Max Welz, University of Zurich

Dissertation Prize

Many quantities of scientific, political, or public interest are measured through categorical variables. A categorical variable is characterized by only taking values in a known countable range. Examples include survey items where a respondent chooses from prespecified response options such as “strongly disagree”, “disagree”, “neutral”, “agree”, “strongly agree” , income groups such as “low”, “middle”, “high”, or counting variables like one’s number of children (0, 1, 2,…). However, low-quality data points might be present in a sample of a categorical variables due to (but not limited to) inattentive responses, data entry errors, or improper use of measurement tools. The thesis of Max Welz studies the effects of low-quality categorical data on statistical analyses and proposes novel methods for robustly analyzing such data, with a special focus on psychometric analyses. It turns out that having already 5% or less data points being low-quality in a categorical sample suffices to invalidate the results of popular analysis methods. In contrast, the proposed novel methods remain robust to the adverse effects of low-quality data points and help identify them. For instance, the proposed methods can be used to answer questions such as “Which survey respondents have been inattentive?”, “When has a respondent started responding carelessly due to fatigue in lengthy surveys?”, or “For which categorical data points is a statistical model misspecified?” The thesis furthermore shows that its proposed methods have unusual but attractive theoretical properties. In the spirit of open science, all methods are implemented in free, publicly available open-source software packages.

About the Speaker

Max Welz is a postdoctoral researcher in statistics at the University of Zurich in the group of Carolin Strobl. He received his PhD in statistics in 2025 from Erasmus University Rotterdam (EUR) under the supervision of Patrick Groenen and Andreas Alfons. During his doctoral studies, he spent a semester at Massachusetts Institute of Technology in the United States. He furthermore holds MSc and BSc degrees from EUR and University of Mannheim, respectively. Max’ research is focused on robust statistical methods for the analysis of categorical data, which are often marred by low-quality observations, such as careless responses or noisy measurements in rating data. He currently works on robust estimation methods for latent variable models with ordinal variables, particularly psychometric structural equation models. His work has been recognized by awards by the Institute of Mathematical Statistics and the Dutch-Flemish Classification Society, as well as publications in international peer-reviewed journals, such as Psychometrika.