Author : Seymour Sudman
Publisher :
Page : 262 pages
File Size : 27,46 MB
Release : 1976
Category : Mathematics
ISBN :
Discusses numerous sampling methods with emphasis on the less expensive techniques.
Author : Edward Blair
Publisher : SAGE Publications
Page : 191 pages
File Size : 26,87 MB
Release : 2014-12-02
Category : Social Science
ISBN : 1483346919
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.
Author : Gary T. Henry
Publisher : SAGE Publications
Page : 150 pages
File Size : 17,58 MB
Release : 1990-08-01
Category : Social Science
ISBN : 1506320341
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.
Author : Rens van de Schoot
Publisher : Routledge
Page : 270 pages
File Size : 45,60 MB
Release : 2020-02-13
Category : Psychology
ISBN : 1000760944
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.
Author : Peter Tryfos
Publisher : Wiley
Page : 456 pages
File Size : 42,50 MB
Release : 1996-02-16
Category : Science
ISBN : 9780471047278
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.
Author : John J. Benedetto
Publisher : Springer Science & Business Media
Page : 423 pages
File Size : 34,83 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1461201438
A state-of-the-art edited survey covering all aspects of sampling theory. Theory, methods and applications are discussed in authoritative expositions ranging from multi-dimensional signal analysis to wavelet transforms. The book is an essential up-to-date resource.
Author : Johnnie Daniel
Publisher : SAGE Publications
Page : 321 pages
File Size : 24,62 MB
Release : 2011-04-25
Category : Social Science
ISBN : 145222305X
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.
Author : S. T. Buckland
Publisher : Springer
Page : 285 pages
File Size : 19,77 MB
Release : 2015-08-08
Category : Medical
ISBN : 3319192191
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.
Author : Patricia L. Smith
Publisher : SIAM
Page : 115 pages
File Size : 48,85 MB
Release : 2000-01-01
Category : Mathematics
ISBN : 9780898718478
How does a marble manufacturer know that the color will be consistent throughout the products being made? How can you tell if liquid at the bottom of a container is the same consistency as at the top? How does a pellet manufacturer know if the pellets are consistently the same size? How does a chemical manufacturer know if the percent purity in a sample is representative of the whole batch? These and similar questions are answered in A Primer for Sampling Solids, Liquids, and Gases: Based on the Seven Sampling Errors of Pierre Gy. Statisticians are well trained in sampling techniques if the sample is well defined. Examples of such samples include industrial parts in manufacturing, invoices in business processes, and people in surveys. However, what if the sampling unit isn't well defined? What if you are sampling bulk material such as a pile of coal? Author Patricia L. Smith illustrates what to look for in sampling devices and procedures to obtain correct samples from bulk materials. She gives sampling guidelines that can be applied immediately and shows how to analyze protocols to uncover sampling problems. Smith presents the ideas of Pierre Gy in lay terms so that his concepts and principles can be easily grasped and applied. She conveys Gy's intuitive meaning while preserving his original ideas. Synonyms have been used for some technical terms to avoid confusion.
Author : National Research Council
Publisher : National Academies Press
Page : 191 pages
File Size : 39,41 MB
Release : 2013-09-03
Category : Mathematics
ISBN : 0309287812
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
