Handbook of Web Surveys

SILVIA BIFFIGNANDI is a professor at the Center for Statistics and Analysis of Sample Surveys, University of Bergamo, Bergamo, Italy.JELKE BETHLEHEM is affiliated with Statistics Netherlands, a Division of Methodology and Quality, The Netherlands.

• Preface xi1 The Road To Web Surveys 11.1 Introduction 11.2 Theory 21.2.1 The Everlasting Demand for Statistical Information 21.2.2 Traditional Data Collection 81.2.3 The Era of Computer-Assisted Interviewing 111.2.4 The Conquest of the Web 131.2.5 Web Surveys and Other Sources 231.2.6 Historic Summary 281.2.7 Present-Day Challenges and Opportunities 281.2.8 Conclusions from Modern-Day Challenges 301.2.9 Thriving in the Modern-Day Survey World 301.3 Application 311.3.1 Blaise 311.4 Summary 39Key Terms 41Exercises 42References 442 About Web Surveys 472.1 Introduction 472.2 Theory 502.2.1 Typical Survey Situations 512.2.2 Why Online Data Collection? 562.2.3 Areas of Application 602.2.4 Trends in Web Surveys 622.3 Application 642.4 Summary 68Key Terms 68Exercises 69References 713 A Framework For Steps and Errors In Web Surveys 733.1 Introduction 733.2 Theory 753.3 Application 883.4 Summary 89Key Terms 90Exercises 90References 914 Sampling For Web Surveys 934.1 Introduction 934.2 Theory 954.2.1 Target Population 954.2.2 Sampling Frames 984.2.3 Basic Concepts of Sampling 1034.2.4 Simple Random Sampling 1064.2.5 Determining the Sample Size 1094.2.6 Some Other Sampling Designs 1124.2.7 Estimation Procedures 1184.3 Application 1234.4 Summary 128Key Terms 129Exercises 130References 1315 Errors In Web Surveys 1335.1 Introduction 1335.2 Theory 1425.2.1 Measurement Errors 1425.2.2 Nonresponse 1645.3 Application 1745.3.1 The Safety Monitor 1745.3.2 Measurement Errors 1755.3.3 Nonresponse 1775.4 Summary 179Key Terms 180Exercises 182References 1856 Web Surveys and Other Modes of Data Collection 1896.1 Introduction 1896.1.1 Modes of Data Collection 1896.1.2 The Choice of the Modes of Data Collection 1906.2 Theory 1946.2.1 Face-to-Face Surveys 1946.2.2 Telephone Surveys 2006.2.3 Mail Surveys 2066.2.4 Web Surveys 2116.2.5 Mobile Web Surveys 2156.3 Application 2226.4 Summary 230Key Terms 231Exercises 233References 2357 Designing A Web Survey Questionnaire 2377.1 Introduction 2377.2 Theory 2407.2.1 The Road Map Toward a Web Questionnaire 2407.2.2 The Language of Questions 2497.2.3 Basic Concepts of Visualization 2527.2.4 Answers Types (Response Format) 2587.2.5 Web Questionnaires and Paradata 2717.2.6 Trends in Web Questionnaire Design and Visualization 2787.3 Application 2817.4 Summary 282Key Terms 283Exercises 284References 2868 Adaptive and Responsive Design 2918.1 Introduction 2918.2 Theory 2948.2.1 Terminology 2948.2.2 Quality and Cost Functions 2988.2.3 Strategy Allocation and Optimization 3018.3 Application 3098.4 Summary 316Key Terms 316Exercises 317References 3189 Mixed-Mode Surveys 3219.1 Introduction 3219.2 The Theory 3269.2.1 What is Mixed-Mode? 3269.2.2 Why Mixed-Mode? 3349.3 Methodological Issues 3439.3.1 Preventing Mode Effects Through Questionnaire Design 3469.3.2 How to Mix Modes? 3509.3.3 How to Compute Response Rates? 3549.3.4 Avoiding and Adjusting Mode Effects for Inference 3599.3.5 Mixed-Mode by Businesses and Households 3709.4 Application 3849.5 Summary 386Key Terms 388Exercises 388References 39010 The Problem of Under-Coverage 39910.1 Introduction 39910.2 Theory 40510.2.1 The Internet Population 40510.2.2 A Random Sample from the Internet Population 40610.2.3 Reducing the Non-Coverage Bias 41010.2.4 Mixed-Mode Data Collection 41310.3 Application 41410.4 Summary 417Key Terms 418Exercises 419References 42111 The Problem of Self-Selection 42311.1 Introduction 42311.2 Theory 43111.2.1 Basic Sampling Theory 43111.2.2 A Self-Selection Sample from the Internet Population 43411.2.3 Reducing the Self-Selection Bias 43911.3 Applications 44411.3.1 Application 1: Simulating Self-Selection Polls 44411.3.2 Application 2: Sunday Shopping in Alphen a/d Rijn 44811.4 Summary 451Key Terms 452Exercises 453References 45512 Weighting Adjustment Techniques 45712.1 Introduction 45712.2 Theory 46312.2.1 The Concept of Representativity 46312.2.2 Post-Stratification 46512.2.3 Generalized Regression Estimation 47712.2.4 Raking Ratio Estimation 48612.2.5 Calibration Estimation 49012.2.6 Constraining the Values of Weights 49112.2.7 Correction Using a Reference Survey 49212.3 Application 50012.4 Summary 506Key Terms 508Exercises 509References 51213 Use of Response Propensities 51313.1 Introduction 51313.2 Theory 51713.2.1 A Simple Random Sample With Nonresponse 51713.2.2 A Self-Selection Sample 52013.2.3 The Response Propensity Definition 52113.2.4 Models for Response Propensities 52213.2.5 Correction Methods Based on Response Propensities 52913.3 Application 53513.3.1 Generation of the Population 53613.3.2 Generation of Response Probabilities 53713.3.3 Generation of the Sample 53713.3.4 Computation of Response Propensities 53713.3.5 Matching Response Propensities 53713.3.6 Estimation of Population Characteristics 54013.3.7 Evaluating the Results 54113.3.8 Model Sensitivity 54213.4 Summary 542Key Terms 543Exercises 544References 54614 Web Panels 54914.1 Introduction 54914.2 Theory 55514.2.1 Under-Coverage 55514.2.2 Recruitment 55714.2.3 Nonresponse 56314.2.4 Representativity 57714.2.5 Weighting Adjustment for Panels 58014.2.6 Panel Maintenance 58214.3 Applications 58514.3.1 Application 1: The Web Panel Pilot of Statistics Netherlands 58514.3.2 Application 2: The U.K. Polling Disaster 58914.4 Summary 592Key Terms 593Exercises 593References 595Index 599