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Discover the latest developments and current practices in survey sampling Survey sampling is an important component of research in many fields, and as the importance of survey sampling continues to grow, sophisticated sampling techniques that are both economical and scientifically reliable are essential to planning statistical research and the design of experiments. Sampling Statistics presents estimation techniques and sampling concepts to facilitate the application of model-based procedures to survey samples. The book begins with an introduction to standard probability sampling concepts, which provides the foundation for studying samples selected from a finite population. The development of the theory of complex sampling methods is detailed, and subsequent chapters explore the construction of estimators, sample design, replication variance estimation, and procedures such as nonresponse adjustment and small area estimation where models play a key role. A final chapter covers analytic studies in which survey data are used for the estimation of parameters for a subject matter model. The author draws upon his extensive experience with survey samples in the book's numerous examples. Both the production of "general use" databases and the analytic study of a limited number of characteristics are discussed. Exercises at the end of each chapter allow readers to test their comprehension of the presented concepts and techniques, and the references provide further resources for study. Sampling Statistics is an ideal book for courses in survey sampling at the graduate level. It is also a valuable reference for practicing statisticians who analyze survey data or are involved in the design of sample surveys.
In a clear, readable style, this timely volume provides the information needed to design and execute audit samples for the appraisal, evaluation, and validation of financial and accounting data. With this material, auditors and analysts can accomplish such required functions as evaluating program performance and determining the reliability of financial records and statements more quickly and accurately. Designed as a practical, reliable, on-the-job reference -- with a minimum of statistical theory and formulas -- Practical Statistical Sampling for Auditors blends statistical sampling with other acceptable auditing techniques ... emphasizes the significance of error analysis and audit appraisal ... examines audit and statistical stratification ... advocates the use of minimum samples ... emphasizes the use of replication to support audit decisions ... and outlines the advantages and limitations of various audit sampling schemes. Further, Practical Statistical Sampling for Auditors offers such handy features as chapter summaries, computer printouts, real-life examples, a time-saving table of minimum sample sizes, an easy-to-use glossary, a detailed index, and numerous literature citations, helping auditors; accountants; program, budget, and management analysts; comptrollers; and financial managers to apply statistical methods in consonance with Auditing Standards. Book jacket.
Acceptance Sampling in Quality Control, Third Edition presents the state of the art in the methodology of sampling while integrating both theory and best practices. It discusses various standards, including those from the ISO, MIL-STD and ASTM and explores how to set quality levels. The book also includes problems at the end of each chapter with solutions. This edition improves upon the previous editions especially in the areas of software applications and compliance sampling plans. New to the Third Edition: Numerous Microsoft Excel templates to address sampling plans are used. Commercial software applications are discussed at the end of many chapters. Discussion of quick switching systems has been expanded to account for the considerable recent activity in this area. Added discussion of zero acceptance number chained quick switching systems.
This book is aimed at those with responsibilities for audit, risk and control - auditors of course - but also finance directors, audit committee members, project accountants, systems designers and other professionals too. Working under pressure, these people often need to take account of theory and best practice but strike a balance with the practical demands of their workplace. This book’s practical emphasis on meeting the ever-changing needs of clients and auditees will benefit a wide audience by helping readers to: ¢ select a suitable, practical sampling approach ¢ appreciate the statistical implications ¢ evaluate the results of audit testing ¢ take account of risk and control evaluation in targeting valuable audit resources. It does this by laying out the principles behind a concept and then grounding them in ’real life’ cases for the reader to work through. These are accompanied by suggested solutions which, while not definitive answers, do provide valuable advice and guidance. Finally the range of appendices, including a complete copy of the statement of auditing standards, SAS 430, make this book an essential resource for everyone concerned about modern auditing.
This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques.
Now available in paperback, this book provides a comprehensive account of survey sampling theory and methodology suitable for students and researchers across a variety of disciplines. It shows how statistical modeling is a vital component of the sampling process and in the choice of estimation technique. The first textbook that systematically extends traditional sampling theory with the aid of a modern model assisted outlook. Covers classical topics as well as areas where significant new developments have taken place.
Provides--in an organized and compact source--a comprehensive guide to the principles of sampling design and statistical analysis methods. Reviews the principles of inference, sampling and statistical design, and hypothesis formulation, all with special reference to ecological data. Includes an impact study illustrating the principles presented. Contains a key to five broad categories of environmental studies--as well as examples and examines specific topics that apply to any environmental study. Provides a comprehensive bibliography which is cross-referenced to the text and keyed to a specific topic code (types of methods and environments studied).
The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice. These related aspects of survey methodology rarely appear together under a single connected roof, making this book a unique combination of materials for teaching, research and practice in survey sampling. Basic knowledge of probability theory and statistical inference is assumed, but no prior exposure to survey sampling is required. The first part focuses on the design-based approach to finite population sampling. It contains a rigorous coverage of basic sampling designs, related estimation theory, model-based prediction approach, and model-assisted estimation methods. The second part stems from original research conducted by the authors as well as important methodological advances in the field during the past three decades. Topics include calibration weighting methods, regression analysis and survey weighted estimating equation (EE) theory, longitudinal surveys and generalized estimating equations (GEE) analysis, variance estimation and resampling techniques, empirical likelihood methods for complex surveys, handling missing data and non-response, and Bayesian inference for survey data. The third part provides guidance and tools on practical aspects of large-scale surveys, such as training and quality control, frame construction, choices of survey designs, strategies for reducing non-response, and weight calculation. These procedures are illustrated through real-world surveys. Several specialized topics are also discussed in detail, including household surveys, telephone and web surveys, natural resource inventory surveys, adaptive and network surveys, dual-frame and multiple frame surveys, and analysis of non-probability survey samples. This book is a self-contained introduction to survey sampling that provides a strong theoretical base with coverage of current research trends and pragmatic guidance and tools for conducting surveys.
This edition is a reprint of the second edition published by Cengage Learning, Inc. Reprinted with permission. What is the unemployment rate? How many adults have high blood pressure? What is the total area of land planted with soybeans? Sampling: Design and Analysis tells you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches sampling using real data sets from social sciences, public opinion research, medicine, public health, economics, agriculture, ecology, and other fields. The book is accessible to students from a wide range of statistical backgrounds. By appropriate choice of sections, it can be used for a graduate class for statistics students or for a class with students from business, sociology, psychology, or biology. Readers should be familiar with concepts from an introductory statistics class including linear regression; optional sections contain the statistical theory, for readers who have studied mathematical statistics. Distinctive features include: More than 450 exercises. In each chapter, Introductory Exercises develop skills, Working with Data Exercises give practice with data from surveys, Working with Theory Exercises allow students to investigate statistical properties of estimators, and Projects and Activities Exercises integrate concepts. A solutions manual is available. An emphasis on survey design. Coverage of simple random, stratified, and cluster sampling; ratio estimation; constructing survey weights; jackknife and bootstrap; nonresponse; chi-squared tests and regression analysis. Graphing data from surveys. Computer code using SAS® software. Online supplements containing data sets, computer programs, and additional material. Sharon Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She has been recognized as Fellow of the American Statistical Association, elected member of the International Statistical Institute, and recipient of the Gertrude M. Cox Statistics Award and the Deming Lecturer Award. Formerly Dean’s Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a freelance statistical consultant and writer. Visit her website at www.sharonlohr.com.