selected as an outcome because these changes mark important transitions

that impact employment opportunities and time to completion (due to changes in course

requirements), among other things, that less drastic major changes (i.e., those that occur within-

college) may not entail.


Analyses

Descriptive statistics were utilized to explore the magnitude of transfer shock

experienced by students. We used between-group ANOVA comparisons as well as multiple

regression with stepwise entry to determine which student and contextual characteristics were

associated with greater first-semester GPA shock. Predictors included incoming college, type of

prior institution, gender, race, number of credits earned at prior institution (coded 0/1 as entering

as sophomore or lower vs. upperclassman), and transfer characteristics (ACT score, HSGPA,

number of transfer hours, transfer GPA).

To understand how transfer shock is associated with STEM major persistence, we relied

on binary logistic regression. In this case there were three events of interest: a student changes

their college of enrollment, a student changes from a STEM college to a non-STEM college, and

a student leaves the institution each of which was coded dichotomously (i.e., the event occurred

or not). For these analyses, only students whose first semester was at least two years prior to the

data collection point (in 2013) were included so that the models were not biased by students who

had only just entered the university. Each of these outcome variables were dichotomous.

Six blocks of variables were included: (1) transfer shock, (2) race and gender variables,

(3) transfer related variables (GPA, credit hours, institution), (4) university entry characteristics

(first college of enrollment), (5) first semester characteristics (credit hours), and (6) key

interactions between shock and institution type, entry college, race, and gender. Within each

block, the Forward LR (likelihood ratio) variable selection method was used. Transfer shock was

entered first by itself to allow us to inspect its unique contribution as well as its contribution once

other variables entered the model.

Results

We first examined the magnitude of transfer shock. Descriptive statistics for the full

sample showed that transfer shock (defined as the difference between transfer GPA and first

semester GPA) was substantial with the average transfer student’s GPA dropping .633 points

(SD = 0.960)1. Comparing students who transferred from two-year and four-year institutions, we

found significant and noteworthy differences in shock with two-year transfers experiencing an

average drop of 0.769 points (SD = 0.949), while four-year transfers experienced an 0.478 point

1 Inspection of the descriptive statistics indicates that transfer GPA, GPA at the four-year institution, and “shock” variables were normally distributed in this sample.


drop (SD = 0.949; t(11,282) = 16.175, p < .001, d = 0.31 [medium effect]). See Figure 1, which

shows the effect of college of entry as well. Regression analyses were used to explore the impact

of college of entry.

[insert Figure 1 about here]

Factors Associated with Transfer Shock

To better understand the factors associated with greater transfer shock, we conducted a

hierarchical multiple linear regression to predict the magnitude of GPA shock from student

characteristics in addition to college of enrollment and transfer institution sector. Stepwise entry

was used with blocks of related variables to build from previous analyses: (1) transferring

institution sector[2-year vs. 4-year] and entry college), (2) an interaction term of institution and

entry college, (3) student characteristics (e.g. race, gender), (4) transfer variables (credit hours

transferred, cumulative transfer GPA); and (5) post-entry characteristics (class level, credit hours

attempted). Table 2 shows the results of these regression analyses.

[insert Table 2 about here]

The regression results confirmed our expectations in terms of which colleges of

enrollment showed the greatest GPA shock: Science/Math and Engineering were among the

colleges with the largest transfer shock (0.47 GPA points and 0.40 points, respectively,

compared to Architecture students who showed average levels of shock and served as the

reference group). As with the t-test results, coming from a two-year institution was shown to be

associated with greater transfer shock (0.19 GPA points more shock than four-year transfers).

Other characteristics associated with greater transfer shock included being female

(associated with slightly more shock of 0.07 GPA points) and being an African American student

(associated with a slightly larger [0.13 GPA points] shock than white students). Transferring

more credits and being a junior or senior at entry were both associated with slightly less shock:

0.04 GPA points for every additional 10 credit hours transferred and 0.05 points less shock for

being an upperclassman at entry. Interestingly, a higher transfer GPA was associated with 0.29

points more shock for every 1 GPA point increase at the transfer institution. Finally, attempting

more credit hours in the first semester at the four-year institution was associated with less shock

(-0.1 points for each additional credit hour). The overall model accounted for just 22% of the


variance, indicating that although some important variables were identified, there is clear room

for improvement in predicting shock.

We were also interested in whether type of transfer institution and college of entry

showed an interaction effect. This expectation was confirmed and the two STEM colleges of

entry, Science/Math and Engineering, yielded the only significant interactions. Entering as a

two-year transfer from either college was associated with about .2 GPA points of additional

shock. When looking at the raw descriptive statistics (i.e., not controlling for other variables as in

the regression), the typical 2-year engineering transfer student showed a 1.0 GPA point drop in

GPA in the first semester while the 4-year transfer showed 0.6 points. In science/math, the GPA

effects were 1.2 and 0.7 points, respectively. These are clearly substantial drops.

Only about half of transfer applicants had ACT/SAT scores or high school GPAs on

record and, often, these values were on record because of previous, unsuccessful, applications to

the institution. Therefore, these variables were omitted from previous analyses to retain the full,

unbiased sample. However, we were interested in whether academic quality measures from high

school would affect the models. When high school GPA and test scores were added to the

regression analyses described above, we found that, among the restricted sample that remained,

students with higher ACT (or converted SAT) scores experienced somewhat less shock—about

.1 GPA points for every 5 ACT points. Likewise, a higher HS GPA was associated with a .1

decrease in transfer shock for every 1 GPA point change. Both are very small effects.

Transfer shock and STEM major persistence and institutional retention

To understand the relationship between transfer shock and institutional and STEM major

persistence we used binomial logistic regression to predict three outcomes: changing colleges,

leaving a STEM college for a non-STEM college, or leaving the university entirely. For these

analyses, only students whose first semester was at least two years prior to the data collection

point were included (reducing the sample size to N = 7, 966). Table 3 shows the full results with

all blocks of variables entered. We used blocks of predictors similar to the previous analyses.

Shock was entered alone as well as in combination with the other predictors to look at its

unique effect on the outcomes of interest. When entered alone, shock was significantly

associated with the likelihood of leaving a major (Exp(B) = 1.184) and institutional departure

(Exp(B) = 2.279). Adding other variables substantially reduced the effect of shock on leaving a

college of enrollment (Exp(B) = 1.045), but only modestly decreased the effect on institution


departure (Exp(B) = 2.161). These odds ratios indicate that students with one GPA point more of

shock are 4% more likely to leave their college of enrollment, but 116% more likely to leave the

institution.

Controlling for shock, race/ethnicity and gender had variable effects on the outcomes of

interest. Compared to white students, African American, Native American, and Asian students

were more likely to leave the institution (all over 34% more likely to leave than white students).

Despite a greater chance of leaving the institution, Asian students were much less likely to

change majors in general (Exp(B) = 0.473) and when in STEM fields (Exp(B) = 0.196). Women

were found to be slightly more likely to leave STEM majors (Exp(B) = 1.090) and more likely to

leave the institution (Exp(B) = 1.295).

Transfer characteristics were also important, although transfer institutional sector was not

a significant predictor in the presence of other variables in the model. Students who transferred

more hours were somewhat less likely to leave the institution (Exp(B) = 0. 996), leave STEM

(Exp(B) = 0. 982), or change major (Exp(B) = 0.987), although these effects are small.

Consistent with this finding, students who entered as juniors or seniors by credit hours were less

likely to change their major (Exp(B) = 0.795; this effect is in addition to the total number of

credit hours). Importantly, having a higher transfer GPA had a substantial impact, reducing the

likelihood of leaving the university (Exp(B) =0. 294), which is interesting because it was

associated with greater shock in the previous analyses. Transfer GPA also made students much

less likely to change their major (Exp(B) =0.670), including STEM (Exp(B) = 0.691).

College of entry was also important to each outcome. For leaving a major, several

colleges, including Business and Liberal Arts, were associated with lower probabilities of

changing major while Nursing and Science/Math were associated with a substantially greater

likelihood of changing colleges (Exp(B) = 2.765 and 1.978, respectively). Engineering majors

were no less likely to leave their major than the reference group of Architecture. Consistent with

this finding, among STEM majors, Science/Math majors were much more likely to leave STEM

than Engineering majors (Exp(B) = 1.896). Nursing and Science/Math were also more likely to

leave the institution without graduating, with substantially higher probabilities than other majors

(Exp(B) = 4.156 and 2.831, respectively).

Credit hours attempted, both general and STEM-related, had an unexpected relationship

with changing majors or leaving the university. We expected that attempting a large number of


credit hours would be associated with less academic success in the transfer institution (i.e. more

shock) and might be associated with change of major or institutional departure . However,

taking more credits (including more STEM credits for STEM majors) was associated with a

small decrease (3-4%) in the likelihood of changes to major or institutional departure.

Critical to our analysis of the relationship between transfer shock and academic outcomes

was understanding how shock interacted with important student and institutional characteristics

identified in prior analyses. Therefore, interactions between shock and transferring institution

sector , college of entry, race, and gender were added to the final block. Only a few interactions

emerged in association with institutional departure. Specifically, interactions between shock and

transferring institution sector and initial college of entry were found. Findings showed that

students from 2-year institutions who experienced more shock are more likely to leave (Exp(B) =

1.110) compared to students with less shock or from a four-year institution. These students may

be the most vulnerable to impacts of shock. The interaction of shock and Science/Math majors

surprisingly showed that students who experienced more shock in that major were less likely to

leave the institution (Exp(B) = 0.839).

R2 statistics for logistic regressions cannot be interpreted as percentages as they are for

linear regression, but they do help interpret model fit. Overall, the R2 values for both college

change variables was relatively small (Nagelkerke R2 = .09 for both), meaning that the

significant factors, including shock, are just a small part of the story for transfer students who

change colleges (STEM or any college). More work is needed to identify additional

characteristics that contribute to these changes. In terms of understanding institutional departure,

the model was much more effective (Nagelkerke R2 = .26) with shock acting as important

predictor of this event. Transfer shock (or perhaps more accurately events or characteristics that

lead to transfer shock) is therefore an important consideration in understanding why transfer

students do not complete college degrees even after successful transfer to a four-year institution.

[insert Table 3 about here]

Discussion

The purpose of this study was to examine the magnitude of transfer shock, demographic

and institutional factors that were associated with greater initial transfer shock, and, finally, the


relationship between transfer shock on student persistence in a STEM major and institutional

retentionWe found that transfer students experienced an average of 0.6 GPA drop in their first

semester, but students transferring from a two-year institution experienced more shock (about 0.8

GPA points) and students in STEM colleges (defined as the Science/Mathematics and

Engineering colleges) and especially two-year transfers in STEM colleges experienced the

greatest amount of shock. Science/Mathematics and Engineering majors from two-year

institutions had an average GPA drop of 1.2 and 1.0 points respectively. These are remarkable

changes in GPA for students undergoing an institutional transition.

These substantial changes in academic performance are likely related to the academic

climate, which is more variable between two and four-year institutions than among four-year

institutions (Cejda, 1997; Lazarowicz, 2015). Prior literature has shown that community college

transfers struggle with increased pace of instruction, coursework intensity, and larger class size

(Cejda, 1997; Johnson, 2005) all of which can impact academic performance and contribute to

transfer shock. Although we cannot determine what causes shock from this data, such large

decrements in GPA are clearly problematic; low GPAs must be discouraging to students and

reflect substantial stress in the first transfer semester. These low GPAs also form an impediment

to graduation, internship placement, graduate school, and other academic and career related

opportunities.

In terms of student and institutional level characteristics associated with experiencing greater

transfer shock, we found that students who were African American, female, brought fewer

transfer credits, and had a higher transfer GPA had significantly greater shock with moderate

effect sizes. Of these characteristics, being African American and female are most problematic as

they are immutable factors. The effect of race was not surprising; prior literature suggests that

minority students experience challenges with the college climate (e.g. Harper, 2006, 2009) and

negative views of a campuses’ racial climate can have a damaging impact on both a minority

student’s academic and social life (Hurtado, 1992; Saenz, Marcoulides, Junn, & Young, 1999).

Their transition to a four-year institution may therefore involve greater challenges to academic

integration than other transfer students.

Interestingly, students with higher GPAs also experienced significant transfer shock. It is

possible this finding could be an artifact reflecting a greater disparity in grade distributions or

grading standards across the two institutions, rather than a direct effect of student performance.


Nevertheless, the sudden drop in GPA seems likely to impact students’ perceptions of their

academic success. Students are known to use grades as a means to assess their suitability for a

particular major or college in general; therefore, we believe such substantial drops in GPA as

seen in our analyses would be likely to affect student commitment to their major or finishing a

STEM degree.

In addition to exploring predictors of shock, we used logistic regression to understand the

impact transfer shock had on persistence in a STEM major and institutional retention. Although

transfer shock in the first semester was not associated with STEM major persistence, it was

associated with changing majors in general and longitudinal institutional retention. The effect on

major change was small, where students with more one GPA point of shock were 18% more

likely to change major college during their time at the university. However, the effect of shock

on institutional retention was large, with students experiencing a 1 GPA point increase in shock

being 128% more likely than other students to leave the institution. Whether this is a direct effect

of shock (or other causal factors that cause both shock and major or institutional change) requires

further research with a longitudinal design.

Other factors associated with changes in college major were race; transfer factors,

including bringing fewer credit hours or having a lower transfer GPA; attempting more credit

hours in the first semester after transfer; and entering the CMS. It is important to note that

transferring from a two-year institution did not affect change of major, which was unexpected

given the large effect it had on shock. We suspect that the effect of shock mediated any impact of

coming from a two-year institution. This would mean that whatever factors are associated with

greater difficulties in transitioning from a two-year to four-year institution are captured by that

substantial drop in GPA.

It is also interesting that the effect of transfer GPA on the outcome variables was the

reverse of the effect on shock: higher transfer GPAs were associated with more shock, but less

likelihood of change of major or institutional departure. Future research should explore how

differences in grading rigor across institutions affect students’ perceptions of academic fit. It may

be that students are aware of the differences in grading rigor across institutions and are prepared

for these changes and do not take them into account in making decisions about major and

institutional persistence.2

2 In fact, we have conducted qualitative interviews that indicate two-year transfer students anticipate substantial


Given the substantially larger shock experienced by transfer students in STEM colleges,

we were surprised to find that factors associated with leaving a STEM major were very similar to

those associated with leaving any major which included the effects of race, transfer GPA,

transfer credit hours, and number of credit hours attempted in the first semester. In this analysis,

women were found to be somewhat more likely to leave a STEM college, consistent with prior

research noting women earn fewer STEM degrees (NCES, 2012). Interestingly,

Science/Mathematics majors were significantly more likely than Engineering majors to leave a

STEM college for a non-STEM college. Prior research has not emphasized any differences in

these types of majors in terms of departure. We were surprised that amount of shock did not

predict STEM departure. It may be that shock is so large among all of these students that it is no

longer a significant factor in explaining departure within this group. Therefore, it may be a

critical factor in explaining the overall problem of retaining transfer students in STEM fields (or

may reflect a larger academic adjustment in these fields), but does not serve as a useful predictor

of which STEM transfer students will leave the major. It is also plausible that the act of

successfully completing rigorous gateway STEM courses prompted resilience and commitment

to completing a STEM degree. Future research should explore the post-baccalaureate success of

STEM transfer students. Although they may remain in their major and at the institution, we do

not know how successful these students were in achieving career goals such as high-paying jobs

and entrance to professional and medical schools. The impact of low GPAs may be more critical

to understanding these outcomes.

In examining institutional departure, we found some similarities and differences from the

previous analyses. The effect of transfer hours and transfer GPA was still associated with a

reduced likelihood of departure. Majors in Science/Mathematics was related to a greater

likelihood of leaving as well. Surprisingly, women were also more likely to depart than men.

This is unexpected given that nationally women complete degrees at a higher rate than men

(NCES, 2012), but may reflect some differences in the persistence intentions of female transfer

students or may reflect a regional difference. In contrast with factors associated with changes in

major, we found that Asian students, as well as Native American and African American, students

were more likely to leave the institution. This lends additional support for our contention that

campus climate may impact minority students in important ways. We also found two year

challenges and lower GPA at the four-year institution. Several reported that these dire warnings were overblown.


transfers who experienced more shock were more likely to leave the institution (by about 11%)

and surprisingly, Science/Mathematics majors who experienced more shock were less likely to

leave the institution. This paradoxical finding requires further confirmation and study. Future

research is needed to explore personal characteristics (e.g., resilience or perhaps stubbornness) as

well as institutional factors (e.g., GPA restrictions on changing colleges) that may push students

to stay in a major despite poor grades.

Limitations

An important limitation to our dataset was the partial information we had about students’

high school academic records, including GPA and ACT/SAT test scores. Having complete data

on these factors would allow us to better control for the academic readiness of students who

initially enter two- and four-year institutions, but later transferred to the focal institution. An

obvious explanation for the greater effect of two-year institutions on shock is that less

academically prepared students may choose to attend a two-year institution first. Our partial data

allowed us to determine that high school GPA and test scores did not negate the effect of two-

year institutions on the magnitude of shock, but more complete data would allow a more

confident analysis of this question.

Another important limitation is that we gathered institutional records over time rather

than at a single point in time. Although this was beneficial to our sample size, it meant we were

unable to account for changes in the institution, including changing admissions selectivity or

changes in student support services provided to the transfer students, and its effect on our results.

Our data essentially averages across any of these changes that may have occurred over the 10

years studied. Based on institutional records, it is clear that the high school GPA and admissions

test scores at this institution increased for first-time freshmen during this time period. It is

unknown how much this affected the admissions process for transfer students. The size of the

transfer enrollment did not appear to vary much during this time period, but no data on their

academic records was available.

Implications for Practitioners

Our findings have important implications for practitioners. On the one hand, our findings

show that STEM students do experience greater shock, with markedly low GPAs at the four-year

institution, and suggests that faculty and college personnel working with STEM students,


especially at community colleges, may want to re-evaluate course norms and format to promote

more success at the four-year institution. Although many inter-institution transfer articulation

discussions held by faculty focus on course content, we also suggest that discussions focus on

pace of instruction, time allowed for content review, and modes for assessing content mastery.

Students should also be prepared (possibly through orientation or mentoring programs) for the

changes in academic culture and possible changes in grades. Alignment of instructional

modalities and course norms between two and four-year institutions may limit the transfer shock

experienced by STEM students.

At the same time, our findings are actually very promising for community college

students. Widespread fear of credit loss prompts many students to transfer early (Monaghan &

Attewell, 2014). Our findings showing that transfer students with greater credit accumulations

experience less shock, which provides additional evidence in support of community college

retention and completing an associate’s degree prior to transfer. Future research is needed to

explore what configuration of credits (e.g., taking core vs. major courses at the first institution)

leads to the strongest outcomes. Further, our finding that transfer shock is not associated with

STEM major persistence supports the idea that community colleges are in fact adequately

preparing STEM students for degree completion. This suggests community colleges can provide

an alternative pathway for STEM degree completion and may be an untapped resource in

remaining globally competitive. As community colleges tend to attract greater proportions of

minority and first-generation college students, college administrators should consider increasing

outreach and recruitment efforts into STEM majors as a means for advancing racial and income

equality.

Conclusion

Although transfer shock has been part of the college persistence literature for many years,

considerably less research has explored the combination of student and contextual characteristics

that impact STEM transfer shock or explored how shock can impact STEM academic outcomes.

The results of this study show that transfer shock is clearly a nontrivial problem that

disproportionately affects transfer students from two-year institutions and students in STEM

colleges. Regression analyses also showed that transfer shock in the first semester has real

effects on student persistence in their major and, especially, in their retention at the institution.

Because shock varies by student and contextual characteristics, this research provides avenues


for additional research into early identification and intervention. Two-year transfers in STEM

fields should be studied in more depth, especially through qualitative approaches, to determine

what hurdles and supports they experience in making the transition from two-year to four-year

institutions. Given the increasing use of community colleges as a pathway to STEM careers

(Starobin, Laanan, & Burger, 2010), and the greater use of community colleges by

underrepresented minority groups, promoting the success of such students could have a

substantial impact on increasing the numbers and diversity of STEM graduates in the U.S.


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Table 1

Demographic data for sample by type of transfer institution and entry college (2004 - 2013)

2-year 4-year Native freshmen

non-STEM STEM non-STEM STEM non-STEM STEM

5,383 2,273 4,493 2,010

approx. 16k a

approx. 8k a

Caucasian 90.2% 88.1% 86.0% 80.2% 83.6% 76.5% African-American 4.3% 4.9% 7.5% 10.5% 7.5% 7.1% Native American 1.1% 1.4% .6% .9% 0.7% 0.7% Asian .9% 2.3% 1.5% 2.7% 1.7% 3.3% Hispanic 1.2% 1.4% 2.2% 1.9% 2.2% 2.5% Unknown 1.4% 1.3% 1.3% 2.6% 1.3% 1.4% Male 53.3% 68.6% 46.7% 64.4% 42.2% 68.1% Female 46.7% 31.4% 53.3% 35.6% 57.8% 31.9% ACT b (mean) 22.01 (3.29) 20.54 (3.17) 24.23 (3.89) 22.56 (3.69) 26 28 HS GPA b (mean) 3.52 (0.47) 3.30 (0.53) 3.58 (0.46) 3.36 (0.51) 3.78a Transfer hours (mean)

60.45 (18.15 58.08 (17.23) 62.33 (30.74) 58.47 (27.48) -- --

Transfer GPA (mean)

3.23 (0.46) 3.07 (0.47) 3.10 (0.48) 3.00 (0.47) -- --

Note. a Only reported in aggregate across colleges and without exact numbers by institutional records. b ACT and HS GPA are reported, but those data were only available for about half of the transfer students as these are not required for admission. In some cases, these data are available because the student applied to the university in prior years without enrolling. In other cases, students voluntarily provided this information. Students with lower scores may be more likely to omit these data from their applications for admissions. Therefore, although they are provided here for reference, they are most likely missing systematically (with a positive bias), leading to unacceptable missing data patterns, and are therefore not included in the analyses except where noted.


Table 2

Hierarchical multiple regression results—Final model with significant variables

Variable added to model

Unstandardized Coefficients

Standardized Coefficientsa

Sig. R R2 R2

Change B Std.

Error Beta 2-year institution .154 .024 .024 .142 .020 .074 <.001 Entry college = Science/Math (relative to Architectureb)

.193 .037 .014 .372 .038 .132 <.001

Engineering .217 .047 .010 .303 .036 .119 <.001 Business .224 .050 .003 .303 .026 .121 <.001 Agriculture .230 .053 .003 .429 .036 .111 <.001 Nursing .237 .056 .003 .205 .047 .038 <.001 Liberal Arts .238 .056 .000 .100 .026 .040 <.001 Forestry .238 .057 .000 .291 .050 .052 <.001 Gender (Female=1) .242 .059 .002 .072 .018 .037 <.001 African American (relative to white)c

.245j .060 .001 .140 .033 .036 <.001

Credit hours transferred

.264 .070 .009 -.004 .000 -.096 <.001

Transfer GPA .271 .074 .004 .287 .018 .142 <.001 Credit hours (1st sem.) .470 .221 .147 -.100 .002 -.395 <.001 Entry as Jr./Sr.d .470 .221 .000 -.051 .022 -.026 .021 Interaction 2-yr and Science/Math

.471 .222 .001 .185 .048 .049 <.001

Interaction 2-yr and Engineering

.472 .223 .001 .165 .043 .050 <.001

Notes. a Positive coefficients indicate greater shock, negative indicates less shock. bSeveral race

variables were entered as dummy codes. Only the African American variable was significant. c

Architecture was chosen as the reference group for major because it showed average levels of

shock compared to other majors. d Jr./Sr. = Junior or Senior class ranking at entry by credit hours.

Table 3

Logistic regression predicting three outcomes

Model Left major Left STEM B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) BShock entered alone 0.169 0.029 <.001 1.184 NS 0.8

Block 1 Shock 1st sem. 0.044 0.033 0.185 1.045 NS .77


Block 2 Race/Ethnicity=White as ref. group African-American NS NS .29Native American NS NS .83Asian -0.750 0.302 0.013 0.473 -1.630 0.611 0.008 0.196 .43Hispanic NS NS Non-Resident Alien NS -1.759 1.034 0.089 0.172 Gender NS 0.086 0.130 0.508 1.090 .25

Block 3 2-Year Inst. NS NS Transfer C.H. -0.013 0.002 <.001 0.987 -0.019 0.003 <.001 0.982 -.00

Transfer GPA -0.400 0.068 <.001 0.670 -0.369 0.120 0.002 0.691 -1.2

Block 4 Entry as Jr./Sr. -0.230 0.089 0.010 0.795 NS

Entry College=Architecture as ref. Agriculture -0.660 0.157 <.001 0.517 --

Business -0.428 0.091 <.001 0.652 --

Engineering NS NS

Liberal Arts -0.415 0.092 <.001 0.660 --

Nursing 1.017 0.135 <.001 2.765 -- 1.4

Science/Math 0.682 0.084 <.001 1.978 0.640 0.130 <.001 1.896 1.0

Block 5 C.H. in 1st sem. -0.051 0.008 <.001 0.951 -0.055 0.014 <.001 0.947 -.03

STEM hours in 1st sem. -- -0.053 0.022 0.016 0.949

Block 6 Shock * Sci/Math -.17Shock * 2-year Inst. .10

Fit Statistics

Nagelkerke R2 .086 0.093

Cox & Snell R2 0.051 0.062


Figure 1. Average GPA transfer shock by college of entry and type of transfer institution. Note that

differences for Architecture, Forestry, Human Sciences, and Nursing were non-significant.

0.82

0.40

0.79

0.48

1.04

0.62

0.42

0.61

0.86

1.17

0.57

0.28

0.50

0.37

0.57

0.45

0.280.35

0.69 0.70

0.00

0.20

0.40

0.60

0.80

1.00

1.20

2‐year

4‐year

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