Suchergebnisse

"Sampling provides an up-to-date treatment of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations ... Organized into six sections, the book covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio and regression estimation; sufficient data, model, and design in practical sampling; useful designs such as stratified, cluster and systematic, multistage, double and network sampling; detectability methods for elusive populations; spatial sampling; and adaptive sampling designs."--Publisher

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Author Bio -- Abbreviations -- Symbols -- CHAPTER 1: General introduction -- 1.1. SAMPLING FROM FINITE POPULATIONS -- 1.2. GRAPH, MOTIF, GRAPH PARAMETER -- 1.3. OBSERVATION PROCEDURE -- 1.4. SAMPLE GRAPH, SAMPLING METHOD AND SAMPLING STRATEGY -- BIBLIOGRAPHIC NOTES -- CHAPTER 2: Bipartite incidence graph sampling and weighting -- 2.1. BIPARTITE INCIDENCE GRAPH SAMPLING -- 2.2. INCIDENCE WEIGHTING ESTIMATOR -- 2.3. RAO-BLACKWELLISATION -- 2.4. ILLUSTRATIONS -- BIBLIOGRAPHIC NOTES -- CHAPTER 3: Strategy BIGS-IWE -- 3.1. APPLICABILITY -- 3.2. NETWORK SAMPLING -- 3.3. LINE-INTERCEPT SAMPLING -- 3.4. SAMPLING FROM RELATIONAL DATABASES -- BIBLIOGRAPHIC NOTES -- CHAPTER 4: Adaptive cluster sampling -- 4.1. SPATIAL ACS -- 4.2. EPIDEMIC PREVALENCE ESTIMATION -- 4.3. ACS DESIGNS OVER TIME -- BIBLIOGRAPHIC NOTES -- CHAPTER 5: Snowball sampling -- 5.1. T-WAVE SNOWBALL SAMPLING -- 5.2. STRATEGIES FOR TSBS -- 5.3. ILLUSTRATIONS -- BIBLIOGRAPHIC NOTES -- CHAPTER 6: Targeted random walk sampling -- 6.1. RANDOM WALK IN GRAPHS -- 6.2. TARGETED RANDOM WALK -- 6.3. STRATEGY FOR TRW SAMPLING -- 6.4. ILLUSTRATIONS -- BIBLIOGRAPHIC NOTES -- Bibliography -- Index.

"Over the last few decades, important progresses in the methods of sampling have been achieved. This book draws up an inventory of new methods that can be useful for selecting samples. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods. This book is aimed at experienced statisticians who are familiar with the theory of survey sampling."--Jacket

"Sampling Essentials: A Practical Guide provides comprehensive and detailed guidelines on sampling in social and behavioral research. Broad coverage of key topics, including techniques for conducting online surveys and determining sample size, makes this book an authoritative reference for readers interested in survey methodology or research design. And, the author's non-technical approach is ideal for novice researchers. This book could easily supplement traditional research methods texts, in which sampling is often only covered in one chapter"--

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.

Respondent driven sampling (RDS) is a network sampling technique typically employed for hard-to-reach populations (e.g. drug users, men who have sex with men, people with HIV). Similar to snowball sampling, initial seed respondents recruit additional respondents from their network of friends. The recruiting process repeats iteratively, thereby forming long referral chains. Unlike in snowball sampling, it is crucial to obtain estimates of respondents' personal network size (i.e., number of acquaintances in the target population) and information about who recruited whom. Markov chain theory makes it possible to derive population estimates and sampling weights. We introduce a new Stata program for RDS and illustrate its use.