Positive Propensity and Navy Enlistment

Abstract

This thesis examines the process used to estimate the military enlistment behavior of young men, and seeks to develop measures to improve the process. Enlistment intention is quantified through the construction of two separate propensity measures, the percent positive propensity (PPP) and the Navy propensity index (NPI). These measures are included as explanatory variables in Navy Recruiting Command's current enlistment prediction model, and this model is in turn regressed upon net enlistment contract data. The study compares model performance and forecasting accuracy with and without each of the propensity variables, and examines positive enlistment propensity itself at the regional and local levels. The main conclusions of the study are: (1) Weighted propensity should be the value of choice when using YATS II data to estimate propensity measures. (2) Net contract data should be the preferred form for use in forecasting enlistments. (3) There has been a definite decrease in nationwide positive propensity during the period 1983-1987. (4) There is significant regional variation in the predictive accuracy of the current Navy enlistment model. (5) Residual analysis of positive propensity indicates that much of the variation in propensity is explained by other significant explanatory variables especially local unemployment. The degree to which other factors explain propensity reduces its effectiveness as an explanatory variable in enlistment forecasting models. Theses.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1989
Accession Number
ADA212061

Entities

People

  • Michael K. Crosbie

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Basic Training
  • California
  • Education
  • Enlisted Personnel
  • Geographic Regions
  • Manpower
  • New England
  • Recruiting
  • Recruits
  • Schools
  • Students
  • Training
  • Unemployment
  • United States
  • Universities
  • Urban Areas

Readers

  • Computational Modeling and Simulation
  • Naval Personnel Management