Preface.
1. Methods for Longitudinal Surveys (Peter Lynn).
1.1 Introduction,.
1.2 Types of Longitudinal Surveys,.
1.3 Strengths of Longitudinal Surveys.
1.4 Weaknesses of Longitudinal Surveys.
1.5 Design Features Specific to Longitudinal Surveys.
1.6 Quality in Longitudinal Surveys.
1.7 Conclusions.
References.
2. Sample Design for Longitudinal Surveys (Paul Smith, Peter Lynn and Dave Elliot).
2.1 Introduction.
2.2 Types of Longitudinal Sample Design.
2.3 Fundamental Aspects of Sample Design.
2.4 Other Aspects of Design and Implementation.
2.5 Conclusion.
References.
3. Ethical Issues in Longitudinal Surveys (Carli Lessof).
3.1 Introduction.
3.2 History of Research Ethics.
3.3 Informed Consent.
3.4 Free Choice Regarding Participation.
3.5 Avoiding Harm.
3.6 Participant Confidentiality and Data Protection.
3.7 Independent Ethical Overview and Participant Involvement.
Acknowledgements.
References.
4. Enhancing Longitudinal Surveys by Linking to Administrative Data (Lisa Calderwood and Carli Lessof).
4.1 Introduction.
4.2 Administrative Data as a Research Resource.
4.3 Record Linkage Methodology.
4.4 Linking Survey Data with Administrative Data at Individual Level.
4.5 Ethical and Legal Issues.
4.6 Conclusion.
References.
5. Tackling Seam Bias Through Questionnaire Design (Jeffrey Moore, Nancy Bates, Joanne Pascale and Aniekan Okon).
5.1 Introduction.
5.2 Previous Research on Seam Bias.
5.3 SIPP and its Dependent Interviewing Procedures.
5.4 Seam Bias Comparison - SIPP 2001 and SIPP 2004.
5.5 Conclusions and Discussion.
References.
6. Dependent Interviewing: A Framework and Application.
to Current Research (Annette Jäckle).
6.1 Introduction.
6.2 Dependent Interviewing - What and Why?
6.3 Design Options and their Effects.
6.4 Empirical Evidence.
6.5 Effects of Dependent Interviewing on Data Quality Across Surveys.
6.6 Open Issues.
References.
7. Attitudes Over Time: The Psychology of Panel Conditioning (Patrick Sturgis, Nick Allum and Ian Brunton-Smith).
7.1 Introduction.
7.2 Panel Conditioning.
7.3 The Cognitive Stimulus Hypothesis.
7.4 Data and Measures.
7.5 Analysis.
7.6 Discussion.
References.
8. Some Consequences of Survey Mode Changes in Longitudinal Surveys (Don A. Dillman).
8.1 Introduction.
8.2 Why Change Survey Modes in Longitudinal Surveys?
8.3 Why Changing Survey Mode Presents a Problem.
8.4 Conclusions.
References.
9. Using Auxiliary Data for Adjustment in Longitudinal Research (Dirk Sikkel, Joop Hox and Edith de Leeuw).
9.1 Introduction.
9.2 Missing Data.
9.3 Calibration.
9.4 Calibrating Multiple Waves.
9.5 Differences Between Waves.
9.6 Single Imputation.
9.7 Multiple Imputation.
9.8 Conclusion and Discussion.
References.
10. Identifying Factors Affecting Longitudinal Survey Response (Nicole Watson and Mark Wooden).
10.1 Introduction.
10.2 Factors Affecting Response and Attrition.
10.3 Predicting Response in the HILDA Survey.
10.4 Conclusion.
References.
11. Keeping in Contact with Mobile Sample Members (Mick P. Couper and Mary Beth Ofstedal).
11.1 Introduction.
11.2 The Location Problem in Panel Surveys.
11.3 Case Study 1: Panel Study of Income Dynamics.
11.4 Case Study 2: Health and Retirement Study.
11.5 Discussion.
References.
12. The Use of Respondent Incentives on Longitudinal Surveys (Heather Laurie and Peter Lynn).
12.1 Introduction.
12.2 Respondent Incentives on Cross-Sectional Surveys.
12.3 Respondent Incentives on Longitudinal Surveys.
12.4 Current Practice on Longitudinal Surveys.
12.5 Experimental Evidence on Longitudinal Surveys.
12.6 Conclusion.
Acknowledgements.
References.
13. Attrition in Consumer Panels (Robert D. Tortora).
13.1 Introduction.
13.2 The Gallup Poll Panel.
13.3 Attrition on the Gallup Poll Panel.
13.4 Summary.
References.
14. Joint Treatment of Nonignorable Dropout and Informative Sampling.
for Longitudinal Survey Data (Abdulhakeem A. H. Eideh and Gad Nathan).
14.1 Introduction.
14.2 Population Model.
14.3 Sampling Design and Sample Distribution.
14.4 Sample Distribution Under Informative Sampling and Informative Dropout.
14.5 Sample Likelihood and Estimation.
14.6 Empirical Example - British Labour Force Survey.
14.7 Conclusions.
References.
15. Weighting and Calibration for Household Panels (Ulrich Rendtel and Torsten Harms).
15.1 Introduction.
15.2 Follow-up Rules.
15.3 Design-Based Estimation.
15.4 Calibration, 274.
15.5 Nonresponse and Attrition.
15.6 Summary.
References.
16. Statistical Modelling for Structured Longitudinal Designs (Ian Plewis).
16.1 Introduction.
16.2 Methodological Framework.
16.3 The Data.
16.4 Modelling One Response from One Cohort.
16.5 Modelling One Response from More Than One Cohort.
16.6 Modelling More Than One Response from One Cohort.
16.7 Modelling Variation Between Generations.
16.8 Conclusion.
References.
17. Using Longitudinal Surveys to Evaluate Interventions (Andrea Piesse, David Judkins and Graham Kalton).
17.1 Introduction.
17.2 Interventions, Outcomes and Longitudinal Data.
17.3 Youth Media Campaign Longitudinal Survey.
17.4 National Survey of Parents and Youth.
17.5 Gaining Early Awareness and Readiness for Undergraduate Programs (GEAR UP).
17.6 Concluding Remarks.
References.
18. Robust Likelihood-Based Analysis of Longitudinal Survey Data with Missing Values (Roderick Little and Guangyu Zhang).
18.1 Introduction.
18.2 Multiple Imputation for Repeated-Measures Data.
18.3 Robust MAR Inference with a Single Missing Outcome.
18.4 Extensions of PSPP to Monotone and General Patterns.
18.5 Extensions to Inferences Other than Means.
18.6 Example.
18.7 Discussion.
Acknowledgements.
References.
19. Assessing the Temporal Association of Events Using Longitudinal Complex Survey Data (Norberto Pantoja-Galicia, Mary E. Thompson and Milorad).
S. Kovacevic.
19.1 Introduction.
19.2 Temporal Order.
19.3 Nonparametric Density Estimation.
19.4 Survey Weights.
19.5 Application: The National Population Health Survey.
19.6 Application: The Survey of Labour and Income Dynamics.
19.7 Discussion.
References.
20. Using Marginal Mean Models for Data from Longitudinal Surveys with a Complex Design: Some Advances in Methods (Georgia Roberts, Qunshu Ren and J.N.K. Rao).
20.1 Introduction.
20.2 Survey-Weighted GEE and Odds Ratio Approach.
20.3 Variance Estimation: One-Step EF-Bootstrap.
20.4 Goodness-of-Fit Tests.
20.5 Illustration Using NPHS Data.
20.6 Summary.
References.
21. A Latent Class Approach for Estimating Gross Flows in the Presence of Correlated Classification Errors (Francesca Bassi and Ugo Trivellato).
21.1 Introduction.
21.2 Correlated Classification Errors and Latent Class Modelling.
21.3 The Data and Preliminary Evidence from Them.
21.4 A Model for Correlated Classification Errors in Retrospective Surveys.
21.5 Concluding Remarks.
References.
22. A Comparison of Graphical Models and Structural Equation Models.
for the Analysis of Longitudinal Survey Data )Peter W. F. Smith, Ann Berrington and Patrick Sturgis).
22.1 Introduction.
22.2 Conceptual Framework.
22.3 Graphical Chain Modelling Approach.
22.4 Structural Equation Modelling Approach.
22.5 Model Fitting.
22.6 Results.
22.7 Conclusions.
References.
Index.
Peter Lynn is Professor of Survey Methodology at the Institute for Social and Economic Research, University of Essex. He is responsible for the methodological research programme of the UK Longitudinal Studies Centre and the UK Household Longitudinal Study and has over 20 years of experience in the field of survey methodology.
“This text is a ‘‘must’’ on the bookshelves of those of us who are
engaged day to day in designing, conducting, or analyzing
longitudinal survey data.” (Public Opinion Quarterly, 14
February 2012)
“This text is a ‘‘must’’ on the bookshelves of those of us who are
engaged day to day in designing, conducting, or analyzing
longitudinal survey data.” (Public Opinion Quarterly, 22
March 2012)
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