The Impact of a Peer Mentoring Program on Undergraduate Graduation Rates: A Matched Control Group Design

Abstract

Promoting student retention and graduation rates in higher education is crucial for both individuals and institutions. Prior research has identified student support initiatives that increase retention and student success, such as Peer Mentor Programs (PMP). However, a better understanding of PMP participation outcomes for transfer and under-represented (UR) students is needed. The current study centers on a college-wide PMP at a 4-year university, targeting undergraduate students from multiple disciplines. A matched control group design was employed, comparing PMP participants with matched non-participants. The results revealed that while a positive association was found for first-year students, this association was not found among transfer students. Moreover, PMP participation significantly improved the 4-year graduation rates of UR students, showcasing the program's potential to address equity gaps. The findings encourage further exploration into program design, mechanisms of action, and the expansion of PMP to reduce achievement disparities in higher education.

Keywords

peer mentors mentoring program graduation rates matched control group

Student retention is increasingly becoming one of the most analyzed outcomes in higher education given the implications for both students as well as college and university institutions (Barbera et al., 2020; Jamelske, 2009). Accordingly, numerous initiatives and programs have been implemented across universities to support student retention and success. One popular intervention has been to target students more likely to experience equity gaps with student support services such as tutoring and peer mentoring (Phinney et al., 2011). These programs enhance factors predicting college retention and graduation rates, including non-cognitive, student “life” skills, and socio-emotional variables such as perceptions of support, engagement, and motivation that are critical in understanding academic achievement (Alhadabi & Karpinski, 2021; Boutakidis & Rodriguez, 2022; Dweck & Master, 2009). The current study will examine the impact of a college-wide peer mentor program on students’ graduation rates and will furthermore, assess whether there are differences in program impact depending on status as first-year or transfer students and among students from under-represented (UR) groups.

The Impact of Peer Mentoring Programs

A consensus on what defines a peer mentor program has not been established; however, a review of the literature provides certain key characteristics. First and most important is the peer mentor component, specifically that student participants (i.e. mentees) receive sustained interaction with a fellow student who is more experienced or knowledgeable on the target criteria, and who operates within a program structure and with supervision by staff and/or faculty (Colvin & Ashman, 2010; Crisp & Cruz, 2009). The extent to which peer mentors are trained can vary, but some form of training, orientation, and eligibility qualifications are commonly described (Gershenfeld, 2014). Another important feature is that they are distinct from tutoring programs. In general, peer mentor programs provide forms of support and information related to campus resources, the navigation of administrative tasks, and socio-emotional support (Crisp & Cruz, 2009) rather than direct instructional and subject-level tutoring.

Positive effects from peer mentor programs across a diverse population of students have been shown, although the underlying mechanisms of action require additional investigation. The general expectation is that peer mentor programs develop skillsets related to college success through a peer relationship that may be more accessible, relatable, trusted, and comfortable than more hierarchical instructor or advisor directed interactions (Bunting et al., 2012; Pornpitakpan, 2006). Furthermore, peer mentor programs are characterized by many of the attributes that have been identified as “high impact practices” that are well-established in promoting student success (Kuh et al., 2006). Studies examining the impact of PMPs with student samples from predominantly White ethnic backgrounds have shown that students participating in peer mentor programs have better course grades (Asgari & Carter, 2016; DeMarinis et al., 2017; Wilton et al., 2021) and higher retention rates (Leidenfrost et al., 2011; Wilton et al., 2021). Not only has peer mentoring been shown to be effective across numerous student success outcomes, but also across different subgroups of students, such as students with disabilities (Hillier et al., 2019), students in STEM disciplines (Damkaci et al., 2017), and students from historically under-served and minoritized populations (Moschetti et al., 2018; Venegas-Muggli et al., 2023). For example, studies have shown that first-generation, Pell grant eligible students had improved retention rates as a consequence of peer mentor program participation (Tucker et al., 2020), and that Black college students involved in peer mentoring programs felt a greater sense of belonging, earned better grades and were more likely to be retained (Brooms & Davis, 2017; Graham & McClain, 2019; Schwitzer & Thomas, 1998). Moreover, a study with Latino college first-year students found that students who were mentored via a PMP were significantly less likely (than non-mentored students) to be classified as being academically at risk (Phinney et al., 2011). The general consensus is that peer mentoring programs meet the needs of UR students (Crisp & Cruz, 2009). However, more research is needed in this area given the importance of providing beneficial programs to students from these groups.

Additionally, numerous studies have shown the benefits of peer mentoring for first-year students specifically (e.g. Yomtov et al., 2017). However, few studies examine whether there are similar program outcomes for transfer students involved in peer mentoring programs. For example, peer mentoring programs have been found to improve academic achievement outcomes among low-achieving first-year students (Salinitri, 2005), but transfer students were not included. Many universities offer articulation agreements from 2-year community colleges to their 4-year institutions (Crisp & Nunez, 2014). Transferring students may experience “transfer shock” in adjusting to university life (Hills, 1965; Ishitani, 2008; Townsend & Wilson, 2009) and may benefit from purposeful building of new relationships (Archambault, 2015; Fulton & O’Brien, 2016). As transfer students represent a sizable group of college-attending students, there has been a push to better understand which programs and services are beneficial to their success (Daddona et al., 2021). While it is true that transfer students—whether they are coming from 2-year community colleges which is typically the case or are transferring in from another university—every institution has its own culture and bureaucratic systems. Transfer students (and not just new freshmen) must learn and navigate this new environment and peer mentors, who are experienced students, can certainly help with that process. Clearly, it is important to consider students who start as first year versus those who have transferred as there may be some nuance in terms of who benefits the most from mentee involvement.

Methodological Issues in PMP Research

Within the peer mentor program research, there are also methodological shortcomings that are important to recognize and opportunities for addressing these gaps in forthcoming studies. For example, only a few studies address the problem of selection. Given that certain students may be more likely to participate in a peer mentor program, then it could be assumed that these would be the same students who came into such a program believing it would help and otherwise be better positioned to benefit. Therefore, these are students who would be more likely to be academically successful irrespective of the mentoring intervention. To address this concern, some studies have described the population of students participating in a peer mentor program relative to a comparison, non-mentored group (e.g. Asgari & Carter, 2016; Wilton et al., 2021). Other studies have used more rigorous methods to draw outcome comparisons for mentees and non-mentees by comparing mentees to a waitlist control group (Rockinson-Szapkiw et al., 2021) or by comparing students enrolled in the same course but split into mentoring vs. non-mentoring sections (Moschetti et al., 2018). Nevertheless, there remains a general lack of rigorous selection and comparison controls.

The present study attempts to address this by utilizing a case–control matching methodology to make statistical comparisons and draw conclusions about the effectiveness of a peer mentor program on direct measures of student outcomes (e.g. Rockinson-Szapkiw et al., 2021). Case–control matching techniques are most commonly used in epidemiological research (Gefeller et al., 1998), but offers important advantages in other areas where random assignment to conditions is not practical or possible. The “cases” in this methodology are the student participants who experienced the PMP and the “controls” are selected from the same general population as the cases but have not experienced the PMP. Critically, the controls are paired with each case with one or more controls based on key characteristics thought to potentially influence the outcome being evaluated to reduce their potential confounding effect. The details as to these characteristics are reported in the Results section below.

The use of a matched control group methodology can better establish that participation in peer mentor programs is driving the academic benefits, rather than a pre-existing characteristic of student participants compared to non-participants. The assessment of direct measures of students’ graduation rates, rather than the psycho-social measures related to those outcomes, is another benefit of this study's approach.

Study Aims

The current study is guided by two key aims:
(1) To evaluate the impact of a large-scale college-wide peer mentor program on students’ graduation rates utilizing a methodologically rigorous matched control group design.
This approach addresses two limitations in the prior evaluation literature, specifically a focus on a direct measure of student success and the control of characteristics between participating and non-participating students when evaluating impacts.
(2) To determine differential impacts of the PMP based on students’ matriculation (first-year vs transfers) and UR status to evaluate its generalizability across student populations, and its effectiveness in closing equity gaps in graduation rates.

Methods

Participants

The analytic sample was comprised of a total of 297 undergraduate students enrolled as mentees in the PMP. Of those, 187 enrolled as first-time first-year students and 110 as new transfer students. All participating students were enrolled in one of the five undergraduate serving majors housed within the college that the PMP program was created and implemented. All mentees participated for one semester in the PMP. Additional demographic data of analytic sample is provided in Table 1. Overall, mentees’ characteristics regarding GPA, enrollment type, major at entry, UR status, incoming cohort year, Pell grant status (a proxy for low-income status), and parents’ levels of education were representative of the overall college population. The one exception was gender with female mentees over-represented in the sample relative to the college population (87.2% vs. 55.5%, respectively).

Table 1.
Mentee Demographic Data: Percent of Total and (Number).

Race/Ethnicity Latino/a/x European American/ White Asian American/ Pacific Islander African American/ Black

55.9% (166) 15.2% (45) 14.1% (42) 2.4% (7)

Under-represented Yes No

35% (104) 65% (193)

Matriculation status New freshman New transfer

63% (187) 37% (110)

Gender Female Male

87.2% (259) 12.8% (38)

First generation Yes No Unknown

40.7% (121) 54.2% (161) 5% (15)

Pell grant Yes No

54.9% (163) 45.1% (134)

Peer Mentor Program Description

The College of Health and Human Development Peer Mentor Program (CHHD PMP) at California State University, Fullerton is the focus of this study. The CHHD PMP serves students from undergraduate-serving departments including Child and Adolescent Studies, Human Services, Public Health, Kinesiology as well as students in the School of Nursing. The CHHD PMP guides students transitioning to a 4-year university life, facilitates access to university resources, empowers students in achieving academic success, and develops student leaders with high-impact practices. These peer mentors and mentees form close partnerships in collaborating to access resources, build support in facing obstacles, and achieve mutually constructed goals. Ultimately, the program is designed to improve retention and graduation rates while reducing equity gaps for UR students who face barriers in their educational path.

Mentors were selected through interviews for interpersonal skills, experience in the majors, and GPAs of at least 3.00. They participated in newly created peer mentor training courses in the Spring 2015 semester and were taught as well as supervised by faculty trained during the prior semester, in peer mentor development and instruction the prior semester. PMP mentors were not paid for their participation in the program, but rather were enrolled in a 3-unit semester length course. During their 16-week mentorship courses, mentors received training on topics including active listening, empathic understanding, time management, academic skills, stress management, and available campus resource centers for referrals. Mentors were also trained to provide social support and encourage belonging while becoming problem-solving and accountability partners as needed.

Mentees were recruited from the general population of students with a special effort to invite first-generation students and students new to the campus. There were no requirements for mentees. Mentees selected their mentors based on online mentor profiles on a specially designed PMP website and were assigned their first choice of available mentors from their own majors. Mentorship included weekly electronic contact from the mentors and face-to-face weekly or bi-weekly meetings over the course of the semester with mentors typically assigned between 2 and 5 mentees each. Mentor and mentees worked together collaboratively to set goals for mentees and develop a social support system while exploring the campus. Student-level data was also collected through official university databases.

Measures

For the purposes of this study, 4-year graduation rates for first-time first-year and 2-year graduation rates for new transfers were the outcomes variables assessed. In addition to the matriculation status (first-year vs transfers), students were also identified as belonging to UR vs. non-UR groups. This classification is based on university guidelines that identify Latino/a/ Latinx, Black, Native American/Alaskan Native, Native Hawaiian or other Pacific Islander as UR (CSU Board of Trustees, 2008). All student-level data utilized in the analyses was gathered from official campus records, which included students’ cumulative GPA, initial enrollment status, gender, Pell grant award status, and parents’ level of education. This data also served to generate matched comparison or control groups, as described below.

Results

Determining Comparison Groups

Comparisons between the analytic sample of PMP participants (n = 297) and a matched control group (n = 325) formed the basis of the analyses. The matched control group was generated using a case–control matching process determined by the profiles of the PMP participants. These profiles were based on the following variables: cumulative GPA, enrollment type, major at entry, UR status, incoming cohort year, gender, Pell grant award status, and parents’ levels of education (identified as first-generation status). In almost all cases, the match was based on exact percentages (i.e. equal percentages of Pell grant awardees, females, UR status, enrollment type, and parents’ levels of education) between the PMP analytic sample the control. However, for cumulative GPA, a match tolerance of up to 0.15 points was allowed between PMP and control group participants. Tables 2 and 3 show the comparisons of the analytic and matched control samples on the variables of interest.

Table 2.
Sample Counts from PMP Participants and Matched Controls Group Profiles: First Year.

Cumulative GPA GPA 3.14 3.15

p > .05 (p = .85)

Gender Women 98 98

Men 20 20

Pell status Non-Pell grant 60 60

Pell grant 58 58

First-generation status^a Non-first generation 83 83

First generation 35 35

Major at entry Child & adolescent studies 19 19

Public health 31 31

Human services 9 9

Kinesiology 40 40

Nursing 19 19

Note. ^aIndicates student whose parent(s) has not completed a post-secondary degree.

Table 3.
Sample Counts from PMP Participants and Matched Controls Group Profiles: New Transfers.

Cumulative GPA GPA 3.31 3.33

p > .05 (p = .78)

Gender Women 87 87

Men 8 8

Pell status Non-Pell grant 36 36

Pell grant 60 60

First-generation status^a Non-first generation 52 52

First generation 43 43

Major at entry Child and adolescent Studies 30 30

Public health 8 8

Human services 39 39

Kinesiology 14 14

Nursing 4 4

Note. ^aIndicates student whose parent(s) has not completed a post-secondary degree.

First-Year Students

The first analysis focused on first-year PMP participants (n = 74) and matched controls (n = 134), both with the same year of entry. The single outcome variable was 4-year graduation rate. In order to determine if PMP participants were more likely to graduate in 4 years, Pearson Chi-Square tests were conducted. The Pearson Chi-Square test was significant (Χ² = 13.10, p = .000) indicating a statistically significant difference in 4-year graduation counts between PMP participants and matched controls. The effect size was also calculated using φ, which is appropriate for a 2 × 2 comparison, resulting in a value of 0.25 indicating a medium effect size (Cohen, 1988). This was evident in the markedly different percentages of 4-year graduates. While 47.3% of first-year students in the PMP graduated in 4 years, approximately half as many non-participants (23%) achieved the same result.

New Transfers

The same analysis was also conducted for new transfer students. Transfer students who participated in the PMP (n = 110) were compared against a matched control set of non-participating students (n = 167), utilizing the same profile criteria described earlier. The graduation rate outcome was set at 2 years, as opposed to the 4-year rate for freshman, given that transfer students are typically matriculating into the university with junior-level standing. Pearson Chi-square tests were not significant for this comparison, Χ² = .667, p = .414, φ = .049. While 56.9% of transfer students in the matched control group graduated in 2 years, that number rose to 61.8% of PMP participants. However, that difference was not statistically significant.

Under-represented Samples

The same analyses, as described above, were also used to compare 4-year and 2-year graduation rates for UR students in and out of the PMP. For first-time first-year students (n = 35), 34% of UR students in the PMP graduated in 4 years compared to 12.7% of UR matched controls not in the PMP. Chi-square analyses indicated that a significantly higher number of first-year PMP UR students graduated in 4 years compared to the matched control group, Χ² = 6.89, p = .009. The effect size (φ = .444) indicates a medium effect. As was the case with the overall sample, although UR transfers (n = 69) in the PMP graduated in 2 years at a 3.6% higher rate (63.8 vs. 60.2 percent), that difference was not found to be statistically significant when examining the frequencies in the Chi-square analysis, Χ² = .212, p = .645. Please see Table 4 for a summary of all Chi-square results.

Table 4.
Summary of Chi-square Results: First-year and Transfer Students by UR and Analytic vs Control Group Categories.

First-year Transfers

Total sample Analytic (n = 70) Matched control (n = 135) Analytic (n = 110) Matched control (n = 167)

4 yr/2 yr graduation percentage 47.3 23.0 61.8 56.9

Chi-squared Χ² = 13.10, p = .000 Χ² = .667, p = .414

UR only Analytic (n = 35) Matched control (n = 71) Analytic (n = 69) Matched control (n = 93)

4 yr/2 yr graduation percentage 34.3 12.7 63.8 60.2

Chi-squared Χ² = 6.89, p = .009 Χ² = .212, p = .645

Discussion

The retention and graduation of undergraduate students is a priority in higher education and thus, at the department, college and university level, peer mentor programs are increasingly being implemented to improve student outcomes. It is critical that we assess and better understand which specific outcomes these programs can improve, as well as identifying the types of students who may benefit the most from their involvement in a PMP. The results from the current study revealed that the first-year student participating in a college-wide PMP had higher graduation rates as compared to students who were matched on important characteristics (i.e. cumulative GPA, enrollment type, major at entry, UR status, incoming cohort year, gender, Pell grant award status, parents’ levels of education). This implies that the program serves an important purpose towards the goal of promoting student retention and graduation rates. However, it is important to note that the improved graduation rates were not found across all groups in the current study. Specifically, the findings were not significant for transfer students, although they were trending in the right direction.

Previous studies focused exclusively on how peer mentor programs can improve experiences for first-year students affirm that mentored students benefit from these programs, in many ways. For example, first-year mentored students have reported feeling more integrated and connected to their university at the end of their first semester compared to non-mentored students (Yomtov et al., 2017). A recent study found that first-year, first-time college student mentees had higher GPAs and higher 1-year persistence than students who did not have a mentor (Dixon et al., 2023). However, to our knowledge, no studies have examined differences in program outcomes when comparing first-year students to transfer students to suggest that first-year students may benefit more from these programs than transfer students. It may be the case that transfer students may already have some college competencies that overlap with those that PMPs are designed to create, given their community college experience, and therefore are not positioned to benefit as much from them. Or it may be the case that benefits could not be immediately measured in the short time period after mentorship concluded.

Results of this study also lend support for the use of PMPs in reducing equity gaps in graduation rates between UR and non-UR populations. A significantly higher number of first-year PMP UR students graduated in 4 years compared to the matched control group. In fact, the effect size for UR students was greater than that of the overall sample of first-year students, supporting the contention that while participation in PMPs is beneficial for all first-year students, it may be particularly impactful for UR students. This finding is in line with prior research that highlights how fostering cultural capital and community culture wealth via peer mentoring program may be particularly relevant and impactful for students from Latinx backgrounds, for example (e.g. Alcocer & Martinez, 2018; Hu & Ma, 2010). Moreover, given that past research has found that academic achievement is more closely tied to commitment to remain in college for UR college students compared to White college students (Zea et al., 1997), then this increased GPA for UR students may also, in turn, have implications for their retention. Once again, the results for the transfer population of UR students were not statistically significant.

Limitations

Despite the strengths of this study, it is important to acknowledge that limitations apply. One key limitation is that the findings are based on the results of one PMP from one institution. More evaluative research on peer mentoring programs is needed to better quantify effectiveness and allow for program comparisons. How these programs are implemented, the level of mentor training, and how students are given access to these programs are all likely to impact the effectiveness of peer mentor programs. Moreover, although this study's matched control group design addressed some of the statistical shortcomings of prior studies, one remaining limitation involves the possible difference in initial motivational factors that lead to participation in a peer mentor program (or any student support program for that matter). While past academic success as measured by GPA and other characteristics such as first-generation college student status were controlled for, students motivated to enroll in and participate in a peer mentor program may already be more likely to engage in the kind of behaviors positively associated with increased future academic success and persistence. This is further complicated by the fact that some students may find their way to a PMP based on instructor facilitation or referral (formal or otherwise), and there may be some systematic differences that characterize them.

Further Research

Given the limitations of the present study and the general peer mentoring research to date, future researchers are encouraged to address several key methodological considerations. The issue of selection effects may be the most important, although it is also a particularly difficult challenge to fully overcome as it would require random assignment to a peer mentoring condition and a control condition. Given the existing reputation of peer mentoring programs as effective, it may be difficult to gain the necessary institutional support to expressly withhold that intervention from students as part of a study. An easier implementation may be assessing peer mentor programs within a broader offering of student success interventions. Random assignment to conditions in that case may be more acceptable given that all participants would be receiving some form of student support. At the very least, future studies may consider a pre-assessment of academic motivation, specifically, for students who enroll in a PMP given that this may identify the critical self-selection driver, which then would allow for greater statistical control.

Conclusion

The findings of this study and others indicate that PMPs can provide a beneficial effect on students’ college success. The current study adds nuance to this research by demonstrating that these benefits may not have the same impact across all student subgroups. Furthermore, any student success effort that may also close equity gaps in outcomes like graduation rates deserves particular attention given how robust these inequities appear to be in higher education. While more research is needed to better understand the underlying mechanisms of action, the impact of variations on program design and implementation, and why some groups—such as transfer students—may not benefit, we believe there is sufficient and promising evidence to endorse the expansion of peer mentor programs in higher education contexts.

Race/Ethnicity	Latino/a/x	European American/ White	Asian American/ Pacific Islander	African American/ Black
	55.9% (166)	15.2% (45)	14.1% (42)	2.4% (7)
Under-represented	Yes	No
	35% (104)	65% (193)
Matriculation status	New freshman	New transfer
	63% (187)	37% (110)
Gender	Female	Male
	87.2% (259)	12.8% (38)
First generation	Yes	No	Unknown
	40.7% (121)	54.2% (161)	5% (15)
Pell grant	Yes	No
	54.9% (163)	45.1% (134)

Cumulative GPA	GPA	3.14	3.15
Gender	Women	98	98
Men	20	20
Pell status	Non-Pell grant	60	60
Pell grant	58	58
First-generation status^a	Non-first generation	83	83
First generation	35	35
Major at entry	Child & adolescent studies	19	19
Public health	31	31
Human services	9	9
Kinesiology	40	40
Nursing	19	19

Cumulative GPA	GPA	3.31	3.33
Gender	Women	87	87
Men	8	8
Pell status	Non-Pell grant	36	36
Pell grant	60	60
First-generation status^a	Non-first generation	52	52
First generation	43	43
Major at entry	Child and adolescent Studies	30	30
Public health	8	8
Human services	39	39
Kinesiology	14	14
Nursing	4	4

	First-year		Transfers
4 yr/2 yr graduation percentage	47.3	23.0	61.8	56.9
Chi-squared	Χ² = 13.10, p = .000	Χ² = .667, p = .414
UR only	Analytic (n = 35)	Matched control (n = 71)	Analytic (n = 69)	Matched control (n = 93)
4 yr/2 yr graduation percentage	34.3	12.7	63.8	60.2
Chi-squared	Χ² = 6.89, p = .009	Χ² = .212, p = .645

Footnotes

Acknowledgments

The authors would like to acknowledge the Office of Institutional Effectiveness and Planning at California State University Fullerton for their data and analytic support.

Declaration of Conflicting Interests

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

Ethical Approval

The lead author received prior Institutional Review Board (IRB) approval in collecting the data used in this study. All authors whose names appear on the submission made substantial contributions to the conception or design of the work, research required in preparation of the manuscript, and toward writing of the manuscript submitted for publication.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ioakim Boutakidis

Author Biographies

Ioakim Boutakidis, PhD, is Chair of the Department of Educational Leadership and professor in the Department of Child and Adolescent Studies at California State University, Fullerton. His major research interests include examining factors associated with adolescents’ academic success, and specifically for historically under-served populations.

Guadalupe Espinoza, PhD, is a professor in the Department of Child and Adolescent Studies at California State University, Fullerton. Her major research interests include examining how adolescent peer relationships (e.g., school bullying, cyberbullying, friendships) shape their psychosocial well-being and school adjustment. Much of her research focuses specifically on the experiences of Latinx youth.

Mia Sevier, PhD, is a professor in the department of Human Services at California State University, Fullerton. Her research focuses on relationships and related interventions.

Andrea Sadek is a Master’s student at California State University, Long Beach. Her research centers on the impact of a sense of belonging on academic success.

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Cumulative GPA	GPA	3.14	3.15
Cumulative GPA	GPA	p > .05 (p = .85)
Gender	Women	98	98
Gender	Men	20	20
Pell status	Non-Pell grant	60	60
Pell status	Pell grant	58	58
First-generation status^a	Non-first generation	83	83
First-generation status^a	First generation	35	35
Major at entry	Child & adolescent studies	19	19
	Public health	31	31
	Human services	9	9
	Kinesiology	40	40
	Nursing	19	19

Cumulative GPA	GPA	3.31	3.33
Cumulative GPA	GPA	p > .05 (p = .78)
Gender	Women	87	87
Gender	Men	8	8
Pell status	Non-Pell grant	36	36
Pell status	Pell grant	60	60
First-generation status^a	Non-first generation	52	52
First-generation status^a	First generation	43	43
Major at entry	Child and adolescent Studies	30	30
	Public health	8	8
	Human services	39	39
	Kinesiology	14	14
	Nursing	4	4

	First-year		Transfers
Total sample	Analytic (n = 70)	Matched control (n = 135)	Analytic (n = 110)	Matched control (n = 167)
4 yr/2 yr graduation percentage	47.3	23.0	61.8	56.9
Chi-squared	Χ² = 13.10, p = .000		Χ² = .667, p = .414
UR only	Analytic (n = 35)	Matched control (n = 71)	Analytic (n = 69)	Matched control (n = 93)
4 yr/2 yr graduation percentage	34.3	12.7	63.8	60.2
Chi-squared	Χ² = 6.89, p = .009		Χ² = .212, p = .645