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Chendi Wang has been honoured with our Cora Maas Award for the best-evaluated course on our 2023 Methods School programme, Bayesian Modelling.
Our Methods School offers an all-encompassing curriculum of advanced training in qualitative and quantitative research methods, catering to social science scholars of all levels and career stages.
Named in honour of Cora Maas, whose exceptional teaching inspired countless junior researchers, this prestigious award celebrates pedagogical excellence among our instructors at both summer and winter Methods School programmes.
The recipient of the award was determined by the outcome of the evaluation surveys carried out after our 2023 winter and summer Methods School events.
Our Training Subcommittee, which oversees the award of these prizes, extends its sincere congratulations to Chendi on behalf of the ECPR community.
Chendi has been doing a fine job for a couple of Methods School cycles across different courses. He has a very participant-oriented approach to teaching a complex topic, including critical reflection on the pedagogical approach and willingness to change what does not work.
Chendi Wang is an Assistant Professor of Political Science at the Department of Political Science and Public Administration, VU Amsterdam. He works in comparative politics, political behaviour, political economy and political methodology.
Chendi's current substantive research focuses on the politics of macroeconomic policies, party & electoral politics, and EU politics. Methodologically, his research agenda revolves around Bayesian and nonparametric statistics, measurement and time series analysis.
I am deeply honoured and humbled to receive the 2023 Cora Maas Award. Teaching the Bayesian course has been a passion of mine, and this recognition reaffirms the importance of fostering a deep understanding of methodologies. The Bayesian approach is a powerful tool in political research, yet it is often underutilised.
Chendi Wang, Vrije Universiteit Amsterdam
Keywords: Political Methodology, Methods, Qualitative, Quantitative, Big Data