「EVENT REVIEW」

The workshop covers basic and advanced methods of panel data analysis for social research from an applied perspective. The focus is on individual data from panel surveys (large N, small T). Panel data offer important advantages over cross-sectional data, in particular, the identification of causal effects with relatively weak assumptions and the analysis of individual life-course trajectories. Special methods for panel data analysis are however needed to make use of these advantages. The workshop gives an applied overview. The starting point is the linear fixed effects (FE) regression model and its advantages  compared to alternative models (random effects). The course furthermore covers the modelling of impact functions and growth curves with practical advice for researchers. Finally, useful extensions will be presented, notably the linear FE model with Individual Slopes (FEIS) and the FE logistic regression model (FE Logit). In the workshop, the structure of regression models is explained. The application of statistical models is demonstrated with Stata and real data examples.

Prerequisites

Knowledge of cross-sectional linear and logistic regression is assumed. Knowledge of Stata is an advantage.

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DATE & TIME: 27 - 31 March 2023, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.

PLACE: FMA/01.19 - Feldkirchenstra?e 21

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Social Network Analysis is a comprehensive research framework that is strongly rooted in interdisciplinary theory, data, and practice. It looks at the interconnected world and it maps, measures, and identifies where it is hyperconnected and where it lacks connectivity; who are the key players, what is the role of groups and communities, and how information, engagement, resources, influence travel through social networks; and what all of these imply, for individuals, organizations, and societies at large. It seeks to pinpoint recurrent relational mechanisms and test network building and network disruption scenarios, to do better informed decision-making at all levels – personal, organizational, and in public policy.

In this class, you will learn about the power and limitations of a scientific research framework to, quite literally, change the world. It will be an intense intellectual challenge that starts before this course and hopefully lasts a long time after. Through the assigned readings, you will be challenged to rethink how the world works and connect the previously known dots in a different way. You will struggle through the process of working with imperfect data and software. And, most importantly, you will be challenged to put your research into perspective – to think about ways in which you can make it better, more visible, and actionable.

You are encouraged to bring your own data and work through each level of analysis throughout the week together. If you do not yet have data, that is fine. We provide interesting data, and you can work on research design and the plan for data collection.

We designed the course for an interdisciplinary cohort of participants. We will cover three new things for most who attend: (1) problem perspective and jargon, (2) data and software, and (3) analytic techniques and interpretation of results.

Prerequisites

• Interdisciplinary readings (network science, social network analysis, political network analysis, organizational network analysis, policy network analysis)
• Software fundamentals (tutorials of R and Gephi)

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DATE & TIME: 27 - 31 March 2023, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.

PLACE: FG1/00.06 - Feldkirchenstra?e 21

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Qualitative research comes in many forms. Among many others, it encompasses historical analysis of processes, case comparisons, action research, and grounded theory (to name but a few). Some approaches are meant to build theories, others to test existing theories. Each approach comes with a specific set of data sources. For example, historical research often relies on archival sources, while an ethnography often relies on observations and interviews. It implies that your research project must be designed with attention to your goals, the types of analytical statements you would like to make, and the data sources available. The goal of this course is to introduce you to the main research designs in qualitative research, and to help you identifying the right design for your research, as well as guiding you towards the data sources and analytical methods you would like to use. We will also look at some key aspects of qualitative research, in particular data coding and subsequent analysis of the codes. Last but not least, we will discuss how different designs may be combined. Please note that this course is primarily about qualitative (case-based) research designs. It does not teach a specific analytical technique. Also, this course is not fitting for PhD projects that use quantitative data.

Prerequisites

This is an introductory course, so no prior knowledge required.

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DATE & TIME: This course did not take place and will be made up in July 2023.

Monday, 27 March 2023, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.
Tuesday, 28 March 2023, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.
Wednesday, 29 March, 9.30 - 11.00 a.m.

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This course introduces PhD students to the logic and application of Qualitative Comparative Analysis (QCA) in empirical research in the social sciences. QCA is a systematic comparative method for small to medium n-samples (usually between ~10 and ~50 cases). It can process both qualitative and quantitative data. The underlying logic is rooted in set-theory and Boolean algebra. This means that QCA is not only a technique but also an approach that requires one to rethink certain core assumptions in social science research, such as causality and casing. In this course, we will discuss the main characteristics and operations of QCA, and will outline what QCA means for your research project. You will be made familiar with the method through exercises. Importantly, you will be asked to apply some of the operations to your own dataset (or a given dataset in case you don’t have one yet) so that you will be able to start using the method on your own by the end of the course.

Prerequisites

• You should have taken the Qualitative Research Design course before the QCA course.
• We will use R for data processing. Please make sure you have R running on your laptop, and that you have the QCA package installed: https://cran.r-project.org/web/packages/QCA/index.html
• Preferably, you have a dataset ready. This doesn’t have to be a fully prepared set. In case this comes to early: we can also work with pre-made datasets that the instructor will bring along

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DATE & TIME: This course did not take place and will be made up in July 2023.

Wednesday, 29 March 2023, 11.30 a.m. - 1.00 p.m.
Thursday, 30 March 2023, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.
Friday, 31 March, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.

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DSGE models with financial frictions have become the standard in academia as well as in policy institutions.  This course is designed to familiarize students with the literature on financial frictions in macroeconomics and cutting-edge methods. We will start with a brief introduction to DSGE models and solution methods. We will then incorporate financial frictions into DSGE models to see (i) how financial shocks are transmitted to the economy and (ii) how non-financial shocks are amplified via financial accelerator. Next we will cover models of financial crises and how to integrate them into DSGE framework. We will also explore how financial frictions interact with topics like economic growth and inequality.

Prerequisites

• Prior knowledge of DSGE models and Matlab/Dynare would be helpful but not required.

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PLACE: FMA/01.20 - Feldkirchenstra?e 21

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