Discusses numerous sampling methods with emphasis on the less expensive techniques.

Written for students and researchers who wish to understand the conceptual and practical aspects of sampling, this book is designed to be accessible without requiring advanced statistical training. It covers a wide range of topics, from the basics of sampling to special topics such as sampling rare populations, sampling organizational populations, and sampling visitors to a place. Using cases and examples to illustrate sampling principles and procedures, the book thoroughly covers the fundamentals of modern survey sampling, and addresses recent changes in the survey environment such as declining response rates, the rise of Internet surveys, the need to accommodate cell phones in telephone surveys, and emerging uses of social media and big data.

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.

Sampling is a fundamental topic in the engineering and physical sciences. This new edited book focuses on recent mathematical methods and theoretical developments, as well as some current central applications of the Classical Sampling Theorem. The Classical Sampling Theorem, which originated in the 19th century, is often associated with the names of Shannon, Kotelnikov, and Whittaker; and one of the features of this book is an English translation of the pioneering work in the 1930s by Kotelnikov, a Russian engineer. Following a technical overview and Kotelnikov's article, the book includes a wide and coherent range of mathematical ideas essential for modern sampling techniques. These ideas involve wavelets and frames, complex and abstract harmonic analysis, the Fast Fourier Transform (FFT), and special functions and eigenfunction expansions. Some of the applications addressed are tomography and medical imaging. Topics and features: • Relations between wavelet theory, the uncertainty principle, and sampling • Multidimensional non-uniform sampling theory and algorithms • The analysis of oscillatory behavior through sampling • Sampling techniques in deconvolution • The FFT for non-uniformly distributed data • Filter design and sampling • Sampling of noisy data for signal reconstruction • Finite dimensional models for oversampled filter banks • Sampling problems in MRI. Engineers and mathematicians working in wavelets, signal processing, and harmonic analysis, as well as scientists and engineers working on applications as varied as medical imaging and synthetic aperture radar, will find the book to be a modern and authoritative guide to sampling theory.

Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

An excellent introductory that uses simple numerical illustrations to provide an intuitive understanding of concepts and confirm major results. Describes various methods for sample selection and estimation including unequal probability sampling and the prediction approach. The accompanying disk contains data files for cases and two computer programs--one of which is an interactive DOS program designed to serve as a tutorial by assisting the implementation of complicated sampling formulas.

Contributions to Survey Sampling and Applied Statistics: Papers in Honor of H. O. Hartley covers the significant advances in survey sampling, modeling, and applied statistics. This book is organized into five parts encompassing 20 chapters. The opening part looks into some aspects of statistics, sampling, randomization, predictive estimation, and internal congruency. This part also considers the properties of variance estimation for a specified multiple frame survey design and some sampling designs involving unequal probabilities of selection and robust estimation of a finite population total. The next parts present the analysis and the theoretical and practical aspects of linear models, as well as the applications of time series analysis. These topics are followed by discussions of the testing for outliers in linear regression; the robustness of location estimators; and completeness comparisons among sample sequences. The closing part deals with the properties of norm estimators in regression and geometric programming. This part also provides tables of the normal conditioned on t-distribution. This book will prove useful to mathematicians and statisticians.

Written for students taking research methods courses, this text provides a thorough overview of sampling principles. The author gives detailed, nontechnical descriptions and guidelines with limited presentation of formulas to help students reach basic research decisions, such as whether to choose a census or a sample, as well as how to select sample size and sample type. Intended for students and researchers in the social and behavioral sciences, public health research, marketing research, and related areas, the text provides nonstatisticians with the concepts and techniques they need to do quality work and make good sampling choices.

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.

The main objective of this manual is to present the basic and standard concepts of sampling methods applied to fisheries science. In order to ensure sound fisheries research, it is essential to have reliable data from landing ports, fishery stocks and research surveys. A rational management of fishing resources can then be established to ensure a sustainable exploitation rate and responsible fisheries management, providing long-term benefits for all. This document provides an introduction to sampling theory and introduces the theory of the three worlds (population, sample and sampling), as well as a short revision of probability concepts. It also provides an overview of the simple random, random stratified, cluster and two-stage sampling methods. The expressions for estimating the mean and total of the populations, their sampling distributions, the expected values, the sampling variances and their estimates are included and justified for each of the sampling designs. The document also contains a case study of biological sampling from landing ports and exercises that should be used to further understanding of the objectives of sampling and its advantages for fishery resource studies.