Abstract
The current nursing shortage has challenged colleges to educate nurses at a faster pace than in previous times. Successful completion of the nursing programs and passing the National Council Licensure Examination for Registered Nurses (NCLEX-RN) exam is important for the students, faculty, and nursing programs. The purpose of this retrospective study was to investigate the possible predictors of completing the baccalaureate nursing program at a college in the eastern part of the United States and passing the NCLEX-RN licensure exam. Using Spearman's classical test theory and Seidman's retention formula as theoretical foundations, this study investigated historical data from 2 graduating classes of the Bachelor of Science (BSN) nursing program to determine whether the admission variables of the preprogram grade point averages (GPA), American College Testing (ACT) scores, course grades in anatomy and physiology, and/or the Health Education System Inc. (HESI) Exit Exam scores could predict who was most likely to complete the nursing program, as well as pass the NCLEX-RN.
Sigma Membership
Nu Alpha
Type
Dissertation
Format Type
Text-based Document
Study Design/Type
Descriptive/Correlational
Research Approach
Quantitative Research
Keywords:
Nursing Student Retention, Nursing Program Failure/Success, Bachelor of Science Nursing Programs, Health Education System Inc. Exit Exam, Admission Variables
Advisor
Kathleen Foley-Peres
Second Advisor
Nancy Walters
Third Advisor
Tiffany DePriter
Degree
Doctoral-Other
Degree Grantor
Walden University
Degree Year
2013
Recommended Citation
Elkins, Nancy Susan, "Predictors of retention and passing National Council Licensure Examination for Registered Nurses" (2022). Dissertations. 634.
https://www.sigmarepository.org/dissertations/634
Rights Holder
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Review Type
None: Degree-based Submission
Acquisition
Proxy-submission
Date of Issue
2022-02-23
Full Text of Presentation
wf_yes
Description
This dissertation has also been disseminated through the ProQuest Dissertations and Theses database. Dissertation/thesis number: 3595186; ProQuest document ID: 1443812575. The author still retains copyright.