Measurement Error in Longitudinal Data (Hardcover)
Language: English
Published by Oxford University Press, Oxford, 2021
- Hardcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
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Add to basketItem description from seller
Hardcover. 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 theworld are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions.Measurement Error in Longitudinal Datatackles 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 datacollection, 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 mostimportant 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. Measurement Error in Longitudinal Data tackles the important issue of how to understand and estimate change in the context of imperfect data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …
Seller Inventory # 9780198859987
- Title
- Measurement Error in Longitudinal Data (Hardcover)
- Author
- Alexandru Cernat
- Publisher
- Oxford University Press, Oxford
- Publication year
- 2021
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0198859988
- ISBN 13
- 9780198859987
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
Grand Eagle Retail
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