Improving Surveys with Paradata: Analytic Uses of Process Information(Wiley Series in Survey Methodology)

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

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