I am an expert in data collection and longitudinal data analysis, with a particular focus on advanced methods for estimating and improving data quality. My research investigates how to design, collect, and combine data in a way that produces more accurate and useful insights about the social world.

My work focuses on generalized latent variable models—including Structural Equation Modelling, Latent Class Analysis, and Item Response Theory—applied to longitudinal data. I use these methods to study measurement error, non-response, survey mode effects, and data quality over time.

Increasingly, I’m working at the frontier of new forms of data (such as digital traces and administrative records) and exploring how to integrate multiple data sources to enhance both research quality and relevance. This includes methodological work on linking, harmonizing, and validating disparate forms of social data.

I also develop resources for researchers and practitioners through longitudinalanalysis.com, a platform dedicated to training and guidance on collecting, preparing, and analyzing longitudinal data.

I welcome collaboration with researchers, institutions, and practitioners interested in survey methodology, data quality, longitudinal research, and the integration of diverse data sources.

I teach and design courses in survey methodology, longitudinal data analysis, latent variable models, multilevel models, and statistical programming with R. Alongside university teaching, I offer bespoke training for research teams and organizations, tailored to their specific data needs and challenges.