Jelke Bethlehem - Handbook of Web Surveys

1 Cover

2 Title Page Handbook of Web Surveys Second Edition Silvia Biffignandi University of Bergamo Italy Jelke Bethlehem Faculty of Social and Behavioral Sciences, Institute of Political Science Leiden University The Netherlands contact: biffisil@teletu.it j.g.bethlehem@fsw.leidenuniv.nl website: www.web-survey-handbook.com/

3 Copyright Page

4 Preface

5 Chapter One: The Road to Web Surveys1.1 Introduction 1.2 Theory 1.3 Application 1.4 Summary KEY TERMS EXERCISES REFERENCES

6 Chapter Two: About Web Surveys2.1 Introduction 2.2 Theory 2.3 Application 2.4 Summary KEY TERMS EXERCISES REFERENCES

7 Chapter Three: A Framework for Steps and Errors in Web Surveys3.1 Introduction 3.2 Theory 3.3 Application 3.4 Summary KEY TERMS EXERCISES REFERENCES

8 Chapter Four: Sampling for Web Surveys4.1 Introduction 4.2 Theory 4.3 Application 4.4 Summary KEY TERMS EXERCISES REFERENCES

9 Chapter Five: Errors in Web Surveys5.1 Introduction 5.2 Theory 5.3 Application 5.4 Summary KEY TERMS EXERCISES REFERENCES

10 Chapter Six: Web Surveys and Other Modes of Data Collection6.1 Introduction 6.2 Theory 6.3 Application 6.4 Summary KEY TERMS EXERCISES REFERENCES

11 Chapter Seven: Designing a Web Survey Questionnaire7.1 Introduction 7.2 Theory 7.3 Application 7.4 Summary KEY TERMS EXERCISES REFERENCES

12 Chapter Eight: Adaptive and Responsive Design8.1 Introduction 8.2 Theory 8.3 Application 8.4 Summary KEY TERMS EXERCISES REFERENCES

13 Chapter Nine: Mixed‐mode Surveys9.1 Introduction 9.2 The Theory 9.3 Methodological Issues 9.4 Application 9.5 Summary KEY TERMS EXERCISES REFERENCES

14 Chapter Ten: The Problem of Under‐coverage10.1 Introduction 10.2 Theory 10.3 Application 10.4 Summary KEY TERMS EXERCISES REFERENCES

15 Chapter Eleven: The Problem of Self‐Selection11.1 Introduction 11.2 Theory 11.3 Applications 11.4 Summary KEY TERMS EXERCISES REFERENCES

16 Chapter Twelve: Weighting Adjustment Techniques 12.1 Introduction 12.2 Theory 12.3 Application 12.4 Summary KEY TERMS EXERCISES REFERENCES

17 Chapter Thirteen: Use of Response Propensities13.1 Introduction 13.2 Theory 13.3 Application 13.4 Summary KEY TERMS EXERCISES REFERENCES

18 Chapter Fourteen: Web Panels 14.1 Introduction 14.2 Theory 14.3 Applications 14.4 Summary KEY TERMS EXERCISES REFERENCES

19 Index

20 End User License Agreement

1 Chapter 1 Table 1.1 The first telephone survey in the Netherlands Table 1.2 Penetration of fixed and mobile phone and of Internet (year 2018)Sour... Table 1.3 Household surveys carried out by Statistics Netherlands in the early 19...

2 Chapter 2 Table 2.1 Response rates of the Italcementi survey by country

3 Chapter 3 Table 3.1 Types of detected errors by response mode (averages on total responses:... Table 3.2 Responses to the survey by mod e and percentage composition

4 Chapter 4 Table 4.1 The recruitment process for the LISS panelTable 4.2 Characteristics of the four size strata of the shops

5 Chapter 5Table 5.1 Are individuals or social conditions to blame?Table 5.2 The effect of offering a middle optionTable 5.3 The effect of offering a middle optionTable 5.4 Nonresponse in the Dutch Housing Demand Survey 1981Table 5.5 Response rates in the recruitment phase of the LISS panelTable 5.6 Average value of indicators in SM4 and SM2Table 5.7 Response distribution of “perceived graffiti” by mode in SM4...Table 5.8 Composition of the response of both safety monitorsTable 5.9 Ranges of the response rates (%) in the categories of auxiliary variabl...

6 Chapter 6Table 6.1 Cost and quality aspects of face‐to‐face surveysTable 6.2 Cost and quality aspects of telephone surveysTable 6.3 Cost and quality aspects of mail surveysTable 6.4 Cost and quality aspects of web surveysTable 6.5 Cost and quality aspects of mobile surveys

7 Chapter 7Table 7.1 Frequency of checking e‐mailTable 7.2 The design of the consumers' values experiment

8 Chapter 8Table 8.1 Estimated response propensities for the three strata and three strategi...Table 8.2 Estimated costs per sample element for the three strata and three strat...Table 8.3 Estimated response propensities for the six strata in phases 1, 2, and ...Table 8.4 Estimated marginal costs per sample element for the six strata in phase...

9 Chapter 9Table 9.1 Degree of disparity between data collection modesTable 9.2 Concurrent versus sequential mixed‐mode per surveyTable 9.3 Multipurpose survey on households: methods to compare alterntive approa...Table 9.4 Detected errors in the SCI surveyTable 9.5 Internet access in the worldTable 9.6 Households level Internet access in European countriesTable 9.7 Response rates (%) by age and mode in the ICT‐experimentTable 9.8 Modes, reminders and incentives in different stages of the survey

10 Chapter 10Table 10.1 Summary of simulation results for the variable NEPTable 10.2 Summary of simulation results for the variable NIP

11 Chapter 11Table 11.1 Predictions (seats in parliament) for the Dutch parliamentary election...Table 11.2 Predictions for the presidential election in the United States on Nove...Table 11.3 Summary of simulation results for the variable NEPTable 11.4 Summary of simulation results for the variable NIPTable 11.5 Distribution of the respondents over the towns of Alphen a/d Rijn

12 Chapter 12Table 12.1 Computing post‐stratification weightsTable 12.2 Incomplete population informationTable 12.3 Post‐stratification by Education × AgeTable 12.4 Weighting with the marginal distributionsTable 12.5 The starting situationTable 12.6 Situation after adjusting for ageTable 12.7 Situation after adjusting for educationTable 12.8 Situation after convergenceTable 12.9 The distribution by gender in the response and the populationTable 12.10 The distribution by age in the response and the populationTable 12.11 The distribution of marital status in the response and the populationTable 12.12 Response and population percentage distribution by province of reside...Table 12.13 The distribution of ethnic background in the response and the populat...Table 12.14 The distribution of voting in 2006 in the response and the populationTable 12.15 Post‐stratification weighting with a single variableTable 12.16 Generalized regression estimation with two variablesTable 12.17 Post‐stratification weighting with a single variable

13 Chapter 13Table 13.1 Auxiliary variables in response propensity modelTable 13.2 The lowest and highest response propensityTable 13.3 Cramér's V statistic the strength of the relationship between the auxi...Table 13.4 Multivariate model for the response propensitiesTable 13.5 The response propensity strataTable 13.6 Characteristics of the generated population (size, mean, and standard ...Table 13.7 Characteristics of response probabilities and response propensities

14 Chapter 14Table 14.1 Response rates and R‐indicator for the LISS PanelTable 14.2 Household‐level recruitment data for a fictitious web panelTable 14.3 Response rates in the panel recruitment processTable 14.4 The R‐indicator in the steps of the recruitment processTable 14.5 The relation between recruitment response and OViN data collection mod...Table 14.6 Estimates (unweighted and weighted) for level of education (%)Table 14.7 Estimates (unweighted and weighted) for main activity (%)Table 14.8 Estimates (unweighted and weighted) for main activity (%)

1 Chapter 1 Figure 1.1 Example of an e‐mail survey questionnaire Figure 1.2 A closed question in HTML Figure 1.3 A check‐all‐that‐apply question in HTML Figure 1.4 An open question in HTML Figure 1.5 A numeric question in HTML Figure 1.6 A simple paper questionnaire Figure 1.7 A simple Blaise questionnaire specification Figure 1.8 A Blaise CADI program Figure 1.9 A Blaise CAPI program Figure 1.10 The screen of a CAPI program in Blaise 4 Figure 1.11 The screen of a Blaise 5 web survey on a tablet Figure 1.12 The screen of a Blaise 5 web survey on a smartphone