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BEST PRACTICES TO CREATE AND IMPLEMENTHIGHLY EFFECTIVE WEB SURVEYS

Exclusively combining design and sampling issues, Handbook of Web Surveys 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.

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:

Sampling designs and estimation procedures

Comparing web surveys to face-to-face, telephone, and mail surveys

Errors in web surveys

Mixed-mode surveys

Weighting techniques including post-stratification, generalized regression estimation, and raking ratio estimation

Use of propensity scores to correct bias

Web panels

Real-world examples illustrate the discussed concepts, methods, and techniques, with related data freely available on the book's Website. Handbook of Web Surveys 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.

Tabla de contenidos provista por Syndetics

• Preface(p. xi)
• 1 The Road to web surveys(p. 1)
• 1.1 Introduction(p. 1)
• 1.2 Theory(p. 2)
• 1.2.1 The Everlasting Demand for Statistical Information(p. 2)
• 1.2.2 The Dawn of Sampling Theory(p. 4)
• 1.2.3 Traditional Data Collection(p. 8)
• 1.2.4 The Era of Computer-Assisted Interviewing(p. 10)
• 1.2.5 The Conquest of the Web(p. 12)
• 1.3 Application(p. 21)
• 1.4 Summary(p. 31)
• Key Terms(p. 31)
• Exercises(p. 33)
• References(p. 34)
• 2 About web surveys(p. 37)
• 2.1 Introduction(p. 37)
• 2.2 Theory(p. 40)
• 2.2.1 Typical Survey Situations(p. 40)
• 2.2.2 Why On-Line Data Collection?(p. 45)
• 2.2.3 Areas of Application(p. 48)
• 2.2.4 Trends in Web Surveys(p. 50)
• 2.3 Application(p. 52)
• 2.4 Summary(p. 55)
• Key Terms(p. 56)
• Exercises(p. 56)
• References(p. 58)
• 3 Sampling for web surveys(p. 59)
• 3.1 Introduction(p. 59)
• 3.2 Theory(p. 60)
• 3.2.1 Target Population(p. 60)
• 3.2.2 Sampling Frames(p. 63)
• 3.2.3 Basic Concepts of Sampling(p. 68)
• 3.2.4 Simple Random Sampling(p. 71)
• 3.2.5 Determining the Sample Size(p. 74)
• 3.2.6 Some Other Sampling Designs(p. 76)
• 3.2.7 Estimation Procedures(p. 82)
• 3.3 Application(p. 87)
• 3.4 Summary(p. 92)
• Key Terms(p. 92)
• Exercises(p. 93)
• References(p. 94)
• 4 Errors in Web surveys(p. 97)
• 4.1 Introduction(p. 97)
• 4.2 Theory(p. 103)
• 4.2.1 Measurement Errors(p. 103)
• 4.2.2 Nonresponse(p. 124)
• 4.3 Application(p. 133)
• 4.3.1 The Safety Monitor(p. 133)
• 4.3.2 Measurement Errors(p. 134)
• 4.3.3 Nonresponse(p. 136)
• 4.4 Summary(p. 138)
• Key Terms(p. 138)
• Exercises(p. 140)
• References(p. 143)
• 5 Web surveys and other modes of data collection(p. 147)
• 5.1 Introduction(p. 147)
• 5.1.1 Modes of Data Collection(p. 147)
• 5.1.2 The Choice of the Modes of Data Collection(p. 149)
• 5.2 Theory(p. 152)
• 5.2.1 Face-To-Face Surveys(p. 152)
• 5.2.2 Telephone surveys(p. 158)
• 5.2.3 Mail Surveys(p. 164)
• 5.2.4 Web surveys(p. 169)
• 5.3 Application(p. 174)
• 5.4 Summary(p. 182)
• Key Terms(p. 183)
• Exercises(p. 185)
• References(p. 187)
• 6 Designing a web survey questionnaire(p. 189)
• 6.1 Introduction(p. 189)
• 6.2 Theory(p. 191)
• 6.2.1 The Road Map Toward a Web Questionnaire(p. 191)
• 6.2.2 The Language of Questions(p. 197)
• 6.2.3 Answers Types (Response Format)(p. 200)
• 6.2.4 Basic Concepts of Visualization(p. 211)
• 6.2.5 Web Questionnaires and Paradata(p. 217)
• 6.2.6 Trends in Web Questionnaire Design and Visualization(p. 223)
• 6.3 Application(p. 226)
• 6.4 Summary(p. 228)
• Key Terms(p. 228)
• Exercises(p. 229)
• References(p. 231)
• 7 Mixed-Mode surveys(p. 235)
• 7.1 Introduction(p. 235)
• 7.2 Theory(p. 238)
• 7.2.1 WhatÆs Mixed Mode?(p. 238)
• 7.2.2 Why Mixed Mode?(p. 243)
• 7.2.3 Methodological Issues(p. 248)
• 7.2.4 Mixed Mode for Business Surveys(p. 262)
• 7.2.5 Mixed Mode for Surveys Among Households and Individuals(p. 267)
• 7.3 Application(p. 272)
• 7.4 Summary(p. 274)
• Key Terms(p. 274)
• Exercises(p. 275)
• References(p. 277)
• 8 The problem of undercoverage(p. 281)
• 8.1 Introduction(p. 281)
• 8.2 Theory(p. 287)
• 8.2.1 The Internet Population(p. 287)
• 8.2.2 A Random Sample From the Internet Population(p. 288)
• 8.2.3 Reducing the Noncoverage Bias(p. 290)
• 8.2.4 Mixed-Mode Data Collection(p. 294)
• 8.3 Application(p. 295)
• 8.4 Summary(p. 299)
• Key Terms(p. 299)
• Exercises(p. 300)
• References(p. 302)
• 9 The problem of self-selection(p. 303)
• 9.1 Introduction(p. 303)
• 9.2 Theory(p. 306)
• 9.2.1 Basic Sampling Theory(p. 306)
• 9.2.2 A Self-Selection Sample from the Internet Population(p. 309)
• 9.2.3 Reducing the Self-Selection Bias(p. 314)
• 9.3 Application(p. 319)
• 9.4 Summary(p. 323)
• Key Terms(p. 323)
• Exercises(p. 324)
• References(p. 326)
• 10 Weighting adjustment techniques(p. 329)
• 10.1 Introduction(p. 329)
• 10.2 Theory(p. 334)
• 10.2.1 The Concept of Representativity(p. 334)
• 10.2.2 Poststratification(p. 336)
• 10.2.3 Generalized Regression Estimation(p. 349)
• 10.2.4 Raking Ratio Estimation(p. 358)
• 10.2.5 Calibration Estimation(p. 361)
• 10.2.6 Constraining the Values of Weights(p. 362)
• 10.2.7 Correction Using a Reference Survey(p. 363)
• 10.3 Application(p. 372)
• 10.4 Summary(p. 378)
• Key Terms(p. 379)
• Exercises(p. 380)
• References(p. 383)
• 11 Use of response propensities(p. 385)
• 11.1 Introduction(p. 385)
• 11.2 Theory(p. 389)
• 11.2.1 A Simple Random Sample with Nonresponse(p. 389)
• 11.2.2 A Self-Selection Sample(p. 392)
• 11.2.3 The Response Propensity Definition(p. 393)
• 11.2.4 Models for Response Propensities(p. 394)
• 11.2.5 Correction Methods Based on Response Propensities(p. 401)
• 11.3 Application(p. 406)
• 11.3.1 Generation of the Population(p. 407)
• 11.3.2 Generation of Response Probabilities(p. 408)
• 11.3.3 Generation of the Sample(p. 408)
• 11.3.4 Computation of Response Propensities(p. 408)
• 11.3.5 Matching Response Propensities(p. 409)
• 11.3.6 Estimation of Population Characteristics(p. 411)
• 11.3.7 Evaluating the Results(p. 412)
• 11.3.8 Model Sensitivity(p. 412)
• 11.4 Summary(p. 413)
• Key Terms(p. 414)
• Exercises(p. 414)
• References(p. 416)
• 12 Web Panels(p. 419)
• 12.1 Introduction(p. 419)
• 12.2 Theory(p. 422)
• 12.2.1 Web Panel Definition and Recruitment(p. 422)
• 12.2.2 Use of Web Panels(p. 426)
• 12.2.3 Web Panel Management(p. 427)
• 12.2.4 Response Rates(p. 432)
• 12.2.5 Representativity(p. 443)
• 12.3 Application(p. 449)
• 12.4 Summary(p. 451)
• Key Terms(p. 452)
• Exercises(p. 452)
• References(p. 454)
• Index(p. 459)

Notas de autor provistas por Syndetics

Silvia Biffignandi 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.