My teaching
During my PhD, I work as a teaching assistant at the Faculty of Economics and Business Administration at Ghent University. An overview of my teaching can be found below.
In this capstone course, students bring together the research competences developed earlier in the programme and apply them, in groups, to an original research project within business economics. Working from a research question of their own, they build a full research design (i.e., situating the question in the literature, establishing its scientific and societal relevance, and formulating hypotheses) which they then test using appropriate bivariate analyses and a multivariate regression in SPSS. Students present and defend their work orally at several stages. By the end of the course, they are able to read and process scientific literature, develop a relevant and original research question, write a critical literature review, analyse data and report findings with reference to existing work, motivate and defend their choices, and handle generative AI tools responsibly.
In this course, students will acquire the theoretical and practical knowledge necessary for collecting and analysing data for research. The students learn how the theoretical basis that they have acquired in statistics can be applied in concrete cases. Additionally, new methods are taught, with major focus on the classical linear regression model. Students gain insight into how a concrete statistical or econometric problem can be modeled by means of practical applications. The students also learn to work with SPSS.
This course introduces students to statistical inference: how data from one or more samples can be used to estimate unknown population parameters and to test hypotheses about them. The set-up is deliberately practical, prioritising understanding and interpretation over formal derivation, with the core concepts illustrated throughout by applications and examples from economics. Students become familiar with the cornerstones of statistical analysis, including sample variables and theoretical distributions, confidence intervals, t-tests (null hypotheses, type I and II errors, p-values, and one- versus two-sample settings), analysis of variance (ANOVA), chi-square tests, correlation, and regression.
I supervise multiple master’s theses at Ghent University on a wide range of labour market topics, such as the effects of clothing style, parenthood, Instagram profiles, and care-related career breaks on work outcomes.
This page was last updated on 15 June 2026.