Collecting, Managing and Assessing Data Using Sample Surveys

Stopher Peter

ISBN 10: 0521681871 / ISBN 13: 9780521681872

Published by Cambridge University Press, 2012

About this Item

pp. 560 82 Illus.

Seller Inventory # 5861420

Bibliographic Details

• Title: Collecting, Managing and Assessing Data Using Sample Surveys
• Publisher: Cambridge University Press
• Publication Date: 2012
• Language: English
• ISBN 10: 0521681871
• ISBN 13: 9780521681872
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Collecting, Managing, and Assessing Data Using Sample Surveys provides a thorough, step-by-step guide to the design and implementation of surveys. Beginning with a primer on basic statistics, the first half of the book takes readers on a comprehensive tour through the basics of survey design. Topics covered include the ethics of surveys, the design of survey procedures, the design of the survey instrument, how to write questions and how to draw representative samples. Having shown readers how to design surveys, the second half of the book discusses a number of issues surrounding their implementation, including repetitive surveys, the economics of surveys, web-based surveys, coding and data entry, data expansion and weighting, the issue of non-response, and the documenting and archiving of survey data. The book is an excellent introduction to the use of surveys for graduate students as well as a useful reference work for scholars and professionals.

About the Author

Peter Stopher is Professor of Transport Planning at the Institute of Transport and Logistics Studies at the University of Sydney. He has also been a professor at Northwestern University, Cornell University, McMaster University and Louisiana State University. Professor Stopher has developed a substantial reputation in the field of data collection, particularly for the support of travel forecasting and analysis. He pioneered the development of travel and activity diaries as a data-collection mechanism, and has written extensively on issues of sample design, data expansion, nonresponse biases and measurement issues.