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Abstract

Many students who transfer between postsecondary institutions lose credits, which may sap academic momentum and increase college costs. Despite anecdotal evidence of major credit loss (MCL), where students cannot apply transferred credits toward their major, data limitations have hindered analyses of its magnitude or causes. Using novel administrative data in Texas, we measure MCL for a statewide sample of 2020–2022 vertical transfer students, compare it to estimates of general credit loss (GCL), and examine how it varies across student populations, majors, and universities. Our analyses show that MCL is roughly as prevalent as GCL and varies considerably across universities. Our results suggest promising directions for research and reforms that may mitigate credit loss.

Keywords

colleges community colleges educational policy higher education postsecondary education regression analyses secondary data analysis

Although many college students begin their journey to a bachelor’s degree at a community college, the loss of credits at the time of transfer to a university can sap academic momentum and increase time to degree (Monaghan & Attewell, 2015). There are two primary reasons why students may lose credits: The institution does not accept the credits at all (general credit loss [GCL]), or the credits transfer but do not apply to a specific requirement in the student’s chosen degree plan (major credit loss [MCL]). Qualitative research has revealed that many students experience MCL (Kadlec & Gupta, 2014). However, researchers have been unable to quantitatively examine MCL due to limitations in state and federal data systems.

In this brief, we analyze newly collected data from all public 4-year institutions in Texas to examine the magnitude and predictors of credit loss, with a focus on MCL. We find that both forms of credit loss are widespread, with considerable variation across majors and receiving institutions. Our results highlight potential limitations in the extant literature on credit loss and suggest promising avenues for future research examining the causes and consequences of MCL.

Literature Review

Research on credit loss has produced three clear findings. First, many students lose credits at the time of transfer (Monaghan & Attewell, 2015; Simone, 2014; U.S. Government Accountability Office [GAO], 2017). Simone’s (2014) analysis of National Center for Education Statistics data estimated that two-thirds of first-time transfer students lose some credits and that nearly 40% lose all credits. Second, credit loss varies across institutions. Students who traverse the traditional vertical transfer path (i.e., public 2-year institution to public 4-year institution) experience the least credit loss, whereas students who transfer from private, for-profit colleges to public 4-year institutions tend to lose the most credits (Simone, 2014; U.S. GAO, 2017). Third, credit loss is inversely related to university persistence and baccalaureate attainment (Monaghan & Attewell, 2015).

Researchers have generally measured credit loss in two ways: the number of credits denied by the institution altogether (Giani, 2019; Monaghan & Attewell, 2015; Simone, 2014; U.S. GAO, 2017) or the number of excess credits transfer students accumulated at the time of degree attainment (e.g., Fink et al., 2018). The first approach is limited in that it assumes all credits accepted by the institution apply to the student’s major despite the known phenomenon of MCL (Kadlec & Gupta, 2014). The second approach is limited in that it fails to distinguish between actual credit loss and other explanations of excess credit accumulation, such as course repetition and students enrolling in more courses than necessary for their degree.

Credit loss mechanisms likely vary across sending and receiving institutions, especially in contexts like Texas, where public institutions rely on bilateral articulation agreements—often shaped by university preferences—rather than a statewide transfer agreement (Schudde & Jabbar, 2024; U.S. GAO, 2017). Despite state policies establishing a transferable core curriculum or common course numbering to facilitate credit transfer between institutions, universities still exert considerable autonomy in whether and how credits apply to specific major requirements (Schudde & Jabbar, 2024). Particularly in decentralized higher education systems like Texas, we hypothesize that MCL varies considerably across universities. Established transfer pathways may be more common in some programs than others, which could also contribute to variation in MCL across majors. Despite the known limitations of methods for measuring credit loss (Fink et al., 2018) and theoretical rationale for why MCL is likely to vary across universities, data limitations have prevented more accurate measurement of MCL versus GCL; it is unclear how the availability of clearer measures of MCL and GCL can shift our understanding of the causes and consequences of credit loss.

Methods

We use data from the Texas Education Research Center, which includes person-level K–12, postsecondary education, and workforce data. Beginning in fall 2020, the Texas Higher Education Coordinating Board began collecting student course-level data on every credit that was not accepted or not applied to the student’s major. The data are reported for all first-time transfers from Texas public 2-year institutions to Texas public 4-year institutions who maintain the same major during transfer (i.e., they are accepted into the major that they applied for). The data include a variable that distinguishes MCL from all other reasons for credit loss (e.g., below minimum grade, repeated course), which we combine to measure GCL. Our sample is the population of students who began college during or after the 2010–2011 academic year and who transferred from fall 2020 through spring 2022 (N = 28,969).

We descriptively analyze how GCL and MCL vary across demographic groups and majors at the time of transfer. We then use ordinary least squares (OLS) regression with MCL as the outcome variable controlling for student covariates (demographics, pretransfer academics, major) and institutional fixed effects (for sending and receiving institutions) to better understand variation in credit loss across institutions. We focus on the 4-year institution fixed effects given greater variability in MCL across 4-year institutions compared with 2-year institutions. The appendix (available on the journal website) offers more information regarding our methods.

Results

As shown in Table 1, 83% of vertical transfer students experience any credit loss, 42% experience MCL, and 55% experience GCL. Although variation in credit loss across demographic groups is modest, there is considerable variation across majors regarding the prevalence and magnitude of both GCL and MCL. Among students who experienced any credit loss, MCL is roughly as prevalent as GCL. The mean number of credits lost among students with any credit loss was 4.47 for GCL and 4.01 for MCL. As shown in the final column in Table 1, MCL comprises between 36% and 65% of students’ total credits lost at the time of transfer, depending on their major.

Table 1

Descriptive Characteristics of Credit Loss by Student Demographics and Major

	Among All Transfers				Among Students With Any Credit Loss
	n	ACL %	GCL %	MCL %	n	GCL M	GCL SD	MCL M	MCL SD	MCL Share
Gender^a (reference: male)
Male	11,096	80%	54%	39%	8,857	4.41	5.45	3.88	6.02	41%
Female	17,873	85%*	57%	43%	15,123	4.51	5.48	4.08*	6.30	43%
Race/ethnicity^b (reference: White)
White (non-Hispanic)	9,053	81%	53%	46%	7,350	4.02	5.11	4.52	6.33	46%
Asian (non-Hispanic)	1,645	71%*	56%	25%	1,167	5.50*	5.65	3.01*	5.82	28%
Black (non-Hispanic)	2,838	83%*	64%	34%	2,349	4.87*	5.26	3.16*	5.73	32%
Hispanic/Latino	13,979	85%*	55%	43%	11,900	4.56*	5.66	3.98*	6.23	43%
Other	1,454	83%*	60%	40%	1,214	4.68*	5.68	3.82*	6.01	40%
Economically disadvantaged (reference: yes)
Yes	12,249	82%*	56%	40%	10,072	4.51	5.41	3.79	6.07	41%
No	16,720	83%*	56%	43%	13,908	4.45	5.51	4.16*	6.29	43%
Major area of concentration^c (reference: humanities and liberal arts)
Humanities and liberal arts	3,202	79%	52%	39%	2,544	4.43	5.42	4.10	6.90	42%
Business	5,307	73%*	49%	35%	3,876	4.24	5.30	3.46*	5.73	41%
Communication studies	884	85%*	57%	41%	751	3.72*	4.13	3.32*	4.92	41%
Education and social services	3,555	82%*	54%	45%	2,913	3.83*	4.62	3.99	5.71	46%
Engineering and related	2,071	84%*	54%	41%	1,735	4.86*	6.11	5.11*	7.76	43%
Health	3,218	91%*	65%	48%	2,920	5.40*	5.91	4.08	6.18	41%
Industrial, manual, and construction	459	99%*	48%	75%	455	2.41*	3.61	5.59*	6.32	65%
Literature, linguistics, and fine arts	1,562	80%*	57%	39%	1,254	4.38	5.36	3.65*	5.83	40%
Math and computer science	1,665	84%*	60%	37%	1,393	5.31*	6.43	3.72*	6.03	36%
Natural science	2,359	87%*	58%	46%	2,064	4.79*	6.05	4.70*	6.52	44%
Service-oriented	1,293	88%*	56%	48%	1,140	4.27	5.17	4.18	5.84	46%
Social and behavioral science	3,394	86%	59%	39%	2,935	4.32	5.21	3.61*	5.83	39%
Total	28,969	83%	55%	42%	23,980	4.47	5.47	4.01	6.20	42%

Source. Texas Education Research Center.

Note. The sample includes all students who transferred from a public 2-year institution to a public 4-year institution in Texas between fall 2020 and spring 2022. ACL is a dichotomous indicator of whether students lost one or more credits at the time of transfer. GCL is the number of credits students lost for all reasons apart from MCL. MCL is the number of credits students lost because the credits were not applied to the student’s major. The left panel shows sample statistics for the full sample, including n, and the proportion of students experiencing ACL, GCL, and MCL (note that MCL and GCL are not mutually exclusive). The right panel is restricted to the subsample of students experiencing ACL and illustrates the magnitude of GCL and MCL and the proportion among ACL students who lost major credits. For example, the final (Total) row shows that among 28,969 students transferring from a public 2-year college to a public 4-year college, 83% lost any credit, which translates to 23,980 students as in Column 6. Columns 4 and 5 show that among the 28,969 transfer students, 55% experienced GCL, and 42% experienced MCL. Within the subsample of ACL students, Columns 7 and 8 show the means and standard deviations of GCL, and Columns 9 and 10 show the means and standard deviations of MCL. The final column represents the percentage of a student’s total credit loss due to MCL (e.g., in the final row, 42% total credits lost by ACL students are due to MCL). Because total credit loss is the sum of MCL and GCL, the remaining 58% of total credit loss can be attributed to GCL (we do not show this calculation in this table due to space limitations). We performed t tests to compare the means of credit loss measures by demographic group. Significant differences (p < .05) between demographic groups on credit loss means are indicated by an asterisk. ACL = any credit loss; GCL = general credit loss; MCL = major credit loss.

Male and female are the only gender options in the Texas Education Research Center data.

The “other” racial/ethnic category includes Native American, Native Hawaiian, other Pacific Islander students, and those of unknown race/ethnicity.

Individual majors, measured by two-digit Classification of Instructional Programs codes, are grouped into major areas of concentration according to College Board groupings.

p < .05.

Figure 1 plots the 4-year institutional fixed effects from the OLS regression model of MCL. Each dot on the figure represents the difference in the predicted MCL between the university indicated by the dot and the reference university, which was chosen because it had the largest number of transfer students in the sample.¹ We exclude the names of the universities due to the novelty of the data and our caution in “shaming” universities for their credit loss rates based on our statistical estimates. Figure 1 highlights the considerable variation across universities in the magnitude of MCL experienced by vertical transfer students. We find these differences while controlling for student demographics, pretransfer academics, majors, and community college fixed effects. The appendix (available on the journal website) includes an alternative version of Figure 1 that groups universities based on their Carnegie Classification, but we do not observe a clear pattern between classification and credit loss.

Figure 1.

Variation in major credit loss across destination universities: university fixed effect coefficients from OLS regression model.

Discussion

Despite the known phenomenon of MCL (Kadlec & Gupta, 2014), prior research has not been able to capture it quantitatively. Our results underscore the importance of examining MCL and suggest that extant literature may provide a biased picture of the causes and consequences of credit loss due to the exclusion of MCL. New data, like that collected in Texas, will allow the field to more accurately measure credit loss and examine its predictors and consequences, including its relationship with persistence, attainment, and student debt (e.g., Giani et al., 2024a, 2024b).

Although reform efforts designed to facilitate the transfer of students and credits have largely focused on the role of community colleges (e.g., Bailey et al., 2015), our results provide new empirical evidence regarding the role of universities in shaping credit loss, particularly in decentralized higher education systems, such as Texas, where universities exert considerable autonomy in credit applicability decisions (Schudde & Jabbar, 2024). Our supplementary analysis in the appendix (available on the journal website) suggests broad categorizations, such as institutional classification, may be insufficient to explain institutional variation in MCL. Further evidence capturing university policies and practices related to credit transferability and applicability, interdepartmental or disciplinary variation in MCL, and university faculty’s beliefs and assumptions about course equivalency—and linking those institutional practices and policy information to aggregate credit loss metrics—could further illuminate the mechanisms shaping MCL. We also recommend that future research consider the extent to which variation by institution and major explain variation in MCL as a means of further capturing those mechanisms through decomposition or similar methodological approaches.

Additionally, the field has limited evidence regarding the efficacy of strategies, programs, and interventions that can mitigate credit loss among prospective transfer students, such as the effects of proactive advising, digital tools, or “recommended course sequences” (Schudde et al., 2025) designed to help students understand how their credits may transfer and apply (or not) to specific majors at specific universities. Without adequate information about credit transfer and applicability at prospective universities, students struggle to anticipate whether and how their credits will count toward their degree as they make transfer decisions (Schudde & Jabbar, 2024). Research is needed to determine whether these strategies improve downstream outcomes, such as university persistence, degree attainment, time to degree, credit accumulation, and student debt.

Finally, although our results suggest that prior research excluding MCL from calculations of credit loss may understate the magnitude of credit loss experienced by transfer students, we underscore that our sample is derived from students who traversed the most common and well-established transfer path from public 2-year institutions to public 4-year institutions. Credit loss tends to be higher among students transferring from a public 2-year institution to other public 2-year institutions or private 2- or 4-year colleges (U.S. GAO, 2017), meaning our results are likely lower bound estimates of the extent of credit loss among the full population of transfer students. Future research should examine why and to what extent both GCL and MCL vary for students pursuing less traditional transfer paths, particularly given the prevalence of these transfer pathways (Taylor & Jain, 2017). In addition to the potential of utilizing MCL data in research, we emphasize the importance of ensuring institutional practitioners receive actionable data on MCL so that they can target programmatic reform efforts at majors and courses in which students are losing the greatest number and proportion of credits due to MCL.

Supplemental Material

sj-pdf-1-edr-10.3102_0013189X251410173 – Supplemental material for New Insights on Sources of Credit Loss

Supplemental material, sj-pdf-1-edr-10.3102_0013189X251410173 for New Insights on Sources of Credit Loss by Matt S. Giani, Lauren Schudde and Tasneem Sultana in Educational Researcher

Footnotes

ORCID iDs

Matt S. Giani

Lauren Schudde

Tasneem Sultana

Notes

Authors

MATT S. GIANI, PhD, is a research associate professor in the Department of Sociology and a faculty affiliate with the Texas Behavioral Science and Policy Institute, the Center for Research to Community Impact, and the Population Research Center at the University of Texas at Austin, 305 E. 23rd St., Austin, TX 78712; matt.giani@austin.utexas.edu . His research examines how policies, programs, and interventions promote social mobility or reinforce social stratification in K–12 and higher education, with a focus on strategies that can promote college and career access and success of students from populations historically underrepresented in higher education.

LAUREN SCHUDDE, PhD, is an associate professor in the Department of Educational Leadership and Policy at the University of Texas at Austin, 1912 Speedway, Austin, TX 78712; schudde@austin.utexas.edu . She studies how to ameliorate social inequities in the United States through higher education policy, and her ongoing projects examine dual enrollment pathways between high schools and community colleges, how community college students respond to state and institutional transfer policies, and the influence of developmental-education reforms on student outcomes.

TASNEEM SULTANA, is a doctoral student in the program in higher education leadership and policy at the University of Texas at Austin, 1912 Speedway, Austin, TX 78712; ts39828@my.utexas.edu . Her research examines how life events and policy changes affect student outcomes and their implications for intergenerational mobility.

References

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Supplementary Material

Please find the following supplemental material available below.

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