Call for book chapters
Measurement Error in Longitudinal Data
Editors:
Alexandru Cernat, University of Manchester, UK
Joseph W. Sakshaug, Institute for Employment Research and University of Mannheim, Germany
Publisher:Oxford University Press
It is our great pleasure to invite book chapter proposals for the book Measurement Error in Longitudinal Data published by Oxford University Press.
After a successful workshop in Manchester and an agreement with Oxford University Press we are now inviting book chapters on the topic of measurement error in longitudinal data. The book aims to bring together state of the art knowledge regarding minimizing measurement error during data collection as well as its estimation and correction in different contexts. We are aiming for an interdisciplinary approach and we encourage colleagues from across a range of disciplines to submit an abstract. We especially encourage submissions from: survey research, statistics, psychology, education, health, economics, marketing, and data science. Abstracts should emphasize a measurement error topic relevant to longitudinal data. Possible topics may include (but are not limited to):
- Design of longitudinal data collection to minimize measurement error
- Estimating measurement error using experimental designs
- Statistical models for estimating measurement error
- Ways of correcting for measurement error post-survey data collection
- Impact of measurement error on substantive research and policy making
- The use of adaptive designs to minimize measurement error
- Measurement error in different settings (e.g., admin data, social media data, biological data)
- Measurement error in online panel surveys
We especially welcome state of the art overviews, for example:
- Best practices for minimizing measurement error in longitudinal surveys
- Overview on measurement error in administrative data
- Overview on the use of SEM for correction of measurement error in estimates of change
- Overview on methods for estimating measurement error in longitudinal data
- Overview on the use of equivalence testing and DIF to understand measurement error in longitudinal data
If you are interested to be part of the edited volume please submit an abstract by the 9th of August 2019 to alexandru.cernat@manchester.ac.uk. The abstract should be between 500 and 1,000 words and include: overview of the chapter, aims, and an outline.
Notifications of acceptance will be sent to authors by the 16th of August 2019.
The provisional schedule is:
- 9th of August 2019 – chapter outline due
- 16th of August 2019 – decision letter to be sent
- 31st December 2019 – first draft of chapter due
- 31st of January 2020 – feedback on first draft to be sent
- 1st of April 2020 – second draft of chapter due
- 15th of April 2020 – feedback on second draft to be sent
- 1st of August 2020– final draft of chapter due
Open access. Oxford University Press has agreed to publish the book under open access, conditional on financing the open access fee. We are currently looking for ways to finance the fee. If the open access fee is not covered then the book will be available based on the regular prices offered by Oxford University Press.
Alexandru Cernat, University of Manchester, UK
Joseph W. Sakshaug, Institute for Employment Research and University of Mannheim, Germany