Advances in Longitudinal Survey Methodology 1st Edition by Peter Lynn ISBN 978-1119376934 1119376939

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ISBN 10: 1119376939

ISBN 13: 978-1119376934

Author:  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 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.

Table of contents: 

1 Refreshment Sampling for Longitudinal Surveys
1.1 Introduction
1.2 Principles
1.3 Sampling
1.3.1 Sampling Frame
1.3.2 Screening
1.3.3 Sample Design
1.3.4 Questionnaire Design
1.3.5 Frequency
1.4 Recruitment
1.5 Data Integration
1.6 Weighting
1.7 Impact on Analysis
1.8 Conclusions
References
Notes
2 Collecting Biomarker Data in Longitudinal Surveys
2.1 Introduction
2.2 What Are Biomarkers, and Why Are They of Value?
2.2.1 Detailed Measurements of Ill Health
2.2.2 Biological Pathways
2.2.3 Genetics in Longitudinal Studies
2.3 Approaches to Collecting Biomarker Data in Longitudinal Studies
2.3.1 Consistency and Relevance of Measures Over Time
2.3.2 Panel Conditioning and Feedback
2.3.3 Choices of When and Who to Ask for Sensitive or Invasive Measures
2.3.4 Cost
2.4 The Future
References
3 Innovations in Participant Engagement and Tracking in Longitudinal Surveys
3.1 Introduction and Background
3.2 Literature Review
3.3 Current Practice
3.4 New Evidence on Internet and Social Media for Participant Engagement
3.4.1 Background
3.4.2 Findings
3.4.2.1 MCS
3.4.2.2 Next Steps
3.4.3 Summary and Conclusions
3.5 New Evidence on Internet and Social Media for Tracking
3.5.1 Background
3.5.2 Findings
3.5.3 Summary and Conclusions
3.6 New Evidence on Administrative Data for Tracking
3.6.1 Background
3.6.2 Findings
3.6.3 Summary and Conclusions
3.7 Conclusion
Acknowledgements
References
3.A List of Studies that Responded to the Survey
4 Effects on Panel Attrition and Fieldwork Outcomes from Selection for a Supplemental Study: Evidence from the Panel Study of Income Dynamics
4.1 Introduction
4.2 Conceptual Framework
4.3 Previous Research
4.4 Data and Methods
4.5 Results
4.6 Conclusions
Acknowledgements
References
5 The Effects of Biological Data Collection in Longitudinal Surveys on Subsequent Wave Cooperation
5.1 Introduction
5.2 Literature Review
5.3 Biological Data Collection and Subsequent Cooperation: Research Questions
5.4 Data
5.5 Modelling Steps
5.6 Results
5.7 Discussion and Conclusion
5.8 Implications for Survey Researchers
References
Notes
6 Understanding Data Linkage Consent in Longitudinal Surveys
6.1 Introduction
6.2 Quantitative Research: Consistency of Consent and Effect of Mode of Data Collection
6.2.1 Data and Methods
6.2.2 Results
6.2.2.1 How Consistent Are Respondents about Giving Consent to Data Linkage between Topics?
6.2.2.2 How Consistent Are Respondents about Giving Consent to Data Linkage over Time?
6.2.2.3 Does Consistency over Time Vary between Domains?
6.2.2.4 What Is the Effect of Survey Mode on Consent?
6.3 Qualitative Research: How Do Respondents Decide Whether to Give Consent to Linkage?
6.3.1 Methods
6.3.2 Results
6.3.2.1 How Do Participants Interpret Consent Questions?
6.3.2.2 What Do Participants Think Are the Implications of Giving Consent to Linkage?
6.3.2.3 What Influences the Participant’s Decision Whether or Not to Give Consent?
6.3.2.4 How Does the Survey Mode Influence the Decision to Consent?
6.3.2.5 Why Do Participants Change their Consent Decision over Time?
6.4 Discussion
Acknowledgements
References
Notes
7 Determinants of Consent to Administrative Records Linkage in Longitudinal Surveys: Evidence from Next Steps
7.1 Introduction
7.2 Literature Review
7.3 Data and Methods
7.3.1 About the Study
7.3.2 Consents Sought and Consent Procedure
7.3.3 Analytic Sample
7.3.4 Methods
7.4 Results
7.4.1 Consent Rates
7.4.2 Regression Models
7.4.2.1 Concepts and Variables
7.4.2.2 Characteristics Related to All or Most Consent Domains
7.4.2.3 National Health Service (NHS) Records
7.4.2.4 Police National Computer (PNC) Criminal Records
7.4.2.5 Education Records
7.4.2.5.1 Characteristics Related to Consent to All Education Records
7.4.2.5.2 Department for Education (DfE) and Higher Education Statistics Agency (HESA) records
7.4.2.5.3 Universities and College Admissions Service (UCAS) records
7.4.2.5.4 Student Loans Company (SLC) Records
7.4.2.6 Economic Records
7.4.2.6.1 Characteristics Related to Consent to All Economic Records
7.4.2.6.2 Her Majesty’s Revenue and Customs (HMRC) Records
7.4.2.6.3 Department for Work and Pensions (DWP) Records
7.4.2.6.4 National Insurance Number (NINO)
7.5 Discussion
7.5.1 Summary of Results
7.5.2 Methodological Considerations and Limitations
7.5.3 Practical Implications
References
Notes
8 Consent to Data Linkage: Experimental Evidence from an Online Panel
8.1 Introduction
8.2 Background
8.2.1 Experimental Studies of Data Linkage Consent in Longitudinal Surveys
8.3 Research Questions
8.4 Method
8.4.1 Data
8.4.2 Study 1: Attrition Following Data Linkage Consent
8.4.3 Study 2: Testing the Effect of Type and Length of Data Linkage Consent Questions
8.5 Results
8.5.1 Do Requests for Data Linkage Consent Affect Response Rates in Subsequent Waves? (RQ1)
8.5.2 Do Consent Rates Depend on Type of Data Linkage Requested? (RQ2a)
8.5.3 Do Consent Rates Depend on Survey Mode? (RQ2b)
8.5.4 Do Consent Rates Depend on the Length of the Request? (RQ2c)
8.5.5 Effects on Understanding of the Data Linkage Process (RQ3)
8.5.6 Effects on Perceptions of the Risk of Data Linkage (RQ4)
8.6 Discussion
References
Notes
9 Mixing Modes in Household Panel Surveys: Recent Developments and New Findings
9.1 Introduction
9.2 The Challenges of Mixing Modes in Household Panel Surveys
9.3 Current Experiences with Mixing Modes in Longitudinal Household Panels
9.3.1 The German Socio‐Economic Panel (SOEP)
9.3.2 The Household, Income, and Labour Dynamics in Australia (HILDA) Survey
9.3.3 The Panel Study of Income Dynamics (PSID)
9.3.4 The UK Household Longitudinal Study (UKHLS)
9.3.5 The Korean Labour and Income Panel Study (KLIPS)
9.3.6 The Swiss Household Panel (SHP)
9.4 The Mixed‐Mode Pilot of the Swiss Household Panel Study
9.4.1 Design of the SHP Pilot
9.4.2 Results of the First Wave
9.4.2.1 Overall Response Rates in the Three Groups
9.4.2.2 Use of Different Modes in the Three Groups
9.4.2.3 Household Nonresponse in the Three Groups
9.4.2.4 Individual Nonresponse in the Three Groups
9.5 Conclusion
References
Notes
10 Estimating the Measurement Effects of Mixed Modes in Longitudinal Studies: Current Practice and Issues
10.1 Introduction
10.2 Types of Mixed‐Mode Designs
10.3 Mode Effects and Longitudinal Data
10.3.1 Estimating Change from Mixed‐Mode Longitudinal Survey Data
10.3.2 General Concepts in the Investigation of Mode Effects
10.3.3 Mode Effects on Measurement in Longitudinal Data: Literature Review
10.4 Methods for Estimating Mode Effects on Measurement in Longitudinal Studies
10.5 Using Structural Equation Modelling to Investigate Mode Differences in Measurement
10.6 Conclusion
Acknowledgement
References
Notes
11 Measuring Cognition in a Multi‐Mode Context
11.1 Introduction
11.2 Motivation and Previous Literature
11.2.1 Measurement of Cognition in Surveys
11.2.2 Mode Effects and Survey Response
11.2.3 Cognition in a Multi‐Mode Context
11.2.4 Existing Mode Comparisons of Cognitive Ability
11.3 Data and Methods
11.3.1 Data Source
11.3.2 Analytic Sample
11.3.3 Administration of Cognitive Tests
11.3.4 Methods
11.3.4.1 Item Missing Data
11.3.4.2 Completion Time
11.3.4.3 Overall Differences in Scores
11.3.4.4 Correlations Between Measures
11.3.4.5 Trajectories over Time
11.3.4.6 Models Predicting Cognition as an Outcome
11.4 Results
11.4.1 Item‐Missing Data
11.4.2 Completion Time
11.4.3 Differences in Mean Scores
11.4.4 Correlations Between Measures
11.4.5 Trajectories over Time
11.4.6 Substantive Models
11.5 Discussion
Acknowledgements
References
Notes
12 Panel Conditioning: Types, Causes, and Empirical Evidence of What We Know So Far
12.1 Introduction
12.2 Methods for Studying Panel Conditioning
12.3 Mechanisms of Panel Conditioning
12.3.1 Survey Response Process and the Effects of Repeated Interviewing
12.3.2 Reflection/Cognitive Stimulus
12.3.3 Empirical Evidence of Reflection/Cognitive Stimulus
12.3.3.1 Changes in Attitudes Due to Reflection
12.3.3.2 Changes in (Self‐Reported) Behaviour Due to Reflection
12.3.3.3 Changes in Knowledge Due to Reflection
12.3.4 Social Desirability Reduction
12.3.5 Empirical Evidence of Social Desirability Effects
12.3.6 Satisficing
12.3.7 Empirical Evidence of Satisficing
12.3.7.1 Misreporting to Filter Questions as a Conditioning Effect Due to Satisficing
12.3.7.2 Misreporting to More Complex Filter (Looping) Questions
12.3.7.3 Within‐Interview and Between‐Waves Conditioning in Filter Questions
12.4 Conclusion and Implications for Survey Practice
References
Notes
13 Interviewer Effects in Panel Surveys
13.1 Introduction
13.2 Motivation and State of Research
13.2.1 Sources of Interviewer‐Related Measurement Error
13.2.1.1 Interviewer Deviations
13.2.1.2 Social Desirability
13.2.1.3 Priming
13.2.2 Moderating Factors of Interviewer Effects
13.2.3 Interviewer Effects in Panel Surveys
13.2.4 Identifying Interviewer Effects
13.2.4.1 Interviewer Variance
13.2.4.2 Interviewer Bias
13.2.4.3 Using Panel Data to Identify Interviewer Effects
13.3 Data
13.3.1 The Socio‐Economic Panel
13.3.2 Variables
13.4 The Size and Direction of Interviewer Effects in Panels
13.4.1 Methods
13.4.2 Results
13.4.3 Effects on Precision
13.4.4 Effects on Validity
13.5 Dynamics of Interviewer Effects in Panels
13.5.1 Methods
13.5.2 Results
13.5.2.1 Interviewer Variance
13.5.2.2 Interviewer Bias
13.6 Summary and Discussion
References
Notes
14 Improving Survey Measurement of Household Finances: A Review of New Data Sources and Technologies
14.1 Introduction
14.1.1 Why Is Good Financial Data Important for Longitudinal Surveys?
14.1.2 Why New Data Sources and Technologies for Longitudinal Surveys?
14.1.3 How Can New Technologies Change the Measurement Landscape?
14.2 The Total Survey Error Framework
14.3 Review of New Data Sources and Technologies
14.3.1 Financial Aggregators
14.3.2 Loyalty Card Data
14.3.3 Credit and Debit Card Data
14.3.4 Credit Rating Data
14.3.5 In‐Home Scanning of Barcodes
14.3.6 Scanning of Receipts
14.3.7 Mobile Applications and Expenditure Diaries
14.4 New Data Sources and Technologies and TSE
14.4.1 Errors of Representation
14.4.1.1 Coverage Error
14.4.1.2 Non‐Participation Error
14.4.2 Measurement Error
14.4.2.1 Specification Error
14.4.2.2 Missing or Duplicate Items/Episodes
14.4.2.3 Data Capture Error
14.4.2.4 Processing or Coding Error
14.4.2.5 Conditioning Error
14.5 Challenges and Opportunities
Acknowledgements
References
15 How to Pop the Question? Interviewer and Respondent Behaviours When Measuring Change with Proactive Dependent Interviewing
15.1 Introduction
15.2 Background
15.3 Data
15.4 Behaviour Coding Interviewer and Respondent Interactions
15.5 Methods
15.6 Results
15.6.1 Does the DI Wording Affect how Interviewers and Respondents Behave? (RQ1)
15.6.2 Does the Wording of DI Questions Affect the Sequences of Interviewer and Respondent Interactions? (RQ2)
15.6.3 Which Interviewer Behaviours Lead to Respondents Giving Codeable Answers? (RQ3)
15.6.4 Are the Different Rates of Change Measured with Different DI Wordings Explained by Differences in I and R Behaviours? (RQ4)
15.7 Conclusion
Acknowledgements
References
15.A IP3 Stems of Experimental Dependent Interviewing Questions
15.B IP7 Stems of Experimental Dependent Interviewing Questions
15.C Behaviour Coding Frame
Note
16 Assessing Discontinuities and Rotation Group Bias in Rotating Panel Designs
16.1 Introduction
16.2 Methods for Quantifying Discontinuities
16.3 Time Series Models for Rotating Panel Designs
16.3.1 Rotating Panels and Rotation Group Bias
16.3.2 Structural Time Series Model for Rotating Panels
16.3.3 Fitting Structural Time Series Models
16.4 Time Series Models for Discontinuities in Rotating Panel Designs
16.4.1 Structural Time Series Model for Discontinuities
16.4.2 Parallel Run
16.4.3 Combining Information from a Parallel Run with the Intervention Model
16.4.4 Auxiliary Time Series
16.5 Examples
16.5.1 Redesigns in the Dutch LFS
16.5.2 Using a State Space Model to Assess Redesigns in the UK LFS
16.6 Discussion
References
17 Proper Multiple Imputation of Clustered or Panel Data
17.1 Introduction
17.2 Missing Data Mechanism and Ignorability
17.3 Multiple Imputation (MI)
17.3.1 Theory and Basic Approaches
17.3.2 Single Versus Multiple Imputation
17.3.2.1 Unconditional Mean Imputation and Regression Imputation
17.3.2.2 Last Observation Carried Forward
17.3.2.3 Row‐and‐Column Imputation
17.4 Issues in the Longitudinal Context
17.4.1 Single‐Level Imputation
17.4.2 Multilevel Multiple Imputation
17.4.3 Interactions and Non‐Linear Associations
17.5 Discussion
References
Notes
18 Issues in Weighting for Longitudinal Surveys
18.1 Introduction: The Longitudinal Context
18.1.1 Dynamic Study Population
18.1.2 Wave Non‐Response Patterns
18.1.3 Auxiliary Variables
18.1.4 Longitudinal Surveys as a Multi‐Purpose Research Resource
18.1.5 Multiple Samples
18.2 Population Dynamics
18.2.1 Post‐Stratification
18.2.2 Population Entrants
18.2.3 Uncertain Eligibility
18.3 Sample Participation Dynamics
18.3.1 Subsets of Instrument Combinations
18.3.2 Weights for Each Pair of Instruments
18.3.3 Analysis‐Specific Weights
18.4 Combining Multiple Non‐Response Models
18.5 Discussion
Acknowledgements
References
Note
19 Small‐Area Estimation of Cross‐Classified Gross Flows Using Longitudinal Survey Data
19.1 Introduction
19.2 Role of Model‐Assisted Estimation in Small Area Estimation
19.3 Data and Methods
19.3.1 Data
19.3.2 Estimate and Variance Comparisons
19.4 Estimating Gross Flows
19.5 Models
19.5.1 Generalised Logistic Fixed Effect Models
19.5.2 Fixed Effect Logistic Models for Estimating Gross Flows
19.5.3 Equivalence between Fixed‐Effect Logistic Regression and Log‐Linear Models
19.5.4 Weighted Estimation
19.5.5 Mixed‐Effect Logit Models for Gross Flows
19.5.6 Application to the Estimation of Gross Flows
19.6 Results
19.6.1 Goodness of Fit Tests for Fixed Effect Models
19.6.2 Fixed‐Effect Logit‐Based Estimation of Gross Flows
19.6.3 Mixed Effect Models
19.6.4 Comparison of Models through BRR Variance Estimation
19.7 Discussion
Acknowledgements
References
20 Nonparametric Estimation for Longitudinal Data with Informative Missingness
20.1 Introduction
20.2 Two NEE Estimators of Change
20.3 On the Bias of NEE
20.4 Variance Estimation
20.4.1 NEE (Expression 20.3)
20.4.2 NEE (Expression 20.6)
20.5 Simulation Study
20.5.1 Data
20.5.2 Response Probability Models
20.5.3 Simulation Set‐up
20.5.4 Results
20.6 Conclusions
References
Index
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