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 2019.
26 to 28 June 2019 | by Professor Uri Simonsohn
This PhD-level course focuses on issues that (i) behavioral researchers are likely to encounter as they conduct research, but (ii) may struggle to figure out independently by consulting a textbook or published article. Topics meet this second criterion for one of four reasons:
- They are technically challenging (e.g., When, and what does it mean, to cluster standard errors? What if data severely violate a test’s assumption? How to bootstrap.)
- There isn’t yet consensus among methodologists about them and hence a behavioral researcher will encounter different recommendations on how to proceed depending on the source that’s consulted (e.g., Bayesian vs frequentist inference for lab experiments, analyzing replication results.)
- There is high degree of consensus among methodologists, but the ideas have not yet become mainstream among behavioral researchers (e.g., stimulus sampling)
- There is high degree of consensus among methodologists but such consensus may be premature and behavioral scientists may be better off not following it (e.g., how to correct for publication bias in meta-analysis, testing for moderation in experiments, power analysis). This set will be obviously controversial.
18 June 2019 | 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. Dul et al. 2018).
19 to 21 June 2019 | by Professor David Yermack
This course will survey academic research in the emerging area of FinTech, an interdisciplinary field which studies the application of Information Technology to the Finance area. We will survey the foundational literature of cryptocurrency, blockchains, smart contracts, consensus protocols, and related regulatory issues. Applications of FinTech in areas such as corporate finance, central banking, investment management, real estate, supply chain management, and personal finance will be studied.
24 & 25 June 2019 | 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 (mediator), which in turn influences the dependent variable. Moderation describes the case where the effect of the independent variable X on the dependent variable Y changes across different levels of another variable M (moderator). 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.
1 to 3 July 2019 | by Dr Richard E. Ocejo
This course provides an overview of qualitative research and gives students hands-on experience and practical guidance for conducting their own projects. It specifically focuses on two elements of qualitative research: 1) project design and implementation, including such topics as choosing cases, getting a sample, asking research questions, and using theory; and 2) collecting and analyzing data, including making observations, conducting interviews, and data coding.
1 & 2 July 2019 | by Dr Jason Roos
Many researchers rely on data that are obtained from a wide variety of online sources, including web sites, social media, and external data providers. This course introduces you to procedures for collecting, preparing, analysing, and visualising such data. Participants will learn about core ideas in data visualization, web scraping, and text analysis while gaining practice writing, debugging, and tracking changes to code in R.
3 to 5 July 2019 | by Dr Marc van Essen
The course aims to teach participants how to develop coding questionnaires and code studies. It also shows how to perform data analysis, draw conclusions, and present findings, thus giving some indications on how to get published in a top journal. Moreover, we theoretically discuss and empirically show the benefits and limitations of meta-analysis. We will also provide explanations on how to get your work published in a journal.