A Decision Algorithm for Admitting Students to Advanced Practice Programs in Nursing

The academic records of 738 master's-level nursing students were reviewed to develop an optimal compound decision rule for selecting candidates who would be successful in advanced practice programs. Chi-square analysis was used to establish the association between various combinations of indepe...

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Bibliographic Details
Published inJournal of professional nursing Vol. 24; no. 5; pp. 281 - 284
Main Authors Suhayda, Rosemarie, Hicks, Frank, Fogg, Louis
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.09.2008
W.B. Saunders Company/JNL
Subjects
Academic achievement
Admission criteria for graduate programs in nursing
Admissions
Adult
Advanced practice nurses
Algorithms
Analysis of Variance
Candidates
Chi-Square Distribution
College students
Decision Trees
Education, Nursing, Graduate - organization & administration
Educational Measurement - methods
Female
Graduate record examination
Humans
Illinois
Male
Middle Aged
Nursing
Nursing Education Research
Postgraduate education
Predictive Value of Tests
Predictors
Predictors of success in graduate nursing programs
School Admission Criteria
Student Dropouts - statistics & numerical data
Students, Nursing - statistics & numerical data
Success
Illinois
Graduate record examination
Predictors of success in graduate nursing programs
Admission criteria for graduate programs in nursing
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Summary:The academic records of 738 master's-level nursing students were reviewed to develop an optimal compound decision rule for selecting candidates who would be successful in advanced practice programs. Chi-square analysis was used to establish the association between various combinations of independent variables and dismissal status. Independent variables included cumulative grade point average (CUMGPA), undergraduate nursing grade point average (NSGGPA), and Graduate Record Examination (GRE) scores (quantitative and verbal). Results indicated that the combination of any two independent variables was as predictive of success as was any combination of three independent variables and that the combination of a CUMGPA of 3.25 and NSGGPA of 3.0 predicted success in 99% of the cases. The addition of the GRE scores added no additional predictive value. On the basis of these analyses, we waived the GRE requirement for applicants who met the GPA criteria and developed decision rules for admitting those who did not.
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ISSN:8755-7223
1532-8481
DOI:10.1016/j.profnurs.2007.10.002