The Nile on eBay Advances in Longitudinal Survey Methodology by Peter Lynn
Advances in Longitudinal Survey Methodology Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodologyAdvances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, "Methodology of Longitudinal Surveys", published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting.New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents' consent to data linkage add to the book's relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of:A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequencyAn exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studiesAn examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement.An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.
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Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodology Advances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, "Methodology of Longitudinal Surveys", published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting. New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents' consent to data linkage add to the book's relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of: A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequency An exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studies An examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement. An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.
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Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodology Advances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, "Methodology of Longitudinal Surveys", published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting. New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents' consent to data linkage add to the book's relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of: A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequency An exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studies An examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement. An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.
Author Biography
Peter Lynn is Professor of Survey Methodology and Director of the Institute for Social and Economic Research (ISER), University of Essex. ISER is one of the leading research centres in the world for longitudinal survey methods and Professor Lynn has headed the survey methods programme at ISER since he joined Essex in 2001. Professor Lynn has published more than 60 articles on survey methods topics in top scientific journals, mostly on topics specific to longitudinal surveys, in addition to numerous book chapters, reports and other articles.
Table of Contents
List of Contributors xvii Preface xxiii About the Companion Website xxvii 1 Refreshment Sampling for Longitudinal Surveys 1Nicole Watson and Peter Lynn 1.1 Introduction 1 1.2 Principles 6 1.3 Sampling 7 1.3.1 Sampling Frame 7 1.3.2 Screening 8 1.3.3 Sample Design 10 1.3.4 Questionnaire Design 10 1.3.5 Frequency 11 1.4 Recruitment 13 1.5 Data Integration 14 1.6 Weighting 15 1.7 Impact on Analysis 18 1.8 Conclusions 20 References 22 2 Collecting Biomarker Data in Longitudinal Surveys 26Meena Kumari and Michaela Benzeval 2.1 Introduction 26 2.2 What Are Biomarkers, and Why Are They of Value? 27 2.2.1 Detailed Measurements of Ill Health 28 2.2.2 Biological Pathways 29 2.2.3 Genetics in Longitudinal Studies 31 2.3 Approaches to Collecting Biomarker Data in Longitudinal Studies 32 2.3.1 Consistency and Relevance of Measures Over Time 33 2.3.2 Panel Conditioning and Feedback 35 2.3.3 Choices of When and Who to Ask for Sensitive or Invasive Measures 36 2.3.4 Cost 39 2.4 The Future 40 References 42 3 Innovations in Participant Engagement and Tracking in Longitudinal Surveys 47Lisa Calderwood, Matt Brown, Emily Gilbert and Erica Wong 3.1 Introduction and Background 47 3.2 Literature Review 48 3.3 Current Practice 52 3.4 New Evidence on Internet and Social Media for Participant Engagement 55 3.4.1 Background 55 3.4.2 Findings 56 3.4.2.1 MCS 56 3.4.2.2 Next Steps 57 3.4.3 Summary and Conclusions 58 3.5 New Evidence on Internet and Social Media for Tracking 58 3.5.1 Background 58 3.5.2 Findings 60 3.5.3 Summary and Conclusions 61 3.6 New Evidence on Administrative Data for Tracking 62 3.6.1 Background 62 3.6.2 Findings 63 3.6.3 Summary and Conclusions 67 3.7 Conclusion 68 Acknowledgements 69 References 69 4 Effects on Panel Attrition and Fieldwork Outcomes from Selection for a Supplemental Study: Evidence from the Panel Study of Income Dynamics 74Narayan Sastry, Paula Fomby and Katherine A. McGonagle 4.1 Introduction 74 4.2 Conceptual Framework 75 4.3 Previous Research 77 4.4 Data and Methods 78 4.5 Results 86 4.6 Conclusions 95 Acknowledgements 98 References 98 5 The Effects of Biological Data Collection in Longitudinal Surveys on Subsequent Wave Cooperation 100Fiona Pashazadeh, Alexandru Cernat and Joseph W. Sakshaug 5.1 Introduction 100 5.2 Literature Review 101 5.3 Biological Data Collection and Subsequent Cooperation: Research Questions 106 5.4 Data 108 5.5 Modelling Steps 109 5.6 Results 110 5.7 Discussion and Conclusion 114 5.8 Implications for Survey Researchers 116 References 117 6 Understanding Data Linkage Consent in Longitudinal Surveys 122Annette Jäckle, Kelsey Beninger, Jonathan Burton and Mick P. Couper 6.1 Introduction 122 6.2 Quantitative Research: Consistency of Consent and Effect of Mode of Data Collection 125 6.2.1 Data and Methods 125 6.2.2 Results 128 6.2.2.1 How Consistent Are Respondents about Giving Consent to Data Linkage between Topics? 128 6.2.2.2 How Consistent Are Respondents about Giving Consent to Data Linkage over Time? 130 6.2.2.3 Does Consistency over Time Vary between Domains? 131 6.2.2.4 What Is the Effect of Survey Mode on Consent? 132 6.3 Qualitative Research: How Do Respondents Decide Whether to Give Consent to Linkage? 136 6.3.1 Methods 136 6.3.2 Results 137 6.3.2.1 How Do Participants Interpret Consent Questions? 137 6.3.2.2 What Do Participants Think Are the Implications of Giving Consent to Linkage? 141 6.3.2.3 What Influences the Participant's Decision Whether or Not to Give Consent? 142 6.3.2.4 How Does the Survey Mode Influence the Decision to Consent? 144 6.3.2.5 Why Do Participants Change their Consent Decision over Time? 144 6.4 Discussion 145 Acknowledgements 147 References 148 7 Determinants of Consent to Administrative Records Linkage in Longitudinal Surveys: Evidence from Next Steps 151Darina Peycheva, George Ploubidis and Lisa Calderwood 7.1 Introduction 151 7.2 Literature Review 153 7.3 Data and Methods 155 7.3.1 About the Study 155 7.3.2 Consents Sought and Consent Procedure 156 7.3.3 Analytic Sample 157 7.3.4 Methods 158 7.4 Results 160 7.4.1 Consent Rates 160 7.4.2 Regression Models 163 7.4.2.1 Concepts and Variables 163 7.4.2.2 Characteristics Related to All or Most Consent Domains 164 7.4.2.3 National Health Service (NHS) Records 164 7.4.2.4 Police National Computer (PNC) Criminal Records 167 7.4.2.5 Education Records 167 7.4.2.6 Economic Records 170 7.5 Discussion 173 7.5.1 Summary of Results 173 7.5.2 Methodological Considerations and Limitations 176 7.5.3 Practical Implications 177 References 177 8 Consent to Data Linkage: Experimental Evidence from an Online Panel 181Ben Edwards and Nicholas Biddle 8.1 Introduction 181 8.2 Background 182 8.2.1 Experimental Studies of Data Linkage Consent in Longitudinal Surveys 183 8.3 Research Questions 186 8.4 Method 187 8.4.1 Data 187 8.4.2 Study 1: Attrition Following Data Linkage Consent 187 8.4.3 Study 2: Testing the Effect of Type and Length of Data Linkage Consent Questions 188 8.5 Results 190 8.5.1 Do Requests for Data Linkage Consent Affect Response Rates in SubsequentWaves? (RQ1) 190 8.5.2 Do Consent Rates Depend on Type of Data Linkage Requested? (RQ2a) 191 8.5.3 Do Consent Rates Depend on Survey Mode? (RQ2b) 193 8.5.4 Do Consent Rates Depend on the Length of the Request? (RQ2c) 193 8.5.5 Effects on Understanding of the Data Linkage Process (RQ3) 194 8.5.6 Effects on Perceptions of the Risk of Data Linkage (RQ4) 197 8.6 Discussion 198 References 200 9 Mixing Modes in Household Panel Surveys: Recent Developments and New Findings 204Marieke Voorpostel, Oliver Lipps and Caroline Roberts 9.1 Introduction 204 9.2 The Challenges of Mixing Modes in Household Panel Surveys 205 9.3 Current Experiences with Mixing Modes in Longitudinal Household Panels 207 9.3.1 The German Socio-Economic Panel (SOEP) 207 9.3.2 The Household, Income, and Labour Dynamics in Australia (HILDA) Survey 208 9.3.3 The Panel Study of Income Dynamics (PSID) 209 9.3.4 The UK Household Longitudinal Study (UKHLS) 211 9.3.5 The Korean Labour and Income Panel Study (KLIPS) 212 9.3.6 The Swiss Household Panel (SHP) 213 9.4 The Mixed-Mode Pilot of the Swiss Household Panel Study 214 9.4.1 Design of the SHP Pilot 214 9.4.2 Results of the FirstWave 217 9.4.2.1 Overall Response Rates in the Three Groups 217 9.4.2.2 Use of Different Modes in the Three Groups 217 9.4.2.3 Household Nonresponse in the Three Groups 219 9.4.2.4 Individual Nonresponse in the Three Groups 221 9.5 Conclusion 223 References 224 10 Estimating the Measurement Effects of Mixed Modes in Longitudinal Studies: Current Practice and Issues 227Alexandru Cernat and Joseph W. Sakshaug 10.1 Introduction 227 10.2 Types of Mixed-Mode Designs 230 10.3 Mode Effects and Longitudinal Data 232 10.3.1 Estimating Change from Mixed-Mode Longitudinal Survey Data 233 10.3.2 General Concepts in the Investigation of Mode Effects 233 10.3.3 Mode Effects on Measurement in Longitudinal Data: Literature Review 235 10.4 Methods for Estimating Mode Effects on Measurement in Longitudinal Studies 237 10.5 Using Structural Equation Modelling to Investigate Mode Differences in Measurement 239 10.6 Conclusion 245 Acknowledgement 246 References 246 11 Measuring Cognition in a Multi-Mode Context 250Mary Beth Ofstedal, Colleen A. McClain and Mick P. Couper 11.1 Introduction 250 11.2 Motivation and Previous Literature 251 11.2.1 Measurement of Cognition in Surveys 251 11.2.2 Mode Effects and Survey Response 252 11.2.3 Cognition in a Multi-Mode Context 252 11.2.4 Existing Mode Comparisons of Cognitive Ability 254 11.3 Data and Methods 256 11.3.1 Data Source 256 11.3.2 Analytic Sample 256 11.3.3 Administration of Cognitive Tests 257 11.3.4 Methods 258 11.3.4.1 Item Missing Data 259 11.3.4.2 Completion Time 259 11.3.4.3 Overall Differences in Scores 259 11.3.4.4 Correlations Between Measures 259 11.3.4.5 Trajectories over Time 260 11.3.4.6 Models Predicting Cognition as an Outcome 260 11.4 Results 261 11.4.1 Item-Missing Data 261 11.4.2 Completion Time 262 11.4.3 Differences in Mean Scores 262 11.4.4 Correlations Between Measures 263 11.4.5 Trajectories over Time 263 11.4.6 Substantive Models 265 11.5 Discussion 266 Acknowledgements 268 References 268 12 Panel Conditioning: Types, Causes, and Empirical Evidence of What We Know So Far 272Bella Struminskaya and Michael Bosnjak 12.1 Introduction 272 12.2 Methods for Studying Panel Conditioning 273 12.3 Mechanisms of Panel Conditioning 276 12.3.1 Survey Response Process and the Effects of Repeated Interviewing 276 12.3.2 Reflection/Cognitive Stimulus 279 12.3.3 Empirical Evidence of Reflection/Cognitive Stimulus 280 12.3.3.1 Changes in Attitudes Due to Reflection 280 12.3.3.2 Changes in (Self-Reported) Behaviour Due to Reflection 282 12.3.3.3 Changes in Knowledge Due to Reflection 284 12.3.4 Social Desirability Reduction 285 12.3.5 Empirical Evidence of Social Desirability Effects 285 12.3.6 Satisficing 287 12.3.7 Empirical Evidence of Satisficing 288 12.3.7.1 Misreporting to Filter Questions as a Conditioning Effect Due to Satisficing 288 12.3.7.2 Misreporting to More Complex Filter (Looping) Questions 289 12.3.7.3 Within-Interview and Between-Waves Conditioning in Filter Questions 290 12.4 Conclusion and Implications for Survey Practice 292 References 295 13 Interviewer Effects in Panel Surveys 302Simon Kühne and Martin Kroh 13.1 Introduction 302 13.2 Motivation and State of Research 303 13.2.1 Sources of Interviewer-Related Measurement Error 303 13.2.1.1 Interviewer Deviations 304 13.2.1.2 Social Desirability 305 13.2.1.3 Priming 307 13.2.2 Moderating Factors of Interviewer Effects 307 13.2.3 Interviewer Effects in Panel Surveys 308 13.2.4 Identifying Interviewer Effects 310 13.2.4.1 Interviewer Variance 310 13.2.4.2 Interviewer Bias 311 13.2.4.3 Using Panel Data to Identify Interviewer Effects 312 13.3 Data 313 13.3.1 The Socio-Economic Panel 313 13.3.2 Variables 314 13.4 The Size and Direction of Interviewer Effects in Panels 314 13.4.1 Methods 314 13.4.2 Results 318 13.4.3 Effects on Precision 320 13.4.4 Effects on Validity 321 13.5 Dynamics of Interviewer Effects in Panels 322 13.5.1 Methods 324 13.5.2 Results 324 13.5.2.1 Interviewer Variance 324 13.5.2.2 Interviewer Bias 325 13.6 Summary and Discussion 326 References 329 14 Improving Survey Measurement of Household Finances: A Review of New Data Sources and Technologies 337Annette Jäckle, Mick P. Couper, Alessandra Gaia and Carli Lessof 14.1 Introduction 337 14.1.1 Why Is Good Financial Data Important for Longitudinal Surveys? 338 14.1.2 Why New Data Sources and Technologies for Longitudinal Surveys? 339 14.1.3 How Can New Technologies Change the Measurement Landscape? 340 14.2 The Total Survey Error Framework 341 14.3 Review of New Data Sources and Technologies 343 14.3.1 Financial Aggregators 346 14.3.2 Loyalty Card Data 346 14.3.3 Credit and Debit Card Data 347 14.3.4 Credit Rating Data 348 14.3.5 In-Home Scanning of Barcodes 349 14.3.6 Scanning of Receipts 350 14.3.7 Mobile Applications and Expenditure Diaries 350 14.4 New Data Sources and Technologies and TSE 352 14.4.1 Errors of Representation 352 14.4.1.1 Coverage Error 352 14.4.1.2 Non-Participation Error 353 14.4.2 Measurement Error 355 14.4.2.1 Specification Error 355 14.4.2.2 Missing or Duplicate Items/Episodes 356 14.4.2.3 Data Capture Error 357 14.4.2.4 Processing or Coding Error 357 14.4.2.5 Conditioning Error 357 14.5 Challenges and Opportunities 358 Acknowledgements 360 References 360 15 How to Pop the Question? Interviewer and Respondent Behaviours When Measuring Change with Proactive Dependent Interviewing 368Annette Jäckle, Tarek Al Baghal, Stephanie Eckman and Emanuela Sala 15.1 Introduction 368 15.2 Background 370 15.3 Data 374 15.4 Behaviour Coding Interviewer and Respondent Interactions 376 15.5 Methods 379 15.6 Results 380 15.6.1 Does the DIWording Affect how Interviewers and Respondents Behave? (RQ1) 381 15.6.2 Does theWording of DI Questions Affect the Sequences of Interviewer and Respondent Interactions? (RQ2) 382 15.6.3 Which Interviewer Behaviours Lead to Respondents Giving Codeable Answers? (RQ3) 385 15.6.4 Are the Different Rates of Change Measured with Different DI Wordings Explained by Differences in I and R Behaviours? (RQ4) 386 15.7 Conclusion 388 Acknowledgements 390 References 390 16 Assessing Discontinuities and Rotation Group Bias in Rotating Panel Designs 399Jan A. van den Brakel, Paul A. Smith, Duncan Elliott, Sabine Krieg, Timo Schmid and Nikos Tzavidis 16.1 Introduction 399 16.2 Methods for Quantifying Discontinuities 401 16.3 Time Series Models for Rotating Panel Designs 402 16.3.1 Rotating Panels and Rotation Group Bias 402 16.3.2 Structural Time Series Model for Rotating Panels 404 16.3.3 Fitting Structural Time Series Models 407 16.4 Time Series Models for Discontinuities in Rotating Panel Designs 408 16.4.1 Structural Time Series Model for Discontinuities 409 16.4.2 Parallel Run 410 16.4.3 Combining Information from a Parallel Run with the Intervention Model 411 16.4.4 Auxiliary Time Series 412 16.5 Examples 412 16.5.1 Redesigns in the Dutch LFS 412 16.5.2 Using a State Space Model to Assess Redesigns in the UK LFS 417 16.6 Discussion 419 References 421 17 Proper Multiple Imputation of Clustered or Panel Data 424Martin Spiess, Kristian Kleinke and Jost Reinecke 17.1 Introduction 424 17.2 Missing Data Mechanism and Ignorability 425 17.3 Multiple Imputation (MI) 426 17.3.1 Theory and Basic Approaches 426 17.3.2 Single Versus Multiple Imputation 429 17.3.2.1 Unconditional Mean Imputation and Regression Imputation 430 17.3.2.2 Last Observation Carried Forward 430 17.3.2.3 Row-and-Column Imputation 432 17.4 Issues in the Longitudinal Context 434 17.4.1 Single-Level Imputation 435 17.4.2 Multilevel Multiple Imputation 437 17.4.3 Interactions and Non-Linear Associations 439 17.5 Discussion 441 References 443 18 Issues in Weighting for Longitudinal Surveys 447Peter Lynn and Nicole Watson 18.1 Introduction: The Longitudinal Context 447 18.1.1 Dynamic Study Population 447 18.1.2 Wave Non-Response Patterns 448 18.1.3 Auxiliary Variables 449 18.1.4 Longitudinal Surveys as a Multi-Purpose Research Resource 450 18.1.5 Multiple Samples 450 18.2 Population Dynamics 451 18.2.1 Post-Stratification 451 18.2.2 Population Entrants 453 18.2.3 Uncertain Eligibility 454 18.3 Sample Participation Dynamics 458 18.3.1 Subsets of Instrument Combinations 459 18.3.2 Weights for Each Pair of Instruments 461 18.3.3 Analysis-SpecificWeights 462 18.4 Combining Multiple Non-Response Models 463 18.5 Discussion 465 Acknowledgements 466 References 467 19 Small-Area Estimation of Cross-Classified Gross Flows Using Longitudinal Survey Data 469Yves Thibaudeau, Eric Slud and Yang Cheng 19.1 Introduction 469 19.2 Role of Model-Assisted Estimation in Small Area Estimation 470 19.3 Data and Methods 471 19.3.1 Data 471 19.3.2 Estimate and Variance Comparisons 473 19.4 Estimating Gross Flows 474 19.5 Models 475 19.5.1 Generalised Logistic Fixed Effect Models 475 19.5.2 Fixed Effect Logistic Models for Estimating Gross Flows 476 19.5.3 Equivalence between Fixed-Effect Logistic Regression and Log-Linear Models 477 19.5.4 Weighted Estimation 478 19.5.5 Mixed-Effect Logit Models for Gross Flows 479 19.5.6 Application to the Estimation of Gross Flows 481 19.6 Results 481 19.6.1 Goodness of Fit Tests for Fixed Effect Models 481 19.6.2 Fixed-Effect Logit-Based Estimation of Gross Flows 483 19.6.3 Mixed Effect Models 483 19.6.4 Comparison of Models through BRR Variance Estimation 483 19.7 Discussion 486 Acknowledgements 488 References 488 20 Nonparametric Estimation for Longitudinal Data with Informative Missingness 491Zahoor Ahmad and Li-Chun Zhang 20.1 Introduction 491 20.2 Two NEE Estimators of Change 494 20.3 On the Bias of NEE 497 20.4 Variance Estimation 499 20.4.1 NEE (Expression 20.3) 499 20.4.2 NEE (Expression 20.6) 500 20.5 Simulation Study 501 20.5.1 Data 502 20.5.2 Response Probability Models 502 20.5.3 Simulation Set-up 503 20.5.4 Results 504 20.6 Conclusions 507 References 511 Index 513
Details ISBN1119376939 Series Wiley Series in Probability and Statistics ISBN-10 1119376939 ISBN-13 9781119376934 Format Hardcover Author Peter Lynn DEWEY 300.721 Language English UK Release Date 2021-04-08 AU Release Date 2021-04-08 NZ Release Date 2021-04-08 US Release Date 2021-04-08 Edited by Peter Lynn Pages 544 Publisher John Wiley & Sons Inc Year 2021 Publication Date 2021-04-08 Imprint John Wiley & Sons Inc Place of Publication New York Country of Publication United States Audience Professional & Vocational We've got this
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