Assessing The Quality Of Survey Data

This book introduces the latest methods for assessing the quality and validity of survey 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 show how to detect all manner of non-substantive variation from response styles including acquiescence, respondents' failure to understand questions, inadequate field work standards, interview fatigue, and even the manufacture of (partly) faked interviews.

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

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

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An in-depth look at current issues, new research findings, and interdisciplinary exchange in survey methodology and processing Survey Measurement and Process Quality extends the marriage of traditional survey issues and continuous quality improvement further than any other contemporary volume. It documents the current state of the field, reports new research findings, and promotes interdisciplinary exchange in questionnaire design, data collection, data processing, quality assessment, and effects of errors on estimation and analysis. The book's five sections discuss a broad range of issues and topics in each of five major areas, including * Questionnaire design--conceptualization, design of rating scales for effective measurement, self-administered questionnaires, and more * Data collection--new technology, interviewer effects, interview mode, children as respondents * Post-survey processing and operations--modeling of classification operations, coding based on such systems, editing, integrating processes * Quality assessment and control--total quality management, developing current best methods, service quality, quality efforts across organizations * Effects of misclassification on estimation, analysis, and interpretation--misclassification and other measurement errors, new variance estimators that account for measurement error, estimators of nonsampling error components in interview surveys Survey Measurement and Process Quality is an indispensable resource for survey practitioners and managers as well as an excellent supplemental text for undergraduate and graduate courses and special seminars.

Insightful observations on common question evaluation methods and best practices for data collection in survey research Featuring contributions from leading researchers and academicians in the field of survey research, Question Evaluation Methods: Contributing to the Science of Data Quality sheds light on question response error and introduces an interdisciplinary, cross-method approach that is essential for advancing knowledge about data quality and ensuring the credibility of conclusions drawn from surveys and censuses. Offering a variety of expert analyses of question evaluation methods, the book provides recommendations and best practices for researchers working with data in the health and social sciences. Based on a workshop held at the National Center for Health Statistics (NCHS), this book presents and compares various question evaluation methods that are used in modern-day data collection and analysis. Each section includes an introduction to a method by a leading authority in the field, followed by responses from other experts that outline related strengths, weaknesses, and underlying assumptions. Topics covered include: Behavior coding Cognitive interviewing Item response theory Latent class analysis Split-sample experiments Multitrait-multimethod experiments Field-based data methods A concluding discussion identifies common themes across the presented material and their relevance to the future of survey methods, data analysis, and the production of Federal statistics. Together, the methods presented in this book offer researchers various scientific approaches to evaluating survey quality to ensure that the responses to these questions result in reliable, high-quality data. Question Evaluation Methods is a valuable supplement for courses on questionnaire design, survey methods, and evaluation methods at the upper-undergraduate and graduate levels. it also serves as a reference for government statisticians, survey methodologists, and researchers and practitioners who carry out survey research in the areas of the social and health sciences.

This publication shows readers how to design and conduct a census or sample survey. It explains basic survey concepts and provides information on how to create efficient and high quality surveys. It is aimed at those involved in planning, conducting or managing a survey and at students of survey design courses. This book contains the following information: formulating the survey objectives and design a questionnaire; things to consider when designing a survey (choosing between a sample or a census, defining the survey population, choosing which survey frame to use, possible sources of survey error); determining the sample size, allocate the sample across strata and select the sample; appropriate uses of survey data and methods of point and variance estimation in data analysis; data dissemination and disclosure control; using administrative data, particularly during the design and estimation phases; choosing a collection method (self-enumeration, personal interview or telephone interview, computer-assisted versus paper-based questionnaires); organizing and conducting data collection operations; processing data (all data handling activities between collection and estimation) and using quality control and quality assurance measures to minimize and control errors during various survey steps; and planning and managing a survey. This publication also includes a case study that illustrates the steps in developing a household survey, using the methods and principles presented in the book.