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Measurement Error in Longitudinal Data tackles the important issue of how to understand and estimate change in the context of imperfect data.
List of contents
- 1: Memory Effects as a Source of Bias in Repeated Survey Measurement
- 2: A Methodological Framework for the Analysis of Panel Conditioning Effects
- 3: A longitudinal error framework to support the design and use of integrated datasets
- 4: Modeling Mode Effects for a Panel Survey in Transition
- 5: Estimating Mode Effects in Panel Surveys: A Multitrait Multimethod Approach
- 6: Developing Reliable Measures: An Approach to Evaluating the Quality of Survey Measurement Using Longitudinal Designs
- 7: Assessing and relaxing assumptions in quasi-simplex models
- 8: Modelling error dependence in categorical longitudinal data
- 9: Reliability in Latent Growth Curve Models
- 10: Longitudinal Measurement (Non)Invariance in Latent Constructs: Conceptual Insights, Model Specifications and Testing Strategies
- 11: Measurement invariance with ordered categorical variables: applications in longitudinal survey research
- 12: Self-evaluation, Differential Item Functioning and Longitudinal Anchoring Vignettes
- 13: The Implications of Functional Form Choice on Model Misspecification in Longitudinal Survey Mode Adjustments
- 14: Disappearing errors in a conversion model
- 15: On Total Least Squares Estimation for Longitudinal Errors-in-Variables Models
- 16: Comparison of Reliability in Seventeen European Countries Using the Quasi-Simplex Model
- 17: Establishing measurement invariance across time within an accelerated longitudinal design
About the author
Alexandru Cernat is a senior lecturer in the Social Statistics Department at the University of Manchester. He has a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods and the Cathie Marsh Institute. His research and teaching focus on: survey methodology, longitudinal data, measurement error, latent variable modelling, new forms of data and missing data.
Joseph W. Sakshaug is Deputy Head of Research and Head of the Data Collection and Data Integration Unit in the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nuremberg. He is also University Professor of Statistics in the Department of Statistics at the Ludwig Maximilian University of Munich, and Honorary Professor in the School of Social Sciences at the University of Mannheim. His research and teaching focuses on survey design and estimation, nonresponse and measurement error, and data integration.
Summary
Measurement Error in Longitudinal Data tackles the important issue of how to understand and estimate change in the context of imperfect data.
Additional text
It is definitely an excellent book and a must-read for anybody analysing longitudinal data and/or developing new or modified methods of analysing longitudinal data in any field of study.
