OJS  Vol.8 No.3 , June 2018
Adaptive Classification Methods for Predicting Transitions in the Nursing Workforce
ABSTRACT
Earlier analyses of transitions from licensed practical nurse (LPN) to registered nurse (RN) in the North Carolina (NC) nursing workforce in terms of 11 categorical predictors were limited by not considering parsimonious classifications based on these predictors and by substantial amounts of missing data. To address these issues, we formulated adaptive classification methods. Secondary analyses of data collected by the NC State Board of Nursing were also conducted to demonstrate adaptive classification methods by modeling the occurrence of LPN-to-RN transitions in the NC nursing workforce from 2001-2013. These methods combine levels (values) for one or more categorical predictors into parsimonious classifications. Missing values for a predictor are treated as one level for that predictor so that the complete data can be used in the analyses; the missing level is imputed by combining it with other levels of a predictor. An adaptive nested classification generated the best model for predicting an LPN-to-RN transition based on three predictors in order of importance: year of first LPN licensure, work setting at transition, and age at first LPN licensure. These results demonstrate that adaptive classification can identify effective and parsimonious classifications for predicting dichotomous outcomes such as the occurrence of an LPN-to-RN transition.
Cite this paper
Knafl, G. , Toles, M. , Beeber, A. and Jones, C. (2018) Adaptive Classification Methods for Predicting Transitions in the Nursing Workforce. Open Journal of Statistics, 8, 497-512. doi: 10.4236/ojs.2018.83032.
References
[1]   Jones, C.B., Toles, M., Knafl, G.J. and Beeber, A.S. (2018) An Untapped Resource in the Nursing Workforce: Licensed Practical Nurses Who Transition to Become Registered Nurses. Nursing Outlook, 66, 46-55.
https://doi.org/10.1016/j.outlook.2017.07.007

[2]   Knafl, G.J. and Ding, K. (2016) Adaptive Regression for Modeling Nonlinear Relationships. Springer International Publishing, Switzerland.
https://doi.org/10.1007/978-3-319-33946-7

[3]   Stone, M. (1977) An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion. Journal of the Royal Statistical Society Series B, 39, 44-47.

[4]   Claeskens, G. and Hjort, N.L. (2009) Model Selection and Model Averaging. Cambridge University Press, Cambridge.

[5]   Sclove, S.L. (1987) Application of Model-Selection Criteria to Some Problems in Multivariate Analysis. Psychometrika, 52, 333-343.
https://doi.org/10.1007/BF02294360

[6]   Burman, P. (1989) A Comparative Study of Ordinary Cross-Validation, ν-Fold Cross-Validation and the Repeated Learning-Testing Methods. Biometrika, 76, 503-514.
https://doi.org/10.1093/biomet/76.3.503

[7]   Friedman, J.H. (1991) Multivariate Adaptive Regression Splines. Annals of Statistics, 19, 1-67.
https://doi.org/10.1214/aos/1176347963

[8]   Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1998) Classification and Regression Trees. CRC Press, Boca Raton, FL.

 
 
Top