Paul P. Biemer – författare

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 dataThe Hui-Walter model and othermethods for two indicatorsThe EM algorithm and its role in latentclass model parameter estimationLatent class models for three ormore indicatorsTechniques for interpretation of modelparameter estimatesAdvanced topics in LCA, including sparse data, boundary values, unidentifiability, and local maximaSpecial considerations for analyzing datafrom clustered and unequal probability samples with nonresponseThe current state of LCA and MLCA (multilevel latent class analysis), and an insightful discussion on areas for further researchThroughout 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.

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.

An in-depth look at current issues, new research findings, andinterdisciplinary exchange in survey methodology andprocessing Survey Measurement and Process Quality extends the marriage oftraditional survey issues and continuous quality improvementfurther than any other contemporary volume. It documents thecurrent state of the field, reports new research findings, andpromotes interdisciplinary exchange in questionnaire design, datacollection, data processing, quality assessment, and effects oferrors on estimation and analysis. The book's five sections discuss a broad range of issues and topicsin each of five major areas, including* Questionnaire design--conceptualization, design of rating scalesfor effective measurement, self-administered questionnaires, andmore* Data collection--new technology, interviewer effects, interviewmode, children as respondents* Post-survey processing and operations--modeling of classificationoperations, coding based on such systems, editing, integratingprocesses* Quality assessment and control--total quality management,developing current best methods, service quality, quality effortsacross organizations* Effects of misclassification on estimation, analysis, andinterpretation--misclassification and other measurement errors, newvariance estimators that account for measurement error, estimatorsof nonsampling error components in interview surveys Survey Measurement and Process Quality is an indispensable resourcefor survey practitioners and managers as well as an excellentsupplemental text for undergraduate and graduate courses andspecial seminars.

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

Noted survey experts present recent developments in telephone survey techniques from around the world, describing work in commercial settings, academic research, and governmental statistical agencies. There are reports from the United States, several European countries, and Australia on trends in coverage of household populations, effects due to mode of data collection, and the state of the art in technology. Also covered are choice of target population, sample design, questionnaire construction, interviewing techniques, measurement error issues, nonresponse characteristics, administrative issues, and the use of Computer Assisted Telephone Interviewing (CATI). Includes in-depth reviews of the literature.

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

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book will be an aid to survey statisticians and to research workers who must work with survey data."–Short Book Reviews, International Statistical InstituteMeasurement Errors in Surveys documents the current state of the field, reports new research findings, and promotes interdisciplinary exchanges in modeling, assessing, and reducing measurement errors in surveys. Providing a fundamental approach to measurement errors, the book features sections on the questionnaire, respondents and responses, interviewers and other means of data collection, the respondent-interviewer relationship, and the effects of measurement errors on estimation and data analysis.

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.

WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"This book will be an aid to survey statisticians and to research workers who must work with survey data." –Short Book Reviews, International Statistical Institute

Measurement Errors in Surveys documents the current state of the field, reports new research findings, and promotes interdisciplinary exchanges in modeling, assessing, and reducing measurement errors in surveys. Providing a fundamental approach to measurement errors, the book features sections on the questionnaire, respondents and responses, interviewers and other means of data collection, the respondent-interviewer relationship, and the effects of measurement errors on estimation and data analysis.

An in-depth look at current issues, new research findings, andinterdisciplinary exchange in survey methodology andprocessing Survey Measurement and Process Quality extends the marriage oftraditional survey issues and continuous quality improvementfurther than any other contemporary volume. It documents thecurrent state of the field, reports new research findings, andpromotes interdisciplinary exchange in questionnaire design, datacollection, data processing, quality assessment, and effects oferrors on estimation and analysis. The book''s five sections discuss a broad range of issues and topicsin each of five major areas, including * Questionnaire design--conceptualization, design of rating scalesfor effective measurement, self-administered questionnaires, andmore * Data collection--new technology, interviewer effects, interviewmode, children as respondents * Post-survey processing and operations--modeling of classificationoperations, coding based on such systems, editing, integratingprocesses * Quality assessment and control--total quality management,developing current best methods, service quality, quality effortsacross organizations * Effects of misclassification on estimation, analysis, andinterpretation--misclassification and other measurement errors, newvariance estimators that account for measurement error, estimatorsof nonsampling error components in interview surveys Survey Measurement and Process Quality is an indispensable resourcefor survey practitioners and managers as well as an excellentsupplemental text for undergraduate and graduate courses andspecial seminars.

An in-depth look at current issues, new research findings, andinterdisciplinary exchange in survey methodology andprocessing Survey Measurement and Process Quality extends the marriage oftraditional survey issues and continuous quality improvementfurther than any other contemporary volume. It documents thecurrent state of the field, reports new research findings, andpromotes interdisciplinary exchange in questionnaire design, datacollection, data processing, quality assessment, and effects oferrors on estimation and analysis. The book''s five sections discuss a broad range of issues and topicsin each of five major areas, including * Questionnaire design--conceptualization, design of rating scalesfor effective measurement, self-administered questionnaires, andmore * Data collection--new technology, interviewer effects, interviewmode, children as respondents * Post-survey processing and operations--modeling of classificationoperations, coding based on such systems, editing, integratingprocesses * Quality assessment and control--total quality management,developing current best methods, service quality, quality effortsacross organizations * Effects of misclassification on estimation, analysis, andinterpretation--misclassification and other measurement errors, newvariance estimators that account for measurement error, estimatorsof nonsampling error components in interview surveys Survey Measurement and Process Quality is an indispensable resourcefor survey practitioners and managers as well as an excellentsupplemental text for undergraduate and graduate courses andspecial seminars.

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystemThis 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 DataExplores how machine learning methods can be applied to the design, collection, and analysis of social science dataFilled with examples and illustrations that show how survey data benefits Big Data evaluationCovers methods and applications used in combining Big Data with survey statisticsExamines regulations as well as ethical and privacy issuesBig 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.

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.

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.

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 setsThis 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 researchTotal 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.

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.