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Predicting NCLEX-RN performance: cross-validating an identified classification procedure

J K Waterhouse1, L Bucher, P B Beeman

  • 1College of Nursing, University of Delaware, Newark 19716.

Journal of Professional Nursing : Official Journal of the American Association of Colleges of Nursing
|July 1, 1994
PubMed
Summary
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This study validated a method to predict National Council Licensure Examination for Registered Nurses (NCLEX-RN) success. The validated procedure accurately identified students at risk of NCLEX-RN failure.

Area of Science:

  • Nursing Education
  • Educational Psychology

Background:

  • The National Council Licensure Examination for Registered Nurses (NCLEX-RN) is a critical benchmark for nursing graduates.
  • Predictive models are essential for identifying students who may require additional support to pass the NCLEX-RN.

Purpose of the Study:

  • To cross-validate a previously developed discriminant function for predicting NCLEX-RN performance.
  • To assess the efficacy of the predictive model on a new cohort of nursing graduates.

Main Methods:

  • A discriminant function, initially developed using 15 variables on 313 graduates (1988-1990), was applied to a new sample of 142 graduates (1991-1992).
  • Demographic and academic variables of the two graduate samples were compared.
  • The classification accuracy of the original discriminant function was evaluated on the cross-validation sample.

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Main Results:

  • The cross-validation sample showed significant differences in SAT scores, physiology grades, and nursing grades compared to the original sample.
  • The discriminant function correctly classified 84% of the subjects in the cross-validation sample.
  • The model accurately categorized over 62% of failures and 87% of passes.

Conclusions:

  • The discriminant function demonstrates statistical and ethical soundness for identifying nursing students at risk of NCLEX-RN failure.
  • The findings support the use of this predictive tool in academic advising and intervention strategies.
  • Continued validation and refinement of predictive models are crucial for enhancing NCLEX-RN pass rates.