David de Vaus' classic text Surveys in Social Research provides clear advice on how to plan, conduct and analyse social surveys. It emphasises the links between theory and research, the logic and interpretation of statistics and the practices of social research. This sixth edition has been completely revised and updated, and contains new examples, data and extensive lists of web resources. As well as explaining how to conduct good surveys, de Vaus shows how to become a critical consumer of research. He argues that the logic of surveys and statistics is simply an extension of the logic we use in everyday life; analysis, however, requires creativity and imagination rather than the application of sterile mechanical procedures. The prime goal of research should be to gain accurate understanding and, as a researcher, use methods and techniques which enhance understanding. De Vaus advocates researchers use the method, rather than letting the method use you. Surveys in Social Research is essential reading for students and researchers working with surveys. It assumes no background in statistical analysis, and gives you the tools you need to come to grips with this often challenging field of work.

Human resources is rapidly evolving into a data-rich field but with big data comes big decisions. The best companies understand how to use data to make strategic workforce decisions and gain significant competitive advantage. Human Resource Management: People, Data, and Analytics by Talya Bauer, Berrin Erdogan, David Caughlin, and Donald Truxillo introduces students to the fundamentals of talent management with integrated coverage of data analytics and how they can be used to inform and support decisions about people in an organization. Features tied to SHRM competencies and data exercises give readers hands-on opportunities to practice the analytical and decision-making skills they need to excel in today’s job market. Engaging examples illustrate key HRM concepts and theories, which brings many traditional HRM topics concepts to life. Whether your students are future managers or future HR professionals, they will learn best practices for managing talent across the lifecycle in the changing workplace.

On May 3, 2012, the US Food and Drug Administration announced a public workshop to receive input on “survey methodologies and instruments that can be used to evaluate patients’ and health care providers’ knowledge about the risks of drugs marketed with an approved REMS [risk evaluation and mitigation strategy].” The FDA intended to use this input to help develop guidance to industry regarding best practices for such research. In the announcement to the meeting, entitled “Risk Evaluation and Mitigation Strategy (REMS) Assessments: Social Science Methodologies to Assess Goals Related to Knowledge: Public Workshop,” the FDA provided an issue paper summarizing experience with prior REMS assessment surveys and posing a series of questions for which input was requested from the workshop panel and public. RTI Health Solutions (RTI-HS) participated in the workshop by serving on the invited panel (Alicia Gilsenan and Karol Krotki), presenting to the panel (Kelly Hollis, Sandy Lewis, and Laurie Zografos), and submitting written responses to the FDA to the questions posed in the issues paper accompanying the meeting announcement. This paper provides a brief background and presents the written responses that RTI-HS submitted to the FDA, with modest revisions to add context and clarity to the response for a wider readership. We also briefly discuss upcoming US and European steps in this field.

Survey Development: A Theory-Driven Mixed Methods Approach provides both an overview of standard methods and tools for developing and validating surveys and a conceptual basis for survey development that advocates establishing and testing of hypotheses pertaining to presumptions and score-interpretation and use inferences and mixing quantitative and qualitative methods. The book has 14 chapters which are divided into four parts. Part A includes six chapters that deal with theory and methodology. Part B has five chapters and it gets into the process of constructing the survey using both quantitative and qualitative methods. Part C comprises two chapters devoted to assessing the quality or psychometric properties (reliability and validity) of survey responses. Finally, the one chapter in Part D is an attempt to present a synopsis of what was covered in the previous chapters in regard to developing a survey with the TDMM framework for developing survey and conducting survey research. This provides a full process for survey development intended to yield results that can support valid interpretation and use of scores. Including detailed online resources, this book is suitable for graduate students who use or are responsible for interpretation of survey research and survey data as well as survey methodologists and practitioners who use surveys in their field.

Pediatric epidemiology differs substantially from general epidemiology especially when it comes to ethical, developmental and societal aspects. This unique book addresses biological considerations and ethical and legal questions in dealing with pediatric and adolescent populations. Classic topics, such as how to recruit representative samples, how to deal with confounding variables, and how to work with genetic information are the core areas of the book are also in focus. Last but not the least, this volume adds to the current understanding of global trends in occurrence, transmission, and control of epidemic pediatric diseases. This book not only serves as a textbook for epidemiologists, pediatricians, geneticists, and child and public health specialists but is also a key reference for those embarking on pediatric cohort studies and epidemiological studies involving the pediatric population.

An understanding of the basics, logic, and theory of statistics is essential for agricultural researchers for dealing with the interpretation of data. This volume presents some of the basic and necessary concepts of statistical tools, specifically as applied to the statistics of agriculture and allied fields. It covers basic statistics, design of experiments, sampling techniques, time series, inference outlines, forecasting models, data handling, and statistical software in an easy-to-understand manner that is aimed at students and researchers with little or no mathematical background. In the agriculture scenario, students and researchers face problems that can be addressed with statistical tools, planning of field experiments, collection of data, analysis, interpretation of the data, etc. In this book, statistical theories are discussed with the help of examples from real-life situations in agriculture and allied fields, followed by worked-out examples. Each chapter is followed by a number of problems and questions that will help readers gain confidence in solving those problems. The volume also provides an analysis of how data is important and introduces the reader to using statistical software such as MS Excel, SAS (Statistical Analysis System), JMP, Minitab, and R (from the R Foundation for Statistical Computing).