Book/Printed Material Developing an Air Force Retention Early Warning System Concept and Initial Prototype

Title

• Developing an Air Force Retention Early Warning System Concept and Initial Prototype

Summary

• 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.

Names

• Schulker, David, author
• Project Air Force (U.S.)
• Rand Corporation
• United States. Department of the Air Force

Created / Published

• Santa Monica, CA : RAND Corporation, [2021]

Contents

• Chapter One: Introduction -- Chapter Two: What Information Is Most Relevant to Predicting Retention? -- Chapter Three: Available Sources of Information for Predicting Air Force Retention -- Chapter Four: Modeling Approaches and Performance Levels -- Chapter Five: How Retention Predictions Can Be Used to Generate Warnings -- Chapter Six: Next Steps for Further Development and Implementation -- Appendix A: Creating the Analytic Data File -- Appendix B: Machine Learning Algorithms -- Appendix C: Decomposition Methodology -- Appendix D: Literature Review Methodology -- Appendix E: Considerations and Challenges in Applying Data Science to Air Force Human Resource Problems -- Appendix F: Policy Impact Methodology.

Headings

• -  United States.--Air Force--Recruiting, enlistment, etc.--Mathematical models
• -  United States.--Air Force--Personnel management--Mathematical models

Notes

• -  Title from PDF document (title page; viewed October 15, 2021)
• -  "Prepared for the Department of the Air Force"
• -  "RAND PROJECT AIR FORCE"
• -  Also available on the Internet as a PDF file.
• -  Includes bibliographical references (pages 46-49).
• -  Desciption based on electronic resource
• -  Description based on print version record; resource not viewed.

Medium

• 1 online resource (xii, 49 pages) : illustrations.

Call Number/Physical Location

• MLCM 2023/41683 (U)

Digital Id

• https://hdl.loc.gov/loc.gdc/gdcebookspublic.2024738791

Library of Congress Control Number

• 2024738791

Rights Advisory

• This is non-restricted, fully open content that may be accessed on and off of the Library of Congress campus, with no restrictions, by an unlimited number of users

Access Advisory

• Unrestricted online access

Online Format

• image
• pdf

LCCN Permalink

• https://lccn.loc.gov/2024738791

Additional Metadata Formats

• MARCXML Record
• MODS Record
• Dublin Core Record