Statistics for Real-Life Sample Surveys: Non-Simple-Random Samples And Weighted Data

Dorofeev, Sergey

Softcover

ISBN 10: 0521674654 ISBN 13: 9780521674652

Publisher: Cambridge University Press, 2006

Samples used in social and commercial surveys, especially of the general population, are usually less random (often by design) than many people using them realise. Unless it is understood, this 'non-randomness' can compromise the conclusions drawn from the data. This book introduces the challenges posed by less-than-perfect samples, giving background knowledge and practical guidance for those who have to deal with them. It explains why samples are, and sometimes should be, non-random in the first place; how to assess the degree of non-randomness; when correction by weighting is appropriate and how to apply it; and how the statistical treatment of these samples must be adapted. Extended data examples show the techniques at work. This is a book for practising researchers. It is a reference for the methods and formulae needed to deal with commonly encountered situations and, above all, a source of realistic and implementable solutions.

Product Description:

Statistics For Real-life Sample Surveys

Review:

"I think the authors have succeeded in their aims: this book will be valuable for analysts who are not survey sampling professionals or who have not been formally trained in the technical background, but who nevertheless find themselves having to analyze survey data."
D.J. Hand, Imperial College of Science, Technology and Medicine, ISI Short Book Reviews

"[The authors'] techniques provide practical, realistic and implementable solutions for many real situations where traditional frames or sampling designs are not available.... I believe that the book will interest private and official survey agencies and theoretical and applied statisticians, as well as teachers and students of statistics."
Mariano Ruiz Espejo, Universidad Nacional de Educacion a Distancia, Madrid

"... The book is accurate and well written."
M.G.M. Khan, The American Statistician