Public Law 92-297, passed in 1971, requires that air traffic control specialists (ATCSs) hired after May 16, 1972 retire at age 56. The underlying rationale was that as controllers aged, the cumulative effects of stress, fatigue (from shift work), and age-related cognitive changes created a safety risk (U.S. House of Representatives, 1971). This hypothesis has been considered in two recent studies of en route operational errors (OEs). The Center for Naval Analyses (CNA, 1995) found no relationship between controller age and OEs. Broach (1999) reported that the probability of involvement in an OE increased with age. The purpose of this study was to re-examine the hypothesis that controller age, controlling for experience, was related to OEs. En route OE records (3,054) were matched with non-supervisory ATCS staffing records for the period FY1997-2003. Poisson regression was used to model OE count as a function of the explanatory variables age and experience using the SPSS® version 11.5 General Loglinear (GENLOG) procedure.

Issues of personnel development in air traffic control (ATC) have become a major topic in aviation recruitment and training. Proper selection and training methods are needed in order to reach a high level of efficiency and reliability in ATC. Pilots were considered the most prominent group in aviation for a long time, but with the development of flight guidance technologies came a second operational occupation in aviation: the air traffic controller (ATCO). This volume provides a state-of-the-art overview of controller selection from an impressive collection of international specialists in research and practice. It will prove a valuable and key insight into the demands of air traffic controller selection through its comprehensive and enlightening examination of the current practice in the USA and Europe for the job-analysis requirements of future air traffic management (ATM) systems.

Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The second edition is a substantial update of the first based on the authors’ experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter—group testing and splines. The computing has been completely updated, with the "emmeans" package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated. Features: Requires no prior experience with R, and offers an introduction to the essential features and functions of R Includes numerous examples from medicine, psychology, sports, ecology, and many other areas Integrates extensive R code and output Graphically demonstrates many of the features and properties of various analysis methods Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study Supplemented by a website with data sets, code, and teaching videos Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.