Professional Skills & Academic Methods

Being able to  analyse complex situations is an indispensable task for future leaders but one must also develop the personal skills to implemenet solutions. This module combines management and personal skills which are necessary for leadership functions in the public sector. It takes place in the second semester and consists of a lecture and a module examination.

The course delves into the theory and practice of public sector management, with a focus on strategic management. It introduces students to a variety of analytical techniques useful for policy implementation and managing organizational performance.

Strategic management in public administration involves defining public value, mobilizing legitimacy and support for that value, and developing and deploying the operational capacity to deliver it. By the end of the course, students are able to diagnose an organization’s strategic position and develop a plan for improving its performance; they should also be able to understand the organizational factors likely to affect a given policy’s implementation and to adjust policy design to produce a better outcome.

Related to that, leadership is the ability to influence a group of people towards a goal. Students will improve their leadership capacities through practice, reflection and feedback.

Through the course, participants shall become familiar with the most known negotiations concepts: Integrative / principled approaches (Harvard), the FBI-concept, distributive and tactical approaches. Two-thirds of the course consist of role-play, which is based on practical cases and will be played by all participants. Students will learn to develop own negotiation strategies, the dynamics of negotiation processes and their momentums, building on the differences of positions and interests, communicating appropriately, and developing useful approaches to deal-making.

Scenarios have become an important tool in policy analysis and public policy making. This course introduces students to explorative scenario construction as one central foresight method. Building their own scenarios on a given case step by step, students will learn the building blocks of foresight techniques, reflect on its disciplinary underpinnings and gain insights into the practical application of foresight methodologies for policymaking.

This module provides a comprehensive introduction into quantitative empirical approaches to policy analysis. The course shows how empirical research is carried out and how it can become the base of policy decisions and policy evaluation, and introduces the most widely used quantitative research methods and designs. It covers topics such as descriptive statistics, probabilities, inferential statistics, correlation and regression analysis. 

Students become aware of the potentials and limitations of quantitative methods and can critically evaluate statistics and quantitative studies and their results. To that end, they will advance their methodological skills in quantitative, qualitative and mixed methods, as well as research design.

Field Experiments and Public Policy

This course introduces students to field experiments as a tool for evaluating public policy. The experimental approach is gaining more and more importance in informing public policy, and having a grasp of the key concepts and methods will therefore become an increasingly valuable skill for students. The course will first cover the method of experimental policy evaluation, i.e. how to design and conduct a randomized controlled trial, and how to interpret the results. We will then discuss experimental approaches to recent issues in economic policy and public economics. Special attention will be given to policy interventions where the experimental design incorporates insights from the behavioral sciences. In the course, students will be asked to come up with an experimental design that can be helpful in better understanding a specific policy issue.

Introduction to Quantitative Research: From Design to Analysis

This seminar targets students interested in quantitative analysis who want to improve their research workflow and analytic skills. The course will foster quantitative literacy by reviewing and replicating good examples of quantitative research published in high-ranking academic journals. Additionally, during the lab sessions, students will learn how to use R programming language for quantitative social sciences. The course will cover the basics of data manipulation in R, exploratory and descriptive analysis, bivariate analysis, regression analysis and data visualization. The seminar will also review how to accurately report quantitative empirical findings (e.g. format of tables, description of findings) to meet the standards of academic and professional outlets. During the sessions, students will have the chance to work on their own quantitative projects (for example, master’s thesis or term papers) and receive feedback from the lecturer and the rest of their peers. This course is particularly designed for second-year students writing their thesis based on quantitative data.