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Predicting 36 Month Attrition In The U S Military
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Book Synopsis Predicting 36-Month Attrition in the U. S. Military by : James V. Marrone
Download or read book Predicting 36-Month Attrition in the U. S. Military written by James V. Marrone and published by . This book was released on 2020-06-15 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author analyzes first-term attrition, using administrative data for all accessions across four military service branches in fiscal years 2002 through 2013 to show what characteristics predict attrition across the first 36 months of service.
Book Synopsis Predicting U.S. Army Enlisted Attrition After Initial Entry Training (IET) Using Survival Analysis - Sophisticated Research Modeling Using Medical Information, Dental and Hearing Readiness Important by : U S Military
Download or read book Predicting U.S. Army Enlisted Attrition After Initial Entry Training (IET) Using Survival Analysis - Sophisticated Research Modeling Using Medical Information, Dental and Hearing Readiness Important written by U S Military and published by . This book was released on 2019-08-22 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the first time since 2005, the U.S. Army fell short of its recruiting goal in 2018 by about 6,500 recruits. A strong economy and an increasing pool of recruit candidates who require a waiver to enlist add to the Army's recruitment troubles. Mental health issues, obesity, and other medical issues have become barriers that disqualify recruits from enlisting. For those who are eligible, they complete a training period called Initial Entry Training (IET). After finishing IET, many soldiers do not finish their first-term service obligation. This research continues the research conducted by Speten (2018) on post-IET attrition, with the added benefit of having medical data available in the Person-event Data Environment (PDE), a secure, virtual environment with a database that provides information on manpower, service, personnel, and medical data. Currently, no research has been conducted that uses detailed medical information to predict post-IET attrition. To estimate the expected number of soldiers who attrite at a specific time post-IET and prior to the end of their first-term obligation, we construct survival tree models using time-varying and time-constant covariates. We find several medical covariates that are important in forecasting attrition including dental readiness and hearing readiness. The effectiveness of the models is assessed on independent test sets. They perform well in predicting expected number of attrition, but not in predicting individual soldier attrition.This compilation includes a reproduction of the 2019 Worldwide Threat Assessment of the U.S. Intelligence Community.For the first time in 13 years, the Army did not meet its recruiting goal (Dickstein 2018). This failure to recruit qualified personnel is especially dire in a time when threats from Russia and China continue to grow. One issue that continues to threaten the ability to recruit soldiers is the lack of a qualified pool of candidates. Criminal convictions, mental health issues, obesity, and other medical issues have become roadblocks that disqualify young recruits from enlisting. In the past, the Army has relaxed certain standards and has given waivers to enlistees for conduct, aptitude, or minor medical issues. However, in 2019, Secretary of the Army, Mark Esper, mandated that fewer less-qualified recruits that require waivers be accepted into the ranks (Myers 2018). This research identifies the demographic and medical factors that contribute to first term service obligation attrition of enlisted U.S. Army soldiers who complete Initial Entry Training (IET). We develop a predictive survival model using survival analysis to forecast the probability that a soldier will either leave the service through attrition within the first t years into their first term or will continue to serve in the Army past their initial first term obligation. The data we use is stored and analyzed in the Person-Event Data Environment (PDE). The PDE is a remote cloud computer environment where data is stored centrally and accessed safely from verified users. The remote access feature of the database ensures there are no privacy or security breaches involving personal information. The PDE contains millions of records on Department of Defense employees, military personnel, and their family members. All personally identifiable information in the database is absent and each individual is assigned a unique Person Identifier (PID).
Book Synopsis The Optimal Job-person Match Case for Attrition Reduction by : Peter Greenston
Download or read book The Optimal Job-person Match Case for Attrition Reduction written by Peter Greenston and published by . This book was released on 1997 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this research is to illuminate an important interaction between personal characteristics and organizational factors as they affect first-term attrition. This study tests the hypothesis that first-term completion is positively related to predicted performance on the job and estimates the attrition reduction that would accompany the utilization of better methods for assigning recruits to jobs so as to improve their predicted performance. The testing is conducted with the 1991 accession cohort using the U.S. Army Research Institute for the Behavioral and Social Sciences' Enlisted Panel Research Data Base (EPRDB). Regression analysis is used to test for a relationship between attrition behavior and predicted performance on the job, holding other factors constant. This relationship is then applied to estimate the attrition reduction that could be brought about by increased soldier performance through improved job-person matching procedures such as the Enlisted Personnel Allocation System (EPAS).
Book Synopsis Military Attrition by : United States. General Accounting Office
Download or read book Military Attrition written by United States. General Accounting Office and published by . This book was released on 1997 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Prediction of Attrition Among First Year U.S. Army Recruits by Pre-military Trauma, Sense of Attachment to Caregiver, Pre-military Depression, Risk-taking Behaviors and Sense of Social Support by : David E. Cabrera
Download or read book Prediction of Attrition Among First Year U.S. Army Recruits by Pre-military Trauma, Sense of Attachment to Caregiver, Pre-military Depression, Risk-taking Behaviors and Sense of Social Support written by David E. Cabrera and published by . This book was released on 2006 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Military Attrition by : Carol R. Schuster
Download or read book Military Attrition written by Carol R. Schuster and published by DIANE Publishing. This book was released on 1999-04 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: 25,000 enlisted personnel are being separated from the services in their first 6 months, during or shortly after they complete basic training. This report analyzes historical attrition rates for enlisted personnel who serve at least 6 months, but leave military service before completing their first contract terms. It determines (1) the rate and timing of attrition during enlistees' first terms; (2) the extent of DoD's investment in recruiting and training first-term enlistees; (3) reasons for first-term attrition after training; (4) servicemembers' perceptions of quality-of-life factors that contribute to attrition; and (5) actions the services are taking to reduce enlistees' attrition.
Book Synopsis Insights on the Use of Machine Learning to Predict Retention of Career Soldiers in the United States Army by : Miguel Garcia
Download or read book Insights on the Use of Machine Learning to Predict Retention of Career Soldiers in the United States Army written by Miguel Garcia and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the face of cyclic pressures to appropriately up-scale or down-scale military forces in response to changes in political power and respective surge or withdrawal of forces from theatres of operation, the Department of Defense (DoD) stands to benefit from any techniques which might provide insight into prediction of retention vs. attrition of U.S. Army soldiers. The presumptive solution of Artificial Intelligence (AI) should thus serve as major resources, but the military decision-making process has yet to fully capitalize on it -- though steps have been taken to lay the groundwork for large-scale implementation. As such, opportunities abound for novel implementations of any or a combination of recent advances in data analytics processes and neural learning breakthroughs to gain insights from large volumes of data. Supported by the Army Analytics Group (AAG), this thesis uses the Person-Event Data Environment (PDE) to aggregate 55 distinct datasets within a secure, cloud computing environment in order to build and compare the efficacy of various modeling techniques in R for the prediction of career-long retention of soldiers, both at time of entry and after three years of service, to include: Decision Trees (rpart), Logistic Regression, Naïve Bayes, C5.0 Decision Trees, Random Forests, XGBoost, simple Artificial Neural Networks (nnet), and Deep Neural Networks (neuralnet) -- the latter of which constitutes the military's first foray into Deep Learning in retention research. In the field of AI as applied to predicting U.S. Army attrition, this research furthers the state of the art in terms of breadth from the perspective of the number of datasets accessed, the number of records considered in terms of Active Duty soldiers in the time window, the number and types of different techniques implemented in a single study, the length of prediction (i.e. full career as opposed to single-term enlistment), and consideration of both commissioned officers as well as enlisted soldiers in a single work. The results show the potential and limitations of available demographic, medical, deployment, and training data for predicting whether soldiers will or will not serve a full career until retirement. With cohorts broken down by fiscal year and by officer vs. enlisted, the relative performance of each technique notably varied in Area Under the Curve (AUC): At most prediction points for both enlisted soldiers and officers (though less consistently), Logistic Regression proved the most effective, but XGBoost and Artificial Neural Network methodologies were able to surpass it when hyperparameters were tuned for a specific cohort. When more variables became available after three years, ensemble techniques such as XGBoost and Random Forests would sometimes outperform Logistic Regression, as in the case of commissioned datasets with ancillary features added. While the performance increase after three years is marginal, the relative predictive strength on enlisted datasets at the time of enlistment is encouraging for the prospect of future implementation by military recruiting. Ultimately, while Logistic Regression is consistent (at least as compared to other models) in prediction of career-long attrition and retention given the data in the PDE, it does not yet produce reliable enough AUC for confident use in justifying changes to policy or decisions made by the DoD in terms of real-world recruiting or retention. That being said, it can be surpassed by even the most basic neural networks when hyperparameters are tuned for specific cohorts. Though this research was limited in its application of Deep Learning by (temporary) technical constraints, findings suggest that libraries like Keras and TensorFlow may be capable of revolutionizing this topic as they have in many other fields. Ultimately, this thesis extends the proven validity of established ML techniques from first-term, enlisted-only predictions to full-career predictions for both enlisted soldiers and officers alike while simultaneously establishing a new precedent for the viability of neural networks and Deep Learning for the study of military retention and attrition.
Book Synopsis Attitudes, Aptitudes, and Aspirations of American Youth by : National Research Council
Download or read book Attitudes, Aptitudes, and Aspirations of American Youth written by National Research Council and published by National Academies Press. This book was released on 2003-01-01 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recruiting an all-volunteer military is a formidable task. To successfully enlist one eligible recruit, the Army must contact approximately 120 young people. The National Research Council explores the various factors that will determine whether the military can realistically expect to recruit an adequate fighting force-one that will meet its upcoming needs. It also assesses the military's expected manpower needs and projects the numbers of youth who are likely to be available over the next 20 years to meet these needs. With clearly written text and useful graphics, Attitudes, Aptitudes, and Aspirations of American Youth offers an overview of important issues for military recruiters, touching on a number of important topics including: sex and race, education and aptitude, physical and moral attributes, and military life and working conditions. In addition, the book looks at how a potential recruit would approach the decision to enlist, considering personal, family, and social values, and the options for other employment or college. Building on the need to increase young Americans' "propensity to enlist," this book offers useful recommendations for increasing educational opportunities while in the service and for developing advertising strategies that include concepts of patriotism and duty to country. Of primary value to military policymakers, recruitment officers, and analysts, Attitudes, Aptitudes, and Aspirations of American Youth will also interest social scientists and policy makers interested in youth trends.
Book Synopsis Determining Characteristic Groups to Predict Army Attrition by : Samuel Buttrey
Download or read book Determining Characteristic Groups to Predict Army Attrition written by Samuel Buttrey and published by . This book was released on 1999 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Office of the Deputy Chief of Staff, Personnel (ODCSPER), is charged with managing the Army's military strength levels and forecasting future strength levels for planning purposes. ODCSPER is reformulating its Enlisted Loss Inventory Model (ELIM), which projects losses of first-term enlisted personnel. These projections in turn are passed to a program which is designed to maintain the Army's strength as closely as possible to prescribed levels. These projections are based on characteristic groups, a set of sub-groups of recruits who are similar in terms of sex, education level, term of service and mental category; the presumption has been that attrition rates ought to be different between groups. However in recent years ELIM projections have been unsatisfactory. This study used Classification and Regression Tree methodology (CART) to generate improved c-groups for predicting not only first-term attrition but also early-term behavior and re-enlistment. The most important variables by which to create these groups turn out to be race and gender. Generally white women have the lowest term completion and re-enlistment rates; those for non-white women and white men are similar; and those for non-white men are the highest.
Book Synopsis Prediction of Job Performance by : Robert Vineberg
Download or read book Prediction of Job Performance written by Robert Vineberg and published by . This book was released on 1982 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Literature pertaining to prediction of enlisted military job performance, 1952-1980, was reviewed. The review excluded studies in which training performance or reenlistment is the criterion. Aptitude was the most frequently used predictor and supervisor ratings the most frequent criterion. Relationships among classes of criteria and between predictors and criteria were examined. Major classes of criteria were job proficiency, job performance, and suitability to military service. The following conclusions are supported by the review: (1) For the great majority of jobs, job knowledge tests appear to provide the most practical method of objective measurement; (2) Because job sample tests are very expensive to construct and administer, their use is not practical unless the job is extremely costly or critical; and (3) Use of supervisors' ratings as the only measure of job performance should be restricted to jobs for which motivation, social skill, and response to situational requirements are the only attributes worth measuring. Two promising approaches to improved prediction are the selective use of miniaturized training and assessment centers and the use of self-paced training performance as a predictor. The review includes abstracts of the studies that were reviewed.
Book Synopsis Who Stays, who Leaves? by : James R. Hosek
Download or read book Who Stays, who Leaves? written by James R. Hosek and published by . This book was released on 1989 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Quantitative Model of the Considerations Determining Enlistment and Reenlistment Behavior by : Stuart H. Rakoff
Download or read book A Quantitative Model of the Considerations Determining Enlistment and Reenlistment Behavior written by Stuart H. Rakoff and published by . This book was released on 1988 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project was designed to improve the understanding and modeling of the decisions, made each year by thousands of first-term soldiers, to reenlist in the Army or to leave for civilian jobs and school. A model of the reenlistment decision formulated from a decision-analytic perspective was developed, based on an extensive review of the literature in the areas of military personnel, job satisfaction and job change, and decision theory, as well as from focus groups conducted with first-term soldiers at Fort Benning, Georgia. A multicomponent decision-modeling approach incorporating attitudinal, normative, and affective predictors of reenlistment intent was then developed, along with a set of instruments to capture data on these components. Consistent with previous findings for an enlistment task, the analysis of the pilot test data indicated that the three components predicted reenlistment intent in the following rank order: affect, attitudinal, and normative. The results also suggest that the Army has available tools for influencing these reenlistment decisions that are much more varied than the limited set of mainly economic factors that are now predominant in these programs. Specifically, the affective component dominated the economic variables in predicting reenlistment intent for this limited sample of soldiers, and may be an important reenlistment program and policy lever in the future. Keywords: Military personnel, Retention.
Book Synopsis Modeling Factors Affecting Recruits' Attrition for the U.S. Army Recruiting Command by : Thomas H. Rahe
Download or read book Modeling Factors Affecting Recruits' Attrition for the U.S. Army Recruiting Command written by Thomas H. Rahe and published by . This book was released on 1999 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Validating Future Force Performance Measures (Army Class) by :
Download or read book Validating Future Force Performance Measures (Army Class) written by and published by . This book was released on 2009 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Army needs the best personnel to meet the emerging demands of the 21st century. Accordingly, the Army is seeking recommendations on new experimental predictor measures that could enhance entry-level Soldier selection and classification decisions, in particular, measures of non-cognitive attributes (e.g., interests, values, temperament). The U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) is conducting a longitudinal criterion-related validation research effort to collect data to inform these recommendations. Data on experimental predictors were collected from about 11,000 Soldiers. Training criterion data were collected for differing subsets of the predictor sample in the first of three planned criterion measurement points. Soldiers were drawn from two samples: (a) job-specific samples targeting six entry-level Military Occupational Specialties (MOS) and (b) an Army-wide sample with no MOS-specific requirements. In the analyses reported here, the value of the experimental predictor measures to enhance new Soldier selection was examined. Overall, many of the experimental predictors significantly incremented the Armed Forces Qualification Test (AFQT) in predicting Soldier performance and retention during training. In addition, the experimental predictors generally exhibited smaller subgroup mean differences (by gender, race, and ethnicity) than the AFQT.
Book Synopsis Determining Characteristic Groups to Predict Army Attrition by : Samuel Buttrey
Download or read book Determining Characteristic Groups to Predict Army Attrition written by Samuel Buttrey and published by . This book was released on 1999 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Office of the Deputy Chief of Staff, Personnel (ODCSPER), is charged with managing the Army's military strength levels and forecasting future strength levels for planning purposes. ODCSPER is reformulating its Enlisted Loss Inventory Model (ELIM), which projects losses of first-term enlisted personnel. These projections in turn are passed to a program which is designed to maintain the Army's strength as closely as possible to prescribed levels. These projections are based on characteristic groups, a set of sub-groups of recruits who are similar in terms of sex, education level, term of service and mental category; the presumption has been that attrition rates ought to be different between groups. However in recent years ELIM projections have been unsatisfactory. This study used Classification and Regression Tree methodology (CART) to generate improved c-groups for predicting not only first-term attrition but also early-term behavior and re-enlistment. The most important variables by which to create these groups turn out to be race and gender. Generally white women have the lowest term completion and re-enlistment rates; those for non-white women and white men are similar; and those for non-white men are the highest.
Book Synopsis Assessing Fitness for Military Enlistment by : National Research Council
Download or read book Assessing Fitness for Military Enlistment written by National Research Council and published by National Academies Press. This book was released on 2006-02-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: The U.S. Department of Defense (DoD) faces short-term and long-term challenges in selecting and recruiting an enlisted force to meet personnel requirements associated with diverse and changing missions. The DoD has established standards for aptitudes/abilities, medical conditions, and physical fitness to be used in selecting recruits who are most likely to succeed in their jobs and complete the first term of service (generally 36 months). In 1999, the Committee on the Youth Population and Military Recruitment was established by the National Research Council (NRC) in response to a request from the DoD. One focus of the committee's work was to examine trends in the youth population relative to the needs of the military and the standards used to screen applicants to meet these needs. When the committee began its work in 1999, the Army, the Navy, and the Air Force had recently experienced recruiting shortfalls. By the early 2000s, all the Services were meeting their goals; however, in the first half of calendar year 2005, both the Army and the Marine Corps experienced recruiting difficulties and, in some months, shortfalls. When recruiting goals are not being met, scientific guidance is needed to inform policy decisions regarding the advisability of lowering standards and the impact of any change on training time and cost, job performance, attrition, and the health of the force. Assessing Fitness for Military Enlistment examines the current physical, medical, and mental health standards for military enlistment in light of (1) trends in the physical condition of the youth population; (2) medical advances for treating certain conditions, as well as knowledge of the typical course of chronic conditions as young people reach adulthood; (3) the role of basic training in physical conditioning; (4) the physical demands and working conditions of various jobs in today's military services; and (5) the measures that are used by the Services to characterize an individual's physical condition. The focus is on the enlistment of 18- to 24-year-olds and their first term of service.
Book Synopsis Cash Incentives and Military Enlistment, Attrition, and Reenlistment by : Beth J. Asch
Download or read book Cash Incentives and Military Enlistment, Attrition, and Reenlistment written by Beth J. Asch and published by RAND Corporation. This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
