ERIM Summer School
Seven advanced courses
Would you like to improve your data analysis skills or your competences in a specific field? Then start the summer with one or more short, specialised courses at ERIM Summer School. In a group of at most 30 participants, you will be taught by academics who are experts on the subject matter.
Fully taught in English, our courses are open to graduate students (i.e. research master and PhD level), researchers and professionals from the Netherlands and beyond. Upon completing a course, you will receive a certificate indicating the number of European credits (ECTS) earned.
Please find below a list of courses that will be offered during the ERIM Summer School 2018.
18 & 19 June 2018
by Dr Ioannis Evangelidis
In this course you will learn how to conduct and interpret the results of mediation, moderation, and conditional process analyses. Mediation describes the case where an independent variable X influences another variable M, which then influences the dependent variable Y. Moderation describes the case where the effect of the X on Y changes across different levels of another variable W. Conditional process models combine mediation with moderation. This course is targeted to any person doing social science research that wants to learn how to test for mediation and moderation in his or her data.
20 to 22 June 2018
by Professor David Yermack
This course acquaints students with the fundamentals of blockchains, digital currency, smart contracts, and other important FinTech topics, while providing an overview of the new academic literature in this area. The importance of ideas such as crowdfunding, decentralized governance, and disintermediation will receive special attention.
26 to 28 June 2018
by Dr Richard E. Ocejo
This course provides an overview of qualitative approaches to research. It shows students how qualitative researchers design their projects and some of the common issues they face. Based on in-depth readings of the instructor’s work and that of many others, topics will include asking research questions, collecting and analyzing data, and handling theory, among many others. In addition to regular discussion of the material, students will be required to consider qualitative approaches to their own research interests.
26 to 29 June 2018
by Professor Finn Wynstra
Since its emergence in the 1960s, Purchasing and Supply Management research has developed as a multi-disciplinary field. Young scholars in this field would therefore benefit from a broad review of the (classical) management theories suited for studying PSM research questions. Each of the six interactive lectures in this course will be hosted by a different faculty member, from either the PSM field or a contributing discipline such as Strategy & Organisation, Marketing or Operations Management.
28 & 29 June 2018
by Dr Mathias Drehmann
Why are banks and financial institutions regulated? Why has the focus shifted to systemic risk? What is the financial cycle and how does it interact with the business cycle? This course discusses the main issues highlighted in the theoretical literature, reviews whether these are empirical relevant and draws the link to policy actions.
2 July 2018
by Professor Jan Dul
Necessary (but not sufficient) conditions are widespread in real life and therefore relevant to various research areas, such as management, business research, sociology, and psychology. But until recently no technique was available to identify necessary conditions in datasets. Traditional (regression based) data analysis techniques fail to do so, even though they are frequently applied for exactly that matter. Necessary Condition Analysis (NCA) is a new technique that can do the job (Dul 2016).
4 to 6 July 2018
by Professor Marno Verbeek
The use of panel data models is wide spread in many fields. This course will provide you with an intuitive and practical introduction into the econometrics of panel data. How do you exploit the benefits of panel data in empirical work? How do you avoid the pitfalls, and how do you address the additional complexities of having multi-dimensional data? How do you make sure inferences are valid? The course will combine theoretical sessions in the morning with practical sessions based on Stata in the afternoon.