Master class: Estimating and correcting for measurement error in longitudinal studies
3rd of June 2021: 14:00 – 18:15 (BST)
To mark the launch of the new book Measurement Error in Longitudinal Data the Royal Statistical Society will be hosting a virtual half-day Master class. The Master class will bring together six of the book authors to present the state of the art in statistical models for estimating and correcting for measurement error in longitudinal data. The course will highlight different statistical models as well as how these can be implemented in practice.
Book your place now on the RSS website (ignore the question on dietary restrictions, the event is online):
https://rss.org.uk/training-events/events/events-2021/sections/master-class-estimating-and-correcting-for-measure/#eventoverview
Program - 3rd of June 2021: 14:00 – 18:15 (BST)
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14:00 – 14:15
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Introduction to event and book
Alexandru Cernat (University of Manchester) & Joe Sakshaug (IAB/LMU-Munich)
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14:15 – 14:50
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Developing Reliable Measures: An Approach to Evaluating the Quality of Survey Measurement Using Longitudinal Designs
Duane F. Alwin (Pennsylvania State University)
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14:50 – 15:25
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Comparison of Reliability in Seventeen European Countries Using the Quasi-Simplex Model
Johana Chylíková (Institute of Sociology of the Czech Academy of Sciences)
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15:25 – 16:00
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Modelling error dependence in categorical longitudinal data
Dimitris Pavlopoulos (Vrije Universiteit Amsterdam)
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16:00 – 16:15
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Break
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16:15 – 16:50
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Longitudinal Measurement (Non)Invariance in Latent Constructs
Heinz Leitgöb (University of Eichstätt-Ingolstadt)
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16:50 – 17:25
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Self-evaluation, Differential Item Functioning and Longitudinal Anchoring Vignettes
Omar Paccagnella (University of Padua)
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17:25 – 18:00
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Establishing measurement invariance across time within an accelerated longitudinal design
Maria Pampaka (University of Manchester)
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18:00 – 18:15
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Q&A and concluding remarks
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As part of the event, Oxford University Press will be offering a 30% discount to their books, including Measurement Error in Longitudinal Data.
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Joseph Sakshaug
German Institute for Employment Research
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