Survey Measurement And Process Quality

Comprises 33 papers on measurement error and process quality in surveys. Covers questionnaire design, data collection, post survey processing and operations, quality assessment and control, and error effects on estimation, analyses and interpretation.

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

Enhance the quality of survey results by recognizing and reducing measurement errors. Margins of Error: A Study of Reliability in Survey Measurement demonstrates how and hwy identifying the presence and extent of measurement errors in survey data is essential for improving the overall collection and analysis of the data. The author outlines the consequences of ignoring survey measurement errors and also discusses ways to detect and estimate the impact of these errors. This book also provides recommendations of improving the quality of survey data. Logically organized and clearly written, this book: Deconstructs the data gathering process into six main elements of the response process: question adequacy, comprehension, accessibility, retrieval, motivation, and communication Provides an exhaustive review of valuable reliability estimation techniques that can be applied to survey data Identifies the types of questions and interviewer practices that are essential to the collection of reliable data Addresses hypotheses regarding which survey questions, sources of information, and questionnaire formats produce the most reliable data In conjunction with research data gathered on nearly 500 survey measures and the application of an empirical approach grounded in classical measurement theory, this book discusses the sources of measurement error and provides the tools necessary for improving survey data collection methods. Margins of Error enables statisticians and researchers in the fields of public opinion and survey research to design studies that can detect, estimate, and reduce measurement errors that may have previously gone undetected. This book also serves as a supplemental textbook for both undergraduate and graduate survey methodology courses.

Measurement error and nonresponse represent real threats to the quality of survey data. Unit and item nonresponse, survey modes, question wordings and response formats are potential sources of error. In addition, survey methods are developing further to keep step with a continuously changing world. The identification and proper handling of threats to data quality requires thus continued efforts and research "on" survey methods. The volume contributes to this demand recent developments and research findings. Its 12 chapters address issues of data quality with respect to survey questions and different kinds of surveys and report developments in the field of missing data handling. Key topics include motivated misreporting, audio-recording of open-ended questions, framing effects, MTMM modeling, web, mobile web and mixed-mode research, experience sampling, estimates of change, missing data handling and multiple imputation. Contributions come from Germany, the Netherlands, Spain, the United Kingdom and the United States.

These Guidelines represent the first attempt to provide international recommendations on collecting, publishing, and analysing subjective well-being data.

Focusing on the use of technology in survey research, this book integrates both theory and application and covers important elements of survey research including survey design, implementation and continuing data management.

Praise for the First Edition: "The book makes a valuable contribution by synthesizing current research and identifying areas for future investigation for each aspect of the survey process." —Journal of the American Statistical Association "Overall, the high quality of the text material is matched by the quality of writing . . ." —Public Opinion Quarterly ". . . it should find an audience everywhere surveys are being conducted." —Technometrics This new edition of Survey Methodology continues to provide a state-of-the-science presentation of essential survey methodology topics and techniques. The volume's six world-renowned authors have updated this Second Edition to present newly emerging approaches to survey research and provide more comprehensive coverage of the major considerations in designing and conducting a sample survey. Key topics in survey methodology are clearly explained in the book's chapters, with coverage including sampling frame evaluation, sample design, development of questionnaires, evaluation of questions, alternative modes of data collection, interviewing, nonresponse, post-collection processing of survey data, and practices for maintaining scientific integrity. Acknowledging the growing advances in research and technology, the Second Edition features: Updated explanations of sampling frame issues for mobile telephone and web surveys New scientific insight on the relationship between nonresponse rates and nonresponse errors Restructured discussion of ethical issues in survey research, emphasizing the growing research results on privacy, informed consent, and confidentiality issues The latest research findings on effective questionnaire development techniques The addition of 50% more exercises at the end of each chapter, illustrating basic principles of survey design An expanded FAQ chapter that addresses the concerns that accompany newly established methods Providing valuable and informative perspectives on the most modern methods in the field, Survey Methodology, Second Edition is an ideal book for survey research courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing survey methodologists and any professional who employs survey research methods.

Combining theoretical, methodological, and practical aspects, Latent Class Analysis of Survey Error successfully guides readers through the accurate interpretation of survey results for quality evaluation and improvement. This book is a comprehensive resource on the key statistical tools and techniques employed during the modeling and estimation of classification errors, featuring a special focus on both latent class analysis (LCA) techniques and models for categorical data from complex sample surveys. Drawing from his extensive experience in the field of survey methodology, the author examines early models for survey measurement error and identifies their similarities and differences as well as their strengths and weaknesses. Subsequent chapters treat topics related to modeling, estimating, and reducing errors in surveys, including: Measurement error modeling forcategorical data The Hui-Walter model and othermethods for two indicators The EM algorithm and its role in latentclass model parameter estimation Latent class models for three ormore indicators Techniques for interpretation of modelparameter estimates Advanced topics in LCA, including sparse data, boundary values, unidentifiability, and local maxima Special considerations for analyzing datafrom clustered and unequal probability samples with nonresponse The current state of LCA and MLCA (multilevel latent class analysis), and an insightful discussion on areas for further research Throughout the book, more than 100 real-world examples describe the presented methods in detail, and readers are guided through the use of lEM software to replicate the presented analyses. Appendices supply a primer on categorical data analysis, and a related Web site houses the lEM software. Extensively class-tested to ensure an accessible presentation, Latent Class Analysis of Survey Error is an excellent book for courses on measurement error and survey methodology at the graduate level. The book also serves as a valuable reference for researchers and practitioners working in business, government, and the social sciences who develop, implement, or evaluate surveys.

Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets This book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error. This book: • Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE • Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects • Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors • Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods.