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Overview
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:
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.
Product Details
| ISBN-13: | 9781119376958 |
|---|---|
| Publisher: | Wiley |
| Publication date: | 03/26/2021 |
| Series: | Wiley Series in Probability and Statistics |
| Sold by: | JOHN WILEY & SONS |
| Format: | eBook |
| Pages: | 544 |
| File size: | 5 MB |
About the Author
Table of Contents
List of Contributors xviiPreface 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