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Annotation Sampling methods are integral to the design of surveys and experiments, to the validity of results, and thus to the study of statistics, social science, and a variety other disciplines that use statistical data. Yet most of the available texts on the subject are either quite advanced and theoretical or too applied, descriptive, and lacking statistical results. This title offers a balanced, practical treatment of the techniques and applications of the commonly used procedures for sampling from finite populations. It keeps mathematics to a minimum, but does not avoid them entirely: it features the principle results within the text but provides their derivations in the Appendices to each chapter.
In this book, the authors cover the basic methods and advances within distance sampling that are most valuable to practitioners and in ecology more broadly. This is the fourth book dedicated to distance sampling. In the decade since the last book published, there have been a number of new developments. The intervening years have also shown which advances are of most use. This self-contained book covers topics from the previous publications, while also including recent developments in method, software and application. Distance sampling refers to a suite of methods, including line and point transect sampling, in which animal density or abundance is estimated from a sample of distances to detected individuals. The book illustrates these methods through case studies; data sets and computer code are supplied to readers through the book’s accompanying website. Some of the case studies use the software Distance, while others use R code. The book is in three parts. The first part addresses basic methods, the design of surveys, distance sampling experiments, field methods and data issues. The second part develops a range of modelling approaches for distance sampling data. The third part describes variations in the basic method; discusses special issues that arise when sampling different taxa (songbirds, seabirds, cetaceans, primates, ungulates, butterflies, and plants); considers advances to deal with failures of the key assumptions; and provides a check-list for those conducting surveys.
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.
Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling. The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students. - Covers a wide spectrum of topics on survey sampling and statistics - Serves as an ideal text for graduate students and researchers in survey sampling theory and applications - Contains material on recent developments in survey sampling not covered in other books - Illustrates theories using numerical examples and exercises
This book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.
Sampling is fundamental to nearly every study in the social and policy sciences, yet clear, concise guidance for practitioners and graduate students has been difficult to find. Practical Sampling provides guidance for researchers dealing with the everyday problems of sampling. Using the practical design approach Henry integrates sampling into the overall research design and explains the interrelationships between research design and sampling choices. He lays out alternatives and implications of the choices using four detailed examples to illustrate the alternatives selected and the trade-offs made by applied researchers. The author uses a narrative, conceptual approach throughout the book; mathematical presentations are limited to necessary formulas; and calculations are kept to the absolute minimum, making it an easily approachable book for any researcher, student or professional across the social sciences.
This book is a multi-purpose document. It can be used as a text by teachers, as a reference manual by researchers, and as a practical guide by statisticians. It covers 1165 references from different research journals through almost 1900 citations across 1194 pages, a large number of complete proofs of theorems, important results such as corollaries, and 324 unsolved exercises from several research papers. It includes 159 solved, data-based, real life numerical examples in disciplines such as Agriculture, Demography, Social Science, Applied Economics, Engineering, Medicine, and Survey Sampling. These solved examples are very useful for an understanding of the applications of advanced sampling theory in our daily life and in diverse fields of science. An additional 173 unsolved practical problems are given at the end of the chapters. University and college professors may find these useful when assigning exercises to students. Each exercise gives exposure to several complete research papers for researchers/students.
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.
Comprehensive Sampling and Sample Preparation is a complete treatment of the theory and methodology of sampling in all physical phases and the theory of sample preparation for all major extraction techniques. It is the perfect starting point for researchers and students to design and implement their experiments and support those experiments with quality-reviewed background information. In its four volumes, fundamentals of sampling and sample preparation are reinforced through broad and detailed sections dealing with Biological and Medical, Environmental and Forensic, and Food and Beverage applications. The contributions are organized to reflect the way in which analytical chemists approach a problem. It is intended for a broad audience of analytical chemists, both educators and practitioners of the art and can assist in the preparation of courses as well in the selection of sampling and sample preparation techniques to address the challenges at hand. Above all, it is designed to be helpful in learning more about these topics, as well as to encourage an interest in sampling and sample preparation by outlining the present practice of the technology and by indicating research opportunities. Sampling and Sample preparation is a large and well-defined field in Analytical Chemistry, relevant for many application areas such as medicine, environmental science, biochemistry, pharmacology, geology, and food science. This work covers all these aspects and will be extremely useful to researchers and students, who can use it as a starting point to design and implement their experiments and for quality-reviewed background information There are limited resources that Educators can use to effectively teach the fundamental aspects of modern sample preparation technology. Comprehensive Sampling and Sample Preparation addresses this need, but focuses on the common principles of new developments in extraction technologies rather than the differences between techniques thus facilitating a more thorough understanding Provides a complete overview of the field. Not only will help to save time, it will also help to make correct assessments and avoid costly mistakes in sampling in the process Sample and sample preparation are integral parts of the analytical process but are often less considered and sometimes even completely disregarded in the available literature. To fill this gap, leading scientists have contributed 130 chapters, organized in 4 volumes, covering all modern aspects of sampling and liquid, solid phase and membrane extractions, as well as the challenges associated with different types of matrices in relevant application areas
Reviews sampling methods used in surveys: simple random sampling, systematic sampling, stratification, cluster and multi-stage sampling, sampling with probability proportional to size, two-phase sampling, replicated sampling, panel designs, and non-probability sampling. Kalton discusses issues of practical implementation, including frame problems and non-response, and gives examples of sample designs for a national face-to-face interview survey and for a telephone survey. He also treats the use of weights in survey analysis, the computation of sampling errors with complex sampling designs, and the determination of sample size.