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.

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.

Second Edition offers a comprehensive presentation of scientific sampling principles and shows how to design a sample survey and analyze the resulting data. Demonstrates the validity of theorems and statements without resorting to detailed proofs.

Practical Sampling Techniques for Infrared Analysis provides a single-source guide to sample handling for routine analysis in infrared spectroscopy using commercially available instrumentation and accessories. Following a review of infrared spectroscopic theory, chapters consider individual techniques such as transmission methodology (e.g., solution cells, KBr pellets), internal reflectance, diffuse reflectance, photoacoustic FT-IR, infrared microscopy, GC/FT-IR, and quantitative analysis. In addition, two chapters elaborate on both typical and unusual samples and problems encountered in industrial laboratories and the process by which a spectroscopist chooses the most effective technique. Various short courses on infrared analysis are also listed. Practical Sampling Techniques for Infrared Analysis will be an important guide for all professional analytical chemists and technicians.

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.

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.