Date of Conferral

2019

Degree

Ph.D.

School

Nursing

Advisor

Leslie Hussey

Abstract

Predicting retention and time to graduation within accelerated online and a hybrid RN-to-BSN programs are significant elements in leveraging the pipeline of qualified RNs with BSN degrees, but the literature lacks significant accounts of retention and time to graduation outcomes within these programs and predictive algorithm developments to offset high attrition rates. The purpose of this study was to quantitatively examine the relationships between pre-entry attributes, academic integration, and institutional characteristics on retention and time to graduation within accelerated online RN-to-BSN programs in order to begin developing a global predictive retention algorithm. This study was guided by Tinto's theories of integration and student departure (1975, 1984, 1993) and Rovai's composite persistence model. Retrospective datasets from 390 student academic records were obtained. Findings of this study revealed pre-entry GPA, number of education credits, enrollment status, 1st and 2nd course grades and GPA index scores, failed course type, size and geographic region, admission GPA standards, prerequisite criteria, academic support and retention methods were statistically significant predictors of retention and timely graduation (p <.05). A decision tree model was performed in SPSS modeler to compare multiple regression and binary logistic regression results, yielding a 96% accuracy rate on retention predictions and a 46 % on timely graduation predictions. Recommendations for future research are to examine other variables that may be associated with retention and time to graduation for results can be used to redevelop accurate predictive retention models. Having accurate predictive retention models will affect positive social change because RN-to-BSN students that successfully complete a BSN degree will impact the quality and safety of patient care.

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