Understanding Persistence of At-Risk Students in Higher Education Enrollment Management Using Multiple Linear Regression and Network Analysis

dc.contributor.authorGilstrap, Donald L.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2020-07-16T13:44:13Z
dc.date.available2020-07-16T13:44:13Z
dc.date.issued2019-09-26
dc.description.abstractThis article presents research on persistence among at-risk students using network analysis following multiple linear regression (MLR). Data on a population of enrolled undergraduate students at an urban-serving university over several years (P = 35,239) is tested using multiple linear regression. Variables interacting at different dimensions of the model are analyzed using IBM SPSS Neural Networks (2017) and Cytoscape (2018) to show network linkages. While variables may not be significant in MLR models where anomalies are ruled out, network analysis takes these anomalies into account and reveals complex layers of interactions between and among variables. Findings show that loans of any kind contribute to attrition while financial aid of any kind contributes to persistence and offsets attrition from loans.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationGilstrap, D. (2019): Understanding Persistence of At-Risk Students in Higher Education Enrollment Management Using Multiple Linear Regression and Network Analysis. The Journal of Experimental Education, 88(3). DOI: https://doi.org/10.1080/00220973.2019.1659217
dc.identifier.doi10.1080/00220973.2019.1659217
dc.identifier.urihttp://ir.ua.edu/handle/123456789/6791
dc.languageEnglish
dc.language.isoen_US
dc.publisherRoutledge
dc.subjectAt-risk
dc.subjectcomplex systems
dc.subjectfinancial aid
dc.subjecthigher education
dc.subjectnetwork analysis
dc.subjectpersistence
dc.subjectDISSIPATIVE STRUCTURES
dc.subjectCONDITIONED EMERGENCE
dc.subjectRETENTION
dc.subjectEducation & Educational Research
dc.subjectPsychology, Educational
dc.subjectPsychology
dc.titleUnderstanding Persistence of At-Risk Students in Higher Education Enrollment Management Using Multiple Linear Regression and Network Analysisen_US
dc.typetext
dc.typeArticle
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