Predictors of success and failure for ADN students on the NCLEX-RN
by Benefiel, Diane, Ed.D., CALIFORNIA STATE UNIVERSITY, FRESNO, 2011, 134 pages; 3456526

Abstract:

The purpose of this study was to: 1) analyze the relationship of preprogram and nursing program variables on National Council Licensure Examination for Registered Nurses (NCLEX-RN) success and failure, and 2) develop a model to predict success and failure on the NCLEX-RN. The convenience sample was comprised of 245 spring, summer, and fall midterm 2010 graduates from two large Central California community college associate degree in nursing (ADN) programs who took the NCLEX-RN of the three-year test plan cycle, which began April 2010. The sample was: 85.7% female, 14.3% male, 24% Hispanic, 33.1% White/non-Hispanic, 5.3% African American, 18% Asian, 1.2% American Indian, with most students (31.4%) between 31–40 years old. The NCLEX-RN first-time pass rate was 86.1%.

The study was a non-experimental, quantitative, retrospective, correlational design, which analyzed 11 preadmission and five nursing program variables with NCLEX-RN success and failure. The chi-square and t-test analysis indicated a significant relationship between prenursing GPA, type of student (traditional, contract, LVN to RN, transfer), Assessment Technologies Institute (ATI) Test of Essential Academic Skills (TEAS) composite score, TEAS English and reading subtest scores, number of attempts on the TEAS, first semester GPA, nursing GPA, ATI Comprehensive Predictor score, number of course repeats, and time from graduation to NCLEX-RN. Gender, ethnicity, age, TEAS science and math subtest scores did not have a significant relationship to NCLEX-RN success. Four models were developed using backward stepwise logistic regression to predict NCLEX-RN success and failure. Model 1 includes the traditional and contract students, all study variables, TEAS composite score (versus TEAS subtest scores), and has a 92.3% overall predictive accuracy. Model 2 has the same variables as Model 1 except with TEAS subtest scores versus the composite score, and a 91.7% overall prediction accuracy. Model 3 includes LVN to RN students, all study variables minus TEAS variables and first semester GPA, and is 100% accurate in predicting NCLEX-RN pass and fail rates. Model 4 includes all students minus TEAS variables and first semester GPA. The overall predication accuracy of Model 4 is 90.2% with the highest accuracy in predicting fails (47.1%) between Models 1, 2, and 4.

 
AdviserDebra Harris
SchoolCALIFORNIA STATE UNIVERSITY, FRESNO
SourceDAI/B 72-08, p. , Jun 2011
Source TypeDissertation
SubjectsNursing; Health education; Higher education
Publication Number3456526
Adobe PDF Access the complete dissertation:
 

» Find an electronic copy at your library.
  Use the link below to access a full citation record of this graduate work:
  http://gateway.proquest.com/openurl%3furl_ver=Z39.88-2004%26res_dat=xri:pqdiss%26rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation%26rft_dat=xri:pqdiss:3456526
  If your library subscribes to the ProQuest Dissertations & Theses (PQDT) database, you may be entitled to a free electronic version of this graduate work. If not, you will have the option to purchase one, and access a 24 page preview for free (if available).

About ProQuest Dissertations & Theses
With over 2.3 million records, the ProQuest Dissertations & Theses (PQDT) database is the most comprehensive collection of dissertations and theses in the world. It is the database of record for graduate research.

The database includes citations of graduate works ranging from the first U.S. dissertation, accepted in 1861, to those accepted as recently as last semester. Of the 2.3 million graduate works included in the database, ProQuest offers more than 1.9 million in full text formats. Of those, over 860,000 are available in PDF format. More than 60,000 dissertations and theses are added to the database each year.

If you have questions, please feel free to visit the ProQuest Web site - http://www.proquest.com - or call ProQuest Hotline Customer Support at 1-800-521-3042.