Short courses information

About your 3SRM short courses: complement your methodological skills!

These short courses (4-hour long) are optional and free of charge -- i.e. they are part of your weekly training package covered by the fee you have paid for a 1-week in-person main course. They can be useful in different ways: the morning cross-cutting short courses provide you with ‘a broader perspective’ and enable you to reflect beyond your more targeted main course(s), while the late afternoon supplemental short courses focus on specific skills that can be useful in connection with numerous 3SRM main courses – or simply enable you to complement your methodological skills. We have arranged that the afternoon courses finish at 17:00, so that you still have time after this to work on daily tasks related to your main course.

There is no need to register in advance to these short courses – you just come and attend if you wish to do so. You will receive a certificate of attendance from MethodsNET if you attend all four successive daily sessions of a given course. These courses do not stand for ECTS Credits.

Morning cross-cutting short courses (Tuesdays-Fridays 8:30-9:30)

[M1] Philosophy of Science, by Patrick T. Jackson (week 1, June 20-24)

Philosophy of science is one of those topics that every research methodology presumes, but is rarely explicit about. Debates in the philosophy of science can help to illuminate the commitments that research tacitly and implicitly make, and equip us to make more informed decisions about our own research designs and approaches. This course will survey the history of the philosophy of science in the European tradition, and offer a vocabulary for more informed discussions of tricky conceptual issues involved in the production of knowledge that is in some sense valid.

[M2] Research Approaches and Designs in the Social Sciences [Instructors: information follows shortly](week 2, June 27-30)

Most social scientists operate within a single overall research tradition, and all too many have little understanding of other approaches. This short course introduces three different ways of thinking about studying the social world: variance-based, case-based and interpretivist approaches. Through a series of conversations between leading methodologists from different traditions, we will explore how and why the approaches diverge on core methodological topics such as what are concepts and theories and how do we use them, and how we can make valid/trustworthy claims about the empirical world. After you have taken the course, you will be better able to understand the claims and evidence of research from other traditions on their own terms.

Late afternoon supplemental short courses (Mondays-Thursdays 16:00-17:00)

[L1] Math Refresher, by Júlia Koltai (week 1, June 19-23, and week 2, June 26-29; same course repeated twice)

This short course is designed to refresh mathematical background frequently used in quantitative social science research. The course will provide an overview of the essential concepts required for competent analysis using quantitative approaches in social science. The topics include dimensions of calculus, linear algebra and probability theory, which are most commonly applied in social science research. Therefore, instead of a comprehensive mathematical approach, the course will cover the most critical concepts and approaches, which are usually behind widely used tools. For each topic, the application of the given mathematical concept will also be mentioned, and literature will be suggested for the deeper understanding.

[L2] Missing Data, by Levente Littvay (week 1, June 19-23)

What do we do when we have holes in our datasets? Maybe a survey respondent did not answer a question. Or they did not respond to any of the questions. Maybe your mouse died in the middle of the study. One thing's for sure, we always do something, even if we are not conscious about the decisions we make. Software defaults, most often, do not offer the optimal solution to the problems missing data pose. In fact, best practices for missing data correction requires deep theoretical thinking. In this short we cover how to think about, and how to think theoretically about missing data and what good (and bad) practices, analytical solutions exist for their alleviation. On the first day we discuss how to avoid having missing data, the second how to think theoretically about missing data. The last two days will revolve around analytical solutions to alleviate item and unit level nonresponse.

[L3] Data Visualization, by Akos Mate (week 2, June 26-29)

Visualizing data is an integral part of any project, from exploration to the final analysis. This course covers five major topics to help participants build engaging and informative figures. First, the fundamental ideas of data visualization and their practical applications. Second, an overview of how different types of visualizations are suited to different types of data. Third, we extend this discussion to visualize uncertainty and quantitative models (instead of using large regression tables). The fourth topic is perception and cognition, to help with design considerations. Finally, we will review various tools for visualizing data (such as Python, R, etc.).