Date: 20th of June
Location: 8th floor, 11 Portland Street. More details on location.
Program
| 09:30 – 09:45 | Introduction |
| 09:45 – 11:15 | Latent variable modelling Establishing measurement invariance across time within an accelerated longitudinal design – Link to slides Maria Pampaka Using Latent State-Trait Theory to Analyze Intensive Longitudinal Data – Link to slides Sebastian Castro Alvarez, Laura F. Bringmann, Jorge N. Tendeiro Measuring normal cognitive ageing – Link to slides Nick Shryane, Renata Manousos |
| 11:45 – 13:15 | Data collection I Using Reverse Coded and Fictitious Issues Questions for Measurement Error Estimation – Link to slides Emilia Peytcheva, Johnathan Conzelmann, Ashley Wilson, Jennifer Wine Measuring expenditure with a mobile app: How do probability and nonprobability panels compare? Carina Cornesse, Annette Jäckle, Alexander Wenz Reading Deviations and Measurement Errors Tarek Al Baghal, Jennifer Kelley Panel mixed-mode effects: does switching modes in probability-based online panels influence measurement error? – Link to slides Sebastian Kocar, Nicholas Biddle |
| 13:15 – 14:15 | Lunch |
| 14:15 – 15:45 | Statistical models for longitudinal data I Measurement Error and Precision Medicine – Link to slides Michael Wallace, Dylan Spicker A joint modelling approach to relate within-individual variability in a repeatedly measured exposure to a future outcome – Link to slides Richard Parker, Harvey Goldstein, Jon Heron, Laura Howe, George Leckie, Kate Tilling A new longitudinal measurement error model with application to physical activity assessment instruments in a large biomarker validation study – Link to slides Victor Kipnis, Douglas Midthune, Raymond J. Carroll, Laurence S. Freedman Towards Generalized Error-in-Variables Models. Extended Corrected Score Functions and Partially Identified Measurement Error Correction – Link to slides Thomas Augustin |
| 16:15 – 17:15 | Keynote: Annelies Blom – Memory effects in surveys |
| From 19:00 | Conference dinner (Abode Hotel, 107 Piccadilly, see details) |