Predictors of Successful Nursing Education Outcomes: A Study of the North Carolina Central University's Nursing Program.

The primary purpose of this study was to specify the variables that would play the greatest role in predicting success of North Carolina Central University (NCCU) nursing graduates in the National Certification Licensure Examination for Registered Nurses (NCLEX-RN). Participants for this study include a conveniece sample of 39 students who graduated from NCCU School of Nursing and sat for the 2006 version of the NCLEX-RN. The predictor variables are represented by student scores in the assessment tests in some 18 courses in the nursing program of the university. Discriminant analysis was used to identify which of the variables adopted are significant in predicting success on the NCLEX-RN. For this study, only variables with a p value of less or equal to 0.05 were considered statistically significant. Of the 18 predictor variables employed in this study, 11 were statistically significant in predicting who passed or failed the NCLEX-RN. These significant variables include: ATI Critical Thinking, ATI TEAS Comp, ATI Percentile Reading, ATI Percentile Math, ATI Percentile English, NLN Percentile Adult I, NLN Percentile Adult II, NLN Percentile Pediatric, Mental Percentile ATI, Pharmacology Percentile ATI, and Fundamental Percentile ATI. Their levels of significance range from an alpha of p These findings suggest that a student's performance in these tests, more than any others offered in the program, are the strongest measures of success in the NCLEX-RN by students that graduate from NCCU'S Nursing program. Introduction The United States has been experiencing a significant shortage of registered nurses in the health care industry since the last decade and half or so. During this period, annual pass rates in the NCLEX-RN have consistendy declined. According to the National Council of State Boards of Nursing (2002) the annual pass rate in nursing licensure examination has decreased from 90.2 percent in 1995 to 83.8 percent in 2000. At NCCU, the pass rates have seen considerable fluctuations in the same period, but increased steadily from 65 percent in 2003 to 91 percent in 2007. In response to these fluctuating pass rates, nurse educators are trying to develop an appropriate mechanism that would assure accurate identification of the factors that would predict success in nursing licensure examinations. Nursing practitioners and nurse educators alike are also aware that employers in the health sector increasingly view the success rate of nurses on the NCLEX-RN as a direct measure of program quality. These practitioners believe that reliable predictions of success on the NCLEX-RN can be very important in terms of enhancing the awareness of program stakeholders as well as having a better understanding of productive student behaviors (Beeman and Waterhouse, 2001). This awareness is not lost to the Nursing faculty at NCCU, which led Professor Marion Gooding, now retired, to encourage me to consider doing this study. It took a poor performance in 2001 of 65 percent pass rate on the NCLEX-RN to alert program managers that some intervention was needed. Determined to shore up the declining pass rates of NCCU students on this licensure examination, the nursing department decided to embark on this study. The objective of this study was to develop a mechanism that would accurately delineate the courses that have the most predictive power in terms of success in the licensure examination. Literature Review A considerable amount of research has been done in attempts to adequately delineate variables that would be useful in predicting success in the NCLEX-RN. In these efforts, many cognitive factors such as course grades, grade point averages, critical thinking tests, etc and non-cognitive variables such as interviews and psychological tests, etc, have been employed, with variable degrees of success. Blackman and Darmawan (2004) employed psychological assessment factors in predicting medical students' success with unreliable result. …