title: Decision-Tree Analysis for Predicting First-Time Pass/Fail Rates for the NCLEX-RN® in Associate Degree Nursing Students.
abstract: BACKGROUND
  Little evidence shows the use of decision-tree algorithms in identifying predictors and analyzing their associations with pass rates for the NCLEX-RN(®) in associate degree nursing students. This longitudinal and retrospective cohort study investigated whether a decision-tree algorithm could be used to develop an accurate prediction model for the students' passing or failing the NCLEX-RN.
  
  
  METHODS
  This study used archived data from 453 associate degree nursing students in a selected program. The chi-squared automatic interaction detection analysis of the decision trees module was used to examine the effect of the collected predictors on passing/failing the NCLEX-RN.
  
  
  RESULTS
  The actual percentage scores of Assessment Technologies Institute®'s RN Comprehensive Predictor(®) accurately identified students at risk of failing. The classification model correctly classified 92.7% of the students for passing.
  
  
  CONCLUSIONS
  This study applied the decision-tree model to analyze a sequence database for developing a prediction model for early remediation in preparation for the NCLEXRN. [J Nurs Educ. 2016;55(8):454-457.].
authors.forename: Hsiu-Chin
authors.initials: HC
authors.lastname: Chen
authors.forename: Sean
authors.initials: S
authors.lastname: Bennett
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003661:Decision Support Techniques; D003663:Decision Trees; D004507:Education, Nursing, Associate; D004521:Educational Measurement; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D055815:Young Adult