Memory effects as a source of bias in repeated survey measurement


Rettig, Tobias ; Blom, Annelies G.



Document Type: Book chapter
Year of publication: 2021
Book title: Measurement error in longitudinal data
Page range: 3-18
Publisher: Cernat, Alexandru ; Sakshaug, Joseph W.
Place of publication: Oxford ; New York, NY
Publishing house: Oxford University Press
ISBN: 978-0-19-885998-7 , 978-0-19-189244-8
Publication language: English
Institution: Außerfakultäre Einrichtungen > SFB 884
School of Social Sciences > Methoden d. empirischen Sozialforschung insbes. Internet Panel Survey-Forschung (Blom 2017-)
Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department B
Subject: 300 Social sciences, sociology, anthropology
Abstract: 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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