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Toward a Comprehensive Model Predicting Credit Loss in Vertical Transfer

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Title: Toward a Comprehensive Model Predicting Credit Loss in Vertical Transfer
Language: English
Authors: Matt S. Giani (ORCID 0000-0001-7257-7253), Lauren Schudde (ORCID 0000-0003-3851-1343), Tasneem Sultana (ORCID 0009-0005-4272-3949)
Source: American Educational Research Journal. 2026 63(2):227-269.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 43
Publication Date: 2026
Sponsoring Agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
Contract Number: P2CHD042849
T32HD007081
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Two Year Colleges
Descriptors: College Transfer Students, College Credits, Majors (Students), Student Characteristics, Institutional Characteristics, Courses, Academic Achievement, Community Colleges, Public Colleges, For Profit Colleges, Private Colleges
Geographic Terms: Texas
DOI: 10.3102/00028312251409063
ISSN: 0002-8312
1935-1011
Abstract: A growing body of research has documented extensive credit loss among transfer students. However, the field lacks theoretically driven and empirically supported frameworks that can guide credit loss research and reforms. We developed and tested a novel framework designed to address this gap using unique administrative credit loss data from Texas. Our results demonstrate how the likelihood of credit loss varies across course characteristics, majors, pretransfer academics, student characteristics, and sending and receiving institutions. Additionally, we disentangled general credit loss from major credit loss and examined how they vary across institutions, majors, and the combination of both. The extensive variation in credit loss among universities in particular underscores the need for future research and reform.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499457
Database: ERIC
FullText Text:
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PubType: Academic Journal
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  Data: Toward a Comprehensive Model Predicting Credit Loss in Vertical Transfer
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  Data: <searchLink fieldCode="AR" term="%22Matt+S%2E+Giani%22">Matt S. Giani</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7257-7253">0000-0001-7257-7253</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lauren+Schudde%22">Lauren Schudde</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3851-1343">0000-0003-3851-1343</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tasneem+Sultana%22">Tasneem Sultana</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-4272-3949">0009-0005-4272-3949</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22American+Educational+Research+Journal%22"><i>American Educational Research Journal</i></searchLink>. 2026 63(2):227-269.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 43
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  Data: 2026
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  Data: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
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  Data: <searchLink fieldCode="DE" term="%22College+Transfer+Students%22">College Transfer Students</searchLink><br /><searchLink fieldCode="DE" term="%22College+Credits%22">College Credits</searchLink><br /><searchLink fieldCode="DE" term="%22Majors+%28Students%29%22">Majors (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Institutional+Characteristics%22">Institutional Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Courses%22">Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Community+Colleges%22">Community Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Colleges%22">Public Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22For+Profit+Colleges%22">For Profit Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Private+Colleges%22">Private Colleges</searchLink>
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  Data: A growing body of research has documented extensive credit loss among transfer students. However, the field lacks theoretically driven and empirically supported frameworks that can guide credit loss research and reforms. We developed and tested a novel framework designed to address this gap using unique administrative credit loss data from Texas. Our results demonstrate how the likelihood of credit loss varies across course characteristics, majors, pretransfer academics, student characteristics, and sending and receiving institutions. Additionally, we disentangled general credit loss from major credit loss and examined how they vary across institutions, majors, and the combination of both. The extensive variation in credit loss among universities in particular underscores the need for future research and reform.
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  Data: EJ1499457
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