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Over the past ten years, maintenance career fields in the U.S. Air Force have been negatively affected by a series of events that have resulted in an experience shortage. Although there has been an improvement in Total Force manning since 2015, several skill levels are still experiencing shortages. To bridge the experience shortfall, the U.S. Government Accountability Office called for an Air Force retention strategy tailored to retain experienced maintainers. The RAND Corporation was asked to explore whether individual characteristics, economic and geographic factors, and the new Blended Retirement System (BRS) could provide additional insights into what predicts retention of this workforce. This report focuses primarily on aircraft maintenance career fields, with some attention to munitions and logistics career fields as resources permitted. The authors undertake two analytic approaches to examine the underlying determinants of retention. First, they use logistic regression to determine how strongly a variety of individual and environmental characteristics are associated with decisions to reenlist, extend an enlistment, or separate from the Air Force; second, they use RAND's Dynamic Retention Model to estimate how the new BRS will affect maintenance, munitions, and logistics career fields when those in the new system reach retention decision points. The authors find that changes in individual characteristics and environmental variables have improved retention in the maintenance, munitions, and logistics career fields. Although much of what influences retention is beyond the Air Force's control, the authors offer a number of recommendations and identify areas of emphasis that could be exploited.
"This monograph provides an empirical analysis of the enlistment, attrition, and reenlistment effects of bonuses, applying statistical models that control for such other factors as recruiting resources, in the case of enlistment and deployments in the case of reenlistment, and demographics. Enlistment and attrition models are estimated for the Army and our reenlistment model approach is twofold. The Army has greatly increased its use of reenlistment bonuses since FY 2004, and we begin by providing an in-depth history of the many changes in its reenlistment bonus program during this decade. We follow this with two independent analyses of the effect of bonuses on Army reenlistment. As we show, the results from the models are consistent, lending credence to the robustness of the estimates. One approach is extended to the Navy, the Marine Corps, and the Air Force, to obtain estimates of the effect of bonuses on reenlistment for all services. We also estimate an enlistment model for the Navy. The estimated models are used to address questions about the cost-effectiveness of bonuses and their effects in offsetting other factors that might adversely affect recruiting and retention, such as changes in the civilian economy and frequent deployments"--P. iii.
A study of how Air Force enlisted personnel helped shape the fi%ture Air Force and foster professionalism among noncommissioned officers in the 195Os.
This book surveys the entire field of body composition as it relates to performance. It includes a clear definition of terminology and a discussion of the various methods for measuring body composition. The authored papers represent a state-of-the-art review of this controversial field and address questions such as: What is a better measure of body compositionâ€"body fat or lean body mass? Does being overweight for one's height really affect performance? The book also addresses the issue of physical appearance as it relates to body fatness and performance. It includes an in-depth discussion of many of the topics of interest to those involved in sports medicine and exercise physiology.
RAND Project Air Force was tasked with developing a new capability for planners: a retention early warning system (REWS) that alerts policymakers when a subgroup of U.S. Air Force (USAF) military members is at risk for future shortages. The goal of the research project was to develop a forecasting model for retention, operationalized within a prototype decision-support application, that can alert decisionmakers to emerging problems and thus allow them enough time to consider adjusting accession and retention policies before shortages occur. The authors' overall approach to designing the system drew on widely used paradigms for solving data science problems. These paradigms emphasize understanding the business problem, drawing on a wide array of data sources and types, testing several flexible prediction approaches to optimize performance, and operationalizing the information for decisionmaking. To gain an understanding of the data sources that would be desirable for this application, the authors performed an extensive review of the turnover literature and identified gaps in existing USAF data collection efforts.