title: Predicting success for baccalaureate graduates on the NCLEX-RN.
abstract: The purpose of this retrospective study was to identify predictors of success for baccalaureate nursing graduates on the National Council Licensure Examination-Registered Nurse (NCLEX-RN). Subjects (505) were graduates of a baccalaureate nursing program in the southeastern United States from 1993 to 1998. The outcome variable was pass/fail on the NCLEX-RN on the first attempt. Predictor variables included type of student (freshman admission, transfer, second degree), age at the time of licensing examination, gender, performance on selected prenursing courses, performance in all junior and senior nursing courses, cumulative grade point average (GPA) at graduation and the Mosby AssessTest score. Results showed a significant relationship between number of Cs, Ds, and Fs in nursing courses and NCLEX-RN results. Students who passed the NCLEX-RN had significantly higher average GPAs, made fewer grades of C or below, and scored higher on the Mosby AssessTest than students who failed. Nontraditional college-age students tended to have a higher passing rate than did traditional age students. A logistic regression model was developed that correctly predicted 76 per cent of the students who failed based on the information available by the end of the end of the first semester of the senior year. J Prof Nurs 17:121-127, 2001.
authors.affiliation: The University of North Carolina at Greensboro, Greensboro, NC 17402-6172, USA.
authors.forename: S A
authors.initials: SA
authors.lastname: Beeson
authors.forename: G
authors.initials: G
authors.lastname: Kissling
mesh_terms: D000328:Adult; D004508:Education, Nursing, Baccalaureate; D004521:Educational Measurement; D005260:Female; D005544:Forecasting; D006801:Humans; D008008:Licensure, Nursing; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D012189:Retrospective Studies; D012570:School Admission Criteria; D015144:Southeastern United States; D018709:Statistics, Nonparametric