{"product_id":"handbook-of-web-surveys-isbn-9780470603567","title":"Handbook of Web Surveys","description":"\u003cb\u003eBEST PRACTICES TO CREATE AND IMPLEMENTHIGHLY EFFECTIVE WEB SURVEYS\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e   \u003cp\u003eExclusively combining design and sampling issues, \u003ci\u003eHandbook of Web Surveys\u003c\/i\u003e presents a theoretical yet practical approach to creating and conducting web surveys. From the history of web surveys to various modes of data collection to tips for detecting error, this book thoroughly introduces readers to the this cutting-edge technique and offers tips for creating successful web surveys.\u003c\/p\u003e \u003cp\u003eThe authors provide a history of web surveys and go on to explore the advantages and disadvantages of this mode of data collection. Common challenges involving under-coverage, self-selection, and measurement errors are discussed as well as topics including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSampling designs and estimation procedures\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eComparing web surveys to face-to-face, telephone, and mail surveys\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eErrors in web surveys\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eMixed-mode surveys\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eWeighting techniques including post-stratification, generalized regression estimation, and raking ratio estimation\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eUse of propensity scores to correct bias\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eWeb panels\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eReal-world examples illustrate the discussed concepts, methods, and techniques, with related data freely available on the book's Website. \u003ci\u003eHandbook of Web Surveys\u003c\/i\u003e is an essential reference for researchers in the fields of government, business, economics, and the social sciences who utilize technology to gather, analyze, and draw results from data. It is also a suitable supplement for survey methods courses at the upper-undergraduate and graduate levels.\u003c\/p\u003e  PREFACE xi  \u003cp\u003e\u003cb\u003e1 THE ROAD TO WEB SURVEYS 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction, 1\u003c\/p\u003e \u003cp\u003e1.2 Theory, 2\u003c\/p\u003e \u003cp\u003e1.2.1 The Everlasting Demand for Statistical Information, 2\u003c\/p\u003e \u003cp\u003e1.2.2 The Dawn of Sampling Theory, 4\u003c\/p\u003e \u003cp\u003e1.2.3 Traditional Data Collection, 8\u003c\/p\u003e \u003cp\u003e1.2.4 The Era of Computer-Assisted Interviewing, 10\u003c\/p\u003e \u003cp\u003e1.2.5 The Conquest of the Web, 12\u003c\/p\u003e \u003cp\u003e1.3 Application, 21\u003c\/p\u003e \u003cp\u003e1.4 Summary, 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 ABOUT WEB SURVEYS 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction, 37\u003c\/p\u003e \u003cp\u003e2.2 Theory, 40\u003c\/p\u003e \u003cp\u003e2.2.1 Typical Survey Situations, 40\u003c\/p\u003e \u003cp\u003e2.2.2 Why On-Line Data Collection?, 45\u003c\/p\u003e \u003cp\u003e2.2.3 Areas of Application, 48\u003c\/p\u003e \u003cp\u003e2.2.4 Trends in Web Surveys, 50\u003c\/p\u003e \u003cp\u003e2.3 Application, 52\u003c\/p\u003e \u003cp\u003e2.4 Summary, 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 SAMPLING FOR WEB SURVEYS 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction, 59\u003c\/p\u003e \u003cp\u003e3.2 Theory, 60\u003c\/p\u003e \u003cp\u003e3.2.1 Target Population, 60\u003c\/p\u003e \u003cp\u003e3.2.2 Sampling Frames, 63\u003c\/p\u003e \u003cp\u003e3.2.3 Basic Concepts of Sampling, 68\u003c\/p\u003e \u003cp\u003e3.2.4 Simple Random Sampling, 71\u003c\/p\u003e \u003cp\u003e3.2.5 Determining the Sample Size, 74\u003c\/p\u003e \u003cp\u003e3.2.6 Some Other Sampling Designs, 76\u003c\/p\u003e \u003cp\u003e3.2.7 Estimation Procedures, 82\u003c\/p\u003e \u003cp\u003e3.3 Application, 87\u003c\/p\u003e \u003cp\u003e3.4 Summary, 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 ERRORS IN WEB SURVEYS 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction, 97\u003c\/p\u003e \u003cp\u003e4.2 Theory, 103\u003c\/p\u003e \u003cp\u003e4.2.1 Measurement Errors, 103\u003c\/p\u003e \u003cp\u003e4.2.2 Nonresponse, 124\u003c\/p\u003e \u003cp\u003e4.3 Application, 133\u003c\/p\u003e \u003cp\u003e4.3.1 The Safety Monitor, 133\u003c\/p\u003e \u003cp\u003e4.3.2 Measurement Errors, 134\u003c\/p\u003e \u003cp\u003e4.3.3 Nonresponse, 136\u003c\/p\u003e \u003cp\u003e4.4 Summary, 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 WEB SURVEYS AND OTHER MODES OF DATA COLLECTION 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction, 147\u003c\/p\u003e \u003cp\u003e5.1.1 Modes of Data Collection, 147\u003c\/p\u003e \u003cp\u003e5.1.2 The Choice of the Modes of Data Collection, 149\u003c\/p\u003e \u003cp\u003e5.2 Theory, 152\u003c\/p\u003e \u003cp\u003e5.2.1 Face-To-Face Surveys, 152\u003c\/p\u003e \u003cp\u003e5.2.2 Telephone surveys, 158\u003c\/p\u003e \u003cp\u003e5.2.3 Mail Surveys, 164\u003c\/p\u003e \u003cp\u003e5.2.4 Web surveys, 169\u003c\/p\u003e \u003cp\u003e5.3 Application, 174\u003c\/p\u003e \u003cp\u003e5.4 Summary, 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 DESIGNING A WEB SURVEY QUESTIONNAIRE 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction, 189\u003c\/p\u003e \u003cp\u003e6.2 Theory, 191\u003c\/p\u003e \u003cp\u003e6.2.1 The Road Map Toward a Web Questionnaire, 191\u003c\/p\u003e \u003cp\u003e6.2.2 The Language of Questions, 197\u003c\/p\u003e \u003cp\u003e6.2.3 Answers Types (Response Format), 200\u003c\/p\u003e \u003cp\u003e6.2.4 Basic Concepts of Visualization, 211\u003c\/p\u003e \u003cp\u003e6.2.5 Web Questionnaires and Paradata, 217\u003c\/p\u003e \u003cp\u003e6.2.6 Trends in Web Questionnaire Design and Visualization, 223\u003c\/p\u003e \u003cp\u003e6.3 Application, 226\u003c\/p\u003e \u003cp\u003e6.4 Summary, 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 MIXED-MODE SURVEYS 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction, 235\u003c\/p\u003e \u003cp\u003e7.2 Theory, 238\u003c\/p\u003e \u003cp\u003e7.2.1 What is Mixed Mode?, 238\u003c\/p\u003e \u003cp\u003e7.2.2 Why Mixed Mode?, 243\u003c\/p\u003e \u003cp\u003e7.2.3 Methodological Issues, 248\u003c\/p\u003e \u003cp\u003e7.2.4 Mixed Mode for Business Surveys, 262\u003c\/p\u003e \u003cp\u003e7.2.5 Mixed Mode for Surveys Among Households and Individuals, 267\u003c\/p\u003e \u003cp\u003e7.3 Application, 272\u003c\/p\u003e \u003cp\u003e7.4 Summary, 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 THE PROBLEM OF UNDERCOVERAGE 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction, 281\u003c\/p\u003e \u003cp\u003e8.2 Theory, 287\u003c\/p\u003e \u003cp\u003e8.2.1 The Internet Population, 287\u003c\/p\u003e \u003cp\u003e8.2.2 A Random Sample From the Internet Population, 288\u003c\/p\u003e \u003cp\u003e8.2.3 Reducing the Noncoverage Bias, 290\u003c\/p\u003e \u003cp\u003e8.2.4 Mixed-Mode Data Collection, 294\u003c\/p\u003e \u003cp\u003e8.3 Application, 295\u003c\/p\u003e \u003cp\u003e8.4 Summary, 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 THE PROBLEM OF SELF-SELECTION 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction, 303\u003c\/p\u003e \u003cp\u003e9.2 Theory, 306\u003c\/p\u003e \u003cp\u003e9.2.1 Basic Sampling Theory, 306\u003c\/p\u003e \u003cp\u003e9.2.2 A Self-Selection Sample fromthe Internet Population, 309\u003c\/p\u003e \u003cp\u003e9.2.3 Reducing the Self-Selection Bias, 314\u003c\/p\u003e \u003cp\u003e9.3 Application, 319\u003c\/p\u003e \u003cp\u003e9.4 Summary, 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 WEIGHTING ADJUSTMENT TECHNIQUES 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction, 329\u003c\/p\u003e \u003cp\u003e10.2 Theory, 334\u003c\/p\u003e \u003cp\u003e10.2.1 The Concept of Representativity, 334\u003c\/p\u003e \u003cp\u003e10.2.2 Poststratification, 336\u003c\/p\u003e \u003cp\u003e10.2.3 Generalized Regression Estimation, 349\u003c\/p\u003e \u003cp\u003e10.2.4 Raking Ratio Estimation, 358\u003c\/p\u003e \u003cp\u003e10.2.5 Calibration Estimation, 361\u003c\/p\u003e \u003cp\u003e10.2.6 Constraining the Values of Weights, 362\u003c\/p\u003e \u003cp\u003e10.2.7 Correction Using a Reference Survey, 363\u003c\/p\u003e \u003cp\u003e10.3 Application, 372\u003c\/p\u003e \u003cp\u003e10.4 Summary, 378\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 USE OF RESPONSE PROPENSITIES 385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction, 385\u003c\/p\u003e \u003cp\u003e11.2 Theory, 389\u003c\/p\u003e \u003cp\u003e11.2.1 A Simple Random Sample with Nonresponse, 389\u003c\/p\u003e \u003cp\u003e11.2.2 A Self-Selection Sample, 392\u003c\/p\u003e \u003cp\u003e11.2.3 The Response Propensity Definition, 393\u003c\/p\u003e \u003cp\u003e11.2.4 Models for Response Propensities, 394\u003c\/p\u003e \u003cp\u003e11.2.5 Correction Methods Based on Response Propensities, 401\u003c\/p\u003e \u003cp\u003e11.3 Application, 406\u003c\/p\u003e \u003cp\u003e11.3.1 Generation of the Population, 407\u003c\/p\u003e \u003cp\u003e11.3.2 Generation of Response Probabilities, 408\u003c\/p\u003e \u003cp\u003e11.3.3 Generation of the Sample, 408\u003c\/p\u003e \u003cp\u003e11.3.4 Computation of Response Propensities, 408\u003c\/p\u003e \u003cp\u003e11.3.5 Matching Response Propensities, 409\u003c\/p\u003e \u003cp\u003e11.3.6 Estimation of Population Characteristics, 411\u003c\/p\u003e \u003cp\u003e11.3.7 Evaluating the Results, 412\u003c\/p\u003e \u003cp\u003e11.3.8 Model Sensitivity, 412\u003c\/p\u003e \u003cp\u003e11.4 Summary, 413\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 WEB PANELS 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction, 419\u003c\/p\u003e \u003cp\u003e12.2 Theory, 422\u003c\/p\u003e \u003cp\u003e12.2.1 Web Panel Definition and Recruitment, 422\u003c\/p\u003e \u003cp\u003e12.2.2 Use of Web Panels, 426\u003c\/p\u003e \u003cp\u003e12.2.3 Web Panel Management, 427\u003c\/p\u003e \u003cp\u003e12.2.4 Response Rates, 432\u003c\/p\u003e \u003cp\u003e12.2.5 Representativity, 443\u003c\/p\u003e \u003cp\u003e12.3 Application, 449\u003c\/p\u003e \u003cp\u003e12.4 Summary, 451\u003c\/p\u003e \u003cp\u003eKey Terms, 452\u003c\/p\u003e \u003cp\u003eExercises, 452\u003c\/p\u003e \u003cp\u003eReferences, 454\u003c\/p\u003e \u003cp\u003eINDEX 459\u003c\/p\u003e  \u003cb\u003eJelke Bethlehem, PhD\u003c\/b\u003e, is Senior Advisor in the Department of Statistical Methods at Statistics Netherlands and Professor of Statistical Information Processing at the University of Amsterdam. His current research interests include web surveys, computer-assisted survey information collection, graphical techniques in statistics, and user-friendly software for statistical analysis. He is coeditor of \u003ci\u003eComputer Assisted Survey Information Collection, author of Applied Survey Methods: A Statistical Perspective\u003c\/i\u003e, and coauthor of \u003ci\u003eHandbook of Nonresponse in Household Surveys\u003c\/i\u003e, all published by Wiley.  \u003cp\u003e\u003cb\u003eSilvia Biffignandi\u003c\/b\u003e is Professor of Economic and Business Statistics and Director of the Centre for Statistical Analyses and Survey Interviewing (CASI) at the University of Bergamo (Italy). She currently focuses her research in the areas of web surveys, online panels, and official statistics.\u003c\/p\u003e \u003cb\u003eBEST PRACTICES TO CREATE AND IMPLEMENTHIGHLY EFFECTIVE WEB SURVEYS\u003c\/b\u003e  \u003cp\u003e Exclusively combining design and sampling issues, \u003ci\u003eHandbook of Web Surveys\u003c\/i\u003e presents a theoretical yet practical approach to creating and conducting web surveys. From the history of web surveys to various modes of data collection to tips for detecting error, this book thoroughly introduces readers to the this cutting-edge technique and offers tips for creating successful web surveys.\u003c\/p\u003e  \u003cp\u003e The authors provide a history of web surveys and go on to explore the advantages and disadvantages of this mode of data collection. Common challenges involving under-coverage, self-selection, and measurement errors are discussed as well as topics including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e  \u003cp\u003e Sampling designs and estimation procedures\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e  \u003cp\u003e Comparing web surveys to face-to-face, telephone, and mail surveys\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e  \u003cp\u003e Errors in web surveys\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e  \u003cp\u003e Mixed-mode surveys\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e  \u003cp\u003e Weighting techniques including post-stratification, generalized regression estimation, and raking ratio estimation\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e  \u003cp\u003e Use of propensity scores to correct bias\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e  \u003cp\u003e Web panels\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003e Real-world examples illustrate the discussed concepts, methods, and techniques, with related data freely available on the book's Website. \u003ci\u003eHandbook of Web Surveys\u003c\/i\u003e is an essential reference for researchers in the fields of government, business, economics, and the social sciences who utilize technology to gather, analyze, and draw results from data. It is also a suitable supplement for survey methods courses at the upper-undergraduate and graduate levels.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989344665829,"sku":"NP9780470603567","price":191.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470603567.jpg?v=1761783749","url":"https:\/\/k12savings.com\/products\/handbook-of-web-surveys-isbn-9780470603567","provider":"K12savings","version":"1.0","type":"link"}