A comparison of neural network and regression models for Navy retention modeling

This thesis evaluates a possible use of artificial neural networks for military manpower and personnel analysis. Two neural network models were constructed to predict the reenlistment behavior of a select group of individuals in the Navy, from a sample of 680 individuals. The data were extracted from the 1985 DoD Survey of Officer and Enlisted Personnel. Explanatory variables were grouped into demographic/personal, military characteristics, perceived probability of civilian employment, educational level, and satisfaction with military life and military benefits. The first neural network model was compared to a more traditional method of statistical modeling (logistic regression analysis) to determine the strengths and weaknesses of the neural network model. Both models used the same set of 17 variables and were tested using a holdout sample of 100 observations. The neural network model was found to be comparable to the logistic regression model as a predictor, but deficient as a policy analysis model. The second neural network model was constructed using the same data set and architecture as the first neural network model, including the original 17 variables, plus an additional II variables that consisted of variables with and without theoretical foundation for predicting reenlistment. The two neural network models were then compared and found to be similar at predicting reenlistment. Both neural network models were considered to be deficient as tools for policy analysts...

Department dc:contributor.department

Department of Administrative Sciences

Grantor dc:publisher

Monterey, California. Naval Postgraduate School

Year dc:date.issued

1993

Author dc:creator

• Russell, Bradley Steven

Advisors dc:contributor.advisor

• Thomas, George W.
• Dolk, Daniel R.
• Hill, Timothy R.

Statement dc:rights

• This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/10945/39890

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/39890