Details

<p><b>1 Improving Surveys with Paradata: Introduction 1<br /> </b><i>Frauke Kreuter<br /> </i><br /> 1.1 Introduction 1<br /> <br /> 1.2 Paradata and Metadata 3</p> <p>1.3 Auxiliary Data and Paradata 4</p> <p>1.4 Paradata in the Total Survey Error Framework 4</p> <p>1.5 Paradata in Survey Production 5</p> <p>1.6 Special Challenges in the Collection and Use of Paradata 7</p> <p>1.7 Future of Paradata 8</p> <p><b>PART I PARADATA AND SURVEY ERRORS</b></p> <p><b>2 Paradata for Nonresponse Error Investigation 3<br /> </b><i>Frauke Kreuter and Kristen Olson</i></p> <p>2.1 Introduction 3</p> <p>2.2 Sources of Paradata 4</p> <p>2.3 Nonresponse Rates and Nonresponse Bias 10</p> <p>2.4 Paradata and Responsive Designs 20</p> <p>2.5 Paradata and Nonresponse Adjustment 21</p> <p>2.6 Issues in Practice 22</p> <p>2.7 Summary and Take Home Messages 24</p> <p><b>3 Collecting Paradata for Measurement Error Evaluations 33<br /> </b><i>Kristen Olson and Bryan Parkhurst<br /> <br /> </i>3.1 Introduction 33<br /> <br /> 3.2 Paradata and Measurement Error 34</p> <p>3.3 Types of paradata 38</p> <p>3.4 Differences in Paradata by Modes 45</p> <p>3.5 Turning paradata into data sets 51</p> <p>3.6 Summary 55<br /> <br /> <b>4 Analyzing Paradata to Investigate Measurement Error 63<br /> </b><i>Ting Yan and Kristen Olson</i></p> <p>4.1 Introduction 63</p> <p>4.2 Review of Empirical Literature on the Use of Paradata for Measurement Error Investigation 64</p> <p>4.3 Analyzing paradata 66</p> <p>4.4 Four empirical examples 73</p> <p>4.5 Cautions 81</p> <p>4.6 Concluding Remarks 82</p> <p><b>5 Paradata for Coverage Research 89<br /> </b><i>Stephanie Eckman</i></p> <p>5.1 Introduction 89</p> <p>5.2 Housing Unit Frames 93</p> <p>5.3 Telephone Number Frames 101</p> <p>5.4 Household Rosters 103</p> <p>5.5 Population Registers 105</p> <p>5.6 Subpopulation Frames 106</p> <p>5.7 Web Surveys 106</p> <p>5.8 Conclusion 107</p> <p><b>PART II PARADATA IN SURVEY PRODUCTION</b></p> <p><b>6 Design and Management Strategies for Paradata-Driven Responsive</b> <b>Design 117<br /> </b><i>Nicole G. Kirgis and James M. Lepkowski</i></p> <p>6.1 Introduction 117</p> <p>6.2 From Repeated Cross-Section to Continuous Design 118</p> <p>6.3 Paradata Design 123</p> <p>6.4 Key Design Change 1: A New Employment Model 128</p> <p>6.5 Key Design Change 2: Field Efficient Sample Design 130</p> <p>6.6 Key Design Change 3: Replicate Sample Design 131</p> <p>6.7 Key Design Change 4: Responsive Design Sampling of Nonrespondents in a Second Phase 132</p> <p>6.8 Key Design Change 5: Active Responsive Design Interventions 134</p> <p>6.9 Concluding Remarks 135</p> <p><b>7 Using Paradata-Driven Models to Improve Contact Rates 141<br /> </b><i>James Wagner</i></p> <p>7.1 Introduction 141</p> <p>7.2 Background 142</p> <p>7.3 The Survey Setting 144</p> <p>7.4 Experiments: Data and Methods 145</p> <p>7.5 Experiments: Results 157</p> <p>7.6 Discussion 162</p> <p><b>8 Using Paradata to Study Response to Within-Survey Requests 169<br /> </b><i>Joseph W. Sakshaug</i></p> <p>8.1 Introduction 169</p> <p>8.2 Consent to Link Survey and Administrative Records 173</p> <p>8.3 Consent to Collect Biomeasures in Population-Based Surveys 177</p> <p>8.4 Switching Data Collection Modes 179</p> <p>8.5 Income Item Nonresponse and Quality of Income Reports 181</p> <p>8.6 Summary 185</p> <p><b>9 Managing Data Quality Indicators with Paradata-Based Statistical Quality</b> <b>Control Tools 191<br /> </b><i>Matt Jans, Robyn Sirkis and David Morgan</i></p> <p>9.1 Introduction 191</p> <p>9.2 Defining and Choosing Key Performance Indicators (KPIs) 193</p> <p>9.3 KPI Displays and the Enduring Insight of Walter Shewhart 201</p> <p>9.4 Implementation Steps for Survey Analytic Quality Control with Paradata Control Charts 212</p> <p>9.5 A Method for Improving Measurement Process Quality Indicators 214</p> <p>9.6 Reections on SPC, Visual Data Displays, and Challenges to Quality Control 221</p> <p>9.7 Some Advice on Using Charts 223</p> <p> Appendix 225</p> <p><b>10 Paradata as Input to Monitoring Representativeness and Measurement Profiles 233<br /> </b><i>Barry Schouten and Melania Calinescu</i></p> <p>10.1 Introduction 233</p> <p>10.2 Measurement profiles 235</p> <p>10.3 Tools for monitoring nonresponse and measurement profiles 238</p> <p>10.4 Monitoring and improving response: a demonstration using the LFS 243</p> <p>10.5 Including paradata observations on households and persons 254</p> <p>10.6 General discussion 256</p> <p>10.7 Take home messages 257</p> <p><b>PART III SPECIAL CHALLENGES</b></p> <p><b>11 Paradata in Web Surveys 263<br /> </b><i>Mario Callegaro</i></p> <p>11.1 Survey data types 263</p> <p>11.2 Collection of paradata 264</p> <p>11.3 Typology of paradata in web surveys 265</p> <p>11.4 Using paradata to change the survey in real time: adaptive scripting 273</p> <p>11.5 Paradata in online panels 274</p> <p>11.6 Software to collect paradata 274</p> <p>11.7 Analysis of paradata: levels of aggregation 275</p> <p>11.8 Privacy and ethical issues in collecting web survey paradata 276</p> <p>11.9 Summary and conclusions on paradata in web surveys 277</p> <p><b>12 Modeling Call Record Data: Examples from Cross-Sectional and Longitudinal Surveys 283<br /> </b><i>Gabriele B. Durrant, Julia D'Arrigo and Gerrit Müller</i></p> <p>12.1 Introduction 283</p> <p>12.2 Call record data 285</p> <p>12.3 Modeling approaches 287</p> <p>12.4 Illustration of call record data analysis using two example datasets 294</p> <p>12.5 Summary 305</p> <p><b>13 Bayesian Penalized Spline Models for Statistical Process Monitoring of Survey Paradata Quality Indicators 311<br /> </b><i>Joseph L. Schafer</i></p> <p>13.1 Introduction 311</p> <p>13.2 Overview of splines 316</p> <p>13.3 Penalized splines as linear mixed models 323</p> <p>13.4 Bayesian methods 327</p> <p>13.5 Extensions 330</p> <p><b>14 The Quality of Paradata: A Literature Review 341<br /> </b><i>Brady T. West and Jennifer Sinibaldi</i></p> <p>14.1 Introduction 341</p> <p>14.2 Existing Studies Examining the Quality of Paradata 342</p> <p>14.3 Possible Mechanisms Leading to Error in Paradata 354</p> <p>14.4 Take Home Messages 357</p> <p><b>15 The Effects of Errors in Paradata on Weighting Class Adjustments: A Simulation Study 363<br /> </b><i>Brady T. West</i></p> <p>15.1 Introduction 363</p> <p>15.2 Design of Simulation Studies 367</p> <p>15.3 Simulation Results 372</p> <p>15.4 Take Home Messages 386</p> <p>15.5 Future Research 388</p> <p>Topic Index 393</p>

<p><b>FRAUKE KREUTER</b> is Associate Professor in the Joint Program in Survey Methodology at the University of Maryland; Professor of Statistics at Ludwig Maximilian University of Munich, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nuremberg, Germany.</p>

<p><b>Explore the practices and cutting-edge research on the new and exciting topic of paradata</b></p> <p>Paradata are measurements related to the process of collecting survey data. <i>Improving Surveys with Paradata: Analytic Uses of Process Information</i> is the most accessible and comprehensive contribution to this up-and-coming area in survey methodology.</p> <p>Featuring contributions from leading experts in the field, <i>Improving Surveys with Paradata: Analytic Uses of Process Information</i> introduces and reviews issues involved in the collection and analysis of paradata. The book presents readers with an overview of the indispensable techniques and innovative research on improving survey quality and total survey error. Along with several case studies, topics include:</p> <ul> <li>Using paradata to monitor fieldwork activity in face-to-face, telephone, and web surveys</li> <li>Guiding intervention decisions during data collection</li> <li>Analysis of measurement, nonresponse, and coverage error via paradata</li> </ul> <p>Providing a practical, encompassing guide to the subject of paradata, the book is aimed at both producers and users of survey data. <i>Improving Surveys with Paradata: Analytic Uses of Process Information</i> is an essential resource for researchers in academia, government, and the private sector. The book also serves as an excellent resource for courses on data collection, survey methodology, and nonresponse and measurement error.</p>

US