Measurement Error in Longitudinal Data

Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions.

Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world.

This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.

About the Author:
Alexandru Cernat, Senior Lecturer in Social Statistics, University of Manchester, Joseph W. Sakshaug, University Professor of Statistics, Ludwig Maximilian University of Munich

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.