Peruse the history of survey research and the essential conceptsfor data quality. With an emphasis on total survey error, theauthors review principles and concepts in the field and examineimportant unresolved issues in survey methods. Spanning a range oftopics dealing with the quality of data collected through thesurvey process, they focus on such key issues as: * Major sources of survey error, examining the origins of eacherror source most successful methods for reducing errors from thosesources * Methods most often used in practice for evaluating the effectsof the source on total survey error * Implications of improving survey quality for organizationalmanagement and costs

Statistical Methods and the Improvement of Data Quality contains the proceedings of The Small Conference on the Improvement of the Quality of Data Collected by Data Collection Systems, held on November 11-12, 1982, in Oak Ridge, Tennessee. The conference provided a forum for discussing the use of statistical methods to improve data quality, with emphasis on the problems of data collection systems and how to handle them using state-of-the-art techniques. Comprised of 16 chapters, this volume begins with an overview of some of the limitations of surveys, followed by an annotated bibliography on frames from which the probability sample is selected. The reader is then introduced to sample designs and methods for collecting data over space and time; response effects to behavior and attitude questions; and how to develop and use error profiles. Subsequent chapters focus on principles and methods for handling outliers in data sets; influence functions, outlier detection, and data editing; and application of pattern recognition techniques to data analysis. The use of exploratory data analysis as an aid in modeling and statistical forecasting is also described. This monograph is likely to be of primary benefit to students taking a general course in survey sampling techniques, and to individuals and groups who deal with large data collection systems and are constantly seeking ways to improve the overall quality of their data.

This state-of-the-art volume provides insight into the recent developments in survey research. It covers topics like: survey modes and response effects, bio indicators and paradata, interviewer and survey error, mixed-mode panels, sensitive questions, conducting web surveys and access panels, coping with non-response, and handling missing data. The authors are leading scientists in the field, and discuss the latest methods and challenges with respect to these topics. Each of the book’s eight parts starts with a brief chapter that provides an historical context along with an overview of today’s most critical survey methods. Chapters in the sections focus on research applications in practice and discuss results from field studies. As such, the book will help researchers design surveys according to today’s best practices. The book’s website www.survey-methodology.de provides additional information, statistical analyses, tables and figures. An indispensable reference for practicing researchers and methodologists or any professional who uses surveys in their work, this book also serves as a supplement for graduate or upper level-undergraduate courses on survey methods taught in psychology, sociology, education, economics, and business. Although the book focuses on European findings, all of the research is discussed with reference to the entire survey-methodology area, including the US. As such, the insights in this book will apply to surveys conducted around the world.

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This is a book for any researcher using any kind of survey data. It introduces the latest methods of assessing the quality and validity of such data by providing new ways of interpreting variation and measuring error. By practically and accessibly demonstrating these techniques, especially those derived from Multiple Correspondence Analysis, the authors develop screening procedures to search for variation in observed responses that do not correspond with actual differences between respondents. Using well-known international data sets, the authors exemplify how to detect all manner of non-substantive variation having sources such as a variety of response styles including acquiescence, respondents′ failure to understand questions, inadequate field work standards, interview fatigue, and even the manufacture of (partly) faked interviews.