Highlighted past presentations
Transition to multimode data collection: understanding the measurement dimension
Estimating systematic measurement error – NCRM Methods festival 2018
Nurse effects in survey biomarkers – NCRM Methods festival 2018
Full list of presentations
Invited talks
- City University/ESS/NatCen (16 Mar 2021): Transition to multimode data collection: understanding the measurement dimension
- Australian National University (13 Dec 2019): Estimating stochastic survey response errors using the multitrait-multierror model.
- Swinburne University (10 Dec 2019): Introduction to measurement error in the social sciences.
- University of Mannheim (23 Sep 2019): Measurement error in non-probability panels, a comparison.
- Interviewer Workshop, University of Nebraska (27 Feb 2019): Investigating the Use of Nurse Paradata in Understanding Nonresponse to Biological Data Collection.
- Methodology and Statistics Utrecht University (18 Feb 2019): Nurse effects on non-response in survey-based biomeasures.
- Center for the Economics of Aging (MEA) Max-Planck-Institute – Munchen (13 Feb 2019): Nurse effects on non-response in survey-based biomeasures.
- Center for Big Data Research in Health Seminar, University of New South Wales (25 Jun 2018): Understanding Measurement Error in Survey Data.
- Center for Health Economics Seminar, Monash University (6 Jun 2018): Estimating stochastic survey response errors using the Multitrait-Multierrror model.
- Utrecht University Methods seminar (19 Feb 2018): Estimating systematic response errors using the multitrait-multierror model.
- University College London QSS seminar (16 Nov 2017): Implementing mixed modes in longitudinal studies: opportunities and challenges.
- NCRM Autumn School – Manchester (2 Sep 2016): Statistical challenges of measuring longitudinal and life course biomarker data.
- NCRM Methods Festival – Bath (5 Jul 2016): Missing Data in Bio-social Research: Issues, Practice and Recommendations.
- Keynote for North-West DTC Symposium – Manchester (3 Nov 2015): From PISA to captain Picard: the impact of measurement error in the social sciences